欠測が中間変数に依存する場合の multiple imputation による解析と欠測のある対象者を除いた解析のシミュレーションによる比較

We conducted a simulation study to see how much bias reduction occurred where an intermediate variable was included in imputation model but not in analysis model for multiple imputation method, if missing depends on the intermediate variable. We compared the results with those of complete case analysis. We assumed the causal pathway that obesity causes coronary heart disease (CHD) only through hypertension as an intermediate variable. We further assumed that the missing on the CHD depends on hypertension status. The data sets of systolic blood pressure, occurrence of CHD and missing on the CHD were obtained by the normal and binomial random number generator. Using these data sets, we obtained parameter estimates of obesity by logistic regression model for the multiple imputation and the complete case analysis. The variable of hypertension was included in the imputation model only for the multiple imputation. The proportions of missing data were set to 10, 20, and 30 % and the number of simulation for each condition was set to 2000 times. Analyses with complete data sets were also conducted. Unbiased estimates were obtained by applying the multiple imputation where the intermediate variable was included in the imputation model only.