Inproceedings,
Combining Analysis Results from Multiply Imputed Categorical Data
page Paper SP03. (2013)Dades censurades; Imputació múltiple; SAS.
Abstract
Multiple imputation (MI) is a methodology for dealing with missing data that has been steadily gaining wide usage in clinical trials. Various methods have been developed and are readily available in SAS PROC MI for multiple imputation of both continuous and categorical variables. MI produces multiple copies of the original dataset, where missing data are filled in with values that differ slightly between imputed datasets. Each of these datasets is then analyzed using a standard statistical method for complete data, and the results from all imputed datasets are combined (pooled) for overall inference using Rubin’s rules which account for the uncertainty associated with imputed values. Rubin’s pooling methodology is very general and is essentially the same no matter what kind of statistic is estimated at the analysis stage for each imputed dataset. However, the combination rules assume that the estimates are asymptotically normally distributed, which may not always be the case. For example, the Cochran-Mantel- Haenszel (CMH) test and the Mantel-Haenszel (MH) estimate of the common odds ratio are often used in analysis of categorical data, and they produce statistics that are not normally distributed. In this case, normalizing transformations need to be applied to the statistics estimated from each imputed dataset before the Rubin’s combination rules can be applied. In this paper, we show how this can be done for the two aforementioned statistics and explore some operating characteristics of the significance tests based on the applied normalizing transformations. We also show how to obtain combined estimates of binomial proportions and their difference between treatment arms.
