249
Views
0
CrossRef citations to date
0
Altmetric
Methodology

Considerations for Using Multiple Imputation in Propensity Score-Weighted Analysis – A Tutorial with Applied Example

ORCID Icon &
Pages 835-847 | Published online: 07 Jul 2022

References

  • Eiset AH, Aoun MP, Stougaard M, et al. The association between long-distance migration and PTSD prevalence in Syrian refugees. BMC Psychiatry. 2022;22:363. doi:10.1186/s12888-022-03982-4
  • Mollica RF, Caspi-Yavin Y, Bollini P, Truong T, Tor S, Lavelle JW. The Harvard Trauma Questionnaire: validating a cross-cultural instrument for measuring torture, trauma, and posttraumatic stress disorder in Indochinese refugees. J Nerv. 1992;180(2):111–116. doi:10.1097/00005053-199202000-00008
  • Brookhart MA, Schneeweiss S, Rothman KJ, Glynn RJ, Avorn J, Stürmer T. Variable selection for propensity score models. Am J Epidemiol. 2006;163(12):1149–1156. doi:10.1093/aje/kwj149
  • Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res. 2011;46(3):399–424. doi:10.1080/00273171.2011.568786
  • Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34(28):3661–3679. doi:10.1002/sim.6607
  • Cole SR, Hernán MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol. 2008;168(6):656–664. doi:10.1093/aje/kwn164
  • Austin PC. Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis. Stat Med. 2016;35(30):5642–5655. doi:10.1002/sim.7084
  • Chan KCG, Yam SCP, Zhang Z. Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting. J R Stat Soc Ser B Stat Methodol. 2016;78(3):673–700. doi:10.1111/rssb.12129
  • Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiol Camb Mass. 2000;11(5):550–560. doi:10.1097/00001648-200009000-00011
  • Lee BK, Lessler J, Stuart EA. Weight trimming and propensity score weighting. PLoS One. 2011;6(3):e18174. doi:10.1371/journal.pone.0018174
  • Seaman SR, Galati J, Jackson D, Carlin J. What is meant by “missing at random”? Stat Sci. 2013;28(2):257–268. doi:10.1214/13-STS415
  • Rubin DB. Inference and missing data. Biometrika. 1976;63(3):581–592. doi:10.2307/2335739
  • Little RJA. Regression with Missing X’s: a review. J Am Stat Assoc. 1992;87(420):1227–1237. doi:10.2307/2290664
  • Rubin DB. Multiple imputation after 18+ years. J Am Stat Assoc. 1996;91(434):473–489. doi:10.2307/2291635
  • Sterne JAC, White IR, Carlin JB, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393–b2393. doi:10.1136/bmj.b2393
  • Bartlett JW, Seaman SR, White IR, Carpenter JR. Multiple imputation of covariates by fully conditional specification: accommodating the substantive model. Stat Methods Med Res. 2015;24(4):462–487. doi:10.1177/0962280214521348
  • Seaman SR, Bartlett JW, White IR. Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods. BMC Med Res Methodol. 2012;12:46. doi:10.1186/1471-2288-12-46
  • Bartlett JW, Keogh R Smcfcs: multiple imputation of covariates by substantive model compatible fully conditional specification.; 2020. https://CRAN.R-project.org/package=smcfcs. Accessed June 9, 2022.
  • Bartlett JW, Morris TP. Multiple imputation of covariates by substantive-model compatible fully conditional specification. Stata J Promot Commun Stat Stata. 2015;15(2):437–456. doi:10.1177/1536867X1501500206
  • Bartlett JW, Hughes RA. Bootstrap inference for multiple imputation under uncongeniality and misspecification. Stat Methods Med Res. 2020;29:3533–3546. doi:10.1177/0962280220932189
  • Schomaker M, Heumann C. Bootstrap inference when using multiple imputation. Stat Med. 2018;37(14):2252–2266. doi:10.1002/sim.7654
  • Seaman SR, White I. Inverse probability weighting with missing predictors of treatment assignment or missingness. Commun Stat - Theory Methods. 2014;43(16):3499–3515. doi:10.1080/03610926.2012.700371
  • Murray JS. Multiple imputation: a review of practical and theoretical findings. Stat Sci. 2018;33(2):142–159. doi:10.1214/18-STS644
  • Efron B. Bootstrap methods: another look at the jackknife. Ann Stat. 1979;7(1):1–26. doi:10.1214/aos/1176344552
  • Carpenter J, Bithell J. Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat Med. 2000;19(9):1141–1164. doi:10.1002/(SICI)1097-0258(20000515)19:9<1141::
  • Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28(25):3083–3107. doi:10.1002/sim.3697
  • Leyrat C, Seaman SR, White IR, et al. Propensity score analysis with partially observed covariates: how should multiple imputation be used? Stat Methods Med Res. 2019;28(1):3–19. doi:10.1177/0962280217713032
  • Penning de Vries BBL, Groenwold RH. A comparison of two approaches to implementing propensity score methods following multiple imputation. Epidemiol Biostat Public Health. 2017;14(4). doi:10.2427/12630
  • R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing; 2020. Available from: http://www.R-project.org/. Accessed June 9, 2022.
  • Greifer N WeightIt: weighting for covariate balance in observational studies; 2020. Available from: https://CRAN.R-project.org/package=WeightIt. Accessed June 9, 2022.
  • Greifer N Cobalt: covariate balance tables and plots; 2020. Available from: https://CRAN.R-project.org/package=cobalt. Accessed June 9, 2022.
  • Canty A, Ripley B Boot: bootstrap R (S-Plus) functions; 2020.
  • Vaughan D, Dancho M Furrr: apply mapping functions in parallel using futures.; 2020. Available from: https://CRAN.R-project.org/package=furrr. Accessed June 9, 2022.
  • Wickham H, Averick M, Bryan J, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4(43):1686. doi:10.21105/joss.01686
  • Harrell F. Regression Modeling Strategies: With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. 2nd. Springer International Publishing; 2015. https://www.springer.com/la/book/9783319194240. Accessed February 15, 2019.
  • Austin PC, Stuart EA. The performance of inverse probability of treatment weighting and full matching on the propensity score in the presence of model misspecification when estimating the effect of treatment on survival outcomes. Stat Methods Med Res. 2017;26:1654–1670.
  • Bahamyirou A, Blais L, Forget A, Schnitzer ME. Understanding and diagnosing the potential for bias when using machine learning methods with doubly robust causal estimators. Stat Methods Med Res. 2019;28(6):1637–1650. doi:10.1177/0962280218772065
  • Penning de Vries BBL, Smeden M, Van Groenwold RHH. Propensity score estimation using classification and regression trees in the presence of missing covariate data. Epidemiol Med. 2018;7(1). doi:10.1515/em-2017-0020
  • Qu Y, Lipkovich I. Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach. Stat Med. 2009;28(9):1402–1414. doi:10.1002/sim.3549
  • von Hippel PT, Bartlett JW. Maximum likelihood multiple imputation: faster imputations and consistent standard errors without posterior draws. ArXiv12100870 Stat. 2019;36:400–420.