***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: Despite years of research and hundreds of reports on tumor markers in oncology , the number of markers that have emerged as clinical ly useful is pitifully small . Often , initially reported studies of a marker show great promise , but subsequent studies on the same or related markers yield inconsistent conclusions or st and in direct contradiction to the promising results . It is imperative that we attempt to underst and the reasons that multiple studies of the same marker lead to differing conclusions . A variety of method ologic problems have been cited to explain these discrepancies . Unfortunately , many tumor marker studies have not been reported in a rigorous fashion , and published articles often lack sufficient information to allow adequate assessment of the quality of the study or the generalizability of study results . The development of guidelines for the reporting of  tumor marker studies  was a major recommendation of the National Cancer Institute-European Organisation for Research and Treatment of Cancer ( NCI-EORTC ) First International Meeting on Cancer Diagnostics in 2000 . As for the successful CONSORT initiative for r and omized trials and for the STARD statement for diagnostic studies , we suggest guidelines to provide relevant information about the study design , preplanned hypotheses , patient and specimen characteristics , assay methods , and statistical analysis methods . In addition , the guidelines suggest helpful presentations of data and important elements to include in discussion s. The goal of these guidelines is to encourage transparent and complete reporting so that the relevant information will be available to others to help them to judge the usefulness of the data and underst and the context in which the conclusions apply OBJECTIVE To create a simple prediction rule that could perform as well as the 2-h postchallenge plasma glucose ( PCPG ) test to predict those at risk for diabetes . We created a prediction rule in one sample and prospect ively vali date d it for incident diabetes in a separate cohort . RESEARCH DESIGN AND METHODS A cross-sectional analysis with data from the Rancho Bernardo Study ( age 67 + /- 11 years ) to derive a rule predicting abnormal PCPG > /=140 mg/dl , using demographic , clinical , and laboratory data of nondiabetic participants with fasting plasma glucose ( FPG ) < 126 mg/dl . Data from the Health , Aging and Body Composition study ( age 74 + /- 3 years ) were used to prospect ively vali date this rule for incident diabetes and compare it with the predictive ability of the PCPG test . RESULTS Of 1,549 RBS participants , 514 ( 33 % ) had PCPG > /=140 mg/dl . Female sex , age , triglycerides , and FPG were most significantly associated with abnormal PCPG . Based on st and ardized beta-coefficients , we allotted 1 point for female sex , triglycerides > /=150 mg/dl , or FPG 95 - 104 mg/dl . Age > /=70 years or FPG 105 - 115 mg/dl were given 2 points , and FPG 116 - 125 mg/dl received 3 points . In the validation cohort , this simple prediction rule was as good as the 2-h PCPG test for predicting incident diabetes ( C-statistic : 0.71 for both ) . CONCLUSIONS Advanced age , female sex , FPG , and triglycerides were able to predict adults at risk for diabetes equally well as the 2-h PCPG test . Using this rule , clinicians may better identify older persons who should receive intensive lifestyle intervention to prevent type 2 diabetes We performed a Monte Carlo study to evaluate the effect of the number of events per variable ( EPV ) analyzed in logistic regression analysis . The simulations were based on data from a cardiac trial of 673 patients in which 252 deaths occurred and seven variables were cogent predictors of mortality ; the number of events per predictive variable was ( 252/7 =) 36 for the full sample . For the simulations , at values of EPV = 2 , 5 , 10 , 15 , 20 , and 25 , we r and omly generated 500 sample s of the 673 patients , chosen with replacement , according to a logistic model derived from the full sample . Simulation results for the regression coefficients for each variable in each group of 500 sample s were compared for bias , precision , and significance testing against the results of the model fitted to the original sample . For EPV values of 10 or greater , no major problems occurred . For EPV values less than 10 , however , the regression coefficients were biased in both positive and negative directions ; the large sample variance estimates from the logistic model both overestimated and underestimated the sample variance of the regression coefficients ; the 90 % confidence limits about the estimated values did not have proper coverage ; the Wald statistic was conservative under the null hypothesis ; and paradoxical associations ( significance in the wrong direction ) were increased . Although other factors ( such as the total number of events , or sample size ) may influence the validity of the logistic model , our findings indicate that low EPV can lead to major problems Background There is no consensus on the most appropriate approach to h and le missing covariate data within prognostic modelling studies . Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model . Methods Data sets were generated to resemble the skewed distributions seen in a motivating breast cancer example . Multivariate missing data were imposed on four covariates using four different mechanisms ; missing completely at r and om ( MCAR ) , missing at r and om ( MAR ) , missing not at r and om ( MNAR ) and a combination of all three mechanisms . Five amounts of incomplete cases from 5 % to 75 % were considered . Complete case analysis ( CC ) , single imputation ( SI ) and five multiple imputation ( MI ) techniques available within the R statistical software were investigated : a ) data augmentation ( DA ) approach assuming a multivariate normal distribution , b ) DA assuming a general location model , c ) regression switching imputation , d ) regression switching with predictive mean matching ( MICE-PMM ) and e ) flexible additive imputation models . A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained . Results Performing a CC analysis produced unbiased regression estimates , but inflated st and ard errors , which affected the significance of the covariates in the model with 25 % or more missingness . Using SI , underestimated the variability ; result ing in poor coverage even with 10 % missingness . Of the MI approaches , applying MICE-PMM produced , in general , the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms . However , this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50 % or more cases had missing data imposed with a MCAR , MAR or combined mechanism . When the missingness depended on the incomplete covariates , i.e. MNAR , estimates were biased with more than 10 % incomplete cases for all MI approaches . Conclusion The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50 % of the cases have missing data and the missing data are not MNAR This study aim ed to provide the epidemiological model evaluating the risk of developing type 2 diabetes ( T2DM ) in Taiwan periodic health-check population . We derived risk functions using multivariate Cox regression in a r and om half of the sample . Rules based on these risk functions were evaluated in another half . Model coefficients were used to assign each variable a score . 73,961 subjects aged 35 - 74 , were included and followed up with a median 3.15 years . Six predictive models ( PMs ) were developed . PM1 contained simple clinical information , while PM2 contained fasting plasma glucose ( FPG ) based on PM1 , and PM3 further added variables indicating lipid level , liver and kidney . PM4 only included FPG . The capability of published ARIC score model was also evaluated . Eventually we considered score defined nine predictors by PM2 . The area under the ROC curve ( AUC ) was 0.848 ( 95 % CI , 0.829 - 0.868 ) predicting diabetes within 5 years , and also had adequate performance in validation sub sample ( AUC=0.833 , 95 % CI , 0.811 - 0.855 ) . The 5-year T2DM probability can be calculated by : 1 - 0.9743960037 exp((score points -15.0281284 ) ) . We concluded that this diabetes risk score , derived from clinical information combined with FPG is a simple , effective tool to identify individuals at high risk for undiagnosed T2DM AIMS To develop risk prediction models of future diabetes in Mauritian Indians . METHODS Three thous and and ninety-four Mauritian Indians ( 1141 men , aged 20 - 65 years ) without diabetes in 1987 or 1992 were followed up to 1992 or 1998 . Subjects underwent repeated oral glucose tolerance tests and diabetes was diagnosed according to 2006 World Health Organization/International Diabetes Federation criteria . Cox regression models for interval censored data were performed using data from 1544 r and omly selected participants .  Predicted probabilities for diabetes were calculated and vali date d in the remaining 1550 subjects . RESULTS Over 11 years of follow-up , there were 511 cases of diabetes . Among variables tested , family history of diabetes , obesity ( body mass index , waist circumference ) and glucose were significant predictors of diabetes . Predicted probabilities derived from a simple model fitted with sex , family history of diabetes and obesity ranged from 0.05 to 0.64 in men and 0.03 to 0.49 in women . To predict the onset of diabetes , area under the receiver operating characteristic ( ROC ) curve ( AROC ) of predicted probabilities was 0.62 ( 95 % confidence interval , 0.56 - 0.68 ) in men and 0.64 ( 0.59 - 0.69 ) in women . At a cut-off point of 0.12 , the sensitivity and specificity were 0.72 ( 0.71 - 0.74 ) and 0.47 ( 0.45 - 0.49 ) in men and 0.77 ( 0.75 - 0.78 ) and 0.50 ( 0.48 - 0.52 ) in women , respectively . Addition of fasting plasma glucose ( FPG ) to the model improved the prediction slightly [ AROC curve 0.70 ( 0.65 - 0.76 ) in men , 0.71 ( 0.67 - 0.76 ) in women ] . CONCLUSIONS A diabetes prediction model based on obesity and family history yielded moderate discrimination in Mauritian Indians , which was slightly inferior to the model with the FPG but may be useful in low-income countries to promote identification of people at high risk of diabetes Aims /hypothesisA range of prediction rules for the onset of type 2 diabetes have been proposed . However , most studies have been conducted in white groups and it is not clear whether these models apply to Asian population s. The purpose of this study was to construct a simple points model for predicting  incident diabetes among Chinese people . Methods We estimated the 10 year risk of diabetes in a cohort study of middle-aged and elderly participants who were free from diabetes at baseline .  Cox regression coefficients were used to construct the simple points model and the discriminatory ability of the result ing prediction rule was determined using AUC and net reclassification improvement and integrated discrimination improvement statistics . Fivefold r and om splitting was used to test the internal validity and obtain bootstrap estimates of the AUC . Results Of the 2,960 participants without diabetes at the baseline examination , 548 developed type 2 diabetes during a median 10 year follow-up period . Age ( four points ) , elevated fasting glucose ( 11 points ) , body mass index ( eight points ) , triacylglycerol ( five points ) , white blood cell count ( four points ) and a higher HDL-cholesterol ( negative four points ) were found to strongly predict diabetes incidence in a multivariate model . The estimated AUC for the model was 0.702 ( 95 % CI 0.676–0.727 ) . This model performed better than existing prediction models developed in other population s , including the Prospect i ve Cardiovascular Münster , Cambridge , San Antonia and Framingham models for diabetes risk . Conclusions /interpretationWe have constructed a model for predicting the 10 year incidence of diabetes in Chinese people that could be useful for identifying individuals at high risk of diabetes in the Chinese population Objective To develop and vali date a new diabetes risk algorithm ( the QDScore ) for estimating 10 year risk of acquiring diagnosed type 2 diabetes  over a 10 year time period in an ethnically and socioeconomically diverse population . Design Prospect i ve open cohort study using routinely collected data from 355 general practice s in Engl and and Wales to develop the score and from 176 separate practice s to vali date the score . Participants 2 540 753 patients aged 25 - 79 in the derivation cohort , who contributed 16 436 135 person years of observation and of whom 78 081 had an incident diagnosis of type 2 diabetes ; 1 232 832 patients ( 7 643 037 person years ) in the validation cohort , with 37 535 incident cases of type 2 diabetes . Outcome measures A Cox proportional hazards model was used to estimate effects of risk factors in the derivation cohort and to derive a risk equation in men and women . The predictive variables examined and included in the final model were self assigned ethnicity , age , sex , body mass index , smoking status , family history of diabetes , Townsend deprivation score , treated hypertension , cardiovascular disease , and current use of corticosteroids ; 
    Output:
    
Conclusions We found widespread use of poor methods that could jeopardise model development , including univariate pre-screening of variables , categorisation of continuous risk predictors and poor h and ling of missing data .
The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes .