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Cited by 41Advanced Causal Inference TechniquesStatistical Methods in Clinical TrialsMeta-analysis and systematic reviews

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Inverse probability weighting to estimate causal effect of a singular phase in a multiphase randomized clinical trial for multiple myeloma

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Pezzi, Annalisa, Cavo, Michele, Biggeri, Annibale, Zamagni, Elena, Nanni, Oriana·2016·BMC Medical Research Methodology
DOI 10.1186/s12874-016-0253-9PMC PMC5103416
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Abstract

BACKGROUND: Randomization procedure in randomized controlled trials (RCTs) permits an unbiased estimation of causal effects. However, in clinical practice, differential compliance between arms may cause a strong violation of randomization balance and biased treatment effect among those who comply. We evaluated the effect of the consolidation phase on disease-free survival of patients with multiple myeloma in an RCT designed for another purpose, adjusting for potential selection bias due to different compliance to previous treatment phases. METHODS: We computed two propensity scores (PS) to model two different selection processes: the first to undergo autologous stem cell transplantation, the second to begin consolidation therapy. Combined stabilized inverse probability treatment weights were then introduced in the Cox model to estimate the causal effect of consolidation therapy miming an ad hoc RCT protocol. RESULTS: We found that the effect of consolidation therapy was restricted to the first 18 months of the phase (HR: 0.40, robust 95 % CI: 0.17-0.96), after which it disappeared. CONCLUSIONS: PS-based methods could be a complementary approach within an RCT context to evaluate the effect of the last phase of a complex therapeutic strategy, adjusting for potential selection bias caused by different compliance to the previous phases of the therapeutic scheme, in order to simulate an ad hoc randomization procedure. TRIAL REGISTRATION: ClinicalTrials.gov: NCT01134484 May 28, 2010 (retrospectively registered) EudraCT: 2005-003723-39 December 17, 2008 (retrospectively registered).

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  • The study used stabilized inverse probability treatment weighting (sIPTW) to balance characteristics between SSRI/SNRI and psychotherapy groups.

    “Fourth, we balanced the characteristics between patients receiving SSRIs/SNRIs and those receiving psychotherapy using the stabilized inverse probability treatment weighting (sIPTW) approach, which preserves the sample size of the original data and avoids underestimating the variance compared to IPTW [25].”

    From Association between first-line antidepressant use and risk of dementia in older adults: a retrospective cohort study (2023) · cites this paper as [25]

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Research works printed but not linked: 24

  1. Armitage P. The role of randomization in clinical trials. Stat Med. 1982;4:345–52. · DOI
  2. Frangakis CE, Rubin DB. Addressing complications of intention-to treat analysis in the combined presence of all-or-none treatment-noncompliance and subsequent missing outcomes. Biometrika. 1999;86:365–79. · DOI
  3. Mealli F, Rubin D. Assumptions when Analyzing Randomized Experiments with Noncompliance and missing Outcomes. Health Serv Outcome Res Methodol. 2002;3:225–32. · DOI
  4. Mealli F, Imbens GW, Ferro S, Biggeri A. Analyzing a randomized trial on breast self-examination with noncompliance and missing outcomes. Biostatistics. 2004;5:2,207–22. · DOI
  5. Rosenbaum P, Rubin D. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41–55. · DOI · Paper page
  6. Cavo M, Brioli A, Tacchetti P, Zannetti BA, Mancuso K, Zamagni E. Role of consolidation therapy in transplant eligible multiple myeloma patients. Semin Oncol. 2013;40(5):610–7. · DOI
  7. D’Agostino RB. Tutorial in biostatistics. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statist Med. 1998;17:2265–81. · DOI
  8. Becker SO, Ichino A. Estimation of average treatment effects based on propensity scores. Stata J. 2002;2(4):358–77. · DOI
  9. Brookhart MA, Schneeweiss S, Rothman KJ, Glynn RJ, Avorn J, Sturmer T. Variable selection for propensity score models. Am J Epidemiol. 2006;163(12):1149–56. · DOI · Paper page
  10. Pearl J. Letter to the editor. Remarks on the method of propensity score. Statist Med. 2009;28:1415–24. · DOI
  11. Guo SY, Fraser MW. Propensity Score Analysis: Statistical Methods and Applications, Second Edition. Sage; 2015. https://uk.sagepub.com/en-gb/eur/propensity-score-analysis/book238151#description .
  12. Austin P. The performance of different propensity score methods for estimating marginal hazard ratios. Statist Med. 2013;32(16):2837–49. · DOI · Paper page
  13. Austin P. The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments. Statist Med. 2014;33(7):1242–58. · DOI
  14. Xu S, Ross C, Raebel MA, Shetterly S, Blanchette C, Smith D. Use of stabilized inverse propensity score as weight to directly estimate relative risk and its confidence intervals. Value Health. 2010;2(13):273–7. · DOI
  15. Hernan MA, Robins J. Estimating causal effects from epidemiological data. J Epidemiol Community Health. 2006;60:578–86. · DOI
  16. Cole SR, Hernan MA. Constructing Inverse Probability Weights for Marginal Structural models. Am J Epidemiol. 2008;168(6):656–64. · DOI · Paper page
  17. Westreich D, Cole SR, Schisterman EF, Platt RW. A simulation study of finite-sample properties of marginal structural Cox proportional hazards models. Statist Med. 2012;31(19):2098–109. · DOI
  18. Cole SR, Hernan MA. Adjusted survival curves with inverse probability weights. Comput Methods Prog Biomed. 2004;75:45–9. · DOI
  19. Xie J, Liu C. Adjusted Kaplan-Meier Estimator and Log-rank Test with Inverse Probability of Treatment Weighting for Survival Data. Statist. Med. 2005;24(20):3089–110. · DOI
  20. Xu S, Shetterly S, Powers D, Raebel MA, Tsai T, Ho PM, Magic D. Extension of Kaplan-Meier methods in observational studies with time-varying treatment. Value Health. 2012;15(1):167–74. · DOI
  21. Lin DY, Wei LJ. The robust inference for the proportional hazards model. J Am Stat Assoc. 1989;84:1074–8. · DOI · Paper page
  22. Hosmer DW, Royston P. Using Aalen’s linear hazards model to investigate time-varying effects in the proportional hazards regression model. Stata J. 2002;2(4):331–50. · DOI
  23. Huffer FW, McKeague IW. Weighted least squares estimation of Aalen’s additive risk model. J Am Stat Assoc. 1991;86:114–29. · DOI
  24. Cavo M, Tacchetti P, Patriarca F, Petrucci MT, Pantani L, Galli M, et al. Bortezomib with thalidomide plus dexamethasone compared with thalidomide plus dexamethasone as induction therapy before, and consolidation therapy after, double autologous stem-cell transplantation in newly diagnosed multiple myeloma: a randomised phase 3 study. Lancet. 2010;376(9758):2075–85. Erratum in: Lancet. 2011;378(9806):1846. · DOI

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Metadata

Authors
Pezzi, A., Cavo, M., Biggeri, A., Zamagni, E., & Nanni, O.
Affiliations
  • IRCCS Azienda Ospedliero-Universitaria di Bologna Policlinico di Sant'Orsola (IT)
  • Istituto per lo Studio e la Prevenzione Oncologica (IT)
  • Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IT)
Corresponding author
Nanni, Oriana
ORCID
Venue
BMC Medical Research Methodology
Publisher
Springer Science and Business Media LLC
Published
2016-11-09
Volume
16
Issue
1
Pages
150-150
ISSN
1471-2288 (ISSN-L)
DOI
10.1186/s12874-016-0253-9
PMC
PMC5103416
OpenAlex
W2554111497
Cited by
41
References
1
Citations by year
2026: 7 · 2025: 8 · 2023: 6 · 2022: 7 · 2021: 5 · 2020: 3 · 2019: 3 · 2018: 1 · 2017: 1
FWCI
1.39
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Research Methodology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Florence Research (University of Florence) (submittedVersion, cc-by)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Padua Research Archive (University of Padova) (submittedVersion, cc-by)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords
inverse probability weighting, multiple myeloma, consolidation therapy, autologous stem cell transplantation, medication adherence, selection bias, propensity score, disease-free survival, causal effect estimation, time-varying treatment effects
MeSH terms
Adolescent, Adult, Aged, Female, Humans, Male, Middle Aged, Multiple Myeloma, Research Design, Time Factors, Proportional Hazards Models, Randomized Controlled Trials as Topic, Outcome Assessment, Health Care, Clinical Trials, Phase III as Topic, Kaplan-Meier Estimate, Young Adult, Propensity Score

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