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#simulate完整数据
expit < - function（x）{ EXP（X）/（1 + EXP（X））}
n < - 100000x < - mvrnorm（n，mu = c（0,0），Sigma =（c（1,0.2,0.2,1），nrow = 2））x1 < - x [，1]x2 < - x [，2]y < - 1 *（runif（n）<expit（-3 + log（3）* x1 + log（3）* x2））（Y）[1] 0.11052

#estimate x1 = 1 vs x1 = 0的边际风险比，标准化为完整数据＃以后用于MI，我们将编写一个获取数据集并返回此估计值的函数marginalRiskRatio < - function（inputData）{ ymod < - glm（y~x1 + x2，family =“binomial”，data = inputData） #predict风险在x1 = 0下 x1 = 1下的#predict risk risk1 < - expit（coef（ymod）[1] + coef（ymod）[2] * 1 + （ymod）[3] * inputData $ x2） #estimate边际风险比率 （risk1）/（risk0）}
fullData < - data.frame（y = y，x1 = x1，x2 = x2）marginalRiskRatio（fullData）[1] 2.295438

z1 < - x1 / 0.2 ^ 0.5r_y < - 1 *（runif（n）<expit（2.5 + 2 * z1））r_x2 < - 1 *（runif（n）<expit（2.5 + 2 * z1））obsData < - fullDataobsData $ y [r_y == 0] < - NAobsData $ x2 [r_x2 == 0] < - NA

numImps < - 10imps < - （obsData，smtype =“logistic”，smformula =“y~x1 + x2”，  method = c（“”，“”，“norm”），m = numImps）[1] "Outcome variable(s): y"[1] "Passive variables: "[1] "Partially obs. variables: x2"[1] "Fully obs. substantive model variables: x1"[1] "Imputation 1"[1] "Imputing missing outcomes using specified substantive model."[1] "Imputing: x2 using x1 plus outcome"[1] "Imputation 2"[1] "Imputation 3"[1] "Imputation 4"[1] "Imputation 5"[1] "Imputation 6"[1] "Imputation 7"[1] "Imputation 8"[1] "Imputation 9"[1] "Imputation 10"Warning message:In smcfcs.core(originaldata, smtype, smformula, method, predictorMatrix, : Rejection sampling failed 7 times (across all variables, iterations, and imputations). You may want to increase the rejection sampling limit.

estLogRR <- array(0, dim=numImps)for (i in 1:numImps) { [i] <- log(marginalRiskRatio(imps$impDatasets[[i]]))}#pooled estimate of log risk ratio ismean(estLogRR)[1] 0.8325685#and estimate of risk ratioexp(mean(estLogRR))[1] 2.299217