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Methods for estimating subgroup effects in cost-effectiveness analyses that use observational data.
Med Decis Making. 2012 Nov-Dec; 32(6):750-63.MD

Abstract

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.

Authors+Show Affiliations

Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (NK, RG, RR, ZS, RR)Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (NK, RG, RR, ZS, RR)Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (NK, RG, RR, ZS, RR)Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (NK, RG, RR, ZS, RR)Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK (NK, RG, RR, ZS, RR)Travers Department of Political Science, UC Berkeley, Berkeley, CA, USA (JSS)

Pub Type(s)

Journal Article

Language

eng

PubMed ID

22691446

Citation

Kreif, Noemi, et al. "Methods for Estimating Subgroup Effects in Cost-effectiveness Analyses That Use Observational Data." Medical Decision Making : an International Journal of the Society for Medical Decision Making, vol. 32, no. 6, 2012, pp. 750-63.
Kreif N, Grieve R, Radice R, et al. Methods for estimating subgroup effects in cost-effectiveness analyses that use observational data. Med Decis Making. 2012;32(6):750-63.
Kreif, N., Grieve, R., Radice, R., Sadique, Z., Ramsahai, R., & Sekhon, J. S. (2012). Methods for estimating subgroup effects in cost-effectiveness analyses that use observational data. Medical Decision Making : an International Journal of the Society for Medical Decision Making, 32(6), 750-63. https://doi.org/10.1177/0272989X12448929
Kreif N, et al. Methods for Estimating Subgroup Effects in Cost-effectiveness Analyses That Use Observational Data. Med Decis Making. 2012 Nov-Dec;32(6):750-63. PubMed PMID: 22691446.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Methods for estimating subgroup effects in cost-effectiveness analyses that use observational data. AU - Kreif,Noemi, AU - Grieve,Richard, AU - Radice,Rosalba, AU - Sadique,Zia, AU - Ramsahai,Roland, AU - Sekhon,Jasjeet S, Y1 - 2012/06/12/ PY - 2012/6/14/entrez PY - 2012/6/14/pubmed PY - 2013/6/5/medline SP - 750 EP - 63 JF - Medical decision making : an international journal of the Society for Medical Decision Making JO - Med Decis Making VL - 32 IS - 6 N2 - 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. SN - 1552-681X UR - https://www.unboundmedicine.com/medline/citation/22691446/Methods_for_estimating_subgroup_effects_in_cost_effectiveness_analyses_that_use_observational_data_ L2 - http://journals.sagepub.com/doi/full/10.1177/0272989X12448929?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub=pubmed DB - PRIME DP - Unbound Medicine ER -