Which patients benefit most from completing health risk assessments: comparing methods to identify heterogeneity of treatment effects

AbstractMethods for identifying heterogeneity of treatment effects in randomized trials have seen recent advances, yet applying these methods to health services intervention trials has not been well investigated. Our objective was to compare two approaches —predictive risk modeling and model-based recursive partitioning—for identifying subgroups of trial participants with potentially differential response to an intervention involving health risk assessment completion alone (n = 192) versus health risk assessment completion plus telephone-deliv ered health coaching (n = 173). Notably, these approaches have been developed by investigators from distinct disciplines and reported in separate literatures and have generally not been compared in prior work. Furthermore, these methods approach subgroup identification differently and answer rel ated but slightly different questions. The primary outcome for both approaches was prevention health program enrollment by six months. The predictive risk model was developed in two steps, where, first, a single risk score was derived from a logistic regression model with 12 a priori chosen covariat es by the scientific investigator team (c-statistic = 0.63). Then, the treatment effect was calculated within quartiles of risk via interaction in a logistic regression model (c-statistic = 0.69; c-for-benefit = 0.43). The greatest treatment effect was in the second quartile, in which 54 % (22 of 41) of intervention patients a...
Source: Health Services and Outcomes Research Methodology - Category: Statistics Source Type: research