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Abstract Background There are numerous approaches available to analyse data from cluster randomised trials. These include cluster-level summary methods and individual-level methods accounting for clustering, such as generalised estimating equations and generalised linear mixed models. There has been much methodological work showing that estimates of treatment effects can vary depending on the choice of approach, particularly when estimating odds ratios, essentially because the different approaches target different estimands. Methods In this manuscript, we describe the protocol for a planned re-analysis of data from a large number of cluster randomised trials. Our main objective is to examine empirically whether and how odds ratios estimated using different approaches (for both primary and secondary binary outcomes) vary in cluster randomised trials. We describe the methods that will be used to identify the datasets for inclusion and how they will be analysed and reported. Discussion There have been a number of small comparisons of empirical differences between the different approaches to analysis for CRTs. The systematic approach outlined in this protocol will allow a much deeper understanding of when there are important choices around the model approach and in which settings. This will be of importance given the heightened awareness of the importance of estimands and the specification of statistical analysis plans.

Abstract Background Anogenital warts are the second most common sexually transmitted infection diagnosed in sexual health services in England. About 90% of genital warts are caused by human papillomavirus (HPV) types 6 or 11, and half of episodes diagnosed are recurrences. The best and most cost-effective treatment for patients with anogenital warts is unknown. The commonly used treatments are self-administered topical agents, podophyllotoxin (0.15% cream) or imiquimod (5% cream), or cryotherapy with liquid nitrogen. Quadrivalent HPV (qHPV) vaccination is effective in preventing infection, and disease, but whether it has any therapeutic effect is not known. Methods and design To investigate the efficacy of clearance and prevention of recurrence of external anogenital warts by topical treatments, podophyllotoxin 0.15% cream or imiquimod 5% cream, in combination with a three-dose regimen of qHPV or control vaccination. 500 adult patients presenting with external anogenital warts with either a first or subsequent episode of anogenital warts will be entered into this randomised, controlled partially blinded 2 × 2 factorial trial. Discussion The trial is expected to provide the first high-quality evidence of the comparative efficacy and cost-effectiveness of the two topical treatments in current use, as well as investigate the potential benefit of HPV vaccination, in the management of anogenital warts. Trial registration The trial was registered prior to starting recruitment under the following reference numbers: International Standard Randomized Controlled Trial Number (ISRCTN) Registry - ISRCTN32729817 (registered 25 July 2014); European Union Clinical Trials Register (EudraCT) - 2013-002951-14 (registered 26 June 2013).

Abstract Background The classification of phase 3 trials as superiority or non-inferiority has become routine, and it is widely accepted that there are important differences between the two types of trial in their design, analysis and interpretation. Main text There is a clear rationale for the superiority/non-inferiority framework in the context of regulatory trials. The focus of our article is non-regulatory trials with a public health objective. First, using two examples from infectious disease research, we show that the classification of superiority or non-inferiority trials is not always straightforward. Second, we show that several arguments for different approaches to the design, analysis and interpretation of superiority and non-inferiority trials are unconvincing when examined in detail. We consider, in particular, the calculation of sample size (and the choice of delta or the non-inferiority margin), intention-to-treat versus per-protocol analyses, and one-sided versus two-sided confidence intervals. We argue that the superiority/non-inferiority framework is not just unnecessary but can have a detrimental effect, being a barrier to clear scientific thought and communication. In particular, it places undue emphasis on tests for significance or non-inferiority at the expense of estimation. We emphasise that these concerns apply to phase 3 non-regulatory trials in general, not just to those where the classification of the trial as superiority or non-inferiority is ambiguous. Conclusions Guidelines and statistical practice should abandon the sharp division between superiority and non-inferiority phase 3 non-regulatory trials and be more closely aligned to the clinical and public health questions that motivate the trial.

Abstract Background There has been some debate in the literature as to whether baseline values of a measurement of interest at treatment initiation should be treated as an outcome variable as part of a model for longitudinal change or instead used as a predictive variable with respect to the response to treatment. We develop a new approach that involves a combined statistical model for all pre- and post-treatment observations of the biomarker of interest, in which the characteristics of response to treatment are treated as a function of the ‘true’ value of the biomarker at treatment initiation. Methods The modelling strategy developed is applied to a dataset of CD4 counts from patients in the UK Register of HIV Seroconverters (UKR) cohort who initiated highly active antiretroviral therapy (HAART). The post-HAART recovery in CD4 counts for each individual is modelled as following an asymptotic curve in which the speed of response to treatment and long-term maximum are functions of the ‘true’ underlying CD4 count at initiation of HAART and the time elapsed since seroconversion. Following previous research in this field, the models developed incorporate non-stationary stochastic process components, and the possibility of between-patient differences in variability over time was also considered. Results A variety of novel models were successfully fitted to the UKR dataset. These provide reinforcing evidence for findings that have previously been reported in the literature, in particular that there is a strong positive relationship between CD4 count at initiation of HAART and the long-term maximum in each patient, but also reveal potentially important features of the data that would not have been easily identified by other methods of analysis. Conclusion Our proposed methodology provides a unified framework for the analysis of pre- and post-treatment longitudinal biomarker data that will be useful for epidemiological investigations and simulations in this context. The approach developed allows use of all relevant data from observational cohorts in which many patients are missing pre-treatment measurements and in which the timing and number of observations vary widely between patients.