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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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Practical considerations when analyzing discrete survival times using the grouped relative risk model.
Rachel MacKay Altman1, Andrew Henrey2
1Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada. rachelm@sfu.ca.
Lifetime Data Analysis
|October 13, 2017
Summary
Maximum likelihood estimators (MLEs) for grouped relative risk models (GRRMs) can be unreliable in small samples. Penalized score function estimators offer improved performance and efficiency for discrete survival time data analysis.
Area of Science:
- Biostatistics
- Statistical modeling
- Survival analysis
Background:
- Grouped relative risk models (GRRMs) are widely used for discrete survival time data.
- Maximum likelihood estimators (MLEs) are typically efficient but may fail in small samples.
Purpose of the Study:
- To investigate computational issues and ill-behaved MLEs in GRRMs with small sample sizes.
- To compare the performance of MLEs with penalized score function estimators.
Main Methods:
- Analysis of grouped relative risk models (GRRMs) using maximum likelihood estimation (MLE).
- Evaluation of penalized score function estimators for small sample scenarios.
- Development of methods for assessing GRRM fit in small samples.
Main Results:
- MLEs for GRRMs can be unreliable and computationally problematic in small samples.
- Penalized score function estimators demonstrate superior performance and efficiency compared to MLEs.
- Methods for assessing model fit in small samples were developed.
Conclusions:
- Penalized score function estimators are recommended over MLEs for GRRMs with small sample sizes.
- Careful consideration of estimation methods and model fit assessment is crucial for small sample survival data analysis.
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