Li Zhuokai, Liu Hai, Tu Wanzhu
1 Duke Clinical Research Institute, Durham, NC, USA.
2 Gilead Sciences, Inc., Foster City, CA, USA.
Stat Methods Med Res. 2017 Dec;26(6):2909-2918. doi: 10.1177/0962280215615159. Epub 2015 Nov 23.
Health care utilization is an outcome of interest in health services research. Two frequently studied forms of utilization are counts of emergency department (ED) visits and hospital admissions. These counts collectively convey a sense of disease exacerbation and cost escalation. Different types of event counts from the same patient form a vector of correlated outcomes. Traditional analysis typically model such outcomes one at a time, ignoring the natural correlations between different events, and thus failing to provide a full picture of patient care utilization. In this research, we propose a multivariate semiparametric modeling framework for the analysis of multiple health care events following the exponential family of distributions in a longitudinal setting. Bivariate nonparametric functions are incorporated to assess the concurrent nonlinear influences of independent variables as well as their interaction effects on the outcomes. The smooth functions are estimated using the thin plate regression splines. A maximum penalized likelihood method is used for parameter estimation. The performance of the proposed method was evaluated through simulation studies. To illustrate the method, we analyzed data from a clinical trial in which ED visits and hospital admissions were considered as bivariate outcomes.
医疗保健利用情况是卫生服务研究中一个令人关注的结果。两种经常被研究的利用形式是急诊室（ED）就诊次数和住院人数。这些计数共同传达了疾病恶化和成本上升的情况。来自同一患者的不同类型的事件计数构成了一个相关结果的向量。传统分析通常一次对一个这样的结果进行建模，忽略了不同事件之间的自然相关性，因此无法全面呈现患者的医疗保健利用情况。在本研究中，我们提出了一个多变量半参数建模框架，用于在纵向环境中分析遵循指数分布族的多个医疗保健事件。纳入双变量非参数函数以评估自变量的并发非线性影响及其对结果的交互作用。使用薄板回归样条估计平滑函数。采用最大惩罚似然法进行参数估计。通过模拟研究评估了所提出方法的性能。为了说明该方法，我们分析了一项临床试验的数据，其中将急诊室就诊次数和住院人数视为双变量结果。
