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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Meta-analysis of two-arm studies: Modeling the intervention effect from survival probabilities.

C Combescure¹, D S Courvoisier², G Haller²

¹CRC & Division of Clinical Epidemiology, Department of Health and Community Medicine, University of Geneva & University Hospitals of Geneva, Geneva, Switzerland christophe.combescure@hcuge.ch.

Statistical Methods in Medical Research

|December 26, 2012

Summary

This study introduces a generalized method for meta-analyses of survival data, using survival probabilities at multiple time points. This approach offers flexible modeling of intervention effects over time, even with partially proportional hazards.

Keywords:

Meta-analysis multiple time points survival data

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Area of Science:

Biostatistics
Survival Analysis
Medical Research Methodology

Background:

Meta-analyses of two-arm survival studies face challenges when hazard ratios are not consistently reported.
Existing methods, like Moodie et al.'s, use survival probabilities at a single time point, estimating intervention effects as a pooled ratio of log survival probabilities.

Purpose of the Study:

To propose a generalization of Moodie et al.'s method for meta-analyses of survival data.
To enable flexible modeling of intervention effects over time using survival probabilities at multiple time points.
To extend applicability to partially proportional hazards models without requiring baseline survival specification.

Main Methods:

A generalized method using survival probabilities at several time points is proposed.
The approach allows for flexible modeling of intervention effects over time.
Estimation procedures are presented for both fixed and random effects models.

Main Results:

The generalized method accommodates partially proportional hazards models.
It bypasses the need to specify baseline survival functions.
Study-level factors modifying survival functions can be ignored if they do not alter the intervention effect.

Conclusions:

The proposed generalization enhances meta-analysis of survival data by allowing flexible, time-dependent intervention effect modeling.
This method provides a robust alternative when hazard ratios are unavailable or inconsistently reported.
It is applicable to a broader range of survival data scenarios, including partially proportional hazards.