Med Decis Making - Methods for estimating subgroup effects in cost-effectiveness analyses that use observational data.

Tópicos

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Resumo

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.

Resumo Limpo

decis maker requir costeffect estim patient subgroup nonrandom studi propens score ps match invers probabl treatment weight iptw can address overt select bias balanc observ covari treatment group genet match gm match ps individu covari use autom search algorithm direct balanc baselin covari articl compar method estim subgroup effect costeffect analys cea motiv case studi cea pharmaceut intervent drotrecogin alfa drotaa patient subgroup sever sepsi n gm report better covari balanc ps match iptw subgroup high level baselin risk probabl drotaa costeffect rang iptw ps match gm threshold per qualityadjust lifeyear compar method simul studi initi ps correct specifi misspecifi exampl ignor subgroupspecif treatment assign relat perform assess bias root mean squar error rmse estim increment net benefit ps correct specifi invers probabl weight stabl method perform well iptw report lowest rmse subgroupspecif treatment assign ignor ps match iptw report covari imbal bias gm report better balanc less bias precis estim conclud ps correct specifi weight iptw stabl method can provid unbias costeffect estim howev unlik iptw ps match gm relat robust ps misspecif

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