Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration.

OBJECTIVES

We sought to explore to what extent the use of Subpopulation Treatment Effect Pattern Plot (STEPP) may help to identify efficient treatment allocation strategy.

METHODS

The analysis was based on data from the COACH study, in which 1023 patients with heart failure were randomly assigned to three treatments: care-as-usual, basic support, and intensive support. First, using predicted 18-month mortality risk as the stratification basis, a suitable strategy for assigning different treatments to different risk groups of patients was developed. To that end, a graphical exploration of the difference in net monetary benefit (NMB) across treatment regimens and baseline risk was used. Next, the efficiency gains resulting from this proposed subgroup strategy were quantified by computing the difference in NMB between our stratified approach and the best performing population-wide strategy.

RESULTS

The analysis using STEPPs suggested that a differentiated approach, based on offering intensive support to low-risk patients (18-month mortality risk ≤ 0.16) and basic support to intermediate- to high-risk patients (18-month mortality risk > 0.16) would be an economically efficient treatment allocation strategy. This was confirmed in the subsequent cost-effectiveness analysis, where the average gain in NMB resulting from the proposed stratified approach compared to basic support for all was found to be €1312 (95% CI €390-€2346) per patient.

CONCLUSIONS

STEPP provides a systematic approach to assess the interaction between baseline risk and the difference in NMB between competing interventions and to identify cutoffs to stratify patients in a health economically optimal manner.