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Deconstructing the Kaplan-Meier curve: Quantification of treatment effect using the treatment effect process
Sean M Devlin1, John O'Quigley2
1Memorial Sloan Kettering Cancer Center, New York, USA.
Contemporary Clinical Trials
|December 6, 2022
Summary
Elapsed time is a critical factor in survival studies. The treatment effect process offers a sophisticated method to better understand how treatments impact survival over time, revealing complex relationships not evident in standard Kaplan-Meier curves.
Area of Science:
- Biostatistics
- Clinical Trials Methodology
- Survival Analysis
Background:
- Elapsed time is a strong predictor in survival studies.
- Kaplan-Meier curves illustrate overall survival but can obscure time-dependent treatment effects.
- Advanced methods are needed to fully understand treatment impacts on survival over time.
Purpose of the Study:
- To introduce and illustrate the utility of the treatment effect process.
- To demonstrate how this method enhances understanding of time-dependent treatment effects.
- To apply the treatment effect process to a relapse-free survival study.
Main Methods:
- Utilized the treatment effect process, a sophisticated statistical tool.
- Analyzed a recently published study with relapse-free survival as the outcome.
- Interpreted the treatment effect process to reveal complex treatment-time interactions.
Main Results:
- The treatment effect process provided deeper insights into the relationship between time and treatment.
- Identified complex ways treatment influenced survival time.
- Offered a more nuanced understanding than traditional survival curves.
Conclusions:
- The treatment effect process is a powerful tool for analyzing time-dependent treatment effects in survival studies.
- This method significantly enhances the interpretation of treatment impacts on survival.
- Recommended for use in clinical trials to gain a comprehensive understanding of treatment efficacy over time.
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