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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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On the linear in probability model for binary data.

H S Battey¹, D R Cox², M V Jackson³

¹Department of Mathematics, Imperial College London, London, UK.

Royal Society Open Science

|June 21, 2019

Summary

This summary is machine-generated.

This study explores simple least-squares estimates for binary response data, offering a more direct interpretation of parameters compared to standard logistic regression models. The findings are illustrated using a sociological study.

Keywords:

interpretation of parameters logistic model missing values model sensitivity

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Area of Science:

Statistics
Sociology

Background:

Binary response data analysis often employs models linear in the logistic transform of probabilities.
Standard methods can sometimes obscure direct empirical interpretation of underlying parameters.

Purpose of the Study:

To evaluate the advantages and disadvantages of simple least-squares estimates for binary response data.
To explore the utility of linear probability models for parameter interpretation.

Main Methods:

Analysis of binary response data using simple least-squares estimation.
Comparison with models linear in the logistic transform of probabilities.
Illustration through a sociological study.

Main Results:

Simple least-squares estimates can offer a more direct empirical interpretation of parameters.
The linear representation of probabilities has specific advantages and disadvantages.

Conclusions:

Least-squares estimates provide a valuable alternative for analyzing binary response data, particularly when direct parameter interpretation is desired.
The choice of model depends on the specific goals of the analysis and the nature of the data.