Early prediction of median survival among a large AIDS surveillance cohort

Analysis of time-to-event data with nonuniform patient entry and loss to follow-up under a two-stage seamless adaptive design with weibull distribution

In the pharmaceutical industry, a two-stage seamless adaptive design that combines two separate independent clinical trials into a single clinical study is commonly employed in clinical research and development. In practice, in the interest of shortening the development process, it is not uncommon to consider study endpoints with different treatment durations at different stages (Chow and Chang, 2006 ; Maca et al., 2006 ). In this study, our attention is placed on the case where the study endpoints of interest are time-to-event data where the durations at the two stages are different with nonuniform patient entry and losses to follow-up or dropouts. Test statistics for the final analysis based on the combined data are developed under various hypotheses for testing equality, superiority, noninferiority, and equivalence. In addition, formulas for sample size calculation and allocation between the two stages based on the proposed test statistic are derived.

Making Sense of Clinical Trial Data: Is Inverse Probability of Censoring Weighted Analysis the Answer to Crossover Bias?

Ideally, therapeutic interventions are evaluated through randomized clinical trials. These trials are commonly analyzed with an intent-to-treat (ITT) approach, whereby patients are analyzed in their assigned treatment group regardless of actual treatment received. If an interim analysis of such trials demonstrates compelling evidence of a difference in benefit, ethical considerations often dictate that the trial be unblinded and participants be provided access to the more efficacious agent. Because interim analysis may not address longer-term outcomes of interest, important clinical questions such as overall survival benefit—the ultimate test of efficacy to many—may remain unanswered. The ensuing crossover disturbs randomization and may lead to biased longer-term analysis, compromising the utility of clinical data. This has been especially apparent in recent adjuvant and prevention breast cancer trials. We consider four such trials: HERA (Herceptin Adjuvant), NSABP P-1 (National Surgical Adjuvant Breast and Bowel Project Breast Cancer Prevention P-1), MA.17, and BIG 1-98 (Breast International Group 1-98), the long-term outcomes of which were complicated by unblinding and selective crossover. We also discuss the biases associated with ITT analysis and, alternatively, censoring of follow-up data (ie, dropping out) after selective crossover. Moreover, we discuss how the statistical procedure of inverse probability of censoring weighted (IPCW) analysis may be used to account for selective crossover as an alternative to ITT or censoring analysis, as was recently done for the BIG 1-98 trial. Notably, IPCW analysis may be particularly suited for detecting overall survival benefits that otherwise would not be detected with an ITT approach, as reported for the BIG 1-98 trial.