Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models

Robert H. Lyles

doi:10.1198/jasa.2006.s90

March 1, 2006Journal of the American Statistical Association

Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models

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Key Points

To evaluate various regression methods used in biostatistics, including their applications and limitations.
Review of regression methods including linear, logistic, survival, and repeated measures models.
Discussion of the statistical principles underlying each regression technique.
Identification of key differences among linear, logistic, and survival regression in data handling.
Insights into the application of repeated measures for longitudinal data analysis.

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Implication

This review discusses regression methods for analyzing data, highlighting their relevance in biostatistics applications.

Abstract

"Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models." Journal of the American Statistical Association, 101(473), pp. 403–404

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Robert H. Lyles (2006) studied this question.

synapsesocial.com/papers/6a0b7ab2fe339c5eb297e4d1 https://doi.org/10.1198/jasa.2006.s90

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Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models

Key Points

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Implication

Abstract

Discussion

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Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models

Key Points

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Implication

Abstract

Discussion

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