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Therapeutically-induced stable disease in oncology early clinical trials.

ABSTRACT: RATIONALE:The RECIST guideline defines four categories of response to treatment for cancer patients according to post-baseline changes in tumor burden, hence ignoring disease history. However, if left untreated, tumors grow exponentially, implying that pretreatment changes in tumor size are key to thoroughly assess efficacy. We present a model-based approach to estimate the rates of changes in tumor mass, before and after treatment onset. METHODS:Sixty-eight patients were eligible for the analysis of tumor size data from a Phase 1 study evaluating the effect of emactuzumab. In addition to tumor size measured at baseline and every six weeks during treatment, a pre-baseline measurement was gathered for each patient. A longitudinal regression model was used to estimate the rates of tumor size change before and after treatment onset. RESULTS:The median pre-treatment tumor growth exponential rate was equal to 0.022 month-1, corresponding to a tumor size doubling time of 4 months, and the on-treatment median tumor shrinkage exponential rate was equal to 0.001 month-1. Among sixteen patients categorized as stable disease per RECIST, only five had similar slopes before and after treatment while nine actually improved. One patient in particular had a therapeutically induced stabilization of the disease. CONCLUSION:Our analysis emphasizes the importance of collecting pre-baseline scans to distinguish therapeutically induced stable disease from cases where the tumor growth is not perturbed by treatment.

SUBMITTER: Mercier F

PROVIDER: S-EPMC7259628 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Therapeutically-induced stable disease in oncology early clinical trials.

Mercier Francois F Meneses-Lorente Georgina G Grimsey Paul P Phipps Alex A Michielin Francesca F

PloS one 20200529 5

<h4>Rationale</h4>The RECIST guideline defines four categories of response to treatment for cancer patients according to post-baseline changes in tumor burden, hence ignoring disease history. However, if left untreated, tumors grow exponentially, implying that pretreatment changes in tumor size are key to thoroughly assess efficacy. We present a model-based approach to estimate the rates of changes in tumor mass, before and after treatment onset.<h4>Methods</h4>Sixty-eight patients were eligible ...[more]

PMID: 32470048

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Project description:ImportanceGroup sequential designs allow potential early trial termination at the interim analysis, before study completion. Traditional maximum likelihood estimate is commonly used to quantify the treatment effect in group sequential design trials; however, in published clinical trials, a bias-adjusted estimator has rarely been reported.ObjectiveTo emphasize the need for considering overestimation of treatment effect by applying 2 bias-adjusted estimators to previously published, early-terminated oncology clinical trials.Evidence reviewTrials published from 2013 to 2017 were identified by searching MEDLINE and Embase on February 23, 2018. This review was restricted to oncology clinical trials using group sequential designs with a single preplanned interim analysis as well as 2-arm randomized clinical trials that were subsequently stopped for efficacy reasons. Each article was independently reviewed by 3 biostatisticians during text screening, and differences in opinion were resolved by discussion. This report presents the unadjusted hazard ratio (HR) of an experimental arm to a reference arm and 2 bias-adjusted HRs calculated by using the conditional mean-adjusted estimator (CMAE) and weighted CMAE (WCMAE).FindingsIn total, 198 abstracts were screened for eligibility, of which, 19 eligible clinical trials were identified as applicable to the bias-adjusted estimators. Unadjusted HRs ranged from 0.203 (95% CI, 0.150-0.276) to 0.71 (95% CI, 0.60-0.84), number of events at the interim analysis from 58 to 540, and information time from 48% to 82%. In each study, the HRs adjusted by CMAE and WCMAE were higher than the unadjusted HR. Bias-adjusted estimates in large trials (243 and 414 events at the interim analysis) were similar to the unadjusted HR. However, in small trials (eg, with 58 events at the interim analysis), bias-adjusted estimates were highly disparate from the unadjusted HR. In trials with large treatment effects (eg, HRs of 0.20 and 0.22), the difference between unadjusted and bias-adjusted HRs was small even though the number of events at the interim analysis was small; larger differences were observed when the unadjusted HR was greater than 0.5.Conclusions and relevanceIn this systematic review of oncology clinical trials that were stopped for efficacy at the interim analysis, relatively large differences were noted between the unadjusted and adjusted HRs when the number of events at the interim analysis was small or when the unadjusted HR was close to the boundaries. These findings suggest presenting the 2 bias-adjusted HRs along with the unadjusted HR in the data monitoring committee meeting.

Dataset Information

Therapeutically-induced stable disease in oncology early clinical trials.

Publications

Therapeutically-induced stable disease in oncology early clinical trials.

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