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Objective: To assess the potential for crowdsourcing to complement and extend community advisory board (CAB) feedback on HIV clinical trials. Crowdsourcing involves community members attempting to solve a problem and then sharing solutions., Methods: CAB and crowdsourced approaches were implemented in the context of a phase 1 HIV antibody trial to collect feedback on informed consent, participation experiences, and fairness. CAB engagement was conducted through group discussions with members of an HIV CAB. Crowdsourcing involved open events intended to engage the local community, including interactive video modules, animated vignettes, and a creative idea contest. Open coding and analysis of emergent themes were conducted to compare CAB and crowdsourced feedback., Results: The crowdsourcing activities engaged 61 people across three events; nine people engaged in CAB feedback. Compared with CAB participants, crowdsourcing participants had lower levels of education and income, and higher levels of disability and unemployment. Overlap in CAB and crowdsourced feedback included recommendations for enhancing communication and additional support for trial participants. Crowdsourcing provided more detailed feedback on the impact of positive experiences and socio-economic factors on trial participation. CAB feedback included greater emphasis on institutional regulations and tailoring trial procedures. Crowdsourced feedback emphasized alternative methods for learning about trials and concerns with potential risks of trial participation., Conclusion: Conducting crowdsourcing in addition to CAB engagement can yield a broader range of stakeholder feedback to inform the design and conduct of HIV clinical trials. VIDEO ABSTRACT:.

Background/aims: Community engagement is widely acknowledged as an important step in clinical trials. One underexplored method for engagement in clinical trials is crowdsourcing. Crowdsourcing involves having community members attempt to solve a problem and then publicly sharing innovative solutions. We designed and conducted a pilot using a crowdsourcing approach to obtain community feedback on an HIV clinical trial, called the Acceptability of Combined Community Engagement Strategies Study. In this work, we describe and assess the Acceptability of Combined Community Engagement Strategies Study's crowdsourcing activities in order to examine the opportunities of crowdsourcing as a clinical trial community engagement strategy., Methods: The crowdsourcing engagement activities involved in the Acceptability of Combined Community Engagement Strategies Study were conducted in the context of a phase 1 HIV antibody trial (ClinicalTrials.gov identifier: NCT03803605). We designed a series of crowdsourcing activities to collect feedback on three aspects of this clinical trial: the informed consent process, the experience of participating in the trial, and fairness/reciprocity in HIV clinical trials. All crowdsourcing activities were open to members of the general public 18 years of age or older, and participation was solicited from the local community. A group discussion was held with representatives of the clinical trial team to obtain feedback on the utility of crowdsourcing as a community engagement strategy for informing future clinical trials., Results: Crowdsourcing activities made use of innovative tools and a combination of in-person and online participation opportunities to engage community members in the clinical trial feedback process. Community feedback on informed consent was collected by transforming the clinical trial's informed consent form into a series of interactive video modules, which were screened at an open public discussion. Feedback on the experience of trial participation involved designing three fictional vignettes which were then transformed into animated videos and screened at an open public discussion. Finally, feedback on fairness/reciprocity in HIV clinical trials was collected using a crowdsourcing idea contest with online and in-person submission opportunities. Our public discussion events were attended by 38 participants in total; our idea contest received 43 submissions (27 in-person, 16 online). Facebook and Twitter metrics demonstrated substantial engagement in the project. The clinical team found crowdsourcing primarily useful for enhancing informed consent and trial recruitment., Conclusion: There is sufficient lay community interest in open calls for feedback on the design and conduct of clinical trials, making crowdsourcing both a novel and feasible engagement strategy. Clinical trial researchers are encouraged to consider the opportunities of implementing crowdsourcing to inform trial processes from a community perspective.

Background: Non-inferiority (NI) and equivalence clinical trials test whether a new treatment is therapeutically no worse than, or equivalent to, an existing standard of care. Missing data in clinical trials have been shown to reduce statistical power and potentially bias estimates of effect size; however, in NI and equivalence trials, they present additional issues. For instance, they may decrease sensitivity to differences between treatment groups and bias toward the alternative hypothesis of NI (or equivalence)., Aims: Our primary aim was to review the extent of and methods for handling missing data (model-based methods, single imputation, multiple imputation, complete case), the analysis sets used (Intention-To-Treat, Per-Protocol, or both), and whether sensitivity analyses were used to explore departures from assumptions about the missing data., Methods: We conducted a systematic review of NI and equivalence trials published between May 2015 and April 2016 by searching the PubMed database. Articles were reviewed primarily by 2 reviewers, with 6 articles reviewed by both reviewers to establish consensus., Results: Of 109 selected articles, 93% reported some missing data in the primary outcome. Among those, 50% reported complete case analysis, and 28% reported single imputation approaches for handling missing data. Only 32% reported conducting analyses of both intention-to-treat and per-protocol populations. Only 11% conducted any sensitivity analyses to test assumptions with respect to missing data., Conclusion: Missing data are common in NI and equivalence trials, and they are often handled by methods which may bias estimates and lead to incorrect conclusions., (Copyright © 2018 John Wiley & Sons, Ltd.)

We discuss 3 alternative approaches to sample size calculation: traditional sample size calculation based on power to show a statistically significant effect, sample size calculation based on assurance, and sample size based on a decision-theoretic approach. These approaches are compared head-to-head for clinical trial situations in rare diseases. Specifically, we consider 3 case studies of rare diseases (Lyell disease, adult-onset Still disease, and cystic fibrosis) with the aim to plan the sample size for an upcoming clinical trial. We outline in detail the reasonable choice of parameters for these approaches for each of the 3 case studies and calculate sample sizes. We stress that the influence of the input parameters needs to be investigated in all approaches and recommend investigating different sample size approaches before deciding finally on the trial size. Highly influencing for the sample size are choice of treatment effect parameter in all approaches and the parameter for the additional cost of the new treatment in the decision-theoretic approach. These should therefore be discussed extensively., (Copyright © 2018 John Wiley & Sons, Ltd.)

Importance: While guidance on statistical principles for clinical trials exists, there is an absence of guidance covering the required content of statistical analysis plans (SAPs) to support transparency and reproducibility., Objective: To develop recommendations for a minimum set of items that should be addressed in SAPs for clinical trials, developed with input from statisticians, previous guideline authors, journal editors, regulators, and funders., Design: Funders and regulators (n = 39) of randomized trials were contacted and the literature was searched to identify existing guidance; a survey of current practice was conducted across the network of UK Clinical Research Collaboration-registered trial units (n = 46, 1 unit had 2 responders) and a Delphi survey (n = 73 invited participants) was conducted to establish consensus on SAPs. The Delphi survey was sent to statisticians in trial units who completed the survey of current practice (n = 46), CONSORT (Consolidated Standards of Reporting Trials) and SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) guideline authors (n = 16), pharmaceutical industry statisticians (n = 3), journal editors (n = 9), and regulators (n = 2) (3 participants were included in 2 groups each), culminating in a consensus meeting attended by experts (N = 12) with representatives from each group. The guidance subsequently underwent critical review by statisticians from the surveyed trial units and members of the expert panel of the consensus meeting (N = 51), followed by piloting of the guidance document in the SAPs of 5 trials., Findings: No existing guidance was identified. The registered trials unit survey (46 responses) highlighted diversity in current practice and confirmed support for developing guidance. The Delphi survey (54 of 73, 74% participants completing both rounds) reached consensus on 42% (n = 46) of 110 items. The expert panel (N = 12) agreed that 63 items should be included in the guidance, with an additional 17 items identified as important but may be referenced elsewhere. Following critical review and piloting, some overlapping items were combined, leaving 55 items., Conclusions and Relevance: Recommendations are provided for a minimum set of items that should be addressed and included in SAPs for clinical trials. Trial registration, protocols, and statistical analysis plans are critically important in ensuring appropriate reporting of clinical trials.

The problem of choosing a sample size for a clinical trial is a very common one. In some settings, such as rare diseases or other small populations, the large sample sizes usually associated with the standard frequentist approach may be infeasible, suggesting that the sample size chosen should reflect the size of the population under consideration. Incorporation of the population size is possible in a decision-theoretic approach either explicitly by assuming that the population size is fixed and known, or implicitly through geometric discounting of the gain from future patients reflecting the expected population size. This paper develops such approaches. Building on previous work, an asymptotic expression is derived for the sample size for single and two-arm clinical trials in the general case of a clinical trial with a primary endpoint with a distribution of one parameter exponential family form that optimizes a utility function that quantifies the cost and gain per patient as a continuous function of this parameter. It is shown that as the size of the population, N, or expected size, N∗ in the case of geometric discounting, becomes large, the optimal trial size is O(N1/2) or O(N∗1/2). The sample size obtained from the asymptotic expression is also compared with the exact optimal sample size in examples with responses with Bernoulli and Poisson distributions, showing that the asymptotic approximations can also be reasonable in relatively small sample sizes., (© 2016 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: Clinical trials are typically designed using the classical frequentist framework to constrain type I and II error rates. Sample sizes required in such designs typically range from hundreds to thousands of patients which can be challenging for rare diseases. It has been shown that rare disease trials have smaller sample sizes than non-rare disease trials. Indeed some orphan drugs were approved by the European Medicines Agency based on studies with as few as 12 patients. However, some studies supporting marketing authorisation included several hundred patients. In this work, we explore the relationship between disease prevalence and other factors and the size of interventional phase 2 and 3 rare disease trials conducted in the US and/or EU. We downloaded all clinical trials from Aggregate Analysis of ClinialTrials.gov (AACT) and identified rare disease trials by cross-referencing MeSH terms in AACT with the list from Orphadata. We examined the effects of prevalence and phase of study in a multiple linear regression model adjusting for other statistically significant trial characteristics., Results: Of 186941 ClinicalTrials.gov trials only 1567 (0.8%) studied a single rare condition with prevalence information from Orphadata. There were 19 (1.2%) trials studying disease with prevalence <1/1,000,000, 126 (8.0%) trials with 1-9/1,000,000, 791 (50.5%) trials with 1-9/100,000 and 631 (40.3%) trials with 1-5/10,000. Of the 1567 trials, 1160 (74%) were phase 2 trials. The fitted mean sample size for the rarest disease (prevalence <1/1,000,000) in phase 2 trials was the lowest (mean, 15.7; 95% CI, 8.7-28.1) but were similar across all the other prevalence classes; mean, 26.2 (16.1-42.6), 33.8 (22.1-51.7) and 35.6 (23.3-54.3) for prevalence 1-9/1,000,000, 1-9/100,000 and 1-5/10,000, respectively. Fitted mean size of phase 3 trials of rarer diseases, <1/1,000,000 (19.2, 6.9-53.2) and 1-9/1,000,000 (33.1, 18.6-58.9), were similar to those in phase 2 but were statistically significant lower than the slightly less rare diseases, 1-9/100,000 (75.3, 48.2-117.6) and 1-5/10,000 (77.7, 49.6-121.8), trials., Conclusions: We found that prevalence was associated with the size of phase 3 trials with trials of rarer diseases noticeably smaller than the less rare diseases trials where phase 3 rarer disease (prevalence <1/100,000) trials were more similar in size to those for phase 2 but were larger than those for phase 2 in the less rare disease (prevalence ≥1/100,000) trials.

Background: Inequalities in cancer research participation are thought to exist with certain groups under-represented in research populations; however, much of the evidence is based on small-scale studies. The aim of this study was to explore data from in-depth interviews with cancer patients and a large national survey to investigate variation in who is asked to participate in research and who takes part., Methods: Factors associated with research discussion and participation were explored in National Cancer Patient Experience Survey data using multivariate logistic regression and during in-depth interviews with 25 breast cancer patients., Results: Survey data were available for 66,953 cancer patients; 30.4% reported having discussions about, and 18.9% took part in, research. Barriers to participation at staff, patient and trust level were evident; for example, staff were less likely to discuss research with older patients, Asian and black patients were less likely to take part and patients treated at specialist or teaching trusts had higher levels of discussion and participation. Interviews showed that patients' willingness to participate changed over time and was not synonymous with participation as some were ineligible., Conclusion: Some patient groups were less likely to have discussions about or participate in research. Analysis of this variation vis-à-vis the composition of the patient population may be useful to ensure that there is equity regarding the potential benefits of research participation and that research findings are applicable to target populations in the translational model.

Background: Over the last decade, the United Kingdom has invested significant resources in its clinical trial infrastructure. Clinical research networks have been formed, and some general oversight functions for clinical research have been centralised. One of the initiatives is a registration programme for Clinical Trials Units involved in the coordination of clinical trials. An international review panel of experts in clinical trials has been convened for three reviews over time, reviewing applications from Clinical Trials Units in the United Kingdom. The process benefited from earlier work by the National Cancer Research Institute that developed accreditation procedures for trials units involved in cancer trials. This article describes the experience with the three reviews of UK Clinical Trials Units which submitted applications., Purpose: This article describes the evolution and impact of this registration process from the perspective of the current international review panel members, some of whom have served on all reviews, including two done by the National Cancer Research Institute., Process: Applications for registration were invited from all active, non-commercial Clinical Trials Units in the United Kingdom. The invitations were issued in 2007, 2009 and 2012, and applicants were asked to describe their expertise and staffing levels in specific areas. To ensure that the reviews were as objective as possible, a description of expected core competencies was developed and applicants were asked to document their compliance with meeting these. The review panel assessed each Clinical Trials Unit against the competencies. The Clinical Trials Unit registration process has evolved over time with each successive review benefiting from what was learned in earlier ones., Results: The review panel has seen positive changes over time, including an increase in the number of units applying, a greater awareness on the part of host institutions about the trials activity within their organisations, more widespread development of Standard Operating Procedures in key areas and improvements in information technology systems used to host clinical trials databases. Key funders are awarding funds only to registered units, and host institutions are implementing procedures and structures to ensure improved communication between all parties involved in trials within their organisation., Conclusion: The registration process developed in the United Kingdom has helped to ensure that trials units in the United Kingdom are compliant with regulatory standards and can meet acceptable standards of quality in their conduct of clinical trials. There is an increased awareness among funders, host institutions and Clinical Trials Units themselves of the required competencies, and communication between all those involved in trials has increased. The registration process is an effective and financially viable way of ensuring that objective standards are met at a national level., (© The Author(s) 2014.)

In recent months one of the most controversially discussed topics among regulatory agencies, the pharmaceutical industry, journal editors, and academia has been the sharing of patient-level clinical trial data. Several projects have been started such as the European Medicines Agency´s (EMA) "proactive publication of clinical trial data", the BMJ open data campaign, or the AllTrials initiative. The executive director of the EMA, Dr. Guido Rasi, has recently announced that clinical trial data on patient level will be published from 2014 onwards (although it has since been delayed). The EMA draft policy on proactive access to clinical trial data was published at the end of June 2013 and open for public consultation until the end of September 2013. These initiatives will change the landscape of drug development and publication of medical research. They provide unprecedented opportunities for research and research synthesis, but pose new challenges for regulatory authorities, sponsors, scientific journals, and the public. Besides these general aspects, data sharing also entails intricate biostatistical questions such as problems of multiplicity. An important issue in this respect is the interpretation of multiple statistical analyses, both prospective and retrospective. Expertise in biostatistics is needed to assess the interpretation of such multiple analyses, for example, in the context of regulatory decision-making by optimizing procedural guidance and sophisticated analysis methods., (© 2014 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

To quantify uncertainty in a formal manner, statisticians play a vital role in identifying a prior distribution for a Bayesian-designed clinical trial. However, when expert beliefs are to be used to form the prior, the literature is sparse on how feasible and how reliable it is to elicit beliefs from experts. For late-stage clinical trials, high importance is placed on reliability; however, feasibility may be equally important in early-stage trials. This article describes a case study to assess how feasible it is to conduct an elicitation session in a structured manner and to form a probability distribution that would be used in a hypothetical early-stage trial. The case study revealed that by using a structured approach to planning, training and conduct, it is feasible to elicit expert beliefs and form a probability distribution in a timely manner. We argue that by further increasing the published accounts of elicitation of expert beliefs in drug development, there will be increased confidence in the feasibility of conducting elicitation sessions. Furthermore, this will lead to wider dissemination of the pertinent issues on how to quantify uncertainty to both practicing statisticians and others involved with designing trials in a Bayesian manner., (Copyright © 2013 John Wiley & Sons, Ltd.)

In this paper we set out what we consider to be a set of best practices for statisticians in the reporting of pharmaceutical industry-sponsored clinical trials. We make eight recommendations covering: author responsibilities and recognition; publication timing; conflicts of interest; freedom to act; full author access to data; trial registration and independent review. These recommendations are made in the context of the prominent role played by statisticians in the design, conduct, analysis and reporting of pharmaceutical sponsored trials and the perception of the reporting of these trials in the wider community., (Copyright © 2010 John Wiley & Sons, Ltd.)

Concerns about potentially misleading reporting of pharmaceutical industry research have surfaced many times. The potential for duality (and thereby conflict) of interest is only too clear when you consider the sums of money required for the discovery, development and commercialization of new medicines. As the ability of major, mid-size and small pharmaceutical companies to innovate has waned, as evidenced by the seemingly relentless decline in the numbers of new medicines approved by Food and Drug Administration and European Medicines Agency year-on-year, not only has the cost per new approved medicine risen: so too has the public and media concern about the extent to which the pharmaceutical industry is open and honest about the efficacy, safety and quality of the drugs we manufacture and sell. In 2005 an Editorial in Journal of the American Medical Association made clear that, so great was their concern about misleading reporting of industry-sponsored studies, henceforth no article would be published that was not also guaranteed by independent statistical analysis. We examine the precursors to this Editorial, as well as its immediate and lasting effects for statisticians, for the manner in which statistical analysis is carried out, and for the industry more generally., (Copyright © 2010 John Wiley & Sons, Ltd.)

Since the web-based registry ClinicalTrials.gov was launched on 29 February 2000, the pharmaceutical industry has made available an increasing amount of information about the clinical trials that it sponsors. The process has been spurred on by a number of factors including a wish by the industry to provide greater transparency regarding clinical trial data; and has been both aided and complicated by the number of institutions that have a legitimate interest in guiding and defining what should be made available. This article reviews the history of this process of making information about clinical trials publicly available. It provides a reader's guide to the study registries and the databases of results; and looks at some indicators of consistency in the posting of study information., (Copyright © 2010 John Wiley & Sons, Ltd.)

The ICH harmonized tripartite guideline 'Statistical Principles for Clinical Trials', more commonly referred to as ICH E9, was adopted by the regulatory bodies of the European Union, Japan and the USA in 1998. This document united related guidance documents on statistical methodology from each of the three ICH regions, and meant that for the first time clear consistent guidance on statistical principles was available to those conducting and reviewing clinical trials. At the 10th anniversary of the guideline's adoption, this paper discusses the influence of ICH E9 by presenting a perspective on how approaches to some aspects of clinical trial design, conduct and analysis have changed in that time in the context of regulatory submissions in the European Union., (Copyright 2008 John Wiley & Sons, Ltd.)

The Committee on Medical Products for Human Use (CHMP) has recently issued two new guidance documents that are of particular note to statisticians. The purpose of this short note is to give a little background to the origins of these documents and a flavour of some of their most important features. The two guidelines are reproduced as the next two papers in the journal., (Copyright 2006 John Wiley & Sons, Ltd.)

Background: Data from clinical trials where the endpoint is a single survival time are readily analysed by standard methods, most commonly using a semi-parametric proportional hazards approach. However, when the outcome involves two sequential survival times, standard methods may not be applicable., Methods: We consider methods appropriate for the analysis of survival data in clinical trials where there are two distinct, sequential and opposing survival endpoints and where inferences about the second event are of particular interest. Two motivating examples of randomized clinical trials with different designs provide important illustrations of the methodology in practice., Results: Bivariate log-normal survival models are proposed as useful way of modeling such data. These models can be simply implemented in two stages, each of which is a univariate log-normal survival analysis. Different approaches to the analyses are described according to whether a second randomized treatment assignment is made at the time when the first event occurs and the second phase of the study commences. In the absence of a second randomization, the bivariate log-normal model adjusts for selection into the second phase of the study., Conclusions: The investigation of 'treatment sequences' should, wherever possible, be handled by repeat randomization, which can then be followed by valid, unbiased analyses. However, in many clinical trial scenarios, this is simply not possible. In this case, the best approach is to consider the data as arising from an observational study, whilst controlling for all appropriate covariates., Limitations: The approach we describe is appropriate for log-normally distributed data but could be generalised to handle other distributions, although the process of model fitting would be less straight-forward.

The comparisons of treatments in randomized clinical trials may use the analysis of covariance to adjust for patient characteristics. We present theoretical results that describe when such an adjustment would be expected to be beneficial. A distinction is made between covariates that are balanced in the design and those that are assigned by the randomization process. The results support the commonly held view that features balanced in the design of the trial (e.g., by stratification) and those that are strongly predictive of the outcome, and thus considered clinically prognostic, should normally be included in the analysis. For other covariates that are not balanced in the design, the potential benefits of including them in the analysis will depend on the number of patients in the trial. However, there is frequently a set of variables whose relevance is unknown and for which data-dependent methods of selection, based on the data for the current trial, have been proposed. A review of the literature has shown that these methods can produce misleading inferences. The decision as to which covariates to include in the analysis should be specified in the protocol on the basis of data from previous trials on similar patient populations. The methods are illustrated with data from a trial comparing two therapies for treating scalp psoriasis where the clinical importance of patients' age and sex as prognostic factors for efficacy is unknown. We show for what size of future trials it would be beneficial to adjust for these covariates and for what size trials it would not. In all cases, prespecification of variables to be included in the analysis is essential in order to avoid bias.

We develop the idea of using data from the first 'few' patients entered in a clinical trial to estimate the final trial size needed to have specified power for rejecting H0 in favour of H1 if a real difference exists. When comparing means derived from Normally distributed data, there is no important effect on test size, power or expected trial size, provided that a minimum of about 20 degrees of freedom are used to estimate residual variance. Relative advantages and disadvantages of using larger internal pilot studies are presented. These revolve around crude expectations of the final study size, recruitment rate, duration of follow-up and practical constraints on the ability to prevent the circulation of unblinded randomization codes to investigators and those involved in editing and checking data.

Methods for estimating required sample size for comparing two population means have been published. Most involve the use of complicated formulae and tables. These methods are limited to comparing two groups. Although techniques exist to determine sample sizes for comparing more than two groups, they are intrinsically far more complicated. A simple linear nomogram is proposed as a solution to these problems, and its use is illustrated with examples of parallel group, ordered parallel group and factorial designs.

Methods for determining sample size and power when comparing two groups in clinical trials are widely available. Studies comparing three or more treatments are not uncommon but are more difficult to analyse. A linear nomogram was devised to help calculate the sample size required when comparing up to five parallel groups. It may also be used retrospectively to determine the power of a study of given sample size. In two worked examples the nomogram was efficient. Although the nomogram offers only 5% and 1% significance levels and can be used only for up to five treatment groups, this is sufficient for most researchers.

This paper considers the problem of comparing proportions in clinical trials where two parallel groups are studied. The question of what will happen to the response rate of an active therapy if some external factor affects the placebo rate is asked and two different, but similar, answers are proposed. Assuming either a logistic or probit model has substantial influence on the sample size required to detect differences and optimal comparisons are suggested. The idea is illustrated by a clinical trial of treatment for peptic ulcer and suggestions for other possible applications are presented.