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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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Meta-analysis for aggregated survival data with competing risks: a parametric approach using cumulative incidence
Federico Bonofiglio1,2, Jan Beyersmann3, Martin Schumacher4
1Institute of Medical Biometry and Statistics - Medical Center, University of Freiburg, Freiburg, Germany. bono@imbi.uni-freiburg.de.
Research Synthesis Methods
|September 22, 2015
Summary
This study introduces a new meta-analysis method using cumulative incidence function (CIF) ratios to better assess treatment effects on survival when competing risks are present. The approach improves upon traditional hazard ratio (HR) pooling, especially with longer follow-up times.
Area of Science:
- Biostatistics
- Clinical Epidemiology
- Survival Analysis
Background:
- Meta-analyses commonly pool hazard ratios (HRs) for survival endpoints.
- Traditional HR pooling can be misleading when competing risks are present, affecting survival probability interpretation.
- Cumulative incidence functions (CIFs) offer a way to link cause-specific hazards (CSHs) to event probabilities.
Purpose of the Study:
- To develop and evaluate a meta-analysis method using CIF ratios for assessing treatment effects in the presence of competing risks.
- To investigate the impact of follow-up duration on pooled cause-specific HRs and CIF ratios.
- To demonstrate the utility of CIF ratios in revealing treatment effects on cumulative event probabilities.
Main Methods:
- Proposed a method to pool CIF ratios across studies, assuming constant cause-specific hazards (CSHs) to retrieve aggregated competing risks data.
- Developed procedures to compute pooled HRs alongside pooled CIF ratios.
- Assessed the influence of follow-up time on the meta-analysis results.
Main Results:
- Applied the method to a medical example, demonstrating the relevance of follow-up duration for both pooled CSHRs and CIF ratios.
- Showed that CIF ratios can provide additional information on treatment effects on cumulative event probabilities, particularly with sufficient all-cause hazard and follow-up time.
- Highlighted the need for improved reporting of competing risks data in clinical studies.
Conclusions:
- CIF ratios offer a valuable alternative to HRs for meta-analysis of survival endpoints with competing risks.
- Follow-up duration significantly influences the interpretation of treatment effects in competing risks meta-analyses.
- Enhanced reporting of competing risks data is crucial for advancing the reliability and utility of such analyses.
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