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首页> 外文期刊>Pharmaceutical statistics. >To adjust or not to adjust for baseline when analyzing repeated binary responses? The case of complete data when treatment comparison at study end is of interest
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To adjust or not to adjust for baseline when analyzing repeated binary responses? The case of complete data when treatment comparison at study end is of interest

机译：分析重复的二进制响应时是否要调整基线？当研究结束时进行治疗比较时，需要完整的数据

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The benefits of adjusting for baseline covariates are not as straightforward with repeated binary responses as with continuous response variables. Therefore, in this study, we compared different methods for analyzing repeated binary data through simulations when the outcome at the study endpoint is of interest. Methods compared included chi-square, Fisher's exact test, covariate adjusted/unadjusted logistic regression (Adj.logit/Unadj.logit), covariate adjusted/unadjusted generalized estimating equations (Adj.GEE/Unadj.GEE), covariate adjusted/unadjusted generalized linear mixed model (Adj.GLMM/Unadj.GLMM). All these methods preserved the type I error close to the nominal level. Covariate adjusted methods improved power compared with the unadjusted methods because of the increased treatment effect estimates, especially when the correlation between the baseline and outcome was strong, even though there was an apparent increase in standard errors. Results of the Chi-squared test were identical to those for the unadjusted logistic regression. Fisher's exact test was the most conservative test regarding the type I error rate and also with the lowest power. Without missing data, there was no gain in using a repeated measures approach over a simple logistic regression at the final time point. Analysis of results from five phase III diabetes trials of the same compound was consistent with the simulation findings. Therefore, covariate adjusted analysis is recommended for repeated binary data when the study endpoint is of interest. Copyright (c) 2015John Wiley & Sons, Ltd.
机译：对于重复的二进制响应，调整基线协变量的好处并不像对连续响应变量那样直接。因此，在这项研究中，当研究终点的结果令人关注时，我们比较了通过仿真分析重复二进制数据的不同方法。比较的方法包括卡方检验，Fisher精确检验，协变量调整/未调整逻辑回归（Adj.logit / Unadj.logit），协变量调整/未调整的广义估计方程（Adj.GEE / Unadj.GEE），协变量调整/未调整的广义线性混合模型（Adj.GLMM / Unadj.GLMM）。所有这些方法都使I型错误保持在标称水平附近。协变量调整方法与未调整方法相比具有更高的功效，这是因为治疗效果估算值提高了，特别是当基线和结果之间的相关性很强时，即使标准误差明显增加了。卡方检验的结果与未经调整的逻辑回归的结果相同。 Fisher的精确测试是关于I型错误率的最保守测试，并且功耗最低。在没有丢失数据的情况下，在最后一个时间点使用重复测量方法比简单的逻辑回归没有任何好处。同一化合物的五项III期糖尿病试验的结果分析与模拟结果一致。因此，当研究终点有意义时，建议对重复的二进制数据进行协变量调整分析。版权所有（c）2015 John Wiley＆Sons，Ltd.

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