Albert P S, Follmann D A, Barnhart H X
Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland 20892-7938, USA.
Biometrics. 1997 Sep;53(3):1116-24.
A common measure in clinical trials and epidemiologic studies is the number of events such as seizures, hospitalizations, or bouts of disease. Frequently, a binary measure of severity for each event is available but is not incorporated in the analysis. This paper proposes methodology for jointly modeling the number of events and the vector of correlated binary severity measures. Our formulation exploits the notion that a given covariate may affect both outcomes in a similar way. We functionally link the regression parameters for the counts and binary means and discuss a generalized estimating equation (GEE) approach for parameter estimation. We discuss conditions under which the proposed joint modeling approach provides marked gains in efficiency relative to the common procedure of simply modeling the counts, and we illustrate the methodology with epilepsy clinical trial data.
临床试验和流行病学研究中的一个常见指标是诸如癫痫发作、住院或疾病发作等事件的数量。通常，每个事件都有一个二元严重程度指标，但在分析中并未纳入。本文提出了一种方法，用于联合建模事件数量和相关二元严重程度指标向量。我们的公式利用了这样一种概念，即给定的协变量可能以相似的方式影响两个结果。我们在功能上链接计数和二元均值的回归参数，并讨论用于参数估计的广义估计方程（GEE）方法。我们讨论了在哪些条件下，相对于简单地对计数进行建模的常见程序，所提出的联合建模方法在效率上有显著提高，并用癫痫临床试验数据说明了该方法。
