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Inconsistent treatment estimates from mis-specified logistic regression analyses of randomized trials.
1School of Mathematics and Statistics, Newcastle University, Newcastle upon Tyne, U.K.
Statistics in Medicine
|April 15, 2015
Summary
Randomization ensures unbiased estimates for treatment differences in clinical trials when using mean differences. However, omitting covariates can introduce bias when using other measures like odds ratios, even with randomization.
Area of Science:
- Biostatistics
- Clinical Trials
- Statistical Modeling
Background:
- Randomization in clinical trials ensures unbiased estimation of treatment effects when using mean differences.
- However, bias can occur when assessing treatment effects with other summaries, such as odds ratios, if important covariates are omitted, irrespective of randomization or trial size.
Purpose of the Study:
- To present accurate closed-form approximations for asymptotic bias arising from omitted covariates in logistic regression.
- To compare these approximations with existing methods and derive more convenient forms.
Main Methods:
- Development of closed-form approximations for asymptotic bias in logistic regression models.
- Comparison of derived approximations with existing literature.
- Simulation studies to assess the applicability of the approximations for various distributions.
Main Results:
- Accurate closed-form approximations for asymptotic bias due to omitted normally distributed covariates in logistic regression were derived.
- The derived approximations offer insights into the nature of the bias.
- Simulations indicated the utility of these approximations for non-normal distributions as well.
Conclusions:
- Omitting important covariates can lead to bias in treatment effect estimation (e.g., odds ratios) in clinical trials, even with randomization.
- The developed approximations provide a valuable tool for understanding and quantifying this bias.
- The findings are applicable even when the logistic regression model includes additional binary covariates.
Keywords:
asymptotic biasbaseline valueslogistic regressionprobit regressionrandomized clinical trialMore Related Videos
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