Identifying differential responders and their characteristics in clinical trials: Innovative methods for analyzing longitudinal data
Stull, D., & Houghton, K. (2013). Identifying differential responders and their characteristics in clinical trials: Innovative methods for analyzing longitudinal data. Value in Health, 16(1), 164-176. DOI: 10.1016/j.jval.2012.08.2215
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.