RTI Health Solutions, Manchester, UK.
Value Health. 2013 Jan-Feb;16(1):164-76. doi: 10.1016/j.jval.2012.08.2215. Epub 2012 Dec 7.
To present a step-by-step example of the examination of heterogeneity within clinical trial data by using a growth mixture modeling (GMM) approach.
Secondary data from a longitudinal double-blind clinical drug study were used. Patients received enalapril or placebo and were followed for 2 years during the drug component, followed by a 3-year postdrug component. Primary variables of interest were creatinine levels during the drug component and number of hospitalizations in the postdrug component. Latent growth modeling (LGM) methods were used to examine the treatment response variability in the data. GMM methods were applied where substantial variability was found to identify latent (unobserved) subsets of differential responders, using treatment groups as known classes. Post hoc analyses were applied to characterize emergent subgroups.
LGM methods demonstrated a large variability in creatinine levels. GMM methods identified two subsets of patients for each treatment group. Placebo class 2 (7.0% of the total sample) and enalapril class 2 (8.5%) include individuals whose creatinine levels start at 1.114 mg/dl and 1.108 mg/dl, respectively, and show worsening (slopes: 0.023 and 0.017, respectively). Placebo class 1 (43.1%) and enalapril class 1 (41.4%) individuals start with lower creatinine levels (1.082 and 1.083 mg/dl, respectively) and show very minimal change (0.008 and 0.003, respectively). Post hoc analyses revealed significant differences between placebo/enalapril class 1 and placebo/enalapril class 2 in terms of New York Heart Association functional ability, depression, functional impairment, creatinine levels, mortality, and hospitalizations.
GMM methods can identify subsets of differential responders in clinical trial data. This can result in a more accurate understanding of treatment effects.
通过使用增长混合建模（GMM）方法，为临床试验数据中的异质性检查提供一个逐步示例。
使用纵向双盲临床药物研究的二次数据。患者接受依那普利或安慰剂治疗，并在药物治疗阶段随访 2 年，随后进行 3 年的药物治疗后阶段。主要感兴趣的变量是药物治疗阶段的肌酐水平和药物治疗后阶段的住院次数。使用潜在增长模型（LGM）方法检查数据中的治疗反应变异性。在发现大量变异性的情况下，应用 GMM 方法来识别潜在的（未观察到的）不同反应者亚组，使用治疗组作为已知类别。进行事后分析以描述新兴亚组。
LGM 方法表明肌酐水平存在很大的变异性。GMM 方法为每个治疗组确定了两个亚组。安慰剂类 2（总样本的 7.0%）和依那普利类 2（8.5%）包括肌酐水平分别从 1.114 mg/dl 和 1.108 mg/dl 开始且恶化的个体（斜率分别为 0.023 和 0.017）。安慰剂类 1（43.1%）和依那普利类 1（41.4%）个体的肌酐水平起始较低（分别为 1.082 和 1.083 mg/dl），且变化非常小（分别为 0.008 和 0.003）。事后分析显示，在纽约心脏协会功能能力、抑郁、功能障碍、肌酐水平、死亡率和住院率方面，安慰剂/依那普利类 1 和安慰剂/依那普利类 2 之间存在显著差异。
GMM 方法可以识别临床试验数据中不同反应者的亚组。这可以更准确地了解治疗效果。
