RE: Missing covariates

From: "Hui C. Kimko" <koh@georgetown.edu> Subject: RE: Missing covariates Date: Mon, 2 Jul 2001 12:09:15 -0400 Dear Atul, What you are curious about now is exactly what I am thinking. The answers to your questions are dependent on your dataset. Of course, if you are in the phase of building a model to describe your data, it would be difficult to know the impact of using many different ways of handling missing data - sensitivity analyses may give you some hints. So, here I am including a general advice from the FDA. Guidance for Industry, Population Pharmacokinetics, Page 12-13 http://www.fda/gov/cder/guidance/1852fnl.pdf B. Handling Missing Data After assembling data for population analysis, the issue of any missing covariate data should be addressed. Missing data will not automatically invalidate the results provided a good-faith effort is made to capture the missing data and adequate documentation is made regarding why data are unavailable. However, missing data represent a potential source of bias. Thus, every effort should be made to fulfill the protocol requirements concerning the collection and management of data, thereby reducing the amount of missing data. Many subjects may be rich in covariate data, and some may be missing only a small sample of covariates. Excluding all subjects with any covariate data missing in some situations will vastly decrease the sample size. Extreme caution should be taken, but in certain situations, it may be better to impute missing covariate values for some subjects rather than to delete those subjects from the data set. Some simple methods of imputation, including the use of median, mean, or mode for missing values, may be biased and inefficient when predictors are correlated (34). A better method uses maximum likelihood procedures for predicting each predictor from all other predictors. Another method for consideration is multiple imputation, in which several imputed data sets are analyzed to remove the optimistic bias from estimates of precision caused by imputing data and treating is as though it were actually observed (35). However, the performance of imputation techniques in this context is not well-studied, nor is there a wealth of experience on their use. Moreover, imputation of missing covariates adds another layer of assumptions to the model. Imputation procedures should be described, and a detailed explanation provided of how such imputations were done and the underlying assumptions made. The sensitivity of the results of the analysis to the method of imputation of missing data should be tested, especially if the number of missing values is substantial. Sometimes missing concentration data can become a problem in a longitudinal population PK study that is conducted for a long time. If there is a pattern to the missing data, appropriate statistical procedures should be used to address the problem. Such procedures should be described in the population PK analysis report. However, if the concentration data are missing randomly, the process that caused the missing data can be ignored and the observed data can be analyzed without regard to the missing data (36, 37). Good luck ! :-) Huicy *********************************** Hui C. Kimko, PhD Center for Drug Development Science Georgetown University Medical Center voice: 202 687 4332 fax: 202 687 0193