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Missing Confounder Data in Propensity Score Methods for Causal Inference

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Statistical Causal Inferences and Their Applications in Public Health Research

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

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Abstract

Propensity score methods, including weighting, matching, or stratification, have been increasingly used to control potential confounding in observational studies and non-randomized trials to obtain causal effects of treatment or intervention. However, there are few studies to address the missing confounder data problem in propensity score estimation which is unique and different from most missing covariate data problems where the goal is parameter estimation. We will review existing methods for addressing missing confounder data in propensity score methods for causal inference and discuss the gap between current methodology developments in this area and the challenges in analyzing real observational data.

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References

  1. Ali, M., Groenwold, R., Klungel, O.: Covariate selection and assessment of balance in propensity score analysis in the medical literature: a systematic review. J. Clin. Epidemiol. 68 (2), 112–121 (2015)

    Article  Google Scholar 

  2. Angrist, J. D., Imbens, G.W., Rubin, D. B.: Identification of causal effects using instrumental variables (with discussion). J. Am. Stat. Assoc. 91, 444–472 (1996)

    Article  MATH  Google Scholar 

  3. Austin, P.C.: The performance of different propensity score methods for estimating difference in proportions (risk differences or absolute risk reductions) in observational studies. Stat. Med. 29, 2137–2148 (2010)

    Article  MathSciNet  Google Scholar 

  4. Austin, P.C.: The performance of different propensity score methods for estimating marginal hazard ratios. Stat. in Med. 32 (16), 2837–2849 (2013)

    Article  MathSciNet  Google Scholar 

  5. 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, 661–677 (2009)

    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, 3083–3107 (2009)

    Article  MathSciNet  Google Scholar 

  7. 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)

    Article  MathSciNet  Google Scholar 

  8. Belitser, S.V., Martens, E.P., Pestman, W.R., Groenwold, R.H.H., Boer, A., Klungel, O.H.: Measuring balance and model selection in propensity score methods. Pharmacoepidemiol. Drug Saf. 20, 1115–1129 (2011)

    Article  Google Scholar 

  9. Concato, J., et al.: Randomized, controlled trials, observational studies, and the hierarchy of research designs. N. Engl. J. Med. 342 (25), 1887–1892 (2000)

    Article  Google Scholar 

  10. D’Agostino, R., et al.: Examining the impact of missing data on propensity score estimation in determining the effectiveness of SMBG. Health Serv. Outcome Res. Methodol. 2, 291–315 (2011)

    Article  Google Scholar 

  11. D’Agostino, R.B., Rubin, D.B.: Estimating and using propensity scores with partially missing data. J. Am. Stat. Assoc. 95 (451), 749–59 (2000)

    Article  Google Scholar 

  12. Dixon, W., Watson, K.D., Lunt, M., Hyrich, K.L., British Society for Rheumatology Biologics Register Control Centre Consortium, Silman, A.J., Symmons, D.P., on behalf of the British Society for Rheumatology Biologics Register: Serious infection following anti-tumor necrosis factor alpha therapy in patients with rheumatoid arthritis: lessons from interpreting data from observational studies. Arthritis Rheum. 56, 2896–2904 (2007)

    Google Scholar 

  13. Fu, B., Lunt, M., et al.: A threshold hazard model for estimating serious infection risk following anti-tumor necrosis factor therapy in rheumatoid arthritis patients. J. Biopharm. Stat. 23 (2), 461–476 (2013)

    Article  MathSciNet  Google Scholar 

  14. Gran, J.M., Roysland, K., Wolbers, M., Didelez, V., Sterne, J., Ledergerber, B., Furrer, H., von Wyl, V., Aalen, O.: A sequential Cox approach for estimating the causal effect of treatment in the presence of time-dependent confounding applied to data from the Swiss HIV cohort study. Stat. Med. 29, 2757–68 (2010)

    Article  MathSciNet  Google Scholar 

  15. Groenwold, R.H., White, I.R., Donders, A.R.T., Carpenter, J.R., Altman, D.G., Moons, K.G.: Missing covariate data in clinical research: when and when not to use the missing-indicator method for analysis. Can. Med. Assoc. J. 184 (11), 1265–1269 (2012)

    Article  Google Scholar 

  16. Gu, X.S., Rosenbaum, P.R.: Comparison of multivariate matching methods: structures, distances, and algorithms. J. Comput. Graph. Stat. 2, 405–420 (1993)

    Google Scholar 

  17. Hirano, K., Imbens, G.W., Ridder, G.: Efficient estimation of average treatment effects using the estimated propensity score. Econometrica. 71, 1161–1189 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  18. Iacus, S.M., King, G., Porro, G.: Multivariate matching methods that are monotonic imbalance bounding. J. Am. Stat. Assoc. 106, 345–361 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  19. Lunt, M., et al.: Different methods of balancing covariates leading to different effect estimates in the presence of effect modification. Am. J. Epidemiol. 169 (7), 909–917 (2009)

    Article  Google Scholar 

  20. Mitra, R., Reiter, J.P.: A comparison of two methods of estimating propensity scores after multiple imputation. Stat. Methods Med. Res. 25 (1), 188–204 (2016)

    Article  MathSciNet  Google Scholar 

  21. Moodie, E., Delaney, J., Lefebvre, G., Platt, R.: Missing confounding data in marginal structure models: a comparison of inverse probability weighting and multiple imputation. Int. J. Biostat. 4, 1557–4679 (2008)

    Article  MathSciNet  Google Scholar 

  22. Qu, Y., Lipkovich, I.: Propensity score estimation with missing values using a multiple imputation missingness pattern (MIMP) approach. Stat. Med. 28, 1402–414 (2009)

    Article  MathSciNet  Google Scholar 

  23. Robins, J.M., Hernán, M.A., Brumback, B.: Marginal structural models and causal inference in epidemiology. Epidemiology. 11, 550–60 (2000)

    Article  Google Scholar 

  24. Rosenbaum, P.R.: Observational Studies. Springer, New York (2002)

    Book  MATH  Google Scholar 

  25. Rosenbaum, P.R.: Model-based direct adjustment. J. Am. Stat. Assoc. 82, 387–94 (1987)

    Article  MATH  Google Scholar 

  26. Rosenbaum, P.R., Rubin, D.B.: Assessing sensitivity to an unobserved binary covariate in an observational study with binary outcome. J. R. Stat. Soc. Ser. B 45, 212–218 (1983)

    Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  28. Rosenbaum, P.R., Rubin, D.B.: Reducing bias in observational studies using subclassification on the propensity score. J. Am. Stat. Assoc. 79, 516–524 (1984)

    Article  Google Scholar 

  29. Stuart, E.A.: Matching methods for causal inference. Stat. Sci. 25 (1), 1–21 (2010)

    Article  MATH  Google Scholar 

  30. Stürmer, T., Joshi, M., Glynn, R.J., Avorn, J., Rothman, K.J., 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. 59, 431–437 (2006)

    Article  Google Scholar 

  31. Stürmer, T., Schneeweiss, S., Avorn, J., et al.: Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration. Am. J. Epidemiol. 162 (3), 279–289 (2005)

    Article  Google Scholar 

  32. VanderWeele, T.J., Arah, O.A.: Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders. Epidemiology. 22 (1), 42–52 (2011)

    Article  Google Scholar 

  33. VanderWeele, T.J.: Unmeasured confounding and hazard scales: sensitivity analysis for total, direct, and indirect effects. Eur. J. Epidemiol. 28 (2), 113–117 (2013)

    Article  Google Scholar 

  34. Weitzen, S., et al.: Principles for modelling propensity scores in medical research: a systematic literature review. Pharmacoepidemiol. Drug Saf. 13 (12), 841–853 (2004)

    Article  Google Scholar 

  35. Williamson, E., Morley, R., Lucas, A., Carpenter, J.: Propensity scores: from naive enthusiasm to intuitive understanding. Stat. Methods Med. Res. 21 (3), 273–93 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  36. Williamson, E.J., Forbes, A., Wolfe, R.: Doubly robust estimators of causal exposure effects with missing data in the outcome, exposure or a confounder. Stat. Med. 31 (30), 4382–400 (2012)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Administrative Data Research Centre for England & Institute of Child Health, University College London, London, UK

    Bo Fu

  2. MRC Biostatistics Unit, Cambridge Institute of Public Health, Cambridge, UK

    Li Su

Authors
  1. Bo Fu
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  2. Li Su
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Correspondence to Bo Fu .

Editor information

Editors and Affiliations

  1. Department of Epidemiology School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, USA

    Hua He

  2. Christiana Care Health System, Value Institute, Newark, Delaware, USA

    Pan Wu

  3. School of Social Work and Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA

    Ding-Geng (Din) Chen

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Fu, B., Su, L. (2016). Missing Confounder Data in Propensity Score Methods for Causal Inference. In: He, H., Wu, P., Chen, DG. (eds) Statistical Causal Inferences and Their Applications in Public Health Research. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-41259-7_5

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