290
Views
0
CrossRef citations to date
0
Altmetric
Original research

Imputing missing patient-level data and propensity score matching in cost-effectiveness analysis in Crohn's disease

ORCID Icon, , , & ORCID Icon
Pages 445-454 | Received 29 Mar 2021, Accepted 26 May 2021, Published online: 17 Jun 2021

References

  • Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46(3):399–424.
  • Guo S, Fraser MW. Propensity score analysis: statistical methods and applications. Vol. 11. Los Angeles: Sage Publications; 2014.
  • Pan W, Bai H. Propensity score analysis: fundamentals and developments. New York: Guilford Publications; 2015.
  • Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–55.
  • Caldwell PH, Murphy SB, Butow PN, et al. Clinical trials in children. Lancet. 2004;364(9436):803–811.
  • Azur MJ, Stuart EA, Frangakis C, et al. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20(1):40–49.
  • Van Buuren S. Flexible imputation of missing data.  Boca Raton, FL: CRC press; 2018.
  • Jamshidian M, Jalal S. Tests of homoscedasticity, normality, and missing completely at random for incomplete multivariate data. Psychometrika. 2010;75(4):649–674.
  • Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol Methods. 2002;7(2):147.
  • Mitra R, Reiter JP. A comparison of two methods of estimating propensity scores after multiple imputation. Stat Methods Med Res. 2016;25(1):188–204.
  • 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.
  • Penning De Vries B, Groenwold R. Comments on propensity score matching following multiple imputation. Stat Methods Med Res. 2016;25(6):3066–3068.
  • Penning De Vries BB, Groenwold RH. A comparison of two approaches to implementing propensity score methods following multiple imputation. Epidemiol Biostatistics Public Health. 2017;14(4). https://doi.org/https://doi.org/10.2427/12630.
  • Granger E, Sergeant JC, Lunt M. Avoiding pitfalls when combining multiple imputation and propensity scores. Stat Med. 2018;38(26):5120–5132.
  • Ling AY, Montez-Rath ME, Mathur MB, et al. How to apply multiple imputation in propensity score matching with partially observed confounders: a simulation study and practical recommendations. arXiv preprint arXiv:190407408. 2019. Cited 2020 Mar 3. https://arxiv.org/ftp/arxiv/papers/1904/1904.07408.pdf
  • Mack DR, Benchimol EI, Critch J, et al. Canadian association of gastroenterology clinical practice guideline for the medical management of pediatric luminal crohn’s disease. J Can Assoc Gastroenterol. 2018;2(3):e35–e63.
  • Bashir NS, Walters TD, Griffiths AM, et al. Cost-effectiveness and clinical outcomes of early anti–tumor necrosis factor–α intervention in pediatric crohn’s disease. Inflamm Bowel Dis. 2020;26(8):1239–1250.
  • Kugathasan S, Walters T, Dubinsky M, et al. RISK cohort study in crohn’s disease. 2008. Cited 2019 Nov 28. https://prokiids.com/RISK_Study_Description.html
  • Van Buuren S, Groothuis-Oudshoorn K. mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45(3). DOI:https://doi.org/10.18637/jss.v045.i03
  • Raghunathan TE, Solenberger PW, Van Hoewyk J IVEware: imputation and variance estimation software. Ann Arbor, MI: Survey Methodology Program, Survey Research Center, Institute for Social Research, University of Michigan. 2002. Cited 2019 Nov 28. https://www.src.isr.umich.edu/wp-content/uploads/iveware/v0.1/Documentation/ive_user.pdf
  • Ho DE, Imai K, King G, et al. MatchIt: nonparametric preprocessing for parametric causal inference. J Stat Softw. 2011;42(8):1–28.
  • Austin PC. Some methods of propensity‐score matching had superior performance to others: results of an empirical investigation and Monte Carlo simulations. Biometrical J. 2009;51(1):171–184.
  • Austin PC. Optimal caliper widths for propensity‐score matching when estimating differences in means and differences in proportions in observational studies. Pharm Stat. 2011;10(2):150–161.
  • Austin PC, Mamdani MM. A comparison of propensity score methods: a case‐study estimating the effectiveness of post‐AMI statin use. Stat Med. 2006;25(12):2084–2106.
  • Greifer N. Covariate balance tables and plots: a guide to the cobalt package. 2016. Cited 2019 Nov 28. https://cran.microsoft.com/snapshot/2017-08-01/web/packages/cobalt/vignettes/cobalt_basic_use.html
  • Briggs A, Sculpher M, Claxton K. Decision modelling for health economic evaluation. Oxford: Oxford university press; 2006.
  • Rubin DB. Multiple imputation for nonresponse in surveys. Vol. 81. Hoboken, New Jersey: John Wiley & Sons; 2004.
  • D’Agostino RB Jr, Db R. Estimating and using propensity scores with partially missing data. J Am Stat Assoc. 2000;95(451):749–759.
  • Caro JJ, Briggs AH, Siebert U, et al. Modeling good research practices—overview a report of the ISPOR-SMDM modeling good research practices task force–1. Med Decis Making. 2012;32(5):667–677.
  • Bojke L, Claxton K, Sculpher M, et al. Characterizing structural uncertainty in decision analytic models: a review and application of methods. Value Health. 2009;12(5):739–749.
  • Jackson CH, Thompson SG, Sharples LD. Accounting for uncertainty in health economic decision models by using model averaging. J R Stat Soc. 2009;172(2):383–404.
  • Hill J Reducing bias in treatment effect estimation in observational studies suffering from missing data. ISERP WORKING PAPER 04-01 2004. Cited 2019 Nov 28. https://academiccommons.columbia.edu/doi/https://doi.org/10.7916/D8B85G11.
  • Briggs AH. Handling uncertainty in cost-effectiveness models. Pharmacoeconomics. 2000;17(5):479–500.
  • Blough DK, Ramsey S, Sullivan SD, et al. The impact of using different imputation methods for missing quality of life scores on the estimation of the cost‐effectiveness of lung‐volume‐reduction surgery. Health Econ. 2009;18(1):91–101.
  • Briggs A, Clark T, Wolstenholme J, et al. Missing …. presumed at random: cost‐analysis of incomplete data. Health Econ. 2003;12(5):377–392.
  • Faria R, Gomes M, Epstein D, et al. A guide to handling missing data in cost-effectiveness analysis conducted within randomised controlled trials. Pharmacoeconomics. 2014;32(12):1157–1170.
  • Leurent B, Gomes M, Carpenter JR. Missing data in trial‐based cost‐effectiveness analysis: an incomplete journey. Health Econ. 2018;27(6):1024–1040.
  • Manca A, Palmer S, Handling missing data in patient-level cost-effectiveness analysis alongside randomised clinical trials. Appl Health Econ Health Policy. 2005;4(2):65–75. Cited 2019 May 28 http://york.ac.uk/res/herc/research/hedg/wp.htm.
  • Briggs AH, Lozano‐Ortega G, Spencer S, et al. Estimating the cost‐effectiveness of fluticasone propionate for treating chronic obstructive pulmonary disease in the presence of missing data. Value Health. 2006;9(4):227–235.
  • Oostenbrink JB, Al MJ, Rutten-van Mölken MP. Methods to analyse cost data of patients who withdraw in a clinical trial setting. Pharmacoeconomics. 2003;21(15):1103–1112.
  • Manca A, Austin PC Using propensity score methods to analyse individual patient level cost effectiveness data from observational studies. The University of York: Health Economics and Data Group Working Paper. 2008;8:20.
  • Rhoads CH. Problems with tests of the missingness mechanism in quantitative policy studies. Stat Polit Policy. 2012;3(1). Cited 2020 Jun 14. https://doi.org/https://doi.org/10.1515/2151-7509.1012
  • Bennett DA. How can I deal with missing data in my study? Aust N Z J Public Health. 2001;25(5):464–469.
  • Connors J, Basseri S, Grant A, et al. Exclusive enteral nutrition therapy in paediatric Crohn’s disease results in long-term avoidance of corticosteroids: results of a propensity-score matched cohort analysis. J Crohn’s Colitis. 2017;11(9):1063–1070.
  • Walters TD, Kim MO, Denson LA, et al. Increased effectiveness of early therapy with anti-tumor necrosis factor-α vs an immunomodulator in children with Crohn’s disease. Gastroenterology. 2014;146(2):383–391.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.