***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: Prognosis studies are investigations of future events or the evaluation of associations between risk factors and health outcomes in population s of patients ( 1 ) . The results of such studies improve our underst and ing of the clinical course of a disease and assist clinicians in making informed decisions about how best to manage patients . Prognostic research also informs the design of intervention studies by helping define subgroups of patients who may benefit from a new treatment and by providing necessary information about the natural history of a disorder ( 2 ) . There has recently been a rapid increase in the use of systematic review methods to synthesize the evidence on research questions related to prognosis . It is essential that investigators conducting systematic review s thoroughly appraise the method ologic quality of included studies to be confident that a study 's design , conduct , analysis , and interpretation have adequately reduced the opportunity for bias ( 3 , 4 ) . Caution is warranted , however , because inclusion of method ologically weak studies can threaten the internal validity of a systematic review ( 4 ) . This follows abundant empirical evidence that inadequate attention to biases can cause invalid results and inferences ( 5 - 9 ) . However , there is limited consensus on how to appraise the quality of prognosis studies ( 1 ) . A useful framework to assess bias in such studies follows the basic principles of epidemiologic research ( 10 , 11 ) . We focus on 6 areas of potential bias : study participation , study attrition , prognostic factor measurement , confounding measurement and account , outcome measurement , and analysis . The main objectives of our review of review s are to describe methods used to assess the quality of prognosis studies and to describe how well current practice s assess potential biases . Our secondary objective is to develop recommendations to guide future quality appraisal , both within single studies of prognostic factors and within systematic review s of the evidence . We hope this work facilitates future discussion and research on biases in prognosis studies and systematic review s. Methods Literature Search and Study Selection We identified systematic review s of prognosis studies by search ing MEDLINE ( 1966 to October 2005 ) using the search strategy recommended by McKibbon and colleagues ( 12 ) . This strategy combines broad search terms for systematic review s ( systematic review .mp ; meta- analysis .mp ) and a sensitive search strategy for prognosis studies ( cohort , incidence , mortality , follow-up studies , prognos * , predict * , or course ) . We also search ed the reference lists of included review s and method ologic papers to identify other relevant publications . We restricted our search to English- language publications . One review er conducted the search and selected the studies . Systematic review s , defined as review s of published studies with a comprehensive search and systematic selection , were included if they assessed the method ologic quality of the included studies by using 1 or more explicit criteria . We excluded studies if they were meta-analyses of independent patient data only , if their primary goal was to investigate the effectiveness of an intervention or specific diagnostic or screening tests , or if they included studies that were not done on humans . Data Extraction and Synthesis Individual items included in the quality assessment of the systematic review s were recorded as they were reported in the publication ( that is , the information that would be available to readers and future review ers ) . We review ed journal Web sites and contacted the authors of the systematic review s for additional information when authors made such an offer in their original papers . When review s assessed different study design s by using different sets of quality items , we extracted only those items used to assess cohort studies . We constructed a comprehensive list of distinct items that the review s used to assess the quality of their included studies . The full text of each review was screened . All items used by the review authors to assess the quality of studies were extracted into a computerized spreadsheet by 1 review er . Two experienced review ers , a clinical epidemiologist and an epidemiologist , independently synthesized the quality items extracted from the prognosis review s to determine how well the systematic review s assessed potential biases . We did this in 3 steps : 1 ) identified distinct concepts or domains addressed by the quality items ; 2 ) grouped each extracted quality item into the appropriate domain or domains ; and 3 ) identified the domains necessary to assess potential biases in prognosis studies . We then used this information to assess how well the review s ' quality assessment included items from the domains necessary to assess potential biases . After completing each of the first 3 steps , the review ers met to attempt to reach a consensus . The consensus process involved each review er presenting his or her observations and results , followed by discussion and debate . A third review er was available in cases of persistent disagreement or uncertainty . In the first step , all domains addressed by the quality items were identified . The first review er iteratively and progressively defined the domains as items were extracted from the included review s. The second review er defined domains from a r and om list of all extracted quality items . Limited guidance was provided to the review ers so that their assessment s and definitions of domains would be independent . The review ers agreed on a final set of domains that adequately and completely defined all of the extracted items . In the second step , review ers independently grouped each extracted item into the appropriate domains . Review ers considered each extracted item by asking , What is each particular quality item addressing ? or What are the review 's authors getting at with the particular quality assessment item ? . Items were grouped into the domain or domains that best represented the concepts being addressed . For example , the extracted items at least 80 % of the group originally identified was located for follow-up and follow-up was sufficiently complete or does n't jeopardize validity were each independently classified by both review ers as assessing the domain completeness of follow-up adequate , whereas the extracted item quantification and description of all subjects lost to follow-up was classified as assessing the domain completeness of follow-up described . In the third step , we identified the domains necessary to assess potential biases . Each review er considered the ability of the identified domains to adequately address , at least in part , 1 of the following 6 potential biases : 1 ) study participation , 2 ) study attrition , 3 ) prognostic factor measurement , 4 ) confounding measurement and account , 5 ) outcome measurement , and 6 ) analysis . Domains were considered to adequately address part of the framework if information garnered from that domain would inform the assessment of potential bias . For example , both review ers judged that the identified domain study population represents source population or population of interest assessed potential bias in a prognosis study , whereas the domain research question definition did not , although the latter is an important consideration in assessing the inclusion of studies in a systematic review . Finally , on the basis of our previous ratings , we looked at whether each review included items from the domains necessary to assess the 6 potential biases . We calculated the frequency of systematic review s by assessing each potential bias and the number of review s that adequately assessed bias overall . From this systematic synthesis , we developed recommendations for improving quality appraisal in future systematic review s of prognosis studies . We used Microsoft Access and Excel 2002 ( Microsoft Corp. , Redmond , Washington ) for data management and SAS for Windows , version 9.1 ( SAS Institute , Inc. , Cary , North Carolina ) for descriptive statistics . Role of the Funding Sources The funding sources , the Canadian Institutes of Health Research , the Canadian Chiropractic Research Foundation , the Ontario Chiropractic Association , and the Ontario Ministry of Health and Long Term Care , did not have a role in the collection , analysis , or interpretation of the data or in the decision to su bmi t the manuscript for publication . Results We identified 1384 potentially relevant articles . Figure 1 shows a flow chart of studies that were included and excluded . Figure 2 shows the number of review s identified by year of publication . We excluded 131 systematic review s of prognosis studies that did not seem to include any quality assessment of the included studies ; this represented 44 % of prognosis review s. We included 163 review s of prognosis studies in our analysis ( 13 - 175 ) . The most common topics were cancer ( 15 % ) , musculoskeletal disorders and rheumatology ( 13 % ) , cardiovascular ( 10 % ) , neurology ( 10 % ) , and obstetrics ( 10 % ) . Other review s included a wide range of health and health care topics . Sixty-three percent of the review s investigated the association between a specific prognostic factor and a particular outcome ; the remainder investigated multiple prognostic factors or models . The number of primary studies included in each systematic review ranged from 3 to 167 ( median , 18 [ interquartile range , 12 to 31 ] ) . A complete description of the included review s is available from the authors on request . Figure 1 . Flow diagram of inclusion and exclusion criteria of systematic review s. Figure 2 . Number of systematic review s of prognosis studies identified over time . Quality Items One hundred fifty-three review s provided adequate detail to allow extraction of quality items . Eight hundred eighty-two distinct quality items were extracted from the review s. Most review s developed their own set of quality items , with only a few applying criteria from previous review s. Most quality items Study Type – Prevalence ( prospect i ve cohort ) Level of Evidence Pre clinical data showed anticancer effects of statins in melanoma , but meta‐analyses could not demonstrate a reduced melanoma incidence in statin users . Rather than preventing occurrence , statins might reduce growth and metastatic spread of melanomas and ultimately improve survival . In this population ‐based study , we investigated the relationship between statin use and survival of melanoma patients . Patients ≥18 years who were diagnosed with cutaneous melanoma ( Breslow thickness > 1 mm ) and registered in the Eindhoven Cancer Registry and in PHARMO Data base Network between 1 January 1998 and 31 December 2010 were eligible .  The hazard ratio ( HR ) of all‐cause mortality was calculated by employing adjusted time‐dependent and time‐fixed Cox proportional hazard models . Disease‐specific survival was estimated by means of 3‐year relative survival rates ( RSR ) . A control cohort of r and omly selected patients using statins from PHARMO Data base Network matched on age and gender was used to compare RSR of statin users to the general population . After melanoma diagnosis , 171 of 709 patients used statins . Use of statins showed a nonsignificantly decreased hazard of death ( adjusted HR 0.76 , 95 % confidence interval [ CI ] 0.50–1.61 ) . After stratification for gender , male but not female statin users showed a favorable outcome compared to nonusers ( HR 0.57 , 95 % CI 0.32–0.99 ; HR 1.22 , 95 % CI 0.62–2.38 , respectively ) . Three‐year RSR for male statin users tended to be higher than for nonusers ( 91 % vs. 80.5 % , P = 0.06 ) , no differences were observed in women ( 87.1 % vs. 92.5 % , P = 0.76 ) . Statin use was not associated with an improved survival  of melanoma patients . The trend for better survival of male in contrast to female statin users warrants further research Background : Pre clinical evidence from lung cancer cell lines and animal models suggest that statins could have anticancer properties . We investigated whether  statin users had reduced risk of cancer-specific mortality in a population -based cohort of lung cancer patients . Methods : Newly diagnosed lung cancer patients , from 1998 to 2009 , were identified from English cancer registry data and linked to the UK Clinical Practice Research Data link , providing prescription records , and to Office of National Statistics mortality data up to 2012 . Cox regression models were used to calculate HRs for cancer-specific mortality and 95 % confidence intervals ( CI ) by statin use before and after diagnosis , and to adjust these HRs for potential confounders . Results : In 3,638 lung cancer patients , there was some evidence that statin use after diagnosis was associated with reduced lung cancer – specific mortality ( adjusted HR , 0.89 ; 95 % CI , 0.78–1.02 ; P = 0.09 ) . Associations were more marked after 12 prescriptions ( adjusted HR , 0.81 ; 95 % CI , 0.67–0.98 ; P = 0.03 ) and when lipophilic statins were investigated ( adjusted HR , 0.81 ; 95 % CI , 0.70–0.94 ; P = 0.01 ) , but were attenuated in some sensitivity analyses . Furthermore , in 11,051 lung cancer patients , statin use before diagnosis was associated with reduced lung cancer – specific mortality ( adjusted HR , 0.88 ; 95 % CI , 0.83–0.93 ; P < 0.001 ) . Conclusions : There was some evidence that lung cancer patients who used statins , and particularly simvastatin  , had reduced rates of cancer-specific mortality . Impact : These findings should first be confirmed in observational studies , but provide some support for conducting r and omized controlled trials of simvastatin as adjuvant cancer therapy in lung cancer patients . Cancer Epidemiol Biomarkers Prev ; 24(5 ) ; 833–41 . © 2015 AACR Lipid-lowering drugs are used for the prevention of cardiovascular diseases .  Statins are the most commonly used lipid-lowering drugs . Evidence from pre clinical and observational studies suggests that statins might improve the prognosis of breast cancer patients . We analyzed data from the German MARIEplus study , a large prospect i ve population -based cohort of patients aged 50 and older , who were diagnosed with breast cancer between 2001 and 2005 . For overall mortality , breast-cancer specific mortality , and non-breast-cancer mortality , we included 3189 patients with invasive breast cancer stage I – IV , and for recurrence risk 3024 patients with breast cancer stage I – III . We used Cox proportional hazards models to assess the association with self-reported lipid-lowering drug use at recruitment . We stratified by study region , tumor grade , and estrogen/progesterone receptor status , and adjusted for age , tumor size , nodal status , metastases ( stage I – IV only ) , menopausal hormone treatment , mode 
    Output:
    
Statin therapy has potential survival benefit for patients with malignancy .