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Nonparametric Tests for Treatment Effect Leveraging Information on Recurrent and Terminal Events and Physiological
1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Statistics in Medicine
|May 28, 2026
Summary
This study introduces a new statistical test for clinical trials that analyzes both event times and patient health status over time. The method improves power by integrating longitudinal health data, offering a more comprehensive evaluation of treatments for progressive diseases.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Health Outcomes Research
Background:
- Current clinical trial analyses often analyze recurrent/terminal events and longitudinal functional measures separately.
- This separation leads to a loss of statistical power and overlooks the daily impact of treatments on patient well-being.
Purpose of the Study:
- To develop a novel two-sample testing procedure that integrates recurrent/terminal events with longitudinal health status.
- To provide a more comprehensive evaluation of treatment effects in progressive disease clinical trials.
Main Methods:
- Developed a two-sample test analyzing recurrent/terminal events and pulmonary function over time.
- Incorporated pulmonary function as a health history state with a utility function.
- Compared longitudinal profiles of tau-restricted, utility-adjusted event-free times across follow-up windows.
Main Results:
- The proposed test demonstrates good performance in simulations.
- Power analyses show gains when treatments improve both event rates and health history.
- The method was successfully applied to the Azithromycin for Prevention of COPD Exacerbation Trial.
Conclusions:
- Integrating longitudinal health data into event time analyses enhances statistical power and provides a fuller picture of treatment efficacy.
- This approach offers a more sensitive and clinically relevant method for evaluating treatments in progressive diseases.
- The developed methodology is practical and applicable to real-world clinical trial data.
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