Dataset Information

Futility monitoring for randomized clinical trials with non-proportional hazards: An optimal conditional power approach.

ABSTRACT:

Background

Standard futility analyses designed for a proportional hazards setting may have serious drawbacks when non-proportional hazards are present. One important type of non-proportional hazards occurs when the treatment effect is delayed. That is, there is little or no early treatment effect but a substantial later effect.

Methods

We define optimality criteria for futility analyses in this setting and propose simple search procedures for deriving such rules in practice.

Results

We demonstrate the advantages of the optimal rules over commonly used rules in reducing the average number of events, the average sample size, or the average study duration under the null hypothesis with minimal power loss under the alternative hypothesis.

Conclusion

Optimal futility rules can be derived for a non-proportional hazards setting that control the loss of power under the alternative hypothesis while maximizing the gain in early stopping under the null hypothesis.

SUBMITTER: Wang X

PROVIDER: S-EPMC10751393 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

Futility monitoring for randomized clinical trials with non-proportional hazards: An optimal conditional power approach.

Wang Xiaofei X George Stephen L SL

Clinical trials (London, England) 20230627 6

<h4>Background</h4>Standard futility analyses designed for a proportional hazards setting may have serious drawbacks when non-proportional hazards are present. One important type of non-proportional hazards occurs when the treatment effect is delayed. That is, there is little or no early treatment effect but a substantial later effect.<h4>Methods</h4>We define optimality criteria for futility analyses in this setting and propose simple search procedures for deriving such rules in practice.<h4>Re ...[more]

PMID: 37366172

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Project description:While well-established methods for time-to-event data are available when the proportional hazards assumption holds, there is no consensus on the best inferential approach under non-proportional hazards (NPH). However, a wide range of parametric and non-parametric methods for testing and estimation in this scenario have been proposed. To provide recommendations on the statistical analysis of clinical trials where non-proportional hazards are expected, we conducted a simulation study under different scenarios of non-proportional hazards, including delayed onset of treatment effect, crossing hazard curves, subgroups with different treatment effects, and changing hazards after disease progression. We assessed type I error rate control, power, and confidence interval coverage, where applicable, for a wide range of methods, including weighted log-rank tests, the MaxCombo test, summary measures such as the restricted mean survival time (RMST), average hazard ratios, and milestone survival probabilities, as well as accelerated failure time regression models. We found a trade-off between interpretability and power when choosing an analysis strategy under NPH scenarios. While analysis methods based on weighted logrank tests typically were favorable in terms of power, they do not provide an easily interpretable treatment effect estimate. Also, depending on the weight function, they test a narrow null hypothesis of equal hazard functions, and rejection of this null hypothesis may not allow for a direct conclusion of treatment benefit in terms of the survival function. In contrast, non-parametric procedures based on well-interpretable measures like the RMST difference had lower power in most scenarios. Model-based methods based on specific survival distributions had larger power; however, often gave biased estimates and lower than nominal confidence interval coverage. The application of the studied methods is illustrated in a case study with reconstructed data from a phase III oncologic trial.

Dataset Information

Futility monitoring for randomized clinical trials with non-proportional hazards: An optimal conditional power approach.

Background

Methods

Results

Conclusion

Publications

Futility monitoring for randomized clinical trials with non-proportional hazards: An optimal conditional power approach.

Similar Datasets

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