TY - JOUR
T1 - Triplet Matching for Estimating Causal Effects With Three Treatment Arms
T2 - A Comparative Study of Mortality by Trauma Center Level
AU - Nattino, Giovanni
AU - Lu, Bo
AU - Shi, Junxin
AU - Lemeshow, Stanley
AU - Xiang, Henry
N1 - Funding Information:
This work was supported by grant 1R01 HS024263 from the Agency of Healthcare Research and Quality of the U.S. Department of Health and Human Services. We thank Paul Rosenbaum, Chi Song, and Bikram Karmakar for insightful discussions on the matching algorithm and the sensitivity analysis. We also thank the editor, the associate editor, and three anonymous referees for their constructive comments, which have substantially improved the presentation of the methodology.
Publisher Copyright:
© 2020 American Statistical Association.
PY - 2021
Y1 - 2021
N2 - Comparing outcomes across different levels of trauma centers is vital in evaluating regionalized trauma care. With observational data, it is critical to adjust for patient characteristics to render valid causal comparisons. Propensity score matching is a popular method to infer causal relationships in observational studies with two treatment arms. Few studies, however, have used matching designs with more than two groups, due to the complexity of matching algorithms. We fill the gap by developing an iterative matching algorithm for the three-group setting. Our algorithm outperforms the nearest neighbor algorithm and is shown to produce matched samples with total distance no larger than twice the optimal distance. We implement the evidence factors method for binary outcomes, which includes a randomization-based testing strategy and a sensitivity analysis for hidden bias in three-group matched designs. We apply our method to the Nationwide Emergency Department Sample data to compare emergency department mortality among non-trauma, level I, and level II trauma centers. Our tests suggest that the admission to a trauma center has a beneficial effect on mortality, assuming no unmeasured confounding. A sensitivity analysis for hidden bias shows that unmeasured confounders, moderately associated with the type of care received, may change the result qualitatively. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
AB - Comparing outcomes across different levels of trauma centers is vital in evaluating regionalized trauma care. With observational data, it is critical to adjust for patient characteristics to render valid causal comparisons. Propensity score matching is a popular method to infer causal relationships in observational studies with two treatment arms. Few studies, however, have used matching designs with more than two groups, due to the complexity of matching algorithms. We fill the gap by developing an iterative matching algorithm for the three-group setting. Our algorithm outperforms the nearest neighbor algorithm and is shown to produce matched samples with total distance no larger than twice the optimal distance. We implement the evidence factors method for binary outcomes, which includes a randomization-based testing strategy and a sensitivity analysis for hidden bias in three-group matched designs. We apply our method to the Nationwide Emergency Department Sample data to compare emergency department mortality among non-trauma, level I, and level II trauma centers. Our tests suggest that the admission to a trauma center has a beneficial effect on mortality, assuming no unmeasured confounding. A sensitivity analysis for hidden bias shows that unmeasured confounders, moderately associated with the type of care received, may change the result qualitatively. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.
KW - Conditional inference
KW - Evidence factors
KW - Optimal matching algorithm
KW - Propensity score
KW - Sensitivity analysis
UR - http://www.scopus.com/inward/record.url?scp=85082950769&partnerID=8YFLogxK
U2 - 10.1080/01621459.2020.1737078
DO - 10.1080/01621459.2020.1737078
M3 - Article
AN - SCOPUS:85082950769
SN - 0162-1459
VL - 116
SP - 44
EP - 53
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 533
ER -