J Biomed Inform - An empirical approach to model selection through validation for censored survival data.


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Medical prognostic models can be designed to predict the future course or outcome of disease progression after diagnosis or treatment. The existing variable selection methods may be precluded by full model advocates when we build a prediction model owing to their estimation bias and selection bias in right-censored time-to-event data. If our objective is to optimize predictive performance by some criterion, we can often achieve a reduced model that has a little bias with low variance, but whose overall performance is enhanced. To accomplish this goal, we propose a new variable selection approach that combines Stepwise Tuning in the Maximum Concordance Index (STMC) with Forward Nested Subset Selection (FNSS) in two stages. In the first stage, the proposed variable selection is employed to identify the best subset of risk factors optimized with the concordance index using inner cross-validation for optimism correction in the outer loop of cross-validation, yielding potentially different final models for each of the folds. We then feed the intermediate results of the prior stage into another selection method in the second stage to resolve the overfitting problem and to select a final model from the variation of predictors in the selected models. Two case studies on relatively different sized survival data sets as well as a simulation study demonstrate that the proposed approach is able to select an improved and reduced average model under a sufficient sample and event size compared with other selection methods such as stepwise selection using the likelihood ratio test, Akaike Information Criterion (AIC), and lasso. Finally, we achieve better final models in each dataset than their full models by most measures. These results of the model selection models and the final models are assessed in a systematic scheme through validation for the independent performance.

Resumo Limpo

medic prognost model can design predict futur cours outcom diseas progress diagnosi treatment exist variabl select method may preclud full model advoc build predict model owe estim bias select bias rightcensor timetoev data object optim predict perform criterion can often achiev reduc model littl bias low varianc whose overal perform enhanc accomplish goal propos new variabl select approach combin stepwis tune maximum concord index stmc forward nest subset select fnss two stage first stage propos variabl select employ identifi best subset risk factor optim concord index use inner crossvalid optim correct outer loop crossvalid yield potenti differ final model fold feed intermedi result prior stage anoth select method second stage resolv overfit problem select final model variat predictor select model two case studi relat differ size surviv data set well simul studi demonstr propos approach abl select improv reduc averag model suffici sampl event size compar select method stepwis select use likelihood ratio test akaik inform criterion aic lasso final achiev better final model dataset full model measur result model select model final model assess systemat scheme valid independ perform

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