Interim Futility Monitoring Assessing Immune Therapies With a Potentially Delayed Treatment Effect

Abstract

Purpose Introduction of new immune therapies that may have a delayed beneficial effect necessitates re-evaluation of traditional clinical trial designs in oncology. A key design feature of randomized trials is interim futility monitoring, which allows stopping early if the accruing data convincingly demonstrate that the experimental treatment is detrimental or is unlikely to be shown superior to the standard treatment. The appropriateness of futility monitoring is frequently questioned when the effect of the experimental treatment may be delayed (eg, in trials of many immune agents). We examine the advisability of using futility monitoring when there is potential for a delayed treatment effect and make recommendations concerning its use. Methods We evaluated the loss of statistical power when using some common futility interim monitoring rules and a new proposed conservative rule via simulation under varying amounts of treatment-effect delay and varying accrual periods. We also considered scenarios where the experimental treatment starts out being worse than the standard treatment but ends up being better, as may sometimes be the case when an immune therapy is compared with an active standard therapy. Results Some standard methods of futility monitoring can result in an unacceptable loss of power when there is a delayed treatment effect, especially if the accrual period is rapid or the experimental treatment is initially worse. The proposed conservative futility rule has a negligible loss of power in the situations considered. Conclusion Although care must be taken with the choice of futility monitoring when there is a delayed treatment effect, inclusion of appropriate rules can reduce the exposure of patients to ineffective therapies without reducing the probability of correctly identifying effective treatments.

Maximum Somatic Allele Frequency in Combination With Blood-Based Tumor Mutational Burden to Predict the Efficacy of Atezolizumab in Advanced Non-small Cell Lung Cancer: A Pooled Analysis of the Randomized POPLAR and OAK Studies

“…The reason for the deleterious effect of high MSAF on benefits of atezolizumab remains unclear. One possible explanation is that a high MSAF could reflect a substantial tumor burden, which could result in rapid deterioration of general fitness of atezolizumab-treated patients, considering the late onset of the activity of PD-L1 blockade (17,18). Additionally, a high MSAF may indicate a high metastatic burden, which has been found to correlate with hyperprogression after treatment with PD-1/PD-L1 inhibitors (19).…”

Methods for Accommodating Nonproportional Hazards in Clinical Trials: Ready for the Primary Analysis?

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Delayed treatment effects, treatment switching and heterogeneous patient populations: How to design and analyzeRCTsin oncology

“…If, however, the assumption about the delay was not correct, the sample size might be too small and in this case sample size reassessment based on interim is proposed to increase the sample size (and power) if necessary. Korn and Freidlin 28 study the loss in power when futility stopping rules at interim are applied in the setting of delayed onset. To reduce the influence of the suspected delayed onset phase, they propose to modify stopping rules by the additional condition that a reasonable fraction (eg, at least two‐thirds) of the events observed at interim have occurred at least a sufficient time (eg, 3 months) after randomization.…”