Barnhart H X, Sampson A R
Department of Biostatistics, Emory University School of Public Health, Atlanta, Georgia 30322, USA.
Biometrics. 1995 Mar;51(1):195-204.
This paper focuses on the development and study of multiple population models for multivariate random length data, of the type often encountered in clinical trials. If experimental outcomes per subject consist of multiple measurements of a quantitative variable and the number of these measurements, then a multivariate random length vector is observed. For this type of data, the experimental treatment is likely to affect both the quantitative measurements and the number of these measurements. One example of such data is from the National Heart, Lung and Blood Institute Type II coronary intervention study (Brensike et al. (1982; Controlled Clinical Trials 3, 91-111; 1984, Circulation 69, 313-324)). The outcome data consist of vectors of lesion sizes with lengths determined by the number of underlying lesions assessed from the patients' angiograms, where both the numbers and the lesion sizes depend on patients' overall disease status. We propose models which can realistically describe the relationships between the quantitative variables and the number of responses. The asymptotic covariance of the maximum likelihood estimators is obtained. Data from the Type II study are analyzed using this multiple population model.
本文聚焦于多变量随机长度数据的多种总体模型的开发与研究，这类数据常见于临床试验中。如果每个受试者的实验结果由一个定量变量的多次测量以及这些测量的次数组成，那么就会观察到一个多变量随机长度向量。对于这类数据，实验处理可能会同时影响定量测量以及这些测量的次数。此类数据的一个例子来自美国国立心肺血液研究所的II型冠状动脉干预研究（布伦西克等人（1982年；《对照临床试验》第3卷，第91 - 111页；1984年，《循环》第69卷，第313 - 324页））。结果数据由病变大小向量组成，其长度由从患者血管造影中评估的潜在病变数量决定，其中数量和病变大小都取决于患者的整体疾病状况。我们提出了能够实际描述定量变量与反应次数之间关系的模型。获得了最大似然估计量的渐近协方差。使用这种多种总体模型对II型研究的数据进行了分析。
