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SAS 倾向得分匹配:在 PSMATCH 中考虑匹配的观察值小于数据集中可用的总观察值

[英]SAS propensity score matching: Observations considered for matching in PSMATCH is less than the total observations available in the data set

I am using SAS procedure PSMATCH to balance the cohorts.我正在使用 SAS 过程 PSMATCH 来平衡群组。 I am calculating the propensity score separately using logistic regression and then using the generated dataset in PSMATCH using PSDATA.我正在使用逻辑回归分别计算倾向得分,然后使用 PSDATA 在 PSMATCH 中使用生成的数据集。 I am doing multiple iterations of matching (to get the best results) by bringing variation in region, method (Optimal, Greedy and variable ratio), distance variable, caliper value and ratio.我正在通过区域、方法(最优、贪婪和可变比率)、距离变量、卡尺值和比率的变化来进行多次匹配迭代(以获得最佳结果)。 Please find the code below:请在下面找到代码:

proc psmatch data=work.&data_set. region=&region_var.;
    class &cat_var.;
    psdata treatvar = case_cntrl_fl(Treated='1') PS=prop_score;
    match method=&mtch_method.(&k_method.=&k_val.) exact= &.exact_mtch_var. 
    stat=&stat_var. caliper(mult=stddev)=&caliper_var.;
    assess lps ps var=(prop_score &covar_asses.) / plots = (boxplot cloudplot);
    output out(obs=match)=WORK.psm ps=ps lps=lps matchid=_MatchID matchwgt = _MATCHWGT_;
run;

My concern is regarding the number of observation considered for matching (ie All Observations).我担心的是考虑匹配的观察数量(即所有观察)。 The total observation logistic regression data set are Treatment Arm 1: 531 and Treatment Arm 2: 3252 However, in PSMATCH report All observations reported as Treatment Arm 1: 446 and Treatment Arm 2: 2784 The result is consistent irrespective of the variations in PSMATCH methods The total observation logistic regression data set are Treatment Arm 1: 531 and Treatment Arm 2: 3252 However, in PSMATCH report All observations reported as Treatment Arm 1: 446 and Treatment Arm 2: 2784 The result is consistent irrespective of the variations in PSMATCH methods

Can somebody help me understand the possible reason of drop in counts?有人可以帮助我了解计数下降的可能原因吗?

You likely have missing values in your data.您的数据中可能存在缺失值。 If any variable in the proc is missing, that entire row is excluded from the analysis overall.如果 proc 中的任何变量丢失,则该整行将从整体分析中排除。

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