Inference on treatment effects for targeted clinical trials under enrichment design

After completion of a human genome project, the disease targets at molecular level can be identified. As a result, treatment modality for molecular targets can be developed. In practice, targeted clinical trials are usually conducted for evaluation of the possibility and feasibility of the individualized treatment of patients. However, the accuracy of diagnostic devices for identification of such molecular targets is usually not perfect. Therefore, some of the patients enrolled in targeted clinical trials with a positive result by the diagnostic device might not have the specific molecular targets and hence the treatment effects of the targeted drugs estimated from targeted clinical trials could be biased for the patient population truly with the molecular targets. Under an enrichment design for targeted clinical trials, we propose to use the EM algorithm and bootstrap method for obtaining the inference of the treatment effects of the targeted drugs in the patient population truly with molecular targets. A simulation study was conducted to empirically investigate the bias and variability of the proposed estimator and the size and power of the proposed testing method. Simulation results demonstrate that the proposed estimator is unbiased with adequate precision and the confidence interval can provide satisfactory coverage probability. In addition, the proposed testing procedure can adequately control the size with sufficient power. A practical example illustrates the utility of the proposed method. Copyright © 2009 John Wiley & Sons, Ltd.     

Statistical inference on censored data for targeted clinical trials under enrichment design

For the traditional clinical trials, inclusion and exclusion criteria are usually based on some clinical endpoints; the genetic or genomic variability of the trial participants are not totally utilized in the criteria. After completion of the human genome project, the disease targets at the molecular level can be identified and can be utilized for the treatment of diseases. However, the accuracy of diagnostic devices for identification of such molecular targets is usually not perfect. Some of the patients enrolled in targeted clinical trials with a positive result for the molecular target might not have the specific molecular targets. As a result, the treatment effect may be underestimated in the patient population truly with the molecular target. To resolve this issue, under the exponential distribution, we develop inferential procedures for the treatment effects of the targeted drug based on the censored endpoints in the patients truly with the molecular targets. Under an enrichment design, we propose using the expectation–maximization algorithm in conjunction with the bootstrap technique to incorporate the inaccuracy of the diagnostic device for detection of the molecular targets on the inference of the treatment effects. A simulation study was conducted to empirically investigate the performance of the proposed methods. Simulation results demonstrate that under the exponential distribution, the proposed estimator is nearly unbiased with adequate precision, and the confidence interval can provide adequate coverage probability. In addition, the proposed testing procedure can adequately control the size with sufficient power. On the other hand, when the proportional hazard assumption is violated, additional simulation studies show that the type I error rate is not controlled at the nominal level and is an increasing function of the positive predictive value. A numerical example illustrates the proposed procedures. Copyright © 2013 John Wiley & Sons, Ltd.