Measuring and Estimating Treatment Effect on Count Outcome in Randomized Trial and Observational Studies |
| |
Authors: | Li Yin |
| |
Institution: | Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden |
| |
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. |
| |
Keywords: | Treatment effect measure Marginal rate difference Finite-sample estimate Confounding covariate Dispersing covariate |
|
|