前後測隨機控制試驗，為常用以評估介入成效的設計，依試驗單位又可分別為個體與集群。若忽略群集中可能的相關，會使推論錯誤的機會增加。在此目的於比較前後測群集隨機試驗下的三種標準平均差估計。

蒙地卡羅模擬法在此是用於比較三種標準平均差估計的相對效力。，三種估計為以後測差異、前後測改變量差異，與利用共變異數分析調整前測下的後測差異所組成的標準平均差。在此以SAS 9.1版以建立前後測群集試驗資料，並假設資料來自一重複測量混合模型。

研究中的三種效果量估計皆為不偏估計，但在混合共變異數分析下，將調整前測當成共變量納入調整的後測差異，所組成的標準平均差其估計效力相對較高。而在改變量差異組成的標準平均之下，其理論分布與採樣分布不符。並且研究發現以總變異建立的效果量估計，在群集資料中當群中相關性低於0.2時，個體與群集效果量估計皆為不偏，但大於0.2後個體效果量估計會出現估計偏誤。

Pretest-posttest randomized control design is a common approach for evaluation of intervention effects. The units of randomization are clusters not individuals for intervention delivered at group levels. Ignoring the intra-cluster correlation may lead to erroneous inferences about the intervention effects. This study aims to compare three estimators of standardized mean difference (SMD) for data obtained from pretest posttest cluster randomized control trials.

A Monte-Carlo simulation study was performed to compare the relative efficiencies of three estimators of SMD using posttest means, change score means or adjusted means in the analysis of covariance with pretest score as a covariate estimators of SMD. The generation of pseudo data uses SAS 9.1 under the assumption that the pretest and posttest scores follow the mixed repeated measure analysis of variance model.

Although these three estimators appeared to be unbiased, using adjusted means in the analysis of covariance with pretest score as a covariate estimator of SMD seemed to have the highest efficiency. The theoretical and observed sampling variations were generally comparable but the theoretical sampling variation for the estimator using the change score appeared to be less reliable. In addition, if intra-cluster correlation is more then 0.2, the effect size estimators under individual will be biased.

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