Adjustment for treatment changes in epilepsy trials: A comparison of causal methods for time-to-event outcomes

The D-Health Trial: a randomised controlled trial of the effect of vitamin D on mortality

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Five participants (one in the vitamin D group and four in the placebo group) were not included as they requested to be withdrawn and their data to be destroyed.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.

Investigation of the efficacy of the short regimen for rifampicin-resistant TB from the STREAM trial

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.

Adjusting for Treatment Switching in Oncology Trials: A Systematic Review and Recommendations for Reporting

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.

ipcwswitch: An R package for inverse probability of censoring weighting with an application to switches in clinical trials

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.

A randomized placebo-controlled trial of vitamin D supplementation for reduction of mortality and cancer: Statistical analysis plan for the D-Health Trial

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.

A framework for the design, conduct and interpretation of randomised controlled trials in the presence of treatment changes

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.