Veridical causal inference using propensity score methods for comparative effectiveness research with medical claims

Abstract

Medical insurance claims are becoming increasingly common data sources to answer a variety of questions in biomedical research. Although comprehensive in terms of longitudinal characterization of disease development and progression for a potentially large number of patients, population-based inference using these datasets require thoughtful modifications to sample selection and analytic strategies relative to other types of studies. Along with complex selection bias and missing data issues, claims-based studies are purely observational, which limits effective understanding and characterization of the treatment differences between groups being compared. All these issues contribute to a crisis in reproducibility and replication of comparative findings using medical claims. This paper offers practical guidance to the analytical process, demonstrates methods for estimating causal treatment effects with propensity score methods for several types of outcomes common to such studies, such as binary, count, time to event and longitudinally varying measures, and also aims to increase transparency and reproducibility of reporting of results from these investigations. We provide an online version of the paper with readily implementable code for the entire analysis pipeline to serve as a guided tutorial for practitioners. The online version can be accessed at https://rydaro.github.io/. The analytic pipeline is illustrated using a sub-cohort of patients with advanced prostate cancer from the large Clinformatics TM Data Mart Database (OptumInsight, Eden Prairie, Minnesota), consisting of 73 million distinct private payer insures from 2001 to 2016.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3

References

  1. Ali, M.S., Groenwold, R.H.H., Belitser, S.V., Pestman, W.R., Hoes, A.W., Roes, K.C.B., de Boer, A., Klungel, O.H.: Reporting of covariate selection and balance assessment in propensity score analysis is suboptimal: a systematic review. J. Clin. Epidemiol. 68(2), 122–131 (2015). https://doi.org/10.1016/J.JCLINEPI.2014.08.011

    Article  Google Scholar 

  2. Andersen, P.K., Perme, M.P.: Pseudo-observations in survival analysis. Stat. Methods Med. Res. 19(1), 71–99 (2010). https://doi.org/10.1177/0962280209105020

    Article  PubMed  Google Scholar 

  3. Andersen, R.: Modern Methods for Robust Regression. Sage, Thousand Oaks, pp. 1–6 (2019)

  4. Austin, P.C.: A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Stat. Med. 27(12), 2037–2049 (2008a). https://doi.org/10.1002/sim.3150

    Article  PubMed  Google Scholar 

  5. Austin, P.C.: Assessing balance in measured baseline covariates when using many-to-one matching on the propensity-score. Pharmacoepidem. Drug Saf. 17, 1218–1225 (2008b). https://doi.org/10.1002/pds.1674

    Article  Google Scholar 

  6. Austin, P.C.: Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat. Med. 28(25), 3083–3107 (2009a). https://doi.org/10.1002/sim.3697

    Article  PubMed  PubMed Central  Google Scholar 

  7. Austin, P.C.: The relative ability of different propensity score methods to balance measured covariates between treated and untreated subjects in observational studies. Med. Decis. Mak. 29(6), 661–677 (2009b). https://doi.org/10.1177/0272989X09341755

    Article  Google Scholar 

  8. Austin, P.C.: Optimal caliper widths for propensity- score matching when estimating differences in means and differences in proportions in observational studies. Pharmaceut. Stat. 10(2), 150–161 (2011a). https://doi.org/10.1002/PST.433

    Article  Google Scholar 

  9. Austin, P.C.: An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar. Behav. Res. 46(3), 399–424 (2011b). https://doi.org/10.1080/00273171.2011.568786

    Article  Google Scholar 

  10. Austin, P.C.: Assessing the performance of the generalized propensity score for estimating the effect of quantitative or continuous exposures on binary outcomes. Stat. Med. 37(11), 1874–1894 (2018). https://doi.org/10.1002/sim.7615

    Article  PubMed  PubMed Central  Google Scholar 

  11. Austin, P.C., Grootendorst, P., Anderson, G.M.: A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study. Stat. Med. 26(4), 734–753 (2007). https://doi.org/10.1002/sim.2580

    Article  PubMed  Google Scholar 

  12. Barocas, D.A., Penson, D.F.: Racial variation in the pattern and quality of care for prostate cancer in the USA: mind the gap. BJU Int. 106, 322–328 (2010). https://doi.org/10.1111/j.1464-410X.2010.09467.x

    Article  PubMed  PubMed Central  Google Scholar 

  13. Bates, D., Venables, W.: “Splines Package| R Documentation.” Accessed April 25, 2020. https://www.rdocumentation.org/packages/splines/versions/3.6.2

  14. Berger, M.L., Sox, H., Willke, R.J., Brixner, D.L., Eichler, H.-G., Goettsch, W., Madigan, D., et al.: Good practices for real-world data studies of treatment and/or comparative effectiveness: recommendations from the joint ISPOR-ISPE special task force on real-world evidence in health care decision making. Pharmacoepidemiol. Drug Saf. 26(9), 1033–1039 (2017). https://doi.org/10.1002/pds.4297

    Article  PubMed  PubMed Central  Google Scholar 

  15. Birnbaum, H.G., Cremieux, P.Y., Greenberg, P.E., LeLorier, J., Ostrander, J., Venditti, L.: Using healthcare claims data for outcomes research and pharmacoeconomic analyses. PharmacoEconomics 16(1), 1–8 (1999). https://doi.org/10.2165/00019053-199916010-00001

    CAS  Article  PubMed  Google Scholar 

  16. Braitman, L.E., Rosenbaum, P.R.: Rare outcomes, common treatments: analytic strategies using propensity scores. Ann. Internal Med. (2002). https://doi.org/10.7326/0003-4819-137-8-200210150-00015

  17. Brookhart, M.A., Schneeweiss, S., Rothman, K.J., Glynn, R.J., Avorn, J., Stürmer, T.: Variable selection for propensity score models. Am. J. Epidemiol. 163(12), 1149–1156 (2006). https://doi.org/10.1093/aje/kwj149

    Article  PubMed  PubMed Central  Google Scholar 

  18. Brookhart, M.A., Richard Wyss, J., Layton, B., Stürmer, T.: Propensity score methods for confounding control in nonexperimental research. Circul. Cardiovasc. Quality Outcomes 6(5), 604–611 (2013). https://doi.org/10.1161/CIRCOUTCOMES.113.000359

    Article  Google Scholar 

  19. Caram, M.E.V., Wang, S., Tsao, P., Griggs, J.J., Miller, D.C., Hollenbeck, B.K., Lin, P., Mukherjee, B.: Patient and provider variables associated with systemic treatment of advanced prostate cancer. Urol. Pract. 6(4), 234–242 (2019a). https://doi.org/10.1097/UPJ.0000000000000020

    Article  PubMed  PubMed Central  Google Scholar 

  20. Caram, M.E.V., Ross, R., Lin, P., Mukherjee, B.: Factors associated with use of sipuleucel-t to treat patients with advanced prostate cancer. JAMA Netw. Open 2(4), e192589 (2019b). https://doi.org/10.1001/jamanetworkopen.2019.2589

    Article  PubMed  PubMed Central  Google Scholar 

  21. CDC: Data Resources| Drug Overdose. Accessed April 25, 2020a. https://www.cdc.gov/drugoverdose/resources/data.html

  22. CMS: Measure Methodology. Accessed April 25, 2020b. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/HospitalQualityInits/Measure-Methodology

  23. Cole, S.R., Hernan, M.A.: Constructing inverse probability weights for marginal structural models. Am. J. Epidemiol. 168(6), 656–664 (2008). https://doi.org/10.1093/aje/kwn164

    Article  PubMed  PubMed Central  Google Scholar 

  24. Conner, S.C., Sullivan, L.M., Benjamin, E.J., LaValley, M.P., Galea, S., Trinquart, L.: Adjusted restricted mean survival times in observational studies. Stat. Med. 38(20), 3832–3860 (2019). https://doi.org/10.1002/sim.8206

    Article  PubMed  PubMed Central  Google Scholar 

  25. D’Agostino Jr., R.B., Rubin, D.: Estimating and using propensity scores with partially missing data. J. Am. Stat. Assoc. 95, 749–759 (2000). https://doi.org/10.2307/2669455

    Article  Google Scholar 

  26. D’Agostino, R., Lang, W., Walkup, M., et al.: Examining the impact of missing data on propensity score estimation in determining the effectiveness of self-monitoring of blood glucose (SMBG). Health Serv. Outcomes Res. Method. 2, 291–315 (2001). https://doi.org/10.1023/A:1020375413191

    Article  Google Scholar 

  27. D’Ascenzo, F., Cavallero, E., Biondi-Zoccai, G., et al.: Use and misuse of multivariable approaches in interventional cardiology studies on drug-eluting stents: a systematic review. J Interv Cardiol. 25(6), 611–621 (2012). https://doi.org/10.1111/j.1540-8183.2012.00753.x

    Article  PubMed  Google Scholar 

  28. Deb, S., Austin, P.C., Tu, J.V., Ko, D.T., David Mazer, C., Kiss, A., Fremes, S.E.: A review of propensity-score methods and their use in cardiovascular research. Can. J. Cardiol. 32(2), 259–265 (2016). https://doi.org/10.1016/J.CJCA.2015.05.015

    Article  PubMed  Google Scholar 

  29. Desai, R.J., Sarpatwari, A., Dejene, S., Khan, N.F., Lii, J., Rogers, J.R., Dutcher, S.K., et al.: Comparative effectiveness of generic and brand-name medication use: a database study of US Health Insurance Claims. PLoS Med. (2019). https://doi.org/10.1371/journal.pmed.1002763

  30. Dickstein, C., Gehring, R.: Administrative healthcare data a guide to its origin, content, and application using SAS®. SAS Institute (2014)

  31. Elixhauser, A., Claudia Steiner, D., Harris, R., Coffey, R.M.: Comorbidity measures for use with administrative data. Med. Care 36(1), 8–27 (1998). https://doi.org/10.1097/00005650-199801000-00004

    CAS  Article  PubMed  Google Scholar 

  32. FDA: Best practices for conducting and reporting pharmacoepidemiologic safety studies using electronic healthcare data sets (2011)

  33. FDA: Framework for FDA’s real-world evidence program (2018)

  34. Garrido, M.M., Kelley, A.S., Paris, J., Roza, K., Meier, D.E., Morrison, R.S., Aldridge, M.D.: Methods for constructing and assessing propensity scores. Health Serv. Res. 49, 1701–1720 (2014). https://doi.org/10.1111/1475-6773.12182

    Article  PubMed  PubMed Central  Google Scholar 

  35. Grimes, D.A.: Epidemiologic research using administrative databases. Obstet. Gynecol. 116(5), 1018–1019 (2010). https://doi.org/10.1097/AOG.0b013e3181f98300

    Article  PubMed  Google Scholar 

  36. HCUP: Clinical Classifications Software (CCS) for ICD-10-PCS (Beta Version). Accessed April 25, 2020. https://www.hcup-us.ahrq.gov/toolssoftware/ccs10/ccs10.jsp

  37. Hernán, M.A., Robins, J.M.: Using big data to emulate a target trial when a randomized trial is not available. Am. J. Epidemiol. 183(8), 758–764 (2016). https://doi.org/10.1093/aje/kwv254

    Article  PubMed  PubMed Central  Google Scholar 

  38. Hirano, K., Imbens, G.W.: The propensity score with continuous treatments. In: Applied Bayesian modeling and causal inference from incomplete-data perspectives: an essential journey with Donald Rubin’s statistical family. Wiley Blackwell, London, pp. 73–84 (2005). https://doi.org/10.1002/0470090456.ch7

  39. Ho, D., Imai, K., King, G., Stuart, E.: Package ‘MatchIt’ title nonparametric preprocessing for parametric causal inference (2018). https://doi.org/10.1093/pan/mpl013

  40. Hoffman, R.M., Gilliland, F.D., Eley, J.W., et al.: Racial and ethnic differences in advanced-stage prostate cancer: the Prostate Cancer Outcomes Study. J. Natl. Cancer Inst. 93(5), 388–395 (2001). https://doi.org/10.1093/jnci/93.5.388

    CAS  Article  PubMed  Google Scholar 

  41. Hoover, K.W., Tao, G., Kent, C.K., Aral, S.O.: Epidemiologic research using administrative databases: garbage in, garbage out. Obstet. Gynecol. 117(3), 729 (2011). https://doi.org/10.1097/AOG.0b013e31820cd18a

    Article  PubMed  Google Scholar 

  42. Imai, K., Ratkovic, M.: Covariate balancing propensity score. J. R. Stat. Soc. B 76, 243–263 (2014). https://doi.org/10.1111/rssb.12027

    Article  Google Scholar 

  43. Imbens, G.: Nonparametric estimation of average treatment effects under exogeneity: a review. Rev. Econ. Stat. 86, 4–29 (2004). https://doi.org/10.1162/003465304323023651

    Article  Google Scholar 

  44. Izurieta, H.S., Xiyuan, W., Yun, L., Chillarige, Y., Wernecke, M., Lindaas, A., Pratt, D., et al.: Zostavax vaccine effectiveness among US elderly using real-world evidence: addressing unmeasured confounders by using multiple imputation after linking beneficiary surveys with medicare claims. Pharmacoepidemiol. Drug Saf. 28(7), 993–1001 (2019). https://doi.org/10.1002/pds.4801

    Article  PubMed  Google Scholar 

  45. Jackevicius, C.A., Tu, J.V., Krumholz, H.M., Austin, P.C., Ross, J.S., Stukel, T.A., Koh, M., Chong, A., Ko, D.T.: Comparative effectiveness of generic Atorvastatin and Lipitor® in patients hospitalized with an acute coronary syndrome. J. Am. Heart Assoc. 5(4), e003350 (2016). https://doi.org/10.1161/JAHA.116.003350

    Article  PubMed  PubMed Central  Google Scholar 

  46. Joffe, M.M., Ten Have, T.R., Feldman, H.I., Kimmel, S.E.: Model selection, confounder control, and marginal structural models. Am. Stat. 58(4), 272–279 (2004). https://doi.org/10.1198/000313004X5824

    Article  Google Scholar 

  47. Johnson, M.L., Crown, W., Martin, B.C., Dormuth, C.R., Siebert, U.: Good research practices for comparative effectiveness research: analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: the ISPOR good research practices for retrospective database analysis task force report—part III. Value Health 12(8), 1062–1073 (2009). https://doi.org/10.1111/J.1524-4733.2009.00602.X

    Article  PubMed  Google Scholar 

  48. Lee, B.K., Lessler, J., Stuart, E.A.: Improving propensity score weighting using machine learning. Stat. Med. 29(3), 337–346 (2010). https://doi.org/10.1002/sim.3782

    Article  PubMed  PubMed Central  Google Scholar 

  49. Lee, B.K., Lessler, J., Stuart, E.A.: Weight trimming and propensity score weighting. Edited by Giuseppe Biondi-Zoccai. PLoS ONE 6(3), e18174 (2011). https://doi.org/10.1371/journal.pone.0018174

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  50. Li, F., Morgan, K.L., Zaslavsky, A.M.: Balancing covariates via propensity score weighting. J. Am. Stat. Assoc. 113(521), 390–400 (2018). https://doi.org/10.1080/01621459.2016.1260466

    CAS  Article  Google Scholar 

  51. Lumley, T.: R package “Survey” https://cran.r-project.org/web/packages/survey/index.html (2020)

  52. Lunceford, J.K., Davidian, M.: Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study (2017)

  53. Morgan, S.L., Todd, J.J.: A diagnostic routine for the detection of consequential heterogeneity of causal effects. Sociol. Methodol. 38(1), 231–282 (2008). https://doi.org/10.1111/j.1467-9531.2008.00204.x

    Article  Google Scholar 

  54. Motheral, B., Brooks, J., Clark, M.A., Crown, W.H., Davey, P., Hutchins, D., Martin, B.C., Stang, P.: A checklist for retrospective database studies—report of the ISPOR task force on retrospective databases. Value Health 6(2), 90–97 (2003). https://doi.org/10.1046/J.1524-4733.2003.00242.X

    Article  PubMed  Google Scholar 

  55. Nidey, N., Carnahan, R., Carter, K.D., Strathearn, L., Bao, W., Greiner, A., Jelliffee-Pawlowski, L., Tabb, K.M., Ryckman, K.: Association of mood and anxiety disorders and opioid prescription patterns among postpartum women. Am. J. Addict. (2020). https://doi.org/10.1111/ajad.13028

    Article  PubMed  Google Scholar 

  56. Noe, M.H., Shin, D.B., Doshi, J.A., Margolis, D.J., Gelfand, J.M.: Prescribing patterns associated with biologic therapies for psoriasis from a United States medical records database. J. Drugs Dermatol. JDD 18(8), 745–750 (2019)

    PubMed  Google Scholar 

  57. Normand, S.-L.T., Landrum, M.B., Guadagnoli, E., Ayanian, J.Z., Ryan, T.J., Cleary, P.D., McNeil, B.J.: Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: a matched analysis using propensity scores. J. Clin. Epidemiol. 54(4), 387–398 (2001). https://doi.org/10.1016/S0895-4356(00)00321-8

    CAS  Article  PubMed  Google Scholar 

  58. O’Neal, W.T., Sandesara, P.B., Claxton, J.S., MacLehose, R.F., Chen, L.Y., Bengtson, L.G.S., Chamberlain, A.M., Norby, F.L., Lutsey, P.L., Alonso, A.: Provider specialty, anticoagulation prescription patterns, and stroke risk in atrial fibrillation. J. Am. Heart Assoc. (2018). https://doi.org/10.1161/JAHA.117.007943

  59. Patel, C.J., Burford, B., Ioannidis, J.P.A.: Assessment of vibration of effects due to model specification can demonstrate the instability of observational associations. J. Clin. Epidemiol. 68(9), 1046–1058 (2015). https://doi.org/10.1016/j.jclinepi.2015.05.029

    Article  PubMed  PubMed Central  Google Scholar 

  60. Perkins, S.M., Wanzhu, T., Underhill, M.G., Zhou, X.-H., Murray, M.D.: The use of propensity scores in pharmacoepidemiologic research. Pharmacoepidemiol. Drug Saf. 9(2), 93–101 (2000). https://doi.org/10.1002/(SICI)1099-1557(200003/04)9:2%3c93:AID-PDS474%3e3.0.CO;2-I

    CAS  Article  PubMed  Google Scholar 

  61. Robins, J.: A new approach to causal inference in mortality studies with a sustained exposure period-application to control of the healthy worker survivor effect. Math. Model. 7(9–12), 1393–1512 (1986). https://doi.org/10.1016/0270-0255(86)90088-6

    Article  Google Scholar 

  62. Rosenbaum, P.R.: Model-based direct adjustment. J. Am. Stat. Assoc. 82(398), 387–394 (1987). https://doi.org/10.1080/01621459.1987.10478441

    Article  Google Scholar 

  63. Rosenbaum, P.R., Rubin, D.B.: The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41–55 (1983)

  64. Rosenbaum, P.R., Rubin, D.B.: Reducing bias in observational studies using subclassification on the propensity score. J. Am. Stat. Assoc. 79(387), 516 (1984). https://doi.org/10.2307/2288398

    Article  Google Scholar 

  65. Rosenbaum, P.R., Rubin, D.B.: The bias due to incomplete matching. Biometrics 41(1), 103–116 (1985a). https://doi.org/10.2307/2530647

    CAS  Article  PubMed  Google Scholar 

  66. Rosenbaum, P., Rubin, D.: Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am. Stat. 39(1), 33–38 (1985b). https://doi.org/10.2307/2683903

    Article  Google Scholar 

  67. Royston, P., Parmar, M.K.: Restricted mean survival time: an alternative to the hazard ratio for the design and analysis of randomized trials with a time-to-event outcome. BMC Med. Res. Methodol. 13, 152 (2013). https://doi.org/10.1186/1471-2288-13-152

    Article  PubMed  PubMed Central  Google Scholar 

  68. Rubin, D.B.: Estimating causal effects of treatments in randomized and nonrandomized studies 1. J. Educ. Psychol. 66, 688 (1974)

  69. Rubin, D., Thomas, N.: Matching using estimated propensity scores: relating theory to practice. Biometrics 52(1), 249–264 (1996). https://doi.org/10.2307/2533160

    CAS  Article  PubMed  Google Scholar 

  70. Rubin, D.B.: Causal inference using potential outcomes: design, modeling, decisions. (2005). https://doi.org/10.1198/016214504000001880

  71. Schneeweiss, S., Avorn, J.: A review of uses of health care utilization databases for epidemiologic research on therapeutics. J. Clin. Epidemiol. 58(4), 323–337 (2005). https://doi.org/10.1016/j.jclinepi.2004.10.012

    Article  PubMed  Google Scholar 

  72. Setoguchi, S., Sebastian Schneeweiss, M., Brookhart, A., Glynn, R.J., Francis Cook, E.: Evaluating uses of data mining techniques in propensity score estimation: a simulation study. Pharmacoepidemiol. Drug Saf. 17(6), 546–555 (2008). https://doi.org/10.1002/pds.1555

    Article  PubMed  PubMed Central  Google Scholar 

  73. Sherman, R.E., Anderson, S.A., Dal Pan, G.J., Gray, G.W., Gross, T., Hunter, N.L., LaVange, L., et al.: Real-world evidence—what is it and what can it tell us? N. Engl. J. Med. 375(23), 2293–2297 (2016). https://doi.org/10.1056/NEJMsb1609216

    Article  PubMed  Google Scholar 

  74. Shi, X., Wellman, R., Heagerty, P.J., Nelson, J.C., Cook, A.J.: Safety surveillance and the estimation of risk in select populations: flexible methods to control for confounding while targeting marginal comparisons via standardization. Stat. Med. 39(4), 369–386 (2020). https://doi.org/10.1002/sim.8410

    Article  PubMed  Google Scholar 

  75. Snowden, J., Rose, S., Mortimer, K.: Implementation of G-computation on a simulated data set: demonstration of a causal inference technique. Am. J. Epidemiol. (2011). https://academic.oup.com/aje/article/173/7/731/104142

  76. Stefanski, L.A., Boos, D.D.: The calculus of M-estimation. Am. Stat. 56(1), 29–38. (2002). https://doi.org/10.1198/000313002753631330

  77. Stuart, E.A.: Matching methods for causal inference: a review and a look forward. Stat. Sci. A Rev. J. Inst. Math. Stat. 25(1), 1–21 (2010). https://doi.org/10.1214/09-STS313

    Article  Google Scholar 

  78. Stuart, E.A., DuGoff, E., Abrams, M., Salkever, D., Steinwachs, D.: Estimating causal effects in observational studies using electronic health data: challenges and (some) solutions. EGEMS 1(3), 1038 (2013). https://doi.org/10.13063/2327-9214.1038

  79. Susanti, Y., Pratiwi, H., Sulistijowati, S.H., Liana, T.: P A M estimation, S estimation, and M estimation in robust regression. Int. J. Pure Appl. Math. 91(3), 349–60 (2014). https://doi.org/10.12732/ijpam.v91i3.7

  80. Tian, L., Zhao, L., Wei, L.J.: Predicting the restricted mean event time with the subject’s baseline covariates in survival analysis. Biostatistics 15(2), 222–233 (2014). https://doi.org/10.1093/biostatistics/kxt050

    Article  PubMed  Google Scholar 

  81. Tyree, P.T., Lind, B.K., Lafferty, W.E.: Challenges of using medical insurance claims data for utilization analysis. Am. J. Med. Quality Offic. J. Am. Coll. Med. Quality 21(4), 269–275 (2006). https://doi.org/10.1177/1062860606288774

    Article  Google Scholar 

  82. Uno, H., Brian Claggett, L., Tian, E.I., Gallo, P., Miyata, T., Schrag, D., et al.: Moving beyond the hazard ratio in quantifying the between-group difference in survival analysis. J. Clin. Oncol. 32(22), 2380–2385 (2014). https://doi.org/10.1200/JCO.2014.55.2208

    Article  PubMed  PubMed Central  Google Scholar 

  83. Van Der Weele, T.J., Ding, P.: Sensitivity analysis in observational research: introducing the E-value. Ann. Intern. Med. 167(4), 268–274 (2017). https://doi.org/10.7326/M16-2607

    Article  Google Scholar 

  84. Vander Weele, T.J.: Principles of confounder selection. Eur. J. Epidemiol. (2019). https://doi.org/10.1007/s10654-019-00494-6

  85. Venkatesh, A.K., Mei, H., Kocher, K.E., Granovsky, M., Obermeyer, Z., Spatz, E.S., Rothenberg, C., Krumholz, H.M., Lin, Z.: Identification of emergency department visits in medicare administrative claims: approaches and implications. Acad. Emerg. Med. 24(4), 422–431 (2017). https://doi.org/10.1111/acem.13140

    Article  PubMed  PubMed Central  Google Scholar 

  86. von Elm, E., Altman, D.G., Egger, M., Pocock, S.J., Gøtzsche, P.C., Vandenbroucke, J.P.: The strengthening the reporting of observational studies in epidemiology (STROBE) statement. Guidel. Report. Observ. Stud. Epidemiol. 18(6), 800–804 (2007). https://doi.org/10.1097/ede.0b013e31815776

  87. Ward, E., Jemal, A., Cokkinides, V., Singh, G.K., Cardinez, C., Ghafoor, A., Thun, M.: Cancer disparities by race/ethnicity and socioeconomic status. CA: A Cancer J. Clin. 54, 78–93 (2004). https://doi.org/10.3322/canjclin.54.2.78

  88. Weitzen, S., Lapane, K.L., Toledano, A.Y., Hume, A.L., Mor, V.: Principles for modeling propensity scores in medical research: a systematic literature review. Pharmacoepidemiol. Drug Saf. 13(12), 841–853 (2004). https://doi.org/10.1002/pds.969

    Article  PubMed  Google Scholar 

  89. Westreich, D., Lessler, J., Funk, M.J.: Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J. Clin. Epidemiol. 63(8), 826–833 (2010). https://doi.org/10.1016/J.JCLINEPI.2009.11.020

    Article  PubMed  PubMed Central  Google Scholar 

  90. Wilson, J., Bock, A.: The benefit of using both claims data and electronic medical record data in health care analysis white paper. Optum Insight (2012)

  91. Wood, S., Pya, N., Safken, B.: Package ‘mgcv’ title mixed GAM computation vehicle with automatic smoothness estimation (2018)

  92. Wyss, R., Girman, C.J., LoCasale, R.J., Brookhart, A.M., Stürmer, T.: Variable selection for propensity score models when estimating treatment effects on multiple outcomes: a simulation study. Pharmacoepidemiol. Drug Saf. 22(1), 77–85 (2013). https://doi.org/10.1002/pds.3356

    Article  PubMed  Google Scholar 

  93. Wyss, R., Ellis, A.R., Alan Brookhart, M., Girman, C.J., Funk, M.J., LoCasale, R., Stürmer, T.: The role of prediction modeling in propensity score estimation: an evaluation of logistic regression, BCART, and the covariate-balancing propensity score. Am. J. Epidemiol. 180(6), 645–655 (2014). https://doi.org/10.1093/aje/kwu181

    Article  PubMed  PubMed Central  Google Scholar 

  94. Yao, X.I., Wang, X., Speicher, P.J., Hwang, E.S., Cheng, P., Harpole, D.H., Berry, M.F., Schrag, D., Pang, H.H.: Reporting and guidelines in propensity score analysis: a systematic review of cancer and cancer surgical studies. JNCI: J. Natl. Cancer Inst. (2017). https://doi.org/10.1093/jnci/djw323

  95. Yu, B., Kumbier, K.: Veridical data science. Proc. Natl. Acad. Sci. 117(8), 201901326 (2020). https://doi.org/10.1073/pnas.1901326117

    CAS  Article  Google Scholar 

Download references

Acknowledgements

Dr. Mukherjee is funded by the University of Michigan Comprehensive Cancer Center Core Grant P30 CA 046592, University of Michigan Precision Health Initiative and NSF DMS Grant 1712933.

Funding

Dr. Caram is funded by a Prostate Cancer Foundation Young Investigator Award.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Ryan D. Ross.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 20 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ross, R.D., Shi, X., Caram, M.E.V. et al. Veridical causal inference using propensity score methods for comparative effectiveness research with medical claims. Health Serv Outcomes Res Method (2020). https://doi.org/10.1007/s10742-020-00222-8

Download citation

Keywords

  • Average treatment effect
  • Covariate adjustment
  • Insurance claims
  • Hormone therapy
  • Matching
  • Prostate cancer
  • Reproducibility
  • Sensitivity analysis
  • Veridical data science