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An R-Based Landscape Validation of a Competing Risk Model
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An efficient alternative to the stratified Cox model analysis.

Devan V Mehrotra1, Shu-Chih Su, Xiaoming Li

  • 1Merck Research Laboratories, 351 N. Sumneytown Pike, North Wales, PA 19454, USA. devan_mehrotra@merck.com

Statistics in Medicine
|March 23, 2012
PubMed
Summary
This summary is machine-generated.

This study warns that assuming a constant treatment hazard ratio (HR) in stratified clinical trials can bias results. A proposed two-step analysis offers a safer alternative for time-to-event data, improving accuracy and power.

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Epidemiology

Background:

  • Randomized clinical trials with time-to-event endpoints often use stratified randomization based on prognostic factors.
  • The standard analysis assumes a constant treatment hazard ratio (HR) across strata, using the stratified Cox proportional hazards model.
  • This assumption can lead to biased or underpowered analyses if the HR varies between strata.

Purpose of the Study:

  • To highlight the risks associated with assuming a constant hazard ratio in stratified clinical trials.
  • To propose and evaluate a two-step analysis method for time-to-event data in stratified trials.
  • To offer a more robust and powerful analytical approach compared to the traditional one-step stratified Cox model.

Main Methods:

  • Proposed a two-step approach: estimate stratum-specific log hazard ratios (HRs) using unstratified Cox models.
  • Combined stratum-specific estimates using sample size or 'minimum risk' weights for overall inference.
  • Conducted simulations to compare the proposed method with the standard stratified Cox model.

Main Results:

  • Simulations demonstrated that departures from the constant HR assumption can indeed bias and reduce the power of the standard stratified Cox model.
  • The proposed two-step analysis showed advantages in managing situations where the HR assumption is violated.
  • The method was validated in simulations supporting vaccine clinical trial design.

Conclusions:

  • The traditional one-step stratified Cox model analysis in clinical trials carries risks if the proportional hazards assumption is not uniform across strata.
  • A two-step analysis estimating and combining stratum-specific hazard ratios provides a more reliable and potentially more powerful alternative.
  • This approach enhances the robustness of time-to-event analyses in stratified randomized trials.