Comparison of alternative approaches to trim subjects in the tails of the propensity score distribution.
comparative effectiveness
confounding bias
equipoise
methods
pharmacoepidemiology
propensity score
statistical
Journal
Pharmacoepidemiology and drug safety
ISSN: 1099-1557
Titre abrégé: Pharmacoepidemiol Drug Saf
Pays: England
ID NLM: 9208369
Informations de publication
Date de publication:
10 2019
10 2019
Historique:
received:
05
10
2018
revised:
06
03
2019
accepted:
06
05
2019
pubmed:
7
8
2019
medline:
1
7
2020
entrez:
7
8
2019
Statut:
ppublish
Résumé
In nonexperimental comparative effectiveness research, restricting analysis to subjects with better overlap of covariate distributions, hence greater treatment equipoise, helps balance the groups compared and can improve validity. Three alternative approaches, derived from different perspectives, implement restriction by trimming observations in the tails of the propensity score (PS). Across approaches, we compared the relationships between the overlap in treatment-specific PS distributions and the size of the balanced study population after trimming. The three trimming approaches considered were absolute trimming to the range 0.1<PS<0.9, asymmetric trimming to include subjects in both treatment groups with PS above the 5th percentile of the distribution in the target group and below the 95th percentile in the comparison group, and restriction to preference score values between 0.3 and 0.7. Comparisons of approaches used simulated PSs from beta distributions and two example studies. The magnitude of the C-statistic strongly predicted (R Study populations with high PS C-statistics include only small percentages of subjects in whom valid treatment effects are confidently expected.
Types de publication
Comparative Study
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1290-1298Subventions
Organisme : NIA NIH HHS
ID : AG056479
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG056479
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA174453
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL118255
Pays : United States
Organisme : NICHD NIH HHS
ID : R21 HD080214
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002489
Pays : United States
Informations de copyright
© 2019 John Wiley & Sons, Ltd.
Références
Rothman KJ. Modern epidemiology. Boston: Little, Brown, and Company; 1986:110.
Schneeweiss S, Patrick AR, Stürmer T, et al. Increasing levels of restriction in pharmacoepidemiologic database studies of elderly and comparison with randomized trial results. Med Care. 2007;45(10 Supl 2):S131-S142.
Lund JL, Richardson DB, Stürmer T. The active comparator, new user study design in pharmacoepidemiology: historical foundations and contemporary application. Curr Epidemiol Rep. 2015;2(4):221-228.
Miettinen OS. Design of the Study Base. In: Theoretical epidemiology: Principles of occurrence research in medicine. New York: John Wiley and Sons; 1985:46-68.
Rothman KJ, Gallacher JEJ, Hatch EE. Why representativeness should be avoided. Int J Epidemiol. 2013;42:1013-1014.
Richiardi L, Pizzi C, Pearce N. Commentary: Representativeness is usually not necessary and often should be avoided. Int J Epidemiol. 2013;42(4):1018-1022.
Rubin DB. The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials. Stat Med. 2007;26(1):20-36.
Rubin DB. For objective causal inference, design trumps analysis. Ann Appl Stat. 2008;2(3):808-840.
Rubin DB. On the limitations of comparative effectiveness research. Stat Med. 2010;29(19):1991-1995.
Crump RK, Hotz VJ, Imbens GW, Mitnik OA. Dealing with limited overlap in estimation of average treatment effects. Biometrika. 2009;96(1):187-299.
Stürmer T, Rothman KJ, Avorn J, Glynn RJ. Treatment effects in the presence of unmeasured confounding: dealing with observations in the tails of the propensity score distribution-a simulation study. Am J Epidemiol. 2010;172(7):843-854.
Walker AM, Patrick AR, Lauer MS, et al. A tool for assessing the feasibility of comparative effectiveness research. Comp Eff Res. 2013;3:11-20.
Stürmer T, Joshi M, Glynn RJ, Avorn J, Rothman KJ, Schneeweiss S. A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. J Clin Epidemiol. 2006;59(5):437-447.
Connors AE Jr, Speroff T, Dawson NV, et al. The effectiveness of right heart catheterization in the initial care of critically ill patients. JAMA. 1996;276(11):889-897.
Hirano K, Imbens GW. Estimation of causal effects using propensity score weighting: an application to data on right heart catheterization. Health Serv Outcome Res Methodol. 2001;2(3/4):259-278.
Li F, Morgan KL, Zaslavsky AM. Balancing covariates via propensity score weighting. J Am Stat Assoc. 2018;113(521):390-400.
Emsley R, Lunt M, Pickles A, Dunn G. Implementing double-robust estimators of causal effects. Stata J. 2008;8(3):334-353.
Longmore RB, Yeh RW, Kennedy KF, et al. Clinical referral patterns for carotid artery stenting versus carotid endarterectomy: results from the Carotid Artery Revascularization and Endarterectomy Registry. Circ Cardiovasc Interv. 2011;4(1):88-94.
Kurth T, Walker AM, Glynn RJ, et al. Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. Am J Epidemiol. 2006;163(3):262-270.
Lunt M, Solomon D, Rothman K, et al. Different methods of balancing covariates leading to different effect estimates in the presence of effect modification. Am J Epidemiol. 2009;169(7):909-917.
Cholesterol Treatment Trialists' (CTT) Collaborators. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet. 2012;380:581-590.
Cannon CP, Braunwald E, McCabe CH, et al. Intensive versus moderate lipid lowering with statins after acute coronary syndromes. N Engl J Med. 2004;350(15):1495-1504.
Ridker PM, Wilson PW. A trial-based approach to statin guidelines. JAMA. 2013;310(11):1123-1124.
Heckman J, Ichimura H, Todd P. Matching as an econometric evaluation estimator: evidence from evaluating a job training programme. Rev Econ Stud. 1998;1017-1098.
Lechner M. A note on the common support problem in applied evaluation studies. Discussion Paper 2001-01. Department of Economics, University of St Gallen, St Gallen, Switzerland.
Lechner M. Some practical issues in the evaluation of heterogenous labour market programmes by matching methods. J Roy Statist Soc Ser A. 2002;165(1):59-82.
Caliendo M, Kopeinig S. Some practical guidance for the implementation of propensity score matching. IZA Discussion Paper No. 1588, May 2005. Available at http://www.econstor.eu/handle/10419/33478.
Smith J, Todd P. Does matching overcome LaLonde's critique of nonexperimental estimators? J Econ. 2005;125(1-2):305-353.
Iacus SM, King G, Poro G. Multivariate matching methods that are monotonic imbalance bounding. J Am Stat Assoc. 2011;106(493):345-361.
de los Angeles Resa M, Zubizarreta JR. Evaluation of subset matching methods and forms of covariate balance. Stat Med. 2016;35:4961-4979.
Hainmueller J. Entropy balancing for causal effects: a multivariate reweighting mthod to produce balanced samples in observational studies. Polit Anal. 2012;20(1):25-46.
Rosenbaum PR. Optimal matching of an optimally chosen subset in observational studies. J Comput Graph Stat. 2012;21(1):57-71.