Tumor Burden Modeling Versus Progression-Free Survival for Phase II Decision Making

Abstract

Randomized Phase II oncology trial endpoints for decision making include both progression-free survival (PFS) and change in tumor burden as measured by the sum of longest diameters (SLD) of the target lesions. In addition to observed SLD changes, tumor shrinkage and growth parameters can be estimated from the patient-specific SLD profile over time. The ability of these SLD analyses to identify an active drug is contrasted with that of a PFS analysis through the simulation of Phase II trials via resampling from each of 6 large, Phase II and III trials, 5 of which were positive and one negative. From each simulated Phase II trial, a P value was obtained from 4 analyses-a log-rank test on PFS, a Wilcoxon rank-sum test on the minimum observed percentage change from baseline in SLD, and 2 nonlinear, mixed-effects model analyses of the SLD profiles. All 4 analyses led to approximately uniformly distributed P values in the negative trial. The PFS analysis was the best or nearly the best analysis in the other 5 trials. In only one of the positive studies did the modeling analysis outperform the analysis of the minimum SLD. In conclusion, for the decision to start a Phase III trial based on the results of a randomized Phase II trial of an oncology drug, PFS appears to be a better endpoint than does SLD, whether analyzed through simple SLD endpoints, such as the minimum percentage change from baseline, or through the modeling of the SLD time course to estimate tumor dynamics.

Comparative Effects of CT Imaging Measurement on RECIST End Points and Tumor Growth Kinetics Modeling

“…The primary advantage of the use of quantitative measures of tumor burden in early phase trials is to improve statistical power for detecting treatment effects. During this investigation, newly published analyses suggested that quantitative assessments of tumor burden were no more useful than RECIST‐based categorical assessments or PFS . Our findings are consistent with the hypothesis that the RECIST‐based methods by which tumor measurement data are collected biases these evaluations.…”

“…Our findings are consistent with investigators collecting and curating the quantitative tumor burden data with sufficient precision to support use of RECIST but not to support more computationally intensive methods of evaluating effects of treatments in small clinical trials. As long as this remains the process by which tumor burden data are collected, we would expect to find no consistent advantages to use of quantitative methods (such as tumor size ratio) in small phase II trials over more qualitative time‐to‐event strategies (such as PFS) for predicting impact on overall survival …”

“…Model‐based strategies have had limited testing and require validation. In studies of colorectal cancer therapy and survival outcomes, some have found advantages to continuous tumor measurement metrics, while others have not …”

“…In studies of colorectal cancer therapy and survival outcomes, some have found advantages to continuous tumor measurement metrics, while others have not. [21][22][23] We sought to assess and refine the published FDA longitudinal tumor size model for NSCLC using archived tumor measurement data so that modeling and simulation might lead to smaller, quicker early phase trials for testing new treatments for NSCLC. We intended to evaluate the power of smaller clinical trials with novel end points to detect evidence of anticancer drug treatment effects with archived CRFs from three randomized clinical trials sponsored by the US National Cancer Institute.…”

Progression-Free Survival: Helpful Biomarker or Clinically Meaningless End Point?

“…3 It also performs equally or better to tumor size log ratio in the different treatment arm comparisons and consistently shows robustness when modeled in simulations of small randomized phase II clinical trials. 8- 10 These exercises are particularly timely given the findings of two recently reported studies. Actually, there are some other studies that have suggested that TTG is a valid end point, especially in mCRC.…”