Methods for estimating subgroup effects in cost-effectiveness analyses that use observational data.
Decision makers require cost-effectiveness estimates for patient subgroups. In nonrandomized studies, propensity score (PS) matching and inverse probability of treatment weighting (IPTW) can address overt selection bias, but only if they balance observed covariates between treatment groups. Genetic matching (GM) matches on the PS and individual covariates using an automated search algorithm to directly balance baseline covariates. This article compares these methods for estimating subgroup effects in cost-effectiveness analyses (CEA). The motivating case study is a CEA of a pharmaceutical intervention, drotrecogin alfa (DrotAA), for patient subgroups with severe sepsis (n = 2726). Here, GM reported better covariate balance than PS matching and IPTW. For the subgroup at a high level of baseline risk, the probability that DrotAA was cost-effective ranged from 30% (IPTW) to 90% (PS matching and GM), at a threshold of £20 000 per quality-adjusted life-year. We then compared the methods in a simulation study, in which initially the PS was correctly specified and then misspecified, for example, by ignoring the subgroup-specific treatment assignment. Relative performance was assessed as bias and root mean squared error (RMSE) in the estimated incremental net benefits. When the PS was correctly specified and inverse probability weights were stable, each method performed well; IPTW reported the lowest RMSE. When the subgroup-specific treatment assignment was ignored, PS matching and IPTW reported covariate imbalance and bias; GM reported better balance, less bias, and more precise estimates. We conclude that if the PS is correctly specified and the weights for IPTW are stable, each method can provide unbiased cost-effectiveness estimates. However, unlike IPTW and PS matching, GM is relatively robust to PS misspecification.
Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (NK, RG, RR, ZS, RR)
Noemi
N
Kreif
Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (NK, RG, RR, ZS, RR)
Richard
R
Grieve
Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (NK, RG, RR, ZS, RR)
Rosalba
R
Radice
Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (NK, RG, RR, ZS, RR)
Zia
Z
Sadique
Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (NK, RG, RR, ZS, RR)
Roland
R
Ramsahai
Travers Department of Political Science, UC Berkeley, Berkeley, CA, USA (JSS)
Jasjeet S
JS
Sekhon
D000465:Algorithms; D001331:Automation; D003362:Cost-Benefit Analysis; D006801:Humans; D009010:Monte Carlo Method; D011336:Probability; D019057:Quality-Adjusted Life Years