Sage Journals: Discover world-class research

Abstract

Propensity score matching is widely used to determine the effects of treatments in observational studies. Competing risk survival data are common to medical research. However, there is a paucity of propensity score matching studies related to competing risk survival data with missing causes of failure. In this study, we provide guidelines for estimating the treatment effect on the cumulative incidence function when using propensity score matching on competing risk survival data with missing causes of failure. We examined the performances of different methods for imputing the data with missing causes. We then evaluated the gain from the missing cause imputation in an extensive simulation study and applied the proposed data imputation method to the data from a study on the risk of hepatocellular carcinoma in patients with chronic hepatitis B and chronic hepatitis C.

Keywords

Competing risks hepatocellular carcinoma matching missing causes propensity score.

Get full access to this article

View all access options for this article.

References

Austin

Fine

. Propensity-score matching with competing risks in survival analysis. Stat Med 2019; 38: 751–777.

Austin

. The performance of different propensity score methods for estimating marginal odds ratios. Stat Med 2007a; 26: 3078–3094.

Austin

. Propensity-score matching in the cardiovascular surgery literature from 2004 to 2006: A systematic review and suggestions for improvement. J Thorac Cardiovasc Surg 2007b; 134: 1128–1135.

Mitra

Reiter

. A comparison of two methods of estimating propensity scores after multiple imputation. Stat Methods Med Res 2016; 25: 188–204.

Leyrat

Seaman

White

, et al. Propensity score analysis with partially observed covariates: How should multiple imputation be used?. Stat Methods Med Res 2019; 28: 3–19.

Austin

Grootendorst

Normand

, et al. Conditioning on the propensity score can result in biased estimation of common measures of treatment effect: A Monte Carlo study. Stat Med 2007; 26: 754–768.

Austin

Schuster

. The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study. Stat Methods Med Res 2016; 25: 2214–2237.

Austin

Fine

. Practical recommendations for reporting fine-gray model analyses for competing risk data. Stat Med 2017; 36: 4391–4400.

Goetghebeur

Ryan

. Analysis of competing risks survival data when some failure types are missing. Biometrika 1995; 82: 821–833.

10.

Liang

. Analysis of competing risks data with missing cause of failure under additive hazards model. Stat Sin 2008; 18: 219–234.

11.

Moreno-Betancur

Latouche

. Regression modeling of the cumulative incidence function with missing causes of failure using pseudo-values. Stat Med 2013; 32: 3206–3223.

12.

Lee

Dignam

Han

. Multiple imputation methods for nonparametric inference on cumulative incidence with missing cause of failure. Stat Med 2014; 33: 4605–4626.

13.

Kim

Han

Kim

, et al. Higher risk of hepatocellular carcinoma in chronic hepatitis B vs chronic hepatitis C after achievement of virologic response. J Viral Hepat 2017; 24: 990–997.

14.

Lee

Cronin

Gail

, et al. Multiple imputation methods for inference on cumulative incidence with missing cause of failure. Biomet J 2011; 53: 974–993.

15.

Bakoyannis

Siannis

Touloumi

. Modelling competing risks data with missing cause of failure. Stat Med 2010; 29: 3172–3185.

16.

Stuart

. Developing practical recommendations for the use of propensity scores: Discussion of ‘a critical appraisal of propensity score matching in the medical literature between 1996 and 2003’by peter Austin. Stat Med 2008; 27: 2062–2065.

17.

Stuart

. Matching methods for causal inference: A review and a look forward. Stat Sci 2010; 25: 1.

18.

Austin

. The performance of different propensity score methods for estimating marginal hazard ratios. Stat Med 2013; 32: 2837–2849.

19.

Gayat

Resche-Rigon

Mary

, et al. Propensity score applied to survival data analysis through proportional hazards models: a Monte Carlo study. Pharm Stat 2012; 11: 222–229.

20.

Zhou

Latouche

Rocha

, et al. Competing risks regression for stratified data. Biometrics 2011; 67: 661–670.

21.

Zhou

Fine

Latouche

, et al. Competing risks regression for clustered data. Biostatistics 2012; 13: 371–383.

22.

Zhou

Latouche

. crrSC: Competing risks regression for stratified and clustered data. R package version 1.1, https://CRAN.R-project.org/package=crrSC (2013, accessed 13 May 2021).

23.

Ishwaran

Gerds

Kogalur

, et al. Random survival forests for competing risks. Biostatistics 2014; 15: 757–773.

24.

Ishwaran

Kogalur

. Fast Unified Random Forests for Survival, Regression, and Classification (RF-SRC). R package version 2.9.3 https://cran.r-project.org/web/packages/randomForestSRC/index.html (2021, accessed 13 May 2021).

25.

Gray

. cmprsk: Subdistribution Analysis of Competing Risks. R package version 2.2–10. https://CRAN.R-project.org/package=cmprsk (2020, accessed 13 May 2021).

26.

Gerds

Ozenne

. riskRegression: Risk Regression Models and Prediction Scores for Survival Analysis with Competing Risks. R package version 2020.02.05. https://CRAN.R-project.org/package=riskRegression (2020, accessed 13 May 2021).

27.

Imai

King

, et al. MatchIt: Nonparametric preprocessing for parametric causal inference. J Stat Softw 2011; 42: 1–28.

28.

Beyersmann

Allignol

Schumacher

. Competing risks and multistate models with R. New York: Springer Science & Business Media, 2012, chapter 5.3, pp. 143–144.

29.

Fine

Gray

. A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 1999; 94: 496–509.

30.

GBD

. Causes of death collaborators. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990–2013: A systematic analysis for the global burden of disease study 2013. Lancet 2013; 385: 117–171.

31.

Rubin

. Inference and missing data. Biometrika 1976; 63: 581–592.

32.

Meng

. Multiple-imputation inferences with uncongenial sources of input. Stat Sci 1994; 9: 538–558.

33.

Craiu

Reiser

. Inference for the dependent competing risks model with masked causes of failure. Lifetime Data Anal 2006; 12: 21–33.

34.

Sen

Banerjee

, et al. A Bayesian approach to competing risks analysis with masked cause of death. Stat Med 2010; 29: 1681–1695.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.17 MB

Multiple imputation analysis for propensity score matching with missing causes of failure: An application to hepatocellular carcinoma data

Abstract

Keywords

Get full access to this article

References

Supplementary Material