Application of the estimand framework for an emulated trial using reference based multiple imputation to investigate informative censoring.
Humans
HIV Infections
/ drug therapy
Pneumonia, Pneumocystis
/ prevention & control
Research Design
/ standards
Antibiotic Prophylaxis
/ methods
Data Interpretation, Statistical
Proportional Hazards Models
AIDS-Related Opportunistic Infections
/ prevention & control
Clinical Trials as Topic
/ methods
Anti-Bacterial Agents
/ therapeutic use
Estimand framework
Informative censoring
Multiple imputation
Sensitivity analysis
Trial emulation
Journal
BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545
Informations de publication
Date de publication:
18 Oct 2024
18 Oct 2024
Historique:
received:
09
10
2023
accepted:
04
10
2024
medline:
19
10
2024
pubmed:
19
10
2024
entrez:
18
10
2024
Statut:
epublish
Résumé
The ICH E9 (R1) addendum on Estimands and Sensitivity analysis in Clinical trials proposes a framework for the design and analysis of clinical trials aimed at improving clarity around the definition of the targeted treatment effect (the estimand) of a study. We adopt the estimand framework in the context of a study using "trial emulation" to estimate the risk of pneumocystis pneumonia, an opportunistic disease contracted by people living with HIV and AIDS having a weakened immune system, when considering two antibiotic treatment regimes for stopping antibiotic prophylaxis treatment against this disease. A "while on treatment" strategy has been implemented for post-randomisation (intercurrent) events. We then perform a sensitivity analysis using reference based multiple imputation to model a scenario in which patients lost to follow-up stop taking prophylaxis. The primary analysis indicated a protective effect for the new regime which used viral suppression as prophylaxis stopping criteria (hazard ratio (HR) 0.78, 95% confidence interval [0.69, 0.89], p < 0.001). For the sensitivity analysis, when we apply the "jump to off prophylaxis" approach, the hazard ratio is almost the same compared to that from the primary analysis (HR 0.80 [0.69, 0.95], p = 0.009). The sensitivity analysis confirmed that the new regime exhibits a clear improvement over the existing guidelines for PcP prophylaxis when those lost to follow-up "jump to off prophylaxis". Our application using reference based multiple imputation demonstrates the method's flexibility and simplicity for sensitivity analyses in the context of the estimand framework for (emulated) trials.
Sections du résumé
BACKGROUND
BACKGROUND
The ICH E9 (R1) addendum on Estimands and Sensitivity analysis in Clinical trials proposes a framework for the design and analysis of clinical trials aimed at improving clarity around the definition of the targeted treatment effect (the estimand) of a study.
METHODS
METHODS
We adopt the estimand framework in the context of a study using "trial emulation" to estimate the risk of pneumocystis pneumonia, an opportunistic disease contracted by people living with HIV and AIDS having a weakened immune system, when considering two antibiotic treatment regimes for stopping antibiotic prophylaxis treatment against this disease. A "while on treatment" strategy has been implemented for post-randomisation (intercurrent) events. We then perform a sensitivity analysis using reference based multiple imputation to model a scenario in which patients lost to follow-up stop taking prophylaxis.
RESULTS
RESULTS
The primary analysis indicated a protective effect for the new regime which used viral suppression as prophylaxis stopping criteria (hazard ratio (HR) 0.78, 95% confidence interval [0.69, 0.89], p < 0.001). For the sensitivity analysis, when we apply the "jump to off prophylaxis" approach, the hazard ratio is almost the same compared to that from the primary analysis (HR 0.80 [0.69, 0.95], p = 0.009). The sensitivity analysis confirmed that the new regime exhibits a clear improvement over the existing guidelines for PcP prophylaxis when those lost to follow-up "jump to off prophylaxis".
CONCLUSIONS
CONCLUSIONS
Our application using reference based multiple imputation demonstrates the method's flexibility and simplicity for sensitivity analyses in the context of the estimand framework for (emulated) trials.
Identifiants
pubmed: 39425034
doi: 10.1186/s12874-024-02364-6
pii: 10.1186/s12874-024-02364-6
doi:
Substances chimiques
Anti-Bacterial Agents
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
245Subventions
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
ID : 324730_149792
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
ID : 324730_149792
Informations de copyright
© 2024. The Author(s).
Références
CHMP. ICH E9 (R1) addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles in clinical trials. 17 Feb 2020. https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e9-r1-addendum-estimands-sensitivity-analysis-clinical-trials-guideline-statistical-principles_en.pdf . Accessed 31 July 2023.
Clark TP, Kahan BC, Philipps A, White I, Carpenter JR. Estimands: bringing clarity and focus to research questions in clinical trials. BMJ Open. 2022;12:e052953.
doi: 10.1136/bmjopen-2021-052953
pubmed: 34980616
pmcid: 8724703
Tan P-T, Cro S, Van Vogt E, Szigeti M, Cornelius VR. A review of the use of controlled multiple imputation in randomised controlled trials with missing outcome data. BMC Med Res Methodol. 2021 Apr 15;21(1):72. https://doi.org/10.1186/s12874-021-01261-6 . PMID: 33858355; PMCID: PMC8048273.
Rehal S, Cro S, Phillips PPJ, Fielding K, Carpenter JR. Handling intecurrent events and missing data in non-inferiority trials using the estimand framework: a tuberculosis case study. Clin Trials. 2023. https://doi.org/10.1177/17407745231176773 .
Chene G. Cohort profile: collaboration of observational HIV epidemiological research Europe (COHERE) in EuroCoord. Int J Epidemiol. 2017;46(3):797–797.
pubmed: 27864413
Hernan MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol. 2016;183(8):758–64.
doi: 10.1093/aje/kwv254
pubmed: 26994063
pmcid: 4832051
Lodi S, et al. Effect of Immediate initiation of antiretroviral treatment in HIV-positive individuals aged 50 years or older. J Acquir Immune Defic Syndr. 2017;3(76).
Atkinson A, Zwahlen M, Barger D, d’Arminio Monforte A, De Wit S, Ghosn J, Girardi E, Johansson V, Morlat P, Mussini C, Noguera-Julian A, Stephan C, Touloumi G, Kirk O, Mocroft A, Reiss P, Miro JM, Carpenter JR, Furrer H. Withholding primary PcP prophylaxis in virally suppressed HIV patients: an emulation of a pragmatic trial in COHERE. Clin Infect Dis. 2020;73(2):195–202. https://doi.org/10.1093/cid/ciaa615 .
Rufibach K. Treatment effect quantification for time-to-event endpoints–estimands, analysis strategies, and beyond. Pharm Stat. 2019;18:145–65.
doi: 10.1002/pst.1917
pubmed: 30478869
Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. New Jersey: Wiley; 2002.
Keogh RH, Gran JM, Seaman SR, Davies G, Vansteelandt S. Causal inference in survival analysis using longitudinal observational data: sequential trials and marginal structural models. Stat Med. 2023;42:2191. https://doi.org/10.1002/sim.8718 .
doi: 10.1002/sim.8718
pubmed: 37086186
pmcid: 7614580
Atkinson A, Kenward MG, Clayton T, Carpenter JR. Reference-based sensitivity analysis for time-to-event data. Pharm Stat. 2019;18(6):645–58.
doi: 10.1002/pst.1954
pubmed: 31309730
pmcid: 6899641
Pham TM, Tweed CD, Carpenter JR, Kahan BC, Nunn AJ, Crook AM, Esmail H, Goodall R, Phillips PPJ, White IR. Rethinking intercurrent events in defining estimands for tuberculosis trials. Clin Trials. 2022;19(5):522–33.
doi: 10.1177/17407745221103853
pubmed: 35850542
pmcid: 9523802
Hernan MA. The hazards of hazard ratios. Epidemiology. 2010;1(21):13–5.
doi: 10.1097/EDE.0b013e3181c1ea43
Royston P, Parmar MK. The use of restricted mean survival time to estimate the treatment effect in randomized clinical trials when the proportional hazards assumption is in doubt. Stat Med. 2011;30:2409–21.
doi: 10.1002/sim.4274
pubmed: 21611958
Zhao Y, Herring AH, Zhou H, Mirza AW, Koch GG. A multiple imputation method for sensitivity analysis of time-to-event data with possibly informative censoring. J Biopharm Stat. 2014;24(2):229–53.
doi: 10.1080/10543406.2013.860769
pubmed: 24605967
pmcid: 4009741
Zhao Y, Saville B, Zhou H, Koch G. Sensitivity analysis for missing outcomes in time-to-event data with covariate adjustment. J Biopharm Stat. 2016;26(2):269–79.
doi: 10.1080/10543406.2014.1000549
pubmed: 25635808
Lipkovich I, Ratitch B, O’Kelly M. Sensitivity to censored-at-random assumption in the analysis of time-to-event endpoints. Pharm Stat. 2016;15:216–29.
doi: 10.1002/pst.1738
pubmed: 26997353
Mason A, Gomes M, Grieve R, Ulug P, Powell J, Carpenter JR. Development of a practical approach to expert elicitation for randomised controlled trial with missing health outcomes: application to improve the trial. Clin Trails. 2017;14:357–67.
doi: 10.1177/1740774517711442
Jackson D, White I, Seaman S, Evans H, Baisley K, Carpenter JR. Relaxing the independent censoring assumption in the cox proportional hazards model using multiple imputation. Stat Med. 2014;33:4681–94.
doi: 10.1002/sim.6274
pubmed: 25060703
pmcid: 4282781
Burkoff NS, Metcalfe P, Bartlett J, Ruau D. Gamma imputation tutorial (Jackson 2014). 10 Aug 2016. https://rdrr.io/cran/InformativeCensoring/f/inst/doc/gamma_imputation_Jackson_2014.pdf . Accessed 31 July 2024.