Matching Weights to Simultaneously Compare Three Treatment Groups: Comparison to Three-way Matching.
Propensity score matching is a commonly used tool. However, its use in settings with more than two treatment groups has been less frequent. We examined the performance of a recently developed propensity score weighting method in the three-treatment group setting.



The matching weight method is an extension of inverse probability of treatment weighting (IPTW) that reweights both exposed and unexposed groups to emulate a propensity score matched population. Matching weights can generalize to multiple treatment groups. The performance of matching weights in the three-group setting was compared via simulation to three-way 1:1:1 propensity score matching and IPTW. We also applied these methods to an empirical example that compared the safety of three analgesics.



Matching weights had similar bias, but better mean squared error (MSE) compared with three-way matching in all scenarios. The benefits were more pronounced in scenarios with a rare outcome, unequally sized treatment groups, or poor covariate overlap. IPTW's performance was highly dependent on covariate overlap. In the empirical example, matching weights achieved the best balance for 24 out of 35 covariates. Hazard ratios were numerically similar to matching. However, the confidence intervals were narrower for matching weights.



Matching weights demonstrated improved performance over three-way matching in terms of MSE, particularly in simulation scenarios where finding matched subjects was difficult. Given its natural extension to settings with even more than three groups, we recommend matching weights for comparing outcomes across multiple treatment groups, particularly in settings with rare outcomes or unequal exposure distributions. See video abstract at, http://links.lww.com/EDE/B188.
From the aDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA; bDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA; cDivision of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, MA; dDivision of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA; and eDepartment of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
Kazuki
K
Yoshida
Sonia
S
Hernández-Díaz
Daniel H
DH
Solomon
John W
JW
Jackson
Joshua J
JJ
Gagne
Robert J
RJ
Glynn
Jessica M
JM
Franklin
D000328:Adult; D000700:Analgesics / Q000009:adverse effects; D000701:Analgesics, Opioid / Q000009:adverse effects*; D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000009:adverse effects*; D002318:Cardiovascular Diseases / Q000139:chemically induced*; D016861:Cyclooxygenase Inhibitors / Q000009:adverse effects*; D004812:Epidemiologic Methods; D005260:Female; D050723:Fractures, Bone / Q000139:chemically induced*; D006471:Gastrointestinal Hemorrhage / Q000139:chemically induced*; D006801:Humans; D008297:Male; D008875:Middle Aged; D057216:Propensity Score; D016016:Proportional Hazards Models
D000700:Analgesics; D000701:Analgesics, Opioid; D000894:Anti-Inflammatory Agents, Non-Steroidal; D016861:Cyclooxygenase Inhibitors