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Measuring and Estimating Treatment Effect on Count Outcome in Randomized Trial and Observational Studies

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  • Li Yin
  • Xiaoqin Wang

Abstract

When estimating treatment effect on count outcome of given population, one uses different models in different studies, resulting in non-comparable measures of treatment effect. Here we show that the marginal rate differences in these studies are comparable measures of treatment effect. We estimate the marginal rate differences by log-linear models and show that their finite-sample maximum-likelihood estimates are unbiased and highly robust with respect to effects of dispersing covariates on outcome. We get approximate finite-sample distributions of these estimates by using the asymptotic normal distribution of estimates of the log-linear model parameters. This method can be easily applied to practice.

Suggested Citation

  • Li Yin & Xiaoqin Wang, 2015. "Measuring and Estimating Treatment Effect on Count Outcome in Randomized Trial and Observational Studies," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(5), pages 1080-1095, March.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:5:p:1080-1095
    DOI: 10.1080/03610926.2013.776686
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