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BJP backs Yashwant Sinha's upcoming meeting with Separatists
August 6, 2017 by neha
Jammu and Kashmir [India]: After reports emerged that senior Bharatiya Janata Party ( BJP) leader Yashwant Sinha has decided to meet Separatists in Kashmir, the party has backed the idea.
Speaking to ANI on the same, Minister of State in the Prime Minister's Office (PMO) Dr. Jitendra Singh said that the present government is taking major steps to improve the situation in Kashmir.
"The present government is following an approach of clarity and conviction in Kashmir." he said, comparing it to the work done by the previous government.
He said that nearly fifty-six investigative cases against separatists have been languishing for over two decades.
"Many cases related to the separatists have been pending over all these years. The current government is trying to work on it," he said.
"I'd like to ask the youth of Kashmir what model of freedom movement are they following? Is it the Gandhian model, Martin Luther King model, Mandela model?," Singh added.
He also reiterated the fact that certain Separatist groups only indulge in these wars as they earn a lot through such wars.
Meanwhile, the Enforcement Directorate on Sunday arrested Aslam Wani, close aide of Separatist leader Shabir Shah from Srinagar, in connection with terror funding case.
Wani will be brought to Delhi today.
Earlier, the ED remand of Shabir Shah, a Democratic Freedom Party (DFP) chairman, was extended for six days.
This came after the Enforcement Directorate submitted an application before a Delhi court for further remand of Shah.
In its remand application, the ED submitted before the court that Shah is in continuous contact with anti national elements/ terrorists residing in Pakistan in garb of the Kashmir issue.
On July 26, Shah was produced in the Patiala House Court, following which he was sent to a seven-day ED custody.
On July 25, Shah was arrested from his residence, where he was under house detention for a very long time, in Jammu and Kashmir's Srinagar.
The National Investigation Agency (NIA) has been tough on Separatists regarding the alleged funding by Pakistan for illegal activities in Kashmir.
On July 24, the NIA arrested seven separatists over money laundering charges, for funding terror in the Kashmir Valley.
All seven separatist leaders – Altaf Shah, Ayaz Akbar, Peer Saifullah, Mehraj Kalwal, Shahid-ul-Islam, Naeem Khan and Bitta Karate – were later sent to 10-day NIA custody.
The accused have been charged under various sections of the Indian Penal Code (IPC) and the Unlawful Activities (Prevention) Act.
The NIA visited Srinagar in May to probe the alleged funding by Pakistan for illegal activities in Kashmir, and questioned several Separatist leaders on the issue of raising, collecting and transferring funds via the Hawala route and other channels to fund terror activities in Kashmir.(ANI)
Categories Kashmir, News Tags Kashmir, Separatists, Yashwant Sinha
J-K: School girls celebrate Raksha Bandhan with CRPF soldiers
Lunar eclipse on August 7-8


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IND vs AUS | Sydney pitch is a tailor-made wicket for Ravindra Jadeja, reckons Parthiv Patel
Jadeja was crucial to India restricting Australia in Sydney
After his performance on Day 2, Parthiv Patel heaped praises on Ravindra Jadeja, calling the pitch to be tailor-made for the left-arm spinner. He also pointed out how vital Jadeja is to the Indian team in overseas conditions, doubling as great bowler, key batsman and a fantastic fielder.
At 206/2, Australia were in a prime fashion to take their total well above the 400 run mark, leaving a huge mountain for the visitors to climb, with Marnus Labuschagne and Steve Smith at the crease on a flat Sydney deck. However, the introduction of Ravindra Jadeja, in his second spell, changed the course of the game with the left-arm spinner accounting for a well-set Labuschagne before dismissing Matthew Wade in quick time.
Alongside that, Jadeja's indifferent bounce with his pace as a spinner made it difficult for the Australians to handle. While they succumbed to his spin bowling, Steve Smith stood the testing times before he was undone by a flash of brilliance from Jadeja in the field. It saw former Indian wicketkeeper Parthiv Patel reckon that the pitch is tailor-made for the left-arm spinner.
"Of course, he's a big player, just look at his contribution. If you look at him in the last Test, he bowled impressively and picked up two crucial wickets for the team. With the bat also, he had a match-winning partnership with Ajinkya Rahane. Alongside that, in the field too, he picked up the crucial wicket of Matthew Wade in the last Test. Today too, on a slow wicket, he was right there, it was a tailor-made wicket for him," Parthiv told Cricbuzz.
Parthiv also noted how Jadeja's ability as a fifth-bowler was quintessential to the pacers getting ample rest in between his overs. He also added that it was evident when Saini accounted for Starc with his raw pace.
"This run-out has reduced 20-30 runs for India and he had only one stump to aim, he was possibly the only player from both the teams to affect such a run-out. The fifth bowler is very important. If you look at the load for the bowlers, it is evident that a fifth bowler is a necessity. Navdeep Saini also got a rest only because Jadeja was there and his ball to bounce out Starc was a result of him getting enough rest. He gels in with everything," he added.
The former Indian wicketkeeper also was in awe of Jadeja's abilities to influence a game, stating that having the all-rounder would essentially mean that India play with 12 players, with his ability to change the course of any fixture. He cited the left-hander's knock in the Melbourne Test alongside his bowling as one of the key reasons for India's victory.
"With Jadeja in the playing XI, you can bring in an extra batsman or an extra bowler, something that is very vital in these conditions. If you have Jadeja kind of player, you enter the stadium with 12 players, he's very key to this setup. One of the major reasons for the Melbourne win was his partnership with Rahane and how bowling. Today too, he was helpful in reducing Australia to 350 and was right on point every single time he came to bowl. He adds a lot to the team and Jadeja seals the case for playing 5 bowlers overseas too."

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Home Uncategorized Laicized homosexual abuser celebrates mass for homosexual dissident group
Laicized homosexual abuser celebrates mass for homosexual dissident group
by Jeffrey Miller August 9, 2005
written by Jeffrey Miller August 9, 2005
DETROIT, Aug. 9 (UPI) — Michigan prosecutors are trying to determine if a defrocked Catholic priest violated his probation by celebrating mass for a gay religious group.
Harry Benjamin pleaded no contest in 2003 and served seven months in prison for sexually abusing a teenage boy in the 1980s. The terms of his probation bar him from being around children or teens.
The Detroit Free Press reports that Benjamin celebrated mass in Alexandria, Va., for Dignity, a group of gay Roman Catholics not recognized by the church. The Wayne County Prosecutor's Office learned of this when the organization checked on Benjamin's background.
"We're investigating what he has been doing in terms of his activities with Dignity, whether he's been conducting masses and is in the presence of minors," a spokeswoman for the prosecutor said, adding that Benjamin's probation does not bar him from participating in religious ceremonies.
Benjamin moved to Virginia in 1991 after he was laicized by the Vatican, a step that completely removed him from the priesthood. [Source]
It would be curious to know if Dignity was aware that this ex-priest was a homosexual abuser when they had him say Mass? I would not be surprised at all to find out that they did.
We don't need to reconcile differences
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State sponsored suicide
Some failed endorsements and business start-ups
Sean August 9, 2005 - 8:22 pm
I would not be surprised either. They probably don't consider what he did to be abuse or morally wrong. These people are truly sad, and in some cases, like this guy, dangerous.
Ed August 9, 2005 - 9:47 pm
?"a step which completely removed him from the priesthood." Your a priest forever by the order of Melchizedek. No. He had his faculties revoked but I would say the mass was illicit but probably valid.
BillyHW August 9, 2005 - 9:58 pm
I'm sure Andrew Sullivan will be all over this story. I think I'll start holding my breath…


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List Top 8 Controversial God men Gurus of India
Below is the list of Indians top ten Controversial God men Gurus
Swami Nithyananda
Swami Nithyananda was accused of rape by Indian-born American citizen, Arthi Rao, in June 2012.
She went on Indian television accusing Nithyananda of raping her for several years. Nithyananda again denied the allegations.
Nithyananda landed in a controversy after a purported video footage showing him in a compromising position with an actress was shown by Indian local TV channels.
Asaram Bapu –
Asaram Bapu arrested by Police
Crime – Raping 16 year minor girl
Crime – Land Grabbing
Delhi Gang rape – Asaram said victim was equally responsible for the crime.
Baba Ramdev –
Tehelka magazine had reported financial irregularities and tax evasion by Ramdev's trusts
Senior leader of the Communist Party of India-Marxist Brinda Karat accused the Divya Pharmacy, owned by Ramdev's Divya Yog Mandir Trust, of using human and animal bones in their medicines.
Baba Ramdev claimed that regular practice pranayama popularised by him has successfully 'cured' cancer within six to nine months.
Ramdev babs said that yoga was the way to prevent and cure AIDS.
"We will prove through clinical tests that yoga can cure AIDS. We will offer the cure shortly, in the next couple of years,"
AIDS cannot be prevented by talking free sex and by using condoms.
Ramdev has contended that homosexuality is a disease that is curable
Chandraswami a controversial tantric –
In 1996, he was arrested on charges of defrauding a London-based businessman of $100,000.
The ED has imposed a total penalty of Rs 9 crore (approx) on Chandraswami in 13 cases of Foreign Exchange Regulation Act
CBI investigated him and his role in Rajiv Gandhi assassination case.
Osho Rajneesh
Mostly misunderstood Guru Osho Rajneesh
Wrote many books which created controversies
His Ashram commune was always talk of the town for good or bad reasons
At the Second World Hindu Conference in 1969 he came under criticism when he claimed "any religion which considers life meaningless and full of misery, and teaches the hatred of life, is not a true religion. Religion is an art that shows how to enjoy life."
Jayendra Saraswati –
Jayendra Saraswati is the sixty-ninth Shankaracharya of the Kanchi Kamakoti Peetham.
Kanchi seer Jayendra Saraswati and his junior Vijayendra are the prime accused in the murder of Sankararaman, the manager of the Varadarajaperumal temple at Kanchipuram, in September 3, 2004.
attempt-to-murder' case was filed by an auditor, Radhakrishnan, in Chennai. He alleged that Jayendra Saraswathi sent goons to his house to kill him because he had questioned the missing 83 kg of gold meant for the Kamakshi temple at Kanchipuram.
Sathya Sai Baba of Puttaparthi –
He produced holy ash or vibhuti, gold ornaments and rings from thin air
Accusations - He used to indulge in sexual abuses and pedophilia.
Bikram Choudhury –
He is an Indian yoga teacher and the founder of Bikram Yoga, a form of hot yoga performed in a series of 26 hatha yoga postures done in a hot (105 degrees Fahrenheit/40 degrees Celsius or greater) environment.
In March of 2013 a suit was filed in Los Angeles' superior court against Choudhury by Sarah Baughn, a former "protege", claiming she was denied championship yoga titles and promotions due to her refusal of Choudhury's sexual advances
Tags - Sex Guru Baba India Top Guru Baba
Popular Post on Religion
Sandhya, September 02, 2013
I didn't know about the last tainted 'saint' until now. We don't know how many more are there, waiting to be exposed.
@Sandhya


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Nick Woltemade (born 14 February 2002) is a German professional footballer who plays as a forward or attacking midfielder for  club SV Elversberg on loan from Werder Bremen. He has represented Germany internationally at U16 and U17 youth levels.

Club career
Woltemade joined Werder Bremen in 2010 from TS Woltmershausen. In the 2018–19 season he scored 18 goals and made eight assists in 24 matches for Werder Bremen's U17 team in the U17 Bundesliga.

Having scored seven goals in eight matches for the U19 side Woltemade was first called up to the first-team squad for a Bundesliga match against Bayer Leverkusen on 11 January 2020. On 1 February 2020, he made his professional debut in a 2–1 league defeat away to FC Augsburg, becoming the youngest Bundesliga player in Werder Bremen's history at 17 years, 11 months, and 16 days.

In August 2022, Woltemade joined SV Elversberg, newly promoted to the 3. Liga, on a one-year loan.

Career statistics

References

2002 births
Footballers from Bremen
Living people
German footballers
Association football forwards
Association football midfielders
Germany youth international footballers
Bundesliga players
2. Bundesliga players
3. Liga players
Regionalliga players
SV Werder Bremen players
SV Werder Bremen II players
SV Elversberg players

redpajama_set_name: RedPajamaWikipedia


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Mass. State Reps: The Nangle Gang Still on the Loose
The Nangle Gang Still on the Loose and Riding High on Taxpayers' Money
by Broadside Staff
The four-members of the Nangle Gang have increased their plundering of taxpayer's gold this year. Their take has increased to $120,000 against the wishes of many law-abiding and well-intentioned citizens, including the sitting governor of this commonwealth.
The Nangle Gang consists of its leader, David "Tax the Nonprofits" Nangle (D-Lowell) and members Ann-Margaret "The Creative Economy" Ferrante (D-Gloucester), Sarah "Automatically Register Voters" Peake (D-Provincetown) and Louis "Regulating & Protecting" Kafka (D-Stoughton.
As a result of the Great Pay-Raise Heist earlier this year (https://legiscan.com/MA/text/H58/2017), perpetrated by Robert "The Unindicted Co-conspirator" DeLeo and Stanley "The Gay Cougar" Rosenberg, the Nangle Gang of Four now takes home and EXTRA $60,000 this year for what appears to be not much more than a no-show gig.
At least the old Wild West "Hole-in-the-Wall Gang" seemed to have had a purpose and direction. It rustled cattle and horses, robbed stagecoaches and held up banks to obtain money for life's necessities and pleasures. Although the Nangle Gang does not adhere to any of these nefarious pursuits, it is hard to determine just what the four desperados do for their $120,000 per year.
Each had been appointed to the antiquated title of "Division Chair" by Blazing Saddles DeLeo earlier this year. But what does a Division Chair do except receive $30,000 a year in our precious gold for the title? Good question!
The only reference our staff could find was in the document: Massachusetts House Rules (https://malegislature.gov/Laws/Rules/House). This says that the Division Chairs are responsible for "reviewing the daily Calendar and providing advance notice to committee members in the respective divisions of all matters scheduled for consideration in the Orders of the Day. Said committee chairpersons shall provide information to members of their committees on pending legislation and other matters of business before the House."
In practice, in this modern digital age of computers, our elected officials simply access the House Clerk's Orders of the Day on their smart phones or computers to find out what's on the schedule or agenda for that day's session.
And how many formal sessions has the House held this year – to date? By our count there have been only 13 sessions with a roll call vote, requiring all members to attend. Every other session has been informal where no important legislation gets passed and Orders of the Day don't change much. Typically, Orders of the Day, or the Calendar as prepared by the House Clerk, are only updated before a formal session.
But wait! The House Rules also dictate that the Division Chairs "shall oversee the physical appearance of the Chamber and the various areas under the jurisdiction of the House of Representatives … for the purpose of requesting expeditious appraisals [when necessary] and necessary repairs and renovations to the interior and exterior of the State House." Thus, they are responsible for bringing to the speaker's attention if any repairs to the State House are required.
But isn't there a building and maintenance department in the executive branch that specifically handles such problems or improvements? We think there is something called the Division of Capital Asset Management and Maintenance that would handle any building questions with respect to the State House. Certainly, these professionals appear to be highly qualified to address any issues with building maintenance, upgrades or renovations.
So we remain perplexed at just what the Nangle Gang does for its extra $120,000 per year. We know they are not out rustling cattle or robbing stagecoaches. Those days are long gone. And they are not hiding in the Big Horn Mountains of Northern Wyoming. They don't even have to avoid the law. They are right here – all around us and on Beacon Hill.
But what does each one of the Gang do to earn an extra $30,000 a year? That's on top of their $62,547 base salary and the new $15,000 or $20,000 travel/office allowance.
If there is anyone out there who can tell us what the Nangle Gang does to justify an extra $120,000 per year of our gold, please contact the Boston Broadside to let us know.
We eagerly await answers. ¨
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12 Healthy Food Options You Can Find At McDonald's
Tama2u/Shutterstock
By T.S. Lowry/Dec. 14, 2021 12:42 pm EST
It's no secret that McDonald's is a celebrated fast-food restaurant with chains all over the world. However, it isn't generally associated with a healthy lifestyle, to say the least. Yet, over the years, the fast-food franchise has made an effort to add nutritious options to its menu. Unfortunately, many of those items have come and gone when McDonald's reduced its menu during the pandemic, eliminating a majority of its previously-added healthy options. Salads, the Egg White Delight McMuffin, fruit and yogurt parfaits, artisan grilled chicken sandwiches, and other health-conscious options which were part of the beloved pre-pandemic menu are no longer being served.
We're not sure when or if they'll return, so the following McDonald's healthy food options aren't necessarily as healthy as they once were. Take these items with a grain of salt ... or don't since too much sodium isn't typically part of a healthy diet. Nevertheless, McDonald's is a fast-food gift that keeps on giving. Many foods are fine in moderation, including this list of healthy options you can find at the Golden Arches right now.
1. Apple slices
Although they can't be considered as a full meal, McDonald's apple slices are a healthy side that could adequately accompany any entrée –– whether it's breakfast, lunch, or dinner. According to WebMD, apples can help slow digestion which prevents overeating. The source adds that they contain antioxidants that can help slow the growth of cancer cells –– and that's just scratching the surface of the benefits that apples bring to the table.
As for the portion provided, each bag contains 15 calories, 4 grams of carbs, and 3 grams of sugar. If you're craving a McDonald's meal, then might we recommend substituting the fries for a side of apples the next time you roll through the drive-thru? Given the low-calorie content, a few bags of apple slices isn't even out of the question.
2. Chicken McNuggets
S and S Imaging/Shutterstock
We know what you're thinking ... McDonald's chicken McNuggets are breaded and fried, elements that are associated with unhealthy food. In moderation, these chicken nuggets will bring some serious nutritional value to the table, whether you dine-in or bring your order to your home table. Plus, the McDonald's website states that its nuggets "are made with 100% white meat chicken and no artificial colors, flavors, or preservatives."
In terms of nutritional information, a four-piece order has 170 calories and 9 grams of protein, making this a light meal that's packed with some macro power. Of course, four nuggets also contain 10 grams of both fat and carbs, and they're not necessarily light on cholesterol or sodium. As we said, most foods are okay in moderation, and that rings especially true for chicken McNuggets. Beware about adding too much sauce, which is far easier said than done since McDonald's sauces are extra tempting.
3. Deluxe crispy chicken sandwich
rblfmr/Shutterstock
Although the deluxe crispy chicken sandwich is undoubtedly delicious, it is fried and served on a sizable bun. Over the years, health-conscious people have been predisposed to loathe fried foods and buns (unless they're whole wheat). That said, this all-star chicken sandwich features a potato roll, accompanied by Roma tomatoes and shredded lettuce.
By itself, it can easily be a meal and at 530 calories, it won't break the calorie bank. The deluxe crispy chicken sandwich also has a whopping 27 grams of protein so it might as well be a protein shake, only savory and satisfying by itself. Nevertheless, it also has 26 grams of fat, 47 grams of carbs, and 10 grams of sugar. This crispy chicken sandwich is a total winner if you're looking for a healthier option yet still want a mouth-watering and hearty fast-food meal.
4. Egg McMuffin
N K/Shutterstock
The egg McMuffin is a McDonald's classic. It's also low in calories (310), high in protein (17 grams per sandwich), and has 2 grams of fiber for good measure. Yet it also contains 13 grams of fat, 30 grams of carbs, and 3 grams of sugar. In terms of breakfast sandwiches at the Golden Arches, the egg McMuffin is one of the healthier and lower-calorie options. Did we mention it's delectable? Given the low calories, we wouldn't blame people if they just so happened to order two egg McMuffins. Just think, that would only be 620 calories and you'd be getting 34 grams of protein.
During a time when people are rightfully concerned about the ingredients in their food and what they put in their bodies, people can rest assured that the savory egg McMuffin only contains six basic ingredients: an English muffin, an egg, Canadian bacon, pasteurized processed American cheese, salted butter, and clarified butter. Of course, there are additional ingredients in an English muffin and processed cheese, but we digress.
5. Filet-O-Fish
Robson90/Shutterstock
Yes, McDonald's has a fish sandwich and it actually tastes amazing as the fish flavor is subtle. Enter the Filet-O-Fish, a low-calorie menu item that's worth the hype. Believe it or not dear readers, this McDonald's staple is actually made out of real fish. This sandwich contains a fish filet patty, a regular bun, tartar sauce, and half a slice of pasteurized processed American cheese. All this goodness leads to 380 delightful calories, 18 grams of fat, 39 grams of carbs, 16 grams of protein, 2 grams of fiber, and 5 grams of sugar.
Though tartar sauce and fish are a perfect match made in the kitchen, if you want to drop some sodium and make this sandwich a bit healthier, you can opt for no tartar sauce. Even though the McDonald's menu has been minimized, the Filet-O-Fish is still a low-calorie and high protein option. For many people who need a quick bite on the go, that's a major benefit.
6. Fruit & maple oatmeal
Bloomberg/Getty Images
For years, people have been singing the praises of fruit and oatmeal –– and for good reason. Naturally, McDonald's combined the two and added the fruit & maple oatmeal item to its menu. "Our Fruit and Maple Oatmeal has two full servings of whole-grain oats with a touch of cream and brown sugar. Loaded with red and green apples, cranberries, and two varieties of raisins, our oatmeal makes for a hearty, wholesome breakfast of whole grains and fruit," (via McDonald's).
Needless to say, this isn't your typical fast-food breakfast and it's loaded with elements that scream "healthy lifestyle." In terms of nutrition, this oatmeal contains 320 calories, 4.5 grams of fat, 64 grams of carbs, and 6 grams of protein. It also has 4 grams of fiber and 31 grams of sugar. Given the high sugar content, this isn't a perfect meal per se, yet it does provide plenty of health benefits given the oatmeal and fruit.
7. Hamburger
Joe Raedle/Getty Images
A McDonald's hamburger might sound plain and simple (where's the cheese?), but don't knock it until you try it. This bad boy consists of a regular bun, a 100% beef patty, ketchup, pickle slices, onions, and mustard. Best of all –– aside from the taste –– it only contains 250 calories. It's also accompanied by 9 grams of fat, 31 grams of carbs, 12 grams of protein, 1 gram of fiber, and 6 grams of sugar. You could knock down the sugar a bit by asking for no ketchup, though it's not like the burger is overloaded with the sauce.
Given there are only 250 calories in this burger, might we suggest ordering two hamburgers the next time you venture to McDonald's? While you could certainly add cheese, doing so would raise the calories and other nutritional marks. However, according to McDonald's, their cheeseburger has 3 more grams of protein than the hamburger –– in case you were wondering.
8. Hash brown
A single hash brown isn't typically considered a full meal, yet this delicious item just so happens to be one of the healthier options on the McDonald's menu. The minimal calorie count is in part responsible –– according to the McDonald's website, there are only 140 calories in each hash brown. This version of the hash brown also contains 8 grams of fat, 18 grams of carbs, 2 grams of protein, and 2 grams of fiber.
Although this menu item is more of a breakfast side than a full meal, it's reassuring that it provides some nutritional value without breaking the daily calorie mark. And when made to perfection, it's crunchy on the outside and fluffy on the inside –– just as McDonald's intends its hash browns to be. While it might be better to opt for apple slices on the side, if you're craving a savory item, then the hash brown is always a marvelous option.
9. McChicken
Everything You Need/Shutterstock
The McChicken is worth every penny, though when it's available, the Hot 'n Spicy should be the go-to option. The McChicken is made up of a breaded chicken patty, a regular bun, shredded lettuce, and mayonnaise. The mayonnaise is a nice touch, as all sandwiches deserve to be lathered in a well-matched sauce –– yet if you want to make this sandwich a tad healthier, then you could always request no mayo.
Like many of McDonald's' current healthy options, the McChicken is a good choice because it's low in calories, coming in at 400 calories per sandwich –– and that's with the mayo. While it packs in 14 grams of protein and 1 gram of fiber, it's also important to point out that the McChicken contains 21 grams of fat, 39 grams of carbs, and 5 grams of sugar. There are so many factors that go into a healthy lifestyle, and though the McChicken isn't a perfect option, it's low in calories and high in protein, which is a good place to start.
10. Sausage burrito
The sausage burrito is wrapped in a flour tortilla, which isn't always associated with a healthy diet, but it's a low-calorie item that does have other health benefits. After all, this breakfast burrito contains a scrambled egg, sausage, and vegetables, as well as a slice of pasteurized processed American cheese. One burrito will only put you back 310 calories, and you'll benefit from 13 grams of protein and 1 gram of fiber. On the flip side, the sausage burrito also contains 17 grams of fat, 25 grams of carbs, and 2 grams of sugar. Yet the egg and vegetable mix make this a worthwhile meal and simply put, the flour tortilla and cheese make it utterly delicious. Having the willpower to order just one burrito is a feat in itself, yet don't feel guilty if you happen to order two as that's only a combined 620 calories and you'll get 26 grams of protein.
11. Sausage McMuffin
Perhaps surprisingly, the sausage McMuffin doesn't need an egg to make it whole. This breakfast sandwich is rather delicious with or without an egg as it already contains an English muffin, a sausage patty, a slice of pasteurized processed American cheese, and salted butter. This combination results in 400 calories, 26 grams of fat, 29 grams of carbs, 14 grams of protein, 2 grams of fiber, and 2 grams of sugar.
However, if you opt for a sausage McMuffin with egg, then the calories shoot up to 480, which is still reasonable. If you're focused on low-calorie items that pack some protein, then there are plenty of foods to choose from on the McDonald's breakfast menu, yet the sausage McMuffin is as good a choice as any. Adding a side hash brown will make your meal a total of 540 calories, though macros like carbs will also go up.
12. Select sandwiches without a bun
HEREA MIHAI COSMIN/Shutterstock
When it comes to a healthy diet, there are many elements to consider since there are no one-size-fits-all answers. All the same, there are countless ways to achieve a healthy lifestyle, including low-carb diets. If you find yourself in a crunch but still want to abide by your low-carb ways, then McDonald's can be a good solution –– if you order a sandwich without the bun, that is.
Whether you follow a specific diet or not, ordering select sandwiches without a bun can help reduce the carbs and make them healthier options. Might we suggest a Big Mac or Quarter Pounder with cheese? A Big Mac contains 25 grams of protein and a Quarter Pounder with cheese contains 30 — if you're extra hungry order a Double Quarter Pounder with cheese and fill up with 48 grams of protein.
Fast-food restaurants might not be known for their healthy menu items, but there's typically always something to order if you're willing to bend your diet just a tad. Mostly, you'll find various low-calorie options available at these food franchises, and McDonald's is no exception.


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ENAR Webinar Series (WebENARs)
Role of Statisticians in a Pandemic
10 a.m. to 11:30 a.m. Eastern
Bhramar Mukherjee, PhD
Department of Biostatistics, School of Public Health
Bhramar Mukherjee is John D. Kalbfleisch Collegiate Professor and Chair, Department of Biostatistics; Professor, Department of Epidemiology, University of Michigan (UM) School of Public Health; Research Professor and Core Faculty Member, Michigan Institute of Data Science (MIDAS), University of Michigan. She also serves as the Associate Director for Quantitative Data Sciences, The University of Michigan Rogel Cancer Center. She is the cohort development core co-director in the University of Michigan's institution-wide Precision Health Initiative. Her research interests include statistical methods for analysis of electronic health records, studies of gene-environment interaction, Bayesian methods, shrinkage estimation, analysis of multiple pollutants. Collaborative areas are mainly in cancer, cardiovascular diseases, reproductive health, exposure science and environmental epidemiology. She has co-authored more than 240 publications in statistics, biostatistics, medicine and public health and is serving as PI on NSF and NIH funded methodology grants. She is the founding director of the University of Michigan's summer institute on Big Data. Bhramar is a fellow of the American Statistical Association and the American Association for the Advancement of Science. She is the recipient of many awards for her scholarship, service and teaching at the University of Michigan and beyond. Including the Gertrude Cox Award, from the Washington Statistical Society in 2016 and most recently the L. Adrienne Cupples Award, from Boston University in 2020.
Jeffrey S. Morris, PhD
Department of Biostatistics, Epidemiology and Informatics
Perelman School of Medicine, University of Pennsylvania
Jeffrey S. Morris is Professor and Director of the Division of Biostatistics at the Perelman School of Medicine at the University of Pennsylvania, moving in 2019 after 19 years at the University of Texas M.D. Anderson Cancer Center. He obtained his PhD in Statistics from Texas A&M University under the supervision of Raymond J. Carroll in 2000. His research involves a combination of biomedical collaborative research and statistical methodological research, with a focus on developing flexible methods for integrating information across modern, complex big data including multi-platform genomics data, biomedical imaging data, and wearable devices, with statistical focus in functional data analysis and Bayesian modeling. Additionally, he has gotten involved in numerous COVID-19 related research projects at University of Pennsylvania, and authors the website http://covid-datascience.com. This website contains a blog in which he attempts to use his perspective and skills as a statistical data science to evaluate constantly emerging COVID-19 information, filter out biases, aggregate information together, identify key insights along with a sense of their uncertainty, and communicate them in an accessible balanced way. This blog contains more than 160 posts with upward of 100k views.
Xihong Lin, PhD
Xihong Lin is Professor and Former Chair of Biostatistics, Coordinating Director of the Program in Quantitative Genomics of Harvard TH Chan School of Public Health, and Professor of Statistics at Harvard University, and Associate Member of the Broad Institute of MIT and Harvard. Dr. Lin's research interests lie in development and application of scalable statistical and computational methods for analysis of massive data from genome, exposome and phenome, such as large scale Whole Genome Sequencing studies, integrative analysis of different types of data, biobanks, and complex epidemiological and observational studies. She is an elected member of the US National Academy of Medicine. Dr. Lin received the 2002 Mortimer Spiegelman Award from the American Public Health Association, the 2006 Presidents' Award and the 2017 FN David Award from the Committee of Presidents of Statistical Societies (COPSS). She is the PI of the Outstanding Investigator Award (R35) from the National Cancer Institute, and the contact PI of the Harvard Analysis Center of the Genome Sequencing Program of the National Human Genome Research Institute. She has been active in COVID-19 research.
Usha Govindarajulu, PhD
Center for Biostatistics
Usha Govindarajulu is an Associate Professor in the Center for Biostatistics in the Department of Population Health Sciences of the Icahn School of Medicine at Mount Sinai. She earned an AB from Cornell University, an MS in Natural Resources from University of Michigan, and MS in Biostatistics from George Washington University, and a PhD in Biostatistics from Boston University After this she spent two years as a postdoctoral fellow at Harvard School of Public Health. She then worked for a year as research faculty at Yale University before moving back to Boston and working at Brigham & Women's and Harvard Medical School. After being there about 5 years, she moved to New York and took as a position as an Assistant Professor of Biostatistics at SUNY Downstate School of Public Health. She was there approximately 7 years before leaving to be in her current position. Her research interests are in survival analysis, frailty models, causal inference, genetic epidemiology, and machine learning. She is currently the 2020 Chair-Elect of the Section on Statistical Computing of the American Statistical Association.
Natalie Dean, PhD
Department of Biostatistics, College of Health & Health Professions
Dr. Natalie Dean is an assistant professor in the Department of Biostatistics at the University of Florida specializing in infectious disease epidemiology and study design. She is principal investigator on an NIH R01 to develop and evaluate innovative trial and observational study designs for assessing the efficacy of vaccines targeting emerging pathogens. Dr. Dean received her PhD in Biostatistics from Harvard University. She has been active in science communications during the COVID-19 pandemic, with recently published pieces in the New York Times, Washington Post, Medscape, Boston Review, and BMJ Opinion.
While the topic is very broad, we shall try to: (1) highlight some specific unique challenges based on the nature of the pandemic, e.g. our lack of knowledge about the virus coming in, the urgency to learn act quickly, yet the necessity to think careful and rigorously to avoid false steps and conclusions. (2) clearly communicate the importance of our profession and people with our quantitative skill sets to engage and have a seat at the table to have our perspective heard, both by policymakers and the media, during this crisis.
People in our profession need to have better communication with policymakers, and many in our field might not recognize their potential or the importance of our skillset and perspective to the big decisions going on in society. We hope our panel discussion can inspire more statisticians to get engaged in this way.
Purchase Webinar Recording (11/13/20)
(Almost) All of Entity Resolution
10 a.m. to 12 p.m. Eastern
Rebecca C. Steorts
Assistant Professor, Department of Statistical Science
Rebecca C. Steorts received her B.S. in Mathematics in 2005 from Davidson College, her MS in Mathematical Sciences in 2007 from Clemson University, and her PhD in 2012 from the Department of Statistics at the University of Florida under the supervision of Malay Ghosh, where she was a U.S. Census Dissertation Fellow and was a recipient for Honorable Mention (second place) for the 2012 Leonard J. Savage Thesis Award in Applied Methodology. Rebecca was a Visiting Assistant Professor in 2012--2015, where she worked closely with Stephen E. Fienberg.
Rebecca is currently an Assistant Professor in the Department of Statistical Science at Duke University. She is affiliated faculty in the Departments of Computer Science and Biostatics and Bioinformatics, the information initiative at Duke (iiD), and the Social Science Research Institute.
Rebecca was named to MIT Technology Review's 35 Innovators Under 35 for 2015 as a humanitarian in the field of software. Her work was profiled in the September/October issue of MIT Technology Review and she was recognized with an invited talk at EmTech in November 2015. In addition, Rebecca is a recipient of a NSF CAREER award, a collaborative NSF award, a collaborative grant with the Laboratory of Analytic Sciences (LAS) at NC State University, a Metaknowledge Network Templeton Foundation Grant, the University of Florida (UF) Graduate Alumni Fellowship Award, the U.S. Census Bureau Dissertation Fellowship Award, and the UF Innovation through Institutional Integration Program (I-Cubed) and NSF for development of an introductory Bayesian course for undergraduates. Her research interests are in large scale clustering, record linkage (entity resolution or de-duplication), privacy, network analysis, and machine learning for computational social science applications.
Whether the goal is to estimate the number of people that live in a congressional district, to estimate the number of individuals that have died in an armed conflict, or to disambiguate individual authors using bibliographic data, all these applications have a common theme - integrating information from multiple sources. Before such questions can be answered, databases must be cleaned and integrated in a systematic and accurate way, commonly known as record linkage, de-duplication, or entity resolution. In this article, we review motivational applications and seminal papers that have led to the growth of this area. Specifically, we review the foundational work that began in the 1940's and 50's that have led to modern probabilistic record linkage. We review clustering approaches to entity resolution, semi- and fully supervised methods, and canonicalization, which are being used throughout industry and academia in applications such as human rights, official statistics, medicine, citation networks, among others. Finally, we discuss current research topics of practical importance.
Webinar recording will be available for purchase October 16, 2020.
The Role of Statistics in Transforming EHR Data into Knowledge
Rebecca Hubbard, PhD
Associate Professor of Biostatistics
Dr. Rebecca Hubbard is an Associate Professor of Biostatistics at the University of Pennsylvania. Her research focuses on development and application of statistical methods to improve the validity of analyses using real world data sources including electronic health records and claims data. These methods have been applied across a broad range of research areas including health services research, cancer epidemiology, aging and dementia, and pharmacoepidemiology.
The widespread adoption of electronic health records (EHR) as a means of documenting medical care has created a vast resource for research on health conditions, interventions, and outcomes. Informaticians have played a leading role in the process of extracting "real world data" from EHR, with statisticians playing a more peripheral part. However, statistical insights on study design and inference are key to drawing valid conclusions from this messy and incomplete data source. This webinar will describe the basic structure of EHR data, highlight key challenges to research arising from this data structure, and present an overview of some statistical methods that address these challenges. The discussion of issues related to the structure and quality of EHR data will include: data types and methods for extracting variables of interest; sources of missing data; error in covariates and outcomes extracted from EHR and claims data; and data capture considerations such as informative visit processes and medical records coding procedures. The overall goal of this webinar is to illustrate the unique contribution of statistics to the process of generating knowledge from EHR data and equip participants with some tools for doing so.
Purchase Webinar Recording (6/19/20)
Spatial Statistics for Disease Ecology
Lance A. Waller, Ph.D.
Lance A. Waller, Ph.D. is a Professor in the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University. He is a member of the National Academy of Science Board on Mathematical Sciences and Analytics and has served on National Academies Committees on applied and theoretical statistics, cancer near nuclear facilities, geographic assessments of exposures to Agent Orange, and standoff explosive technologies. His research involves the development of statistical methods for geographic data including applications in environmental justice, epidemiology, disease surveillance, spatial cluster detection, conservation biology, and disease ecology. His research appears in biostatistical, statistical, environmental health, and ecology journals and in the textbook Applied Spatial Statistics for Public Health Data (2004, Wiley). Dr. Waller has also lead two separate T32 training grants, one from NIGMS and the other from NIEHS, and served as the Director of the NHLBI Summer Institute for Research Training in Biostatistics (SIBS) site at Emory for the past 9 years.
The field of disease ecology involves exploration of the multiple, dynamic interactions between pathogens, hosts, and the environment that result in the transmission of disease. Many of these interactions involve spatial or spatiotemporal components that determine the course of an outbreak and may offer potential interventions to stem the extent and duration of an outbreak at the population level. In this webinar, we provide a brief overview of the field of disease ecology, the motivating questions of interest, the nature of data involved, and the interaction of statistical and mathematical modeling addressing these questions with available data. We offer two illustrations relating to the monitoring and analysis of zoonic disease, namely: identifying geographic drivers of the spread of raccoon rabies along the Eastern United States and utilizing environmental data to enhance animal surveillance for plague in California. Both examples utilize concepts and analytic tools from spatial statistics to better understand and monitor geographically-referenced zoonotic diseases in wild animal populations.
Collaboration: Applications of RWE in drug development and methodologies for confounding control and A statistical roadmap for journey from real-world data to real-world evidence
Hongwei Wang, PhD
Dr. Hongwei Wang is currently a Director of Global Medical Affairs Statistics, Data and Statistical Sciences at AbbVie. He held a PhD in Statistics from Rutgers University and his research interests include designing and analyzing real-world studies, network meta-analysis and advanced analytics. Before AbbVie, he worked at Merck and Sanofi.
Applications of RWE in Drug Development and Methodologies for Confounding Control: Real-world evidence (RWE) is playing an increasingly important role in drug development, from early in discovery throughout clinical development program to life-cycle management. RWE can augment randomized clinical trials for regulatory approval, establish the effectiveness and safety profile in routine clinical practice to support reimbursement decision, and constitute an integral part of scientific communication overall. Due to its noninterventional nature, a key challenge of robust RWE generation is to establish causal relationship between exposure and outcome. This talk focuses on several main methodologies for causal inference, consisting of IPTW, MLE, AIPTW, and TMLE using full data and matched data that is derived from propensity score matching, respectively. Following the RWE roadmap outlined in the first talk, practical considerations are given to facilitate the series of decisions for confounding control, such as defining estimand, usage of matching, and choice among different analytic frameworks.
Yixin Fang, PhD
Dr. Yixin Fang is director of Global Medical Affairs (GMA) Statistics at AbbVie. Since he joined AbbVie in January 2019, he has focused his research on real-world studies, comparative effectiveness research, and causal inference. After he received his PhD in statistics from Columbia University in 2006, he had been working in academia for 12 years, teaching young statisticians and doing research in different fields such as machine learning, high-dimensional data analysis, and big data analysis. Motivated by the research of Professor Mark van der Laan, he is promoting the applications of targeted learning in real-world data research, combining his experiences in both machine learning and causal inference.
A statistical roadmap for journey from real-world data to real-world evidence: Randomized controlled clinical trials (RCTs) are the gold standard for evaluating the safety and efficacy of pharmaceutical drugs, but in many cases their costs, duration, limited generalizability, and ethical or technical feasibility have caused some to look for real-world studies as alternatives. On the other hand, real-world data may be much less convincing due to the lack of randomization and the presence of confounding bias. In this presentation, we propose a statistical roadmap to translate real-world data (RWD) to robust real-world evidence (RWE). The roadmap consists of three main stations: (1) defining an estimand translating the research objective into a precise definition of the treatment effect that is to be estimated, (2) constructing an efficient estimator (minimum-variance unbiased estimator) for the estimation of the estimand, and (3) conducting sensitivity analysis to explore the robustness of the inference to deviation from the underlying no-unmeasured confounding assumption. The Food and Drug Administration (FDA) is working on guidelines, with a target to release a draft by 2021, to harmonize RWD applications and monitor the safety and effectiveness of pharmaceutical drugs using RWE. The proposed roadmap aligns with the newly released framework for FDA's RWE Program in December 2018 and we hope this statistical roadmap is useful for statisticians who are eager to embark on their journeys in the real-world research.
The Central Role of Personalized Solution in the Era of Digital Health
Haoda Fu
Enterprise Lead of Machine Learning and Artificial Intelligence Team
Dr. Haoda Fu is a senior research advisor and a enterprise lead for Machine Learning, Artificial Intelligence, and Digital Connected Care from Eli Lilly and Company. Dr. Haoda Fu is a Fellow of ASA (American Statistical Association). He is also an adjunct professor of biostatistics department, Indiana university school of medicine. Dr. Fu received his Ph.D. in statistics from University of Wisconsin - Madison in 2007 and joined Lilly after that. Since he joined Lilly, he is very active in statistics methodology research. He has more than 90 publications in the areas, such as Bayesian adaptive design, survival analysis, recurrent event modeling, personalized medicine, indirect and mixed treatment comparison, joint modeling, Bayesian decision making, and rare events analysis. In recent years, his research area focuses on machine learning and artificial intelligence. His research has been published in various top journals including JASA, JRSS, Biometrics, ACM, IEEE, JAMA, Annals of Internal Medicine etc.. He has been teaching topics of machine learning and AI in large industry conferences including teaching this topic in FDA workshop. He was board of directors for statistics organizations and program chairs, committee chairs such as ICSA, ENAR, and ASA Biopharm session.
Digital health is an important pharmaceutical industry trend in recent years, and it can bring significant disruptive innovation to transform healthcare industry. In this talk, we will provide an introduction on digital health and associated analytics challenges and opportunities. In particular, we will focus on the central role of personalized intervention in the era of digital health.
New Statistical Learning Methods for Optimizing Dynamic Treatment Decision Rules Leading Toward Personalized Health Care
Lu Wang
Dr. Lu Wang is Associate Professor of Biostatistics at the University of Michigan, Ann Arbor, Associate Editor for the Journal of the American Statistical Association. She received her Ph.D. in Biostatistics from Harvard University in 2008 and joined the faculty at the University of Michigan in the same year. Dr. Wang's research focuses on statistical methods for evaluating dynamic treatment regimes, personalized health care, nonparametric and semiparametric regressions, missing data analysis, functional data analysis, and longitudinal (correlated/clustered) data analysis. She has been collaborating with investigators at M.D. Anderson Cancer Center, University of Michigan Medical School, and Harvard School of Public Health during the past 12 years.
In this talk, we present recent advances and statistical developments for evaluating Dynamic Treatment Regimes (DTR), which allow the treatment to be dynamically tailored according to evolving subject-level data. Identification of an optimal DTR is a key component for precision medicine and personalized health care. Specific topics covered in this talk include several recent projects with robust and flexible methods developed for the above research area. We will first introduce a dynamic statistical learning method, adaptive contrast weighted learning (ACWL), which combines doubly robust semiparametric regression estimators with flexible machine learning methods. We will further develop a tree-based reinforcement learning (T-RL) method, which builds an unsupervised decision tree that maintains the nature of batch-mode reinforcement learning. Unlike ACWL, T-RL handles the optimization problem with multiple treatment comparisons directly through a purity measure constructed with augmented inverse probability weighted estimators. T-RL is robust, efficient and easy to interpret for the identification of optimal DTRs. However, ACWL seems more robust against tree-type misspecification than T-RL when the true optimal DTR is non-tree-type. At the end of this talk, we will also present a new Stochastic-Tree Search method called ST-RL for evaluating optimal DTRs.
Purchase Webinar Recording (12/6/19)
Subgroup Identification for Differential Treatment Effects
Wei-Yin Loh
Wei-Yin Loh is Professor of Statistics at the University of Wisconsin, Madison. He received his PhD from Berkeley in 1982. His major research interests are in bootstrap methods and classification and regression trees. He is a fellow of the American Statistical Association and the Institute of Mathematical Statistics and a consultant to government and industry.
Many subgroup identification methods exist but they have not been compared together. To better understand the relative strengths and weaknesses of the methods, we briefly review those with publicly available software (FindIt, GUIDE, Interaction Trees, MOB, Outcome Weighted Estimation, PRIM, ROWSi, Sequential Batting, SIDES, and Virtual Twins) and then compare their performance on seven criteria: (i) variable selection bias, (ii) probability of false discovery, (iii) probability of correct variable identification, (iv) bias in subgroup treatment effect estimates, (v) expected subgroup size, (vi) expected size of subgroup treatment effects, and (vii) subgroup stability. We conclude with a bootstrap solution to performing post-selection inference on the selected subgroups.
Lessons and Strategies for a Career in Academia: A Conversation
10:00 am – 12:00 pm Eastern
Dr. Leslie McClure
Professor & Chair, Department of Epidemiology and Biostatistics
Dornsife School of Public Health at Drexel University
Dr. Elizabeth Stuart
Associate Dean for Education and Professor of Biostatistics, Mental Health, and Health Policy and Management
Johns Hopkins Bloomberg School of Public Health
As a Biostatistician, there are many paths to a successful career. Each has benefits and drawbacks and will depend on an individual's own skills and preferences. In this webinar, Drs. Elizabeth Stuart and Leslie McClure will host a conversation about their academic careers, including providing some strategies for success and describing some of the challenges they've faced. They'll consider important questions, such as: What to look for in a job? How to develop meaningful collaborations (and get out of those that are not productive)? How to prioritize activities with an eye towards promotion (e.g., collaborative and methodological projects)? How to balance teaching, research, and grant requirements? And how to balance all of that with things outside of work? However, the exact direction of the conversation will depend on the questions and engagement from webinar participants.
Machine Learning for Health Care Policy
Dr. Sherri Rose
Associate Professor of Health Care Policy (Biostatistics)
Health care research is moving toward analytic systems that take large health databases and estimate quantities of interest both quickly and robustly, incorporating advances from statistics, machine learning, and computer science. Pressing questions in prediction and causal inference are being answered with machine learning techniques. I will give an overview of the specific challenges related to developing and deploying these statistical algorithms for health policy, including examples from the areas of health plan payment, mental health outcomes, cancer staging, and medical devices. This webinar will be accessible for graduate students with most technical derivations provided in references
Biostatistical Methods for Wearable and Implantable Technology
Dr. Ciprian Crainiceanu
Professor, Department of Biostatistics
Wearable and Implantable Technology (WIT) is rapidly changing the Biostatistics data analytic landscape due to their reduced bias and measurement error as well as to the sheer size and complexity of the signals. In this talk I will review some of the most used and useful sensors in Health Sciences and the ever expanding WIT analytic environment. I will describe the use of WIT sensors including accelerometers, heart monitors, glucose monitors and their combination with ecological momentary assessment (EMA). This rapidly expanding data eco-system is characterized by multivariate densely sampled time series with complex and highly non-stationary structures. I will introduce an array of scientific problems that can be answered using WIT and I will describe methods designed to analyze the WIT data from the micro- (sub-second-level) to the macro-scale (minute-, hour- or day-level) data.
Sensitivity analysis in observational research: introducing the E-value
Dr. Tyler VanderWeele
Professor of Epidemiology
Sensitivity analysis is useful in assessing how robust an association is to potential unmeasured or uncontrolled confounding. This webinar introduces a new measure called the "E-value," which is related to the evidence for causality in observational studies that are potentially subject to confounding. The E-value is defined as the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away a specific treatment–outcome association, conditional on the measured covariates. A large E-value implies that considerable unmeasured confounding would be needed to explain away an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain away an effect estimate. The speaker and his collaborators propose that in all observational studies intended to produce evidence for causality, the E-value be reported or some other sensitivity analysis be used. They suggest calculating the E-value for both the observed association estimate (after adjustments for measured confounders) and the limit of the confidence interval closest to the null. If this were to become standard practice, the ability of the scientific community to assess evidence from observational studies would improve considerably, and ultimately, science would be strengthened.
Reference: VanderWeele, T.J. and Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167:268-274.
Online E-value Calculator: https://mmathur.shinyapps.io/evalue/
Purchase Webinar Recording (9/28/2018)
Evidence Synthesis for Clinical trials: Use of Historical Data and Extrapolation
Sebastian Weber
Associate Director SMC
Novartis Pharma AG
Satrajit Roychoudhury
A Bayesian approach provides the formal framework to incorporate external information into the statistical analysis of a clinical trial. There is an intrinsic interest of leveraging all available information for an efficient design and analysis of clinical trials. The use of external data in trials are nowadays used in earlier phases of drug development (Trippa, Rosnerand Muller, 2012; French, Thomas and Wang, 2012; Hueber et al., 2012), occasionally in phase III trials (French et al., 2012), and also in special areas such as medical devices (FDA, 2010a), orphan indications (Dupont and Van Wilder, 2011) and extrapolation in pediatric studies (Berry, 1989). This allows trials with smaller sample size or with unequal randomization (more subjects on treatment than control). In addition, the Bayesian statistical paradigm is a natural approach for combining information across heterogeneous sources, such as different trials or the adult and pediatric data. In this webinar we'll provide a statistical framework to incorporate trial external evidence with real life examples.
During the first part of the webENAR we will introduce the meta-analytic predictive (MAP) model (Neuenschwander, 2010). The MAP model is a Bayesian hierarchical model which combines the evidence from different sources (usually studies). The MAP model provides a prediction for a future study based on available information while accounting for inherent heterogeneity in the data. This approach can be used widely in different applications of biostatistics.
In the second part of the webENAR we will focus on three key applications of the MAP approach in biostatistics, which are (i) the derivation of informative priors from historical controls, (ii) probability of success and (iii) extrapolation from adult data to pediatrics. These applications will be demonstrated using the R package RBesT, the R Bayesian evidence synthesis tools, which are freely available from CRAN. The aim of the webinar is to teach the MAP approach and enable participants to apply the approach themselves with the help of RBesT.
Programming with hierarchical statistical models: An introduction to the BUGS-compatible NIMBLE system for MCMC and more
11:00 am – 1:00 pm Eastern
Chris Paciorek
Adjunct Professor, Statistical Computing Consultant
This webinar will introduce attendees to the NIMBLE system for programming with hierarchical models in R. NIMBLE
(r-nimble.org) is a system for flexible programming and dissemination of algorithms that builds on the BUGS language for declaring hierarchical models. NIMBLE provides analysts with a flexible system for using MCMC, sequential Monte Carlo and other techniques on user-specified models. It provides developers and methodologists with the ability to write algorithms in an R-like syntax that can be easily disseminated to users. C++ versions of models and algorithms are created for speed, but these are manipulated from R without any need for analysts or algorithm developers to program in C++.
While analysts can use NIMBLE as a drop-in replacement for WinBUGS or JAGS, NIMBLE provides greatly enhanced functionality in a number of ways. The webinar will first show how to specify a hierarchical statistical model using BUGS syntax (including user-defined function and distributions) and fit that model using MCMC (including user customization for better performance). We will demonstrate the use of NIMBLE for biostatistical methods such as semiparametric random effects models and clustering models. We will close with a discussion of how to use the system to write algorithms for use with hierarchical models, including building and disseminating your own methods.
Incorporating Patient Preferences into Regulatory Decision Making
11:00 am - 1:00 pm Eastern
Telba Irony, PhD
Center for Biologics Evaluation and Research
Regulatory authorities and patient advocacy groups have been paving the way towards engaging patients in medical product development and regulatory review. These efforts gave rise and relevance to the development of the Science of Patient Input, or SPI. SPI consists of scientifically valid qualitative and quantitative methods for capturing patient perspective information to incorporate it into product development and regulatory decision making. Two types of patient input, Patient Reported Outcomes (PRO) and Patient Preference Information (PPI) are expected to be captured in accordance with applicable scientific and statistical standards and best practices, and statisticians have a large role to play.
A PRO is a measurement based on a report of a patient's health status that comes directly from the patient, without interpretation of the patient's report by a clinician or anyone else. Some symptoms or other unobservable concepts known only to the patient, such as pain or fatigue, can only be measured by PRO measures.
PPI is a patient's expression of desirability or value of one course of action or selection in contrast to others. It focuses on assessing the importance, or preferences, that patients place on the benefits, harms and other aspects of treatments.
In this Webinar we will introduce key elements concerning elicitation and use of patient preferences (PPI) to inform regulatory decision making. As an example, we will present a study commissioned by the FDA to elicit obese patients' preferences in choosing weight-loss devices and show how these preferences can be used to inform regulatory decision making. We will describe the weight-loss device survey and present the survey results, which have been used to develop a decision-aid tool for regulatory reviewers. The tool provides estimates of patients' benefit-risk tradeoff preferences and also stratifies patients according to their risk-tolerance. We will conclude the Webinar by sharing experiences in using patient preferences in the regulatory process and talking about best statistical practices for eliciting and using patient preference information.
Basket Trials for Development of Targeted Treatment Regimens
Dr. Mithat Gönen
Chief, Biostatistics Service
Cancer clinical trials have traditionally been designed specific to a disease site (breast, lung, colon etc). This paradigm is being challenged by the advent of targeted treatments, regimens targeting molecular alterations in cancer cells. Since targeted treatments are not site-specific the trials evaluating them increasingly include multiple sites where the target is expressed. These trials are often called basket trials. In this WebENAR we will present several possible designs for basket trials: parallel design, aggregation design and hierarchical model-based design; comparing their operating characteristics, strengths and weaknesses. Although their applications have mostly been in oncology so far, basket trials can be used in any disease where targeted treatments can be used in molecularly defined subgroups. We will give examples of publicly available software that can be used to design and analyze basket trials.
Multi-state Models: Methods and Software
Dr. Christopher Jackson
Senior Statistician, MRC Biostatistics Unit, University of Cambridge
School of Clinical Medicine, Cambridge Institute of Public Health
Multi-state models are stochastic processes which describe how an individual moves between a set of discrete states in continuous time. They have been used for two broad classes of data. Firstly, for "panel data": intermittent observations of the state at a finite series of times, for a set of individuals, where transition times are not known. Secondly, for times to multiple events for a set of individuals, so that the state at any time is known. Combinations or slight variants of these two data types are also possible.
Data-driven Decision Making with Application to Precision Medicine
Dr. Eric Laber
In this webinar we will cover the methodological and computational underpinnings of data-driven decision making with application to precision medicine. Planned topics covered include: (i) formalizing optimal decision making through potential outcomes; (ii) regression and classification-based methods for single-stage decision problems; (iii) approximate dynamic programming and direct-search methods for multi-stage decision problems; (iv) interpretability and the research-practice gap; and (v) current research topics and open problems. We do not assume that participants have any prior exposure to these topics; however, it is assumed that participants have at least a masters-level understanding of biostatistics.
The Overlap between Statisticians and Pharmacometricians in Clinical Drug Development and a Case Study
Kenneth G. Kowalski, MS
Kowalski PMetrics Consulting, LLC
Wenping Wang, PhD
Novartis Pharmaceuticals Corporation
This WebENAR will be presented in two parts. The first part will focus on a commentary presented by Ken Kowalski discussing the overlap between statisticians and pharmacometricians working in clinical drug development. Individuals with training in various academic disciplines including pharmacokinetics, pharmacology, engineering and statistics, to name a few, have pursued careers as pharmacometricians. While pharmcometrics has benefitted greatly from advances in statistical methodology, there is considerable tension and skepticism between biostatisticians and pharmacometricians as they apply their expertise to drug development applications. This talk explores some of the root causes for this tension and provides some suggestions for improving collaborations between statisticians and pharmcometricians. The talk concludes with a plea for more statisticians to consider careers as pharmacometrics practitioners. The second part of the WebENAR will highlight a case study presented by Wenping Wang illustrating the application of pharmacokinetic-pharmacodynamic modeling of the time to first flare to support dose justification of Canakinumab in a sBLA submission. The case study will conclude with some observations regarding team interactions between statisticians and pharmacometricians that resulted in a successful sBLA submission.
Evaluation and Use of Surrogate Markers
Dr. Layla Parast
The use of surrogate markers to estimate and test for a treatment effect has been an area of popular research. Given the long follow-up periods that are often required for treatment or intervention studies, appropriate use of surrogate marker information has the potential to decrease required follow-up time. However, previous studies have shown that using inadequate markers or making inappropriate assumptions about the relationship between the primary outcome and the surrogate marker can lead to inaccurate conclusions regarding the treatment effect. Many of the available methods for identifying, validating and using surrogate markers to test for a treatment effect tend to rely on restrictive model assumptions and/or focus on uncensored outcomes. In this course, I will describe different approaches to quantify the proportion of treatment effect explained by surrogate marker information in both a non-survival outcome setting and censored survival outcome setting. One described approach will be a nonparametric method that can accommodate a setting where individuals may experience the primary outcome before the surrogate marker is measured. I will illustrate the procedures using an R package available on CRAN to examine potential surrogate markers for diabetes with data from the Diabetes Prevention Program.
How Credible are Your Conclusions about Treatment Effect When There are Missing Data? Sensitivity Analyses for Time-to-event and Recurrent-event Outcomes
Dr. Michael O'Kelly
Dr. Bohdana Ratitch
Dr. Ilya Lipkovich
Center for Statistics in Drug Development
Most experiments have missing data. When there are missing data, it is useful to provide sensitivity analyses to allow the reader of the account of the research to assess the robustness to the missing data of any conclusions made. Using the pattern-mixture framework, a variety of assumptions can be implemented with regard to categories of missing outcomes. Assumptions that would tend to undermine the alternative hypothesis can be especially useful for assessing robustness of conclusions. Multiple imputation (MI) is one quite straightforward way of implementing such pattern-mixture approaches. While MI is a standard tool for continuous outcomes, recently researchers have come up with ways of implementing MI for other outcomes, such as time-to-event and recurrent-event outcomes. This webinar describes a number of these new applications of the MI idea. The strengths and weaknesses of these approaches are described and illustrated via examples and simulations.
This webinar was not recorded and is not available for on-demand purchase.
Introduction to Clinical Trial Optimization
Dr. Alex Dmitrienko
Mediana Inc.
This webinar focuses on a broad class of statistical problems related to optimizing the design and analysis of Phase II and III trials (Dmitrienko and Pulkstenis, 2017). This general topic has attracted much attention across the clinical trial community due to increasing pressure to reduce implementation costs and shorten timelines in individual trials and development programs.
The Clinical Scenario Evaluation (CSE) framework (Benda et al., 2010) will be described to formulate a general approach to clinical trial optimization and decision-making. Using the CSE approach, main objectives of clinical trial optimization will be formulated, including selection of clinically relevant optimization criteria, identification of sets of optimal and nearly optimal values of the parameters of interest, and sensitivity assessments. Key principles of clinical trial optimization will be illustrated using a problem of identifying efficient and robust multiplicity adjustment strategies in late-stage trials (Dmitrienko et al., 2009; Dmitrienko, D'Agostino and Huque, 2013; Dmitrienko, Paux and Brechenmacher, 2015).
Software tools for applying optimization methods will be presented, including R software (Mediana package) and Windows applications with a graphical user interface (MedianaFixedDesign application).
Purchase Webinar Recording (2/3/17)
Design and Analysis of Genome-Wide Association Studies
Dr. Nilanjan Chatterjee
Bloomberg Distinguished Professor
Department of Biostatistics, Bloomberg School of Public Health
Department of Oncology, School of Medicine
Decreasing cost of large scale genotyping and sequencing technologies is fuelling investigation of association between complex traits and genetic variants across the whole genome using studies of massive sample sizes. Recent genome-wide association studies (GWAS) focused on common variants have already led to the discoveries of thousands of genetic loci across variety of complex traits, including chronic diseases such as cancers, heart diseases and type-2 diabetes. Future studies of less common and rare variants hold further promise for discovery of new genetic loci and better understanding of causal mechanisms underlying existing loci. The webinar will provide brief review of some state of the art design and analysis issues faced in the field. The topics will include sample size requirement and power calculations, methods for single- and multi-marker association testing, estimation of heritability and effect-size distribution, techniques for pleiotropic and Mendelian randomization analyses and genetic risk prediction.
Nonparametric Bayes Biostatistics
Dr. David Dunson
Arts & Sciences Professor of Statistical Science, Mathematics and Electrical & Computer Engineering
This webinar will provide an introduction to the practical use of nonparametric Bayesian methods in the analysis and interpretation of data from biomedical studies. I will start with a very brief review of the Bayesian paradigm, rapidly leading into what is meant by "Nonparametric Bayes." I'll then describe some canonical nonparametric Bayes models, including Dirichlet process mixtures and Gaussian processes. Basic practical properties and approaches for computation will be sketched, and I'll provide a practical motivation through some biomedical applications ranging from genomics to epidemiology to neuroscience. I'll finish up by describing some possibilities in terms of more advanced models that allow the density of a response variable to change flexibly with predictors, while providing practical motivation and implementation details.
Pragmatic Trials in Public Health and Medicine
11:00 am- 1:00 pm Eastern
David M. Murray, Ph.D.
Associate Director for Prevention
Director, Office of Disease Prevention
This webinar will review key issues and their solutions for pragmatic trials in public health and medicine. Pragmatic trials are used increasingly in health care settings to help clinicians choose between options for care. They often involve group- or cluster-randomization, though alternatives to randomized trials are also available. Many current trials rely upon electronic health records as the major source for data. These studies face a variety of challenges in the development and delivery of their interventions, research design, informed consent, data collection, and data analysis. This webinar will review these issues both generally and using examples from the Health Care Systems Collaboratory. The HCS Collaboratory is an NIH funded consortium of nine pragmatic trials that address a variety of health issues and outcomes, all conducted within health care systems, all relying on electronic health records as their primary source of data, with most implemented as a group- or cluster-randomized trial.
Analytic Methods for Functional Neuroimaging Data
10:00 am- 12:00 pm Eastern
F. DuBois Bowman
Dr. Daniel Drake
Dr. Ben Cassidy
Department of Biostatistics, Mailman School of Public Health
Brain imaging scanners collect detailed information on brain function and various aspects of brain structure. When used as a research tool, imaging enables studies to investigate brain function related to emotion, cognition, language, memory, and responses to numerous other external stimuli, as well as resting-state brain function. Brain imaging studies also attempt to determine the functional or structural basis for psychiatric or neurological disorders and to examine the responses of these disorders to treatment. Neuroimaging data, particularly functional images, are massive and exhibit complex patterns of temporal and spatial dependence, which pose analytic challenges. There is a critical need for statisticians to establish rigorous methods to extract information and to quantify evidence for formal inferences. In this webinar, I briefly provide background on various types of neuroimaging data (with an emphasis on functional data) and analysis objectives that are commonly targeted in the field. I also present a survey of existing methods aimed at these objectives and identify particular areas offering opportunities for future statistical contribution.
Bayesian Population Projections
10:00 am - 12:00 pm Eastern
Adrian E. Raftery
Professor of Statistics and Sociology
Projections of countries' future populations, broken down by age and sex, are widely used for planning and research. They are mostly done deterministically, but there is a widespread need for probabilistic projections. I will describe a Bayesian statistical method for probabilistic population projections for all countries. These new methods have been used by the United Nations to produce their most recent population projections for all countries.
Regulatory Perspective on Subgroup Analysis in Clinical Trials
Dr. Mohamed Alosh & Dr. Kathleen Fritsch
Division of Biometrics III, Office of Biostatistics, OTS, CDER, FDA
For a confirmatory clinical trial that established treatment efficacy in the overall population, subgroup analysis aims to investigate the extent of benefits from the therapy for the major subgroups. Consequently, findings from the subgroup analysis play a major role in interpreting the trial results. This presentation focuses on two areas related to subgroup analysis in a confirmatory clinical trial: (i) investigating consistency of treatment effect across subgroups, and (ii) designing a clinical trial with the objective of establishing treatment efficacy in a targeted subgroup in addition to the overall population. The presentation also outlines the regulatory guidelines for subgroup analysis in such trials and provides examples of clinical trials where subgroup analysis played a role in determining the population for treatment use.
Reproducible Research: The Time is Now
Dr. Keith Baggerly
The buzz phrase "Reproducible Research" refers to studies where the raw data and code supplied are enough to let a new investigator exactly match the reported results without a huge amount of effort. "Replicable Research" refers to studies whose methods, when applied to new data, give rise to qualitatively similar results. Particularly as experiments get bigger, more involved, and more expensive, reproducibility should precede replication. Unfortunately, more attention is now being focused on such issues due to some high-profile failures.
In this talk, we first illustrate the issues with some case studies from oncology showing the types of things that can go wrong, the simple nature of the most common mistakes, and what the implications can be: e.g., treating patients incorrectly. We then give some point estimates of how widespread the problems of reproducibility and replicability are thought to be, and discuss some additional problems associated with the replication. We survey tools introduced in the past few years which have made assembling reproducible studies markedly easier, discuss considerations to be applied when considering replication, and give pointers to some resources for further information.
The Statistical Analysis of fMRI Data
10:00 am to 12:00 pm Eastern
Martin Lindquist
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for studying brain activity. During the past two decades fMRI has provided researchers with an unprecedented access to the inner workings of the brain, leading to countless new insights into how the brain processes information. The field that has grown around the acquisition and analysis of fMRI data has experienced a rapid growth in the past several years and found applications in a wide variety of areas. This webinar introduces fMRI and discusses key statistical aspects involved in the analysis of fMRI data. Topics include: (a) an overview of the acquisition and reconstruction of fMRI data; (b) overview of the physiological basis of the fMRI signal; (c) common experimental designs; (d) pre-processing steps; (d) methods for localizing areas activated by a task; (e.) connectivity analysis; and (f.) prediction and brain decoding.
Sparse Logical Models for Interpretable Machine Learning
Cynthia Rudin, PhD, Associate Professor of Statistics, MIT CSAIL and Sloan School of Management, Massachusetts Institute of Technology
Possibly *the* most important obstacle in the deployment of predictive models is the fact that humans simply do not trust them. If it is known exactly which variables were important for the prediction and how they were combined, this information can be very powerful in helping to convince people to believe (or not believe) the prediction and make the right decision. In this talk I will discuss algorithms for making these non-black box predictions including:
"Bayesian Rule Lists" - This algorithm builds a decision list using a probabilistic model over permutations of IF-THEN rules. It competes with the CART algorithm for building accurate-yet-interpretable logical models. It is not a greedy algorithm like CART.
"Falling Rule Lists" - These are decision lists where the probabilities decrease monotonically along the list. These are really useful for medical applications because they stratify patients into risk categories from highest to lowest risk.
"Bayesian Or's of And's" - These are disjunctions of conjunction models (disjunctive normal forms). These models are natural for modeling customer preferences in marketing.
"The Bayesian Case Model" - This is a case-based reasoning clustering method. It provides a prototypical exemplar from each cluster along with the subspace that is important for the cluster.
Statistical Issues in Comparative Effectiveness Research
11:00 am to 1:00 pm EST
Sharon-Lise Normand, Department of Health Care Policy, Harvard Medical School & Department of Biostatistics, Harvard School of Public Health
Comparative Effectiveness Research (CER) refers to a body of research that generates and synthesizes evidence on the comparative benefits and harms of alternative interventions to prevent, diagnose, treat, and monitor clinical conditions, or to improve the delivery of health care. The evidence from CER is intended to support clinical and policy decision making at both the individual and the population level. While the growth of massive health care data sources has given rise to new opportunities for CER, several statistical challenges have also emerged. This tutorial will provide an overview of the types of research questions addressed by CER, review the main statistical methodology currently utilized, and highlight areas where new methodology is required. Inferential issues in the "big data" context are identified. Examples from cardiology and mental illness will illustrate methodological issues.
Statistical Challenges in Genomics High Throughput Data
Presenter: Rafa Irizarry, PhD
Professor of Biostatistics and Computational Biology at the Dana Farber Cancer Center
Professor of Biostatistics at the Harvard School of Public Health
http://rafalab.dfci.harvard.edu/
In this webinar I will give an overview of genomics technologies and the challenges arising when analyzing the data they produce. Specifically, I will focus on microarrays and next generation sequencing technologies. We will cover statistical issues related to preprocessing, normalization, detecting differential expression, and dealing with batch effects.
An Introduction to Dynamic Treatment Regimes
11:00 am to 1:00 pm (EST)
Presenter: Marie Davidian, PhD North Carolina State University
Description: Treatment of patients with chronic diseases or disorders in clinical practice involves a series of decisions made over time. Clinicians adjust, change, modify, or discontinue therapies based on the patient's observed progress, side effects, compliance, and so on, with the goal of "personalizing" treatment to the patient in order to provide the best care. The decisions are typically based on synthesis of the available information on the patient using clinical experience and judgment.
A "dynamic treatment regime," also referred to as an "adaptive treatment strategy," is a set of sequential rules that dictate how to make decisions on treatment of a patient over time. Each rule corresponds to a key decision point at which a decision on which treatment action to take from among the available options must be made. Based on patient information, the rule outputs the next treatment action. Thus, a dynamic treatment regime is an algorithm that formalizes the way clinicians manage patients in practice.
In this presentation, we introduce the notion of a dynamic treatment regime and an appropriate statistical framework in which treatment regimes can be studied. We demonstrate how statistical inference may be made on the effects of different regimes based on data from so-called sequential, multiple assignment, randomized trials (SMARTs). We conclude with a discussion of current challenges, including the development of "optimal" treatment regimes. The material presented is ideal background for the shortcourse on personalized medicine and optimal dynamic treatment regimes to be offered at the ENAR Spring Meeting in March 2015.


Published 04/25/2019 06:49:24 am at 04/25/2019 06:49:24 am in Torch Circle Cutter.
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