Personalized Predictive Medicine and Genomic Clinical Trials：个性化预测医学和基因组的临床试验.pptVIP

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Personalized Predictive Medicine and Genomic Clinical Trials：个性化预测医学和基因组的临床试验.ppt

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Personalized Predictive Medicine and Genomic Clinical Trials：个性化预测医学和基因组的临床试验

It is difficult to have the right single completely defined predictive biomarker identified and analytically validated by the time the pivotal trial of a new drug is ready to start accrual Changes in the way we do phase II trials Adaptive methods for the refinement and evaluation of predictive biomarkers in the pivotal trials in a non-exploratory manner Use of archived tissues in focused “prospective-retrospective” designs based on randomized pivotal trials Multiple Biomarker Design Have identified K candidate binary classifiers B1 , …, BK thought to be predictive of patients likely to benefit from T relative to C Eligibility not restricted by candidate classifiers For notation let B0 denote the classifier with all patients positive Test T vs C restricted to patients positive for Bk for k=0,1,…,K Let S(Bk) be log partial likelihood ratio statistic for treatment effect in patients positive for Bk (k=1,…,K) Let S* = max{S(Bk)} , k* = argmax{S(Bk)} For a global test of significance Compute null distribution of S* by permuting treatment labels If the data value of S* is significant at 0.05 level, then claim effectiveness of T for patients positive for Bk* Let S* = max{S(Bk)} , k* = argmax{S(Bk)} in actual data The new treatment is superior to control for the population defined by k* Repeating the analysis for bootstrap samples of cases provides an estimate of the stability of k* (the indication) an interval estimate of S* (the size of treatment effect for the size of treatment effect in the target population) Adaptive Signature Design Boris Freidlin and Richard Simon Clinical Cancer Research 11:7872-8, 2005 Adaptive Signature DesignEnd of Trial Analysis Compare E to C for all patients at significance level α0 (eg 0.04) If overall H0 is rejected, then claim effectiveness of E for eligible patients Otherwise Otherwise: Using only the first half of patients accrued during the trial, develop a binary classifier that predicts the subset of patients most likely