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[Recent methodological advances in measuring mortality differentials]
Revista Brasileira De Estudos De Populacao
|January 1, 1984
Summary
This study introduces regression models for analyzing differential mortality across multiple factors, especially with small sample sizes. It explains Cox
Area of Science:
- Biostatistics
- Demography
- Epidemiology
Background:
- Analyzing differential mortality with multiple criteria is challenging due to small sample sizes limiting cross-tabulation.
- Regression models offer advanced techniques to overcome these limitations in mortality analysis.
Purpose of the Study:
- To introduce nonspecialists to regression techniques for differential mortality analysis.
- To explain the logic, advantages, and disadvantages of Cox's proportional hazards method.
- To suggest alternatives and discuss extensions for human population mortality studies.
Main Methods:
- Discussion of recent regression-based techniques for multivariate mortality analysis.
- Detailed explanation of Cox's proportional hazards model.
- Exploration of alternatives and extensions like indirect estimation.
Main Results:
- Regression models facilitate differential mortality analysis with simultaneous criteria, even with small samples.
- Cox's proportional hazards model is a common but not always optimal approach.
- Alternative methods and strategies for handling unexplained heterogeneity are presented.
Conclusions:
- Regression techniques, particularly Cox's model and its alternatives, are essential for complex mortality studies.
- Understanding these methods aids in more accurate human population mortality analysis.
- Further research can extend these methods to indirect estimation and unexplained heterogeneity.