Using classification tree analysis to generate propensity score weights

Journal of Evaluation in Clinical Practice

Volumn 23, Issue 4, 2017, Pages 703-712

  Linden, Ariel ^a,b   Yarnold, Paul R ^c  

^a LINDEN CONSULTING GROUP   (United States)

^b UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

^c Optimal Data Analysis   (United States)

Author keywords

causal inference; classification tree analysis; machine learning; propensity score

Indexed keywords

ADULT; ALGORITHM; ARTICLE; CLASSIFICATION TREE ANALYSIS; CONTROLLED STUDY; DISCRIMINANT ANALYSIS; FEMALE; HEALTH CARE COST; HEALTH PROGRAM; HEALTH SERVICE; HUMAN; LOGISTIC REGRESSION ANALYSIS; MALE; MEASUREMENT ACCURACY; PILOT STUDY; PRIORITY JOURNAL; PROBABILITY; PROPENSITY SCORE; RETROSPECTIVE STUDY; STANDARDIZATION; VALIDATION STUDY; COMPUTER SIMULATION; MACHINE LEARNING; MONTE CARLO METHOD; OBSERVATIONAL STUDY; PROCEDURES; STATISTICAL ANALYSIS; STATISTICAL MODEL;

COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; LOGISTIC MODELS; MACHINE LEARNING; MODELS, STATISTICAL; MONTE CARLO METHOD; OBSERVATIONAL STUDIES AS TOPIC; PROPENSITY SCORE;

EID: 85017149860     PISSN: 13561294     EISSN: 13652753     Source Type: Journal    
DOI: 10.1111/jep.12744     Document Type: Article

References (55)

1. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91:444-455.
2. Linden A, Adams J. Evaluating disease management program effectiveness: an introduction to instrumental variables. J Eval Clin Pract. 2006;12:148-154.
3. Rosenbaum PR, Rubin DB. The central role of propensity score in observational studies for causal effects. Biometrika. 1983;70:41-55.
4. Weitzen S, Lapane KL, Toledano AY, Hume AL, Mor V. Principles for modeling propensity scores in medical research: a systematic literature review. Pharmacoepidemiol Drug Saf. 2004;13:841e53.
5. Brookhart MA, Schneeweiss S, Rothman KJ, Glynn RJ, Avorn J, Stürmer T. Variable selection for propensity score models. Am J Epidemiol. 2006;163:1149-1156.
6. Drake C. Effects of misspecification of the propensity score on estimators of treatment effect. Biometrics. 1993;49:1231-1236.
7. Smith J, Todd P. Reconciling conflicting evidence on the performance of propensity-score matching methods. Am Econ Rev. 2001;91:112-118.
8. Cook EF, Goldman L. Asymmetric stratification. An outline for an efficient method for controlling confounding in cohort studies. Am J Epidemiol. 1988;127:626-639.
9. Barosi G, Ambrosetti A, Centra A, et al. Splenectomy and risk of blast transformation in myelofibrosis with myeloid metaplasia. Italian Cooperative Study Group on myeloid with myeloid metaplasia. Blood. 1998;91:3630-3636.
10. McCaffrey DF, Ridgeway G, Morral AR. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychol Methods. 2004;9:403-425.
11. Setoguchi S, Schneeweiss S, Brookhart MA, Glynn RJ, Cook EF. Evaluating uses of data mining techniques in propensity score estimation: a simulation study. Pharmacoepidemiol Drug Saf. 2008;17:546-555.
12. Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Stat Med. 2010;29(3):337-346.
13. Westreich D, Lessler J, Funk MJ. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J Clin Epidemiol. 2010;63(8):826-833.
14. Wyss R, Ellis AR, Brookhart MA, et al. The role of prediction modeling in propensity score estimation: an evaluation of logistic regression, bCART, and the covariate-balancing propensity score. Am J Epidemiol. 2014;180:645-655.
15. Neugebauer R, Schmittdiel JA, van der Laan MJ. A case study of the impact of data-adaptive versus model-based estimation of the propensity scores on causal inferences from three inverse probability weighting estimators. Int J Biostat. 2016;12:131-155.
16. Breiman L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat Science. 2001;16:199-231.
17. Fernández-Delgado M, Cernadas E, Barro S, Amorim D. Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res. 2014;15:3133-3181.
18. Yarnold PR, Soltysik RC. Maximizing Predictive Accuracy. Chicago, IL: ODA Books, 2016. https://doi.org/10.13140/RG.2.1.1368.3286
19. Yarnold PR, Soltysik RC, Bennett CL. Predicting in-hospital mortality of patients with AIDS-related Pneumocystis carinii pneumonia: an example of hierarchically optimal classification tree analysis. Stat Med. 1997;16:1451-1463.
20. Yarnold PR, Soltysik RC. Theoretical distributions of optima for univariate discrimination of random data. Decis Sciences. 1991;22:739-752.
21. Linden A, Yarnold PR. Combining machine learning and propensity score weighting to estimate causal effects in multivalued treatments. J Eval Clin Pract. 2016;22:875-885.
22. Linden A, Yarnold PR, Nallamothu BK. Using machine learning to model dose-response relationships. J Eval Clin Pract. 2016;22:860-867.
23. Linden A, Yarnold PR. Using machine learning to identify structural breaks in single-group interrupted time series designs. J Eval Clin Pract. 2016;22:855-859.
24. Yarnold PR. Discriminating geriatric and non-geriatric patients using functional status information: an example of classification tree analysis via UniODA. Educ Psychol Meas. 1996;56:656-667.
25. Soltysik RC, Yarnold PR. Automated CTA software: fundamental concepts and control commands. Optim Data Anal. 2010;1:144-160.
26. Linden A. Identifying spin in health management evaluations. J Eval Clin Pract. 2011;17:1223-1230.
27. Linden A, Adler-Milstein J. Medicare disease management in policy context. Health Care Financ Rev. 2008;29:1-11.
28. Elith J, Leathwick JR, Hastie T. A working guide to boosted regression trees. J Anim Ecol. 2008;77:802-813.
29. Schonlau M. Boosted regression (boosting): an introductory tutorial and a Stata plugin. Stata J. 2005;5:330-354.
30. Yarnold PR, Linden A. Novometric analysis with ordered class variables: the optimal alternative to linear regression analysis. Optim Data Anal. 2016;5:65-73.
31. Linden A, Adams JL. Using propensity score-based weighting in the evaluation of health management programme effectiveness. J Eval Clin Pract. 2010;16:175-179.
32. Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550-560.
33. Rosenbaum PR. Model-based direct adjustment. J Am Stat Assoc. 1987;82:387-394.
34. Hong G. Marginal mean weighting through stratification: adjustment for selection bias in multilevel data. J Educ Behav Stat. 2010;35:499-531.
35. Linden A, Adams J, Roberts N. Evaluating disease management program effectiveness: an introduction to the bootstrap technique. Dis Manag Health Out. 2005;13:159-167.
36. Linden A, Adams J, Roberts N. The generalizability of disease management program results: getting from here to there. Manag Care Interface. 2004;17:38-45.
37. Flury BK, Reidwyl H. Standard distance in univariate and multivariate analysis. Am Stat. 1986;40:249-251.
38. Linden A, Samuels SJ. Using balance statistics to determine the optimal number of controls in matching studies. J Eval Clin Pract. 2013;19:968-975.
39. Normand SLT, Landrum MB, Guadagnoli E, et al. Validating recommendations for coronary angiography following an acute myocardial infarction in the elderly: a matched analysis using propensity scores. J Clin Epidemiol. 2001;54:387-398.
40. Yarnold PR, Soltysik RC. Optimal Data Analysis: Guidebook with Software for Windows. Washington, D.C.: APA Books; 2005.
41. Yarnold PR, Linden A. Theoretical aspects of the D statistic. Optim Data Anal. 2016;5:171-174.
42. Yarnold PR. Determining jackknife ESS for a CTA model with chaotic instability. Optim Data Anal. 2016;5:11-14.
43. Linden A. LOOCLASS: stata module for generating classification statistics of leave-one-out cross-validation for binary outcomes. Statistical Software Components s458032, Boston College Department of Economics, 2015. Available from: http://ideas.repec.org/c/boc/bocode/s458032.html Accessed on January 12, 2017.
44. Linden A. COVBAL: stata module for generating covariate balance statistics. Statistical Software Components s458188, Boston College Department of Economics, 2016. Available from http://ideas.repec.org/c/boc/bocode/s458188.html Accessed on January 12, 2017.
45. Allison PD. Convergence failures in logistic regression. SAS Global Forum. 2008;360:1-11.
46. Linden A, Adams J, Roberts N. Strengthening the case for disease management effectiveness: unhiding the hidden bias. J Eval Clin Pract. 2006;12:140-147.
47. Stuart EA. Matching methods for causal inference: a review and a look forward. Stat Science. 2010;25:1-21.
48. Linden A, Adams JL. Evaluating health management programmes over time. Application of propensity score-based weighting to longitudinal data. J Eval Clin Pract. 2010;16:180-185.
49. Linden A, Adams JL. Applying a propensity-score based weighting model to interrupted time series data: improving causal inference in program evaluation. J Eval Clin Pract. 2011;17:1231-1238.
50. Linden A, Adams JL. Combining the regression-discontinuity design and propensity-score based weighting to improve causal inference in program evaluation. J Eval Clin Pract. 2012;18(2):317-325.
51. Linden A. Combining propensity score-based stratification and weighting to improve causal inference in the evaluation of health care interventions. J Eval Clin Pract. 2014;20(6):1065-1071.
52. Linden A, Adams J, Roberts N. Evaluating disease management program effectiveness: an introduction to survival analysis. Dis Manag. 2004;7:180-190.
53. Linden A, Yarnold PR. Using data mining techniques to characterize participation in observational studies. J Eval Clin Pract. 2016;22:839-847.
54. Linden A, Roberts N. Disease management interventions: what's in the black box? Dis Manag. 2004;7:275-291.
55. Linden A, Butterworth S, Roberts N. Disease management interventions II: what else is in the black box? Dis Manag. 2006;9:73-85.