Scholarlink

Background <p>The average hazard is a summary measure of event time distributions with a given time window, <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\([0,\tau ],\)</EquationSource> </InlineEquation> and allows intuitive interpretation as an average person-time incidence rate over the time window. This metric is calculated as the ratio of the cumulative incidence probability at <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\tau\)</EquationSource> </InlineEquation> to the restricted mean survival time at <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(\tau\)</EquationSource> </InlineEquation> and can be estimated through non-parametric methods and thus robust. While previously proposed for randomized trials, its use in comparative effectiveness research remains underexplored.</p> Methods <p>We evaluate inference procedures for the difference and ratio of average hazards from two comparative groups, using six common confounding adjustment methods for survival functions, including direct standardization, inverse probability of treatment weighting (IPTW), propensity score matching, empirical likelihood, and augmented IPTW (AIPTW). Extensive simulation studies under varying model specification are conducted to assess bias, variance, coverage probability, and width of confidence interval. We apply the method to data from the preference cohort in the CANVAS study.</p> Results <p>All adjustment methods achieved satisfactory performance; AIPTW was notably robust under partial model misspecification.</p> Conclusions <p>Using difference in average hazards and ratio of average hazards as estimands, when combined with common confounding adjustment methods, is feasible and reliable for comparative effectiveness research. The average hazard-based analysis provides a practical alternative to the traditional hazard ratio approach for quantifying the magnitude of the intervention effect on survival outcomes.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Comparative effectiveness research with average hazard for censored time-to-event outcomes: simulation study and application to observational data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月26日

查看全文
  • Hong Xiong,
  • Jean Connors,
  • Deb Schrag,
  • Hajime Uno

摘要

Background

The average hazard is a summary measure of event time distributions with a given time window, \([0,\tau ],\) and allows intuitive interpretation as an average person-time incidence rate over the time window. This metric is calculated as the ratio of the cumulative incidence probability at \(\tau\) to the restricted mean survival time at \(\tau\) and can be estimated through non-parametric methods and thus robust. While previously proposed for randomized trials, its use in comparative effectiveness research remains underexplored.

Methods

We evaluate inference procedures for the difference and ratio of average hazards from two comparative groups, using six common confounding adjustment methods for survival functions, including direct standardization, inverse probability of treatment weighting (IPTW), propensity score matching, empirical likelihood, and augmented IPTW (AIPTW). Extensive simulation studies under varying model specification are conducted to assess bias, variance, coverage probability, and width of confidence interval. We apply the method to data from the preference cohort in the CANVAS study.

Results

All adjustment methods achieved satisfactory performance; AIPTW was notably robust under partial model misspecification.

Conclusions

Using difference in average hazards and ratio of average hazards as estimands, when combined with common confounding adjustment methods, is feasible and reliable for comparative effectiveness research. The average hazard-based analysis provides a practical alternative to the traditional hazard ratio approach for quantifying the magnitude of the intervention effect on survival outcomes.