The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement

Abstract

Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined "1 variable at a time" (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, "predictive HTE analysis." The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs.

Figure 1.. Schematized and Actual Risk-based Heterogeneous Treatment Effects

A. Schematic results in a trial for a hypothetical intervention which lowers the odds of an outcome by 25% but with an absolute treatment-related harm of 1% This figure schematically depicts outcome risks for a trial testing a hypothetical intervention with an odds ratio of 0.75 but with an absolute treatment-related harm of 1% (shown in the top panel). Observed odds ratios (middle panel) and risk differences (bottom panel) are shown. Overall trial results are dependent on the average risk of the enrolled trial population. When the average risk is ~7% (as above), a well-powered study would detect a positive overall treatment benefit (shown by the horizontal dashed line in the middle and bottom panels). However, a prediction model with a C-statistic of 0.75, generates the risk distribution at the top of the figure. A treatment-by-risk interaction emerges (middle panel). Whether or not this interaction is statistically significant, examination of treatment effects on the absolute risk difference scale (bottom panel) reveals harm in the low risk group and very substantial benefit in the high risk group, both of which are obscured by the overall summary results. Conventional one-variable-at-a-time subgroup analyses are typically inadequate to disaggregate patients into groups that are sufficiently heterogeneous for risk such that benefit-harm trade-offs can misleadingly appear to be consistent across the trial population. Although the figure here shows idealized relations between risk and treatment effects, these relations will be sensitive to how risk is described (i.e. what variables are in the risk model). Baseline risk is logit normal distributed with mu=−3 and sigma=1 (the log odds are normally distributed). Figure adapted from Kent DM et al. JAMA 2007. B. Stratified results of the Randomized Intervention Trial of unstable Angina (RITA)-3 The RITA-3 trial (N=1810) tested early intervention versus conservative management of non-ST-elevation acute coronary syndrome. Results for the outcome of death or non-fatal myocardial infarction at 5 years are shown above, stratified into equal-sized risk quarters using an internally-derived risk model; the highest risk quarter is sub-stratified in halves (groups 4a and 4b). Event rates with 95% confidence intervals (top panel), odds ratios (middle panel), and risk difference (bottom panel) are displayed. The risk model is comprised of the following easily obtainable clinical characteristics: age, sex, diabetes, prior MI, smoking status, heart rate, ST depression, angina severity, left bundle branch block, and treatment strategy. As in the schematic diagram to the left, the average treatment effect seen in the summary results (horizontal dashed line in middle and bottom panels) closely reflect the effect in patients in risk quarter 3, while fully half of patients (q1 and q2) receive no treatment benefit from early intervention. Absolute benefit (bottom panel) in the primary outcome was very pronounced in the eighth of patients at highest risk (4b). A statistically significant risk-by-treatment interaction* can be seen when results are expressed in the odds ratio scale (middle panel). Such a pattern can emerge if early intervention is associated with some procedure-related risks that are evenly distributed over all risk groups, eroding benefit in low risk but not high risk patients, as illustrated schematically in Figure 1A. *The interaction p value is from a likelihood ratio test for adding an interaction between the linear predictor of risk and treatment assignment (one degree of freedom).