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

Li Yin and Xiaoqin Wang

Communications in Statistics - Theory and Methods, 2015, vol. 44, issue 5, 1080-1095

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.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:44:y:2015:i:5:p:1080-1095

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DOI: 10.1080/03610926.2013.776686

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