Nixon R M, Thompson S G
MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, CB2 2SR, UK.
Stat Med. 2003 Sep 15;22(17):2673-92. doi: 10.1002/sim.1483.
Analysis of covariance models, which adjust for a baseline covariate, are often used to compare treatment groups in a controlled trial in which individuals are randomized. Such analysis adjusts for any baseline imbalance and usually increases the precision of the treatment effect estimate. We assess the value of such adjustments in the context of a cluster randomized trial with repeated cross-sectional design and a binary outcome. In such a design, a new sample of individuals is taken from the clusters at each measurement occasion, so that baseline adjustment has to be at the cluster level. Logistic regression models are used to analyse the data, with cluster level random effects to allow for different outcome probabilities in each cluster. We compare the estimated treatment effect and its precision in models that incorporate a covariate measuring the cluster level probabilities at baseline and those that do not. In two data sets, taken from a cluster randomized trial in the treatment of menorrhagia, the value of baseline adjustment is only evident when the number of subjects per cluster is large. We assess the generalizability of these findings by undertaking a simulation study, and find that increased precision of the treatment effect requires both large cluster sizes and substantial heterogeneity between clusters at baseline, but baseline imbalance arising by chance in a randomized study can always be effectively adjusted for.
协方差分析模型用于调整基线协变量，常用于在个体随机分组的对照试验中比较治疗组。这种分析可调整任何基线不平衡情况，通常会提高治疗效果估计的精度。我们在具有重复横断面设计和二元结局的整群随机试验背景下评估这种调整的价值。在这种设计中，每次测量时都会从各个群组中抽取新的个体样本，因此基线调整必须在群组层面进行。使用逻辑回归模型分析数据，并采用群组层面的随机效应来考虑每个群组中不同的结局概率。我们比较了在纳入测量基线时群组层面概率的协变量的模型和未纳入该协变量的模型中估计的治疗效果及其精度。在两个取自月经过多治疗整群随机试验的数据集里，只有当每个群组中的受试者数量较多时，基线调整的价值才会显现出来。我们通过进行模拟研究来评估这些发现的普遍性，发现提高治疗效果的精度既需要较大的群组规模，也需要基线时群组之间存在实质性异质性，但随机研究中偶然出现的基线不平衡总能得到有效调整。
