Combining Multiple Imputation and Inverse-Probability Weighting for Analyzing Response with Missing in the Presence of Covariates

Missing values are frequently seen in data sets of research studiesespecially in medical studies.Therefore, it is essential that the data, especially in medical research should evaluate in terms of the structure of missingness.This study aims to provide new statistical methods for analyzing such data.

Multiple imputation (MI) and inverse-probability weighting (IPW)aretwo common methods whichused to deal with missing data. MI method is more effectiveand complexthan IPW.MI requires a model for the joint distribution of the missing data given the observed data.While IPW need only a model for the probability that a subject has fulldata .Inefficacy in each of these models may causeto serious bias if missingness in dataset is large .Anothermethod that combines these approaches to give a doubly robust estimator.In addition, using of these methodswill demonstrate in the clinical trial data related to postpartum bleeding.

In this article, we examine the performance of IPW/MI relative to MI and IPW alone in terms of bias and efficiency.According to the results of simulation can be said that that IPW/MI have advantages over alternatives.Also results of real data showed that,results of MI/MI doesnot differ with the results of IPW/MIsignificantly.

Problem of missing data are in many studies that causes bias and decreasing efficacy inmodel.In this study, after comparing the results of these techniques,it was concludedthat IPW/MI method has better performance than other methods.