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Morgan KE, White IR, Leyrat C, Stanworth S, Kahan BC. Applying the Estimands Framework to Non-Inferiority Trials: Guidance on Choice of Hypothetical Estimands for Non-Adherence and Comparison of Estimation Methods. Stat Med 2025; 44:e10348. [PMID: 39921280 PMCID: PMC11806244 DOI: 10.1002/sim.10348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/17/2024] [Accepted: 01/06/2025] [Indexed: 02/10/2025]
Abstract
A common concern in non-inferiority (NI) trials is that non-adherence due, for example, to poor study conduct can make treatment arms artificially similar. Because intention-to-treat analyses can be anti-conservative in this situation, per-protocol analyses are sometimes recommended. However, such advice does not consider the estimands framework, nor the risk of bias from per-protocol analyses. We therefore sought to update the above guidance using the estimands framework, and compare estimators to improve on the performance of per-protocol analyses. We argue the main threat to validity of NI trials is the occurrence of "trial-specific" intercurrent events (IEs), that is, IEs which occur in a trial setting, but would not occur in practice. To guard against erroneous conclusions of non-inferiority, we suggest an estimand using a hypothetical strategy for trial-specific IEs should be employed, with handling of other non-trial-specific IEs chosen based on clinical considerations. We provide an overview of estimators that could be used to estimate a hypothetical estimand, including inverse probability weighting (IPW), and two instrumental variable approaches (one using an informative Bayesian prior on the effect of standard treatment, and one using a treatment-by-covariate interaction as an instrument). We compare them, using simulation in the setting of all-or-nothing compliance in two active treatment arms, and conclude both IPW and the instrumental variable method using a Bayesian prior are potentially useful approaches, with the choice between them depending on which assumptions are most plausible for a given trial.
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Affiliation(s)
- Katy E. Morgan
- Department of Medical StatisticsLondon School of Hygiene & Tropical MedicineLondonUK
| | | | - Clémence Leyrat
- Department of Medical StatisticsLondon School of Hygiene & Tropical MedicineLondonUK
| | - Simon Stanworth
- Radcliffe Department of MedicineUniversity of OxfordOxfordUK
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Tuesley KM, Spilsbury K, Webb PM, Pearson SA, Donovan P, Coory MD, Steer CB, Stewart LM, Pandeya N, Protani MM, Dixon-Suen S, Marquart-Wilson L, Jordan SJ. Use of an emulated trial to investigate the association between use of nitrogen-based bisphosphonates and risk of epithelial ovarian cancer. Int J Epidemiol 2024; 53:dyae108. [PMID: 39133937 PMCID: PMC11319644 DOI: 10.1093/ije/dyae108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 08/02/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND Epithelial ovarian cancer (EOC) is the eighth most common cancer in women, with poor survival outcomes. Observational evidence suggests that nitrogen-based bisphosphonate (NBB) use may be associated with reduced risk of EOC, particularly the endometrioid and serous histotypes; however, confounding by indication is a concern. An alternative approach to investigate the chemo-preventive potential of NBBs is to emulate a target trial by identifying all women who initiate use of NBBs and investigate the risk of EOC for continued users compared with discontinued users. METHODS Using population-based linked data, we identified all Australian women aged over 50 years who first used NBBs over 2004-12. We used the year after first use to define treatment for each woman as either continued or discontinued use. We emulated randomization using stabilized inverse probability weights to balance the treatment groups using covariates including age, comorbidities and socioeconomic status. We followed women from treatment assignment until EOC diagnosis, death or 31 December 2013. We assessed the risk of EOC (overall and by histotype) using flexible parametric time-to-event models allowing for time-varying effects, and produced time-varying coefficients. RESULTS Of the 313 383 women in the study, 472 were diagnosed with EOC during follow-up (261 serous EOC), with an average age at diagnosis of 72 years. Continued use of NBBs was associated with reduced risk of EOC overall (HR = 0.87, 95% CI: 0.69, 1.10), and serous EOC (HR = 0.71, 95% CI: 0.53, 0.96), compared with discontinued treatment, with estimates remaining constant over the 9-year follow-up. CONCLUSIONS Results from our emulated trial suggest that in women who initiated NBB treatment, those who continued use had 13% and 29% lower hazards of being diagnosed with EOC overall and serous EOC, respectively, compared with women who discontinued use.
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Affiliation(s)
- Karen M Tuesley
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Katrina Spilsbury
- Institute for Health Research, University of Notre Dame Australia, Fremantle, WA, Australia
| | - Penelope M Webb
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Sallie-Anne Pearson
- School of Population Health, University of New South Wales, Sydney, NSW, Australia
- Centre of Research Excellence in Medicines Intelligence, University of New South Wales, Sydney, NSW, Australia
| | - Peter Donovan
- Clinical Pharmacology Department, Royal Brisbane and Women’s Hospital, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Michael D Coory
- Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Christopher B Steer
- Border Medical Oncology, Albury-Wodonga Regional Cancer Centre, Albury, NSW, Australia
- University of NSW Rural Clinical School, Albury Campus, Albury, NSW, Australia
| | - Louise M Stewart
- School of Population and Global Health, University of Western Australia, Perth, WA, Australia
| | - Nirmala Pandeya
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Melinda M Protani
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Suzanne Dixon-Suen
- Institute for Physical Activity and Nutrition, Deakin University, Geelong, VIC, Australia
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Louise Marquart-Wilson
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Clinical Malaria Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Susan J Jordan
- School of Public Health, Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
- Population Health Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
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3
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Neale RE, Baxter C, Romero BD, McLeod DSA, English DR, Armstrong BK, Ebeling PR, Hartel G, Kimlin MG, O'Connell R, van der Pols JC, Venn AJ, Webb PM, Whiteman DC, Waterhouse M. The D-Health Trial: a randomised controlled trial of the effect of vitamin D on mortality. Lancet Diabetes Endocrinol 2022; 10:120-128. [PMID: 35026158 DOI: 10.1016/s2213-8587(21)00345-4] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 12/03/2021] [Accepted: 12/03/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND The effect of supplementing unscreened adults with vitamin D3 on mortality is unclear. We aimed to determine whether monthly doses of vitamin D3 influenced mortality in older Australians. METHODS We did a randomised, double-blind, placebo-controlled trial of oral vitamin D3 supplementation (60 000 IU per month) in Australians 60 years or older who were recruited across the country via the Commonwealth electoral roll. Participants were randomly assigned (1:1), using automated computer-generated permuted block randomisation, to receive one oral gel capsule of either 60 000 IU vitamin D3 or placebo once a month for 5 years. Participants, staff, and investigators were blinded to study group allocation. The primary endpoint was all-cause mortality assessed in all participants who were randomly assigned. We also analysed mortality from cancer, cardiovascular disease, and other causes. Hazard ratios (HRs) and 95% CIs were generated using flexible parametric survival models. This trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12613000743763. FINDINGS Between Feb 14, 2014, and June 17, 2015, we randomly assigned 21 315 participants, including 10 662 to the vitamin D group and 10 653 to the placebo group. In 4441 blood samples collected from randomly sampled participants (N=3943) during follow-up, mean serum 25-hydroxy-vitamin D concentrations were 77 (SD 25) in the placebo group and 115 (SD 30) nmol/L in the vitamin D group. Following 5 years of intervention (median follow-up 5·7 years [IQR 5·4-6·7]), 1100 deaths were recorded (placebo 538 [5·1%]; vitamin D 562 [5·3%]). 10 661 participants in the vitamin D group and 10 649 participants in the placebo group were included in the primary analysis. Five participants (one in the vitamin D group and four in the placebo group) were not included as they requested to be withdrawn and their data to be destroyed. The HR of vitamin D3 effect on all-cause mortality was 1.04 [95% CI 0·93 to 1·18]; p=0·47)and the HR of vitamin D3 effect on cardiovascular disease mortality was 0·96 (95% CI 0·72 to 1·28; p=0·77). The HR for cancer mortality was 1·15 (95% CI 0·96 to 1·39; p=0·13) and for mortality from other causes it was 0·83 (95% CI 0·65 to 1·07; p=0·15). The odds ratio for the per-protocol analysis was OR 1·18 (95% CI 1·00 to 1·40; p=0·06). In exploratory analyses excluding the first 2 years of follow-up, those randomly assigned to receive vitamin D had a numerically higher hazard of cancer mortality than those in the placebo group (HR 1·24 [95% CI 1·01-1·54]; p=0·05). INTERPRETATION Administering vitamin D3 monthly to unscreened older people did not reduce all-cause mortality. Point estimates and exploratory analyses excluding the early follow-up period were consistent with an increased risk of death from cancer. Pending further evidence, the precautionary principle would suggest that this dosing regimen might not be appropriate in people who are vitamin D-replete. FUNDING The D-Health Trial is funded by National Health and Medical Research Council.
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Affiliation(s)
- Rachel E Neale
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia; School of Public Health, The University of Queensland, Brisbane, Australia.
| | - Catherine Baxter
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Briony Duarte Romero
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Donald S A McLeod
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia; Department of Endocrinology and Diabetes, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Dallas R English
- Melbourne School of Population Health, University of Melbourne, Melbourne, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Australia
| | - Bruce K Armstrong
- School of Public Health, University of Sydney, Sydney, Australia; School of Global and Population Health, University of Western Australia, Perth, Australia
| | - Peter R Ebeling
- Department of Medicine, School of Clinical Sciences, Monash University, Melbourne, Australia
| | - Gunter Hartel
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Michael G Kimlin
- School of Biomedical Science, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Rachel O'Connell
- NHMRC Clinical Trials Centre, University of Sydney, Sydney, Australia
| | - Jolieke C van der Pols
- School of Exercise and Nutrition Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Alison J Venn
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Penelope M Webb
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia; School of Public Health, The University of Queensland, Brisbane, Australia
| | - David C Whiteman
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia; School of Public Health, The University of Queensland, Brisbane, Australia
| | - Mary Waterhouse
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
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Phillips PPJ, Van Deun A, Ahmed S, Goodall RL, Meredith SK, Conradie F, Chiang CY, Rusen ID, Nunn AJ. Investigation of the efficacy of the short regimen for rifampicin-resistant TB from the STREAM trial. BMC Med 2020; 18:314. [PMID: 33143704 PMCID: PMC7640464 DOI: 10.1186/s12916-020-01770-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/31/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The STREAM trial demonstrated that a 9-11-month "short" regimen had non-inferior efficacy and comparable safety to a 20+ month "long" regimen for the treatment of rifampicin-resistant tuberculosis. Imbalance in the components of the composite primary outcome merited further investigation. METHODS Firstly, the STREAM primary outcomes were mapped to alternatives in current use, including WHO programmatic outcome definitions and other recently proposed modifications for programmatic or research purposes. Secondly, the outcomes were re-classified according to the likelihood that it was a Failure or Relapse (FoR) event on a 5-point Likert scale: Definite, Probable, Possible, Unlikely, and Highly Unlikely. Sensitivity analyses were employed to explore the impact of informative censoring. The protocol-defined modified intention-to-treat (MITT) analysis population was used for all analyses. RESULTS Cure on the short regimen ranged from 75.1 to 84.2% across five alternative outcomes. However, between-regimens results did not exceed 1.3% in favor of the long regimen (95% CI upper bound 10.1%), similar to the primary efficacy results from the trial. Considering only Definite or Probable FoR events, there was weak evidence of a higher risk of FoR in the short regimen, HR 2.19 (95%CI 0.90, 5.35), p = 0.076; considering only Definite FoR events, the evidence was stronger, HR 3.53 (95%CI 1.05, 11.87), p = 0.030. Cumulative number of grade 3-4 AEs was the strongest predictor of censoring. Considering a larger effect of informative censoring attenuated treatment differences, although 95% CI were very wide. CONCLUSION Five alternative outcome definitions gave similar overall results. The risk of failure or relapse (FoR) may be higher in the short regimen than in the long regimen, highlighting the importance of how loss to follow-up and other censoring is accounted for in analyses. The outcome of time to FoR should be considered as a primary outcome for future drug-sensitive and drug-resistant TB treatment trials, provided sensitivity analyses exploring the impact of departures from independent censoring are also included.
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Affiliation(s)
- P P J Phillips
- UCSF Center for Tuberculosis, University of California San Francisco, San Francisco, USA.
| | | | - S Ahmed
- MRC Clinical Trials Unit at UCL, London, UK
| | | | | | - F Conradie
- Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
| | - C-Y Chiang
- Division of Pulmonary Medicine, Department of Internal Medicine, Wanfang Hospital, Taipei Medical University, Taipei, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,International Union against Tuberculosis and Lung Disease (the Union), Paris, France
| | - I D Rusen
- Research Division, Vital Strategies, New York, USA
| | - A J Nunn
- MRC Clinical Trials Unit at UCL, London, UK
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Sullivan TR, Latimer NR, Gray J, Sorich MJ, Salter AB, Karnon J. Adjusting for Treatment Switching in Oncology Trials: A Systematic Review and Recommendations for Reporting. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2020; 23:388-396. [PMID: 32197735 DOI: 10.1016/j.jval.2019.10.015] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 10/09/2019] [Accepted: 10/26/2019] [Indexed: 06/10/2023]
Abstract
OBJECTIVES To systematically review the quality of reporting on the application of switching adjustment approaches in published oncology trials and industry submissions to the National Institute for Health and Care Excellence Although methods such as the rank preserving structural failure time model (RPSFTM) and inverse probability of censoring weights (IPCW) have been developed to address treatment switching, the approaches are not widely accepted within health technology assessment. This limited acceptance may partly be a consequence of poor reporting on their application. METHODS Published trials and industry submissions were obtained from searches of PubMed and nice.org.uk, respectively. The quality of reporting in these studies was judged against a checklist of reporting recommendations, which was developed by the authors based on detailed considerations of the methods. RESULTS Thirteen published trials and 8 submissions to nice.org.uk satisfied inclusion criteria. The quality of reporting around the implementation of the RPSFTM and IPCW methods was generally poor. Few studies stated whether the adjustment approach was prespecified, more than a third failed to provide any justification for the chosen method, and nearly half neglected to perform sensitivity analyses. Further, it was often unclear how the RPSFTM and IPCW methods were implemented. CONCLUSIONS Inadequate reporting on the application of switching adjustment methods increases uncertainty around results, which may contribute to the limited acceptance of these methods by decision makers. The proposed reporting recommendations aim to support the improved interpretation of analyses undertaken to adjust for treatment switching.
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Affiliation(s)
- Thomas R Sullivan
- SAHMRI Women & Kids, South Australian Health & Medical Research Institute, Adelaide, Australia; School of Public Health, The University of Adelaide, Adelaide, Australia.
| | - Nicholas R Latimer
- School of Health and Related Research, The University of Sheffield, Sheffield, England, UK
| | - Jodi Gray
- Australian Institute of Health Innovation, Macquarie University, Sydney, Australia; College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Michael J Sorich
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
| | - Amy B Salter
- School of Public Health, The University of Adelaide, Adelaide, Australia
| | - Jonathan Karnon
- College of Medicine and Public Health, Flinders University, Adelaide, Australia
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Grafféo N, Latouche A, Le Tourneau C, Chevret S. ipcwswitch: An R package for inverse probability of censoring weighting with an application to switches in clinical trials. Comput Biol Med 2019; 111:103339. [PMID: 31442762 DOI: 10.1016/j.compbiomed.2019.103339] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 05/30/2019] [Accepted: 06/19/2019] [Indexed: 10/26/2022]
Abstract
In randomized clinical trials (RCT), the analysis is based on the intent-to-treat principle to avoid any selection bias in the constitution of groups. However, estimates of overall survival can be biased when significant crossover occurs because the separation of randomized groups is lost. To handle these switches, the inverse probability of censoring weighting (IPCW) method has been proposed; however, it is still poorly used in RCT, notably because of its complex implementation. In particular, for time-to-event outcomes, it can be difficult to format data, especially when time-dependent covariates have to be managed, with different measurement times between patients. This paper aims to present the R package ipcwswitch with some guidance for the analysis of the treatment effect on survival in a hypothetical setting where all patients would have continued to take the randomization treatment. After a brief recall of the key principles of the IPCW method, each step of the implementation is described using a toy example. The guidelines are illustrated in a case study that aimed at evaluating the benefit of therapy based on tumour molecular profiling for advanced cancers, SHIVA01.
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Affiliation(s)
- Nathalie Grafféo
- INSERMU1153, Statistic and Epidemiologic Research Center Sorbonne Paris Cité (CRESS), ECSTRRA Team, Saint-Louis Hospital, 75010, Paris, France; Paris Diderot University, Paris, France; Aix Marseille Univ, INSERM, IRD, SESSTIM, Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, Marseille, France.
| | - Aurélien Latouche
- EA 4629, Conservatoire national des arts et métiers (Cnam), Paris, France; INSERM U900, Institut Curie, Saint Cloud, France.
| | - Christophe Le Tourneau
- INSERM U900, Institut Curie, Saint Cloud, France; Department of Drug Development and Innovation (D3i), Institut Curie, Paris & Saint-Cloud, France; Paris-Saclay University, Paris, France.
| | - Sylvie Chevret
- INSERMU1153, Statistic and Epidemiologic Research Center Sorbonne Paris Cité (CRESS), ECSTRRA Team, Saint-Louis Hospital, 75010, Paris, France; Paris Diderot University, Paris, France; SBIM, Saint-Louis Hospital, APHP, Paris, France.
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7
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Waterhouse M, English DR, Armstrong BK, Baxter C, Duarte Romero B, Ebeling PR, Hartel G, Kimlin MG, McLeod DS, O'Connell RL, van der Pols JC, Venn AJ, Webb PM, Whiteman DC, Neale RE. A randomized placebo-controlled trial of vitamin D supplementation for reduction of mortality and cancer: Statistical analysis plan for the D-Health Trial. Contemp Clin Trials Commun 2019; 14:100333. [PMID: 30886934 PMCID: PMC6402378 DOI: 10.1016/j.conctc.2019.100333] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 01/25/2019] [Accepted: 02/06/2019] [Indexed: 01/12/2023] Open
Abstract
Background Many observational studies have reported an association between vitamin D and non-skeletal health outcomes. The D-Health Trial was launched to determine if supplementing the older population with high monthly doses of Vitamin D can prevent cancer and premature mortality. The intervention is ongoing but here we provide a detailed statistical analysis plan for the primary and secondary outcomes of the D-Health Trial. Methods/design The D-Health Trial is a double-blind, randomized, placebo-controlled trial. Between February 2014 and May 2015, 21,315 people were randomized in a 1:1 ratio to receive monthly doses of either 60,000 IU of cholecalciferol (vitamin D3) or placebo for five years. The primary outcome is all-cause mortality and the secondary outcomes are total cancer incidence and colorectal cancer incidence. These will be ascertained via linkage to death and cancer registries. The primary analysis for each outcome will follow an intention-to-treat approach; we will use flexible parametric survival models to investigate the association between supplementation and time to an event. We describe in detail sophisticated secondary analyses that consider non-compliance and contamination due to off-study supplementation. Conclusions Publication of this statistical analysis plan in advance of the intervention's completion, and adherence to it, will avoid data-driven analyses of the primary and secondary outcomes and ensure robust reporting of outcomes. Clinical trial registration number Australian New Zealand Clinical Trials Registry: ACTRN12613000743763. Registered on 4 July 2013.
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Affiliation(s)
- Mary Waterhouse
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Dallas R. English
- Melbourne School of Population Health, University of Melbourne, Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Australia
| | | | - Catherine Baxter
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Briony Duarte Romero
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Peter R. Ebeling
- Department of Medicine, School of Clinical Sciences, Monash University, Melbourne, Australia
| | - Gunter Hartel
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Michael G. Kimlin
- Health Research Institute, University of Sunshine Coast, Sippy Downs, Australia
| | - Donald S.A. McLeod
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | | | - Jolieke C. van der Pols
- School of Exercise and Nutrition Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Alison J. Venn
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Penelope M. Webb
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - David C. Whiteman
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Rachel E. Neale
- Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Australia
- School of Public Health, The University of Queensland, Brisbane, Australia
- Corresponding author. QIMR Berghofer Medical Research Institute, Locked Bag 2000, Royal Brisbane Hospital, QLD, 4029, Australia.
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Dodd S, White IR, Williamson P. A framework for the design, conduct and interpretation of randomised controlled trials in the presence of treatment changes. Trials 2017; 18:498. [PMID: 29070048 PMCID: PMC5657109 DOI: 10.1186/s13063-017-2240-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 10/06/2017] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND When a randomised trial is subject to deviations from randomised treatment, analysis according to intention-to-treat does not estimate two important quantities: relative treatment efficacy and effectiveness in a setting different from that in the trial. Even in trials of a predominantly pragmatic nature, there may be numerous reasons to consider the extent, and impact on analysis, of such deviations from protocol. Simple methods such as per-protocol or as-treated analyses, which exclude or censor patients on the basis of their adherence, usually introduce selection and confounding biases. However, there exist appropriate causal estimation methods which seek to overcome these inherent biases, but these methods remain relatively unfamiliar and are rarely implemented in trials. METHODS This paper demonstrates when it may be of interest to look beyond intention-to-treat analysis for answers to alternative causal research questions through illustrative case studies. We seek to guide trialists on how to handle treatment changes in the design, conduct and planning the analysis of a trial; these changes may be planned or unplanned, and may or may not be permitted in the protocol. We highlight issues that must be considered at the trial planning stage relating to: the definition of nonadherence and the causal research question of interest, trial design, data collection, monitoring, statistical analysis and sample size. RESULTS AND CONCLUSIONS During trial planning, trialists should define their causal research questions of interest, anticipate the likely extent of treatment changes and use these to inform trial design, including the extent of data collection and data monitoring. A series of concise recommendations is presented to guide trialists when considering undertaking causal analyses.
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Affiliation(s)
- Susanna Dodd
- Department of Biostatistics, Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3GS UK
| | - Ian R. White
- MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge, CB2 0SR UK
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, Aviation House, 125 Kingsway, London, WC2B 6NH UK
| | - Paula Williamson
- Department of Biostatistics, Institute of Translational Medicine, University of Liverpool, Liverpool, L69 3GS UK
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