No / Designing a large research trial to comprehensively evaluate the impact of a service initiative can be daunting but it is not beyond the skills and abilities of radiographers and non-medical professionals. This paper describes the development of a protocol and operational aspects of a multi-centre randomised controlled trial of radiographer immediate reporting. While the focus of the research is specific, the research design stages are transferable and not context dependent and therefore can be applied to different clinical fields. The intention of this paper is to make transparent and explicit the steps in the design and operation of this research and by doing so, offer an objective reflection on each phase of the process to enhance professional understanding of the practicalities of operating a clinical trial. Keywords

Aim PRECIS (PRagmatic Explanatory Continuum Indicator Summaries 2009) is a tool with a simple wheel format that trialists can use when designing their trials to improve the applicability of results but users highlighted problems. The aim of the study was to produce an improved and validated version of PRECIS, called PRECIS-2 and test this tool out with trial teams designing primary care trials. Methods Brainstorming and a 2-round Delphi survey of authors who cited PRECIS plus user-testing of candidate PRECIS-2 models was followed by validity and reliability testing of the most promising PRECIS-2 candidate using a sample of 15 trials rated by 19 different trialists. The validated PRECIS-2 tool was then used to consider the risk of bias (internal validity) and estimates of treatment effect of a matched set of explanatory (ideal conditions) and pragmatic (real world) trials. The PRECIS-2 website was also created with a database of pragmatic trials and a toolkit for trial groups. This was tested out at the Pragmatic Clinical Trials Unit (PCTU) in London with trial teams designing primary care trials. Results Forty-two people responded to the Delphi and highlighted scoring, domain choice, and tool format as issues. An expert panel of 14 in Toronto provided the basis for a PRECIS-2 model that was then user tested by 19 other methodologists and trialists. After 13 iterations, a PRECIS-2 model with 9 domains (i.e. Eligibility, Recruitment, Setting, Organisation, Flexibility Delivery, Flexibility Adherence, Follow up, Primary Outcome, Primary Analysis) was tested for validity and reliability. Inter-rater reliability was generally good, with eight of nine domains having an ICC over 0.65. Discriminant validity was reasonable for all domains, though with wide confidence intervals. Matching trials taking pragmatic (‘real world’) and explanatory (‘ideal world’) approaches was challenging but we found no indication that a pragmatic approach compromises internal validity. We were unable to extract sufficient information for a planned analysis of estimates of treatment effect. At the PCTU, the tool highlighted differences in opinion with trial team members and demonstrated convergence of opinion following discussion. There was acknowledgment that scoring of PRECIS-2 domains assisted trials teams in considering the intended audience and creation of trials relevant to practice. Useful feedback was obtained to improve the PRECIS-2 tool software for users. Conclusions PRECIS was improved by the addition of scoring and additional domains after consultation with over 80 international trialists. We have a validated PRECIS-2, in the visually appealing wheel format with 9 spokes, which is being made available through an increasingly accessed website. Work at the PCTU improved the usability of the PRECIS-2 website and demonstrated that the tool increases transparency in trial design and assists trialists in considering applicability of trial results. More matching work on the impact of design approaches on effect size is needed, and further data to support the risk of bias results would be valuable.

Indiana University-Purdue University Indianapolis (IUPUI) / The marker strategy design (MSGD) has been proposed to assess and validate predictive markers for targeted therapies and immunotherapies. Under this design, patients are randomized into two strategies: the marker-based strategy, which treats patients based on their marker status, and the non-marker-based strategy, which randomizes patients into treatments independent of their marker status in the same way as in a standard randomized clinical trial. The strategy effect is then tested by comparing the response rate between the two strategies and this strategy effect is commonly used to evaluate the predictive capability of the markers. We show that this commonly used between-strategy test is flawed, which may cause investigators to miss the opportunity to discover important predictive markers or falsely claim an irrelevant marker as predictive. Then we propose new procedures to improve the power of the MSGD to detect the predictive marker effect. One is based on a binary response endpoint; the second is based on survival endpoints. We conduct simulation studies to compare the performance of the MSGD with the widely used marker stratified design (MSFD). Numerical studies show that the MSGD and MSFD has comparable performance. Hence, contrary to popular belief that the MSGD is an inferior design compared with the MSFD, we conclude that using the MSGD with the proposed tests is an efficient and ethical way to find predictive markers for targeted therapies.

The most common type of confirmatory trial is a randomised trial comparing the experimental treatment of interest to a control treatment. Confirmatory trials are expensive and take a lot of time in the planning, set up and recruitment of patients. Efficient methodology in clinical trial design is critical to save both time and money and allow treatments to become available to patients quickly. Often there are data available on the control treatment from a previous trial. These historical data are often used to design new trials, forming the basis of sample size calculations, but are not used in the analysis of the new trial. Incorporating historical control data into the design and analysis could potentially lead to more efficient trials. When the historical and current control data agree, incorporating historical control data could reduce the number of control patients required in the current trial and therefore the duration of the trial, or increase the precision of parameter estimates. However, when the historical and current data are inconsistent, there is a potential for biased treatment effect estimates, inflated type I error and reduced power. We propose two novel weights to assess agreement between the current and historical control data: a probability weight based on tail area probabilities; and a weight based on the equivalence of the historical and current control data parameters. For binary outcome data, agreement is assessed using the posterior distributions of the response probability in the historical and current control data. For normally distributed outcome data, agreement is assessed using the marginal posterior distributions of the difference in means and the ratio of the variances of the current and historical control data. We consider an adaptive design with an interim analysis. At the interim, the agreement between the historical and current control data is assessed using the probability or equivalence probability weight approach. The allocation ratio is adapted to randomise fewer patients to control when there is agreement and revert back to a standard trial design when there is disagreement. The final analysis is Bayesian utilising the analysis approach of the power prior with a fixed weight. The operating characteristics of the proposed design are explored and we show how the equivalence bounds can be chosen at the design stage of the current study to control the maximum inflation in type I error. We then consider a design where a treatment arm is added to an ongoing clinical trial. For many disease areas, there are often treatments in different stages of the development process. We consider the design of a two-arm parallel group trial where it is planned to add a new treatment arm during the trial. This could potentially save money, patients, time and resources. The addition of a treatment arm creates a multiple comparison problem. Dunnett (1955) proposed a design that controls the family-wise error rate when comparing multiple experimental treatments to control and determined the optimal allocation ratio. We have calculated the correlation between test statistics for the method proposed by Dunnett when a treatment arm is added during the trial and only concurrent controls are used for each treatment comparison. We propose an adaptive design where the sample size of all treatment arms are increased to control the family-wise error rate. We explore adapting the allocation ratio once the new treatment arm is added to maximise the overall power of the trial.

To assess non-inferiority of an experimental product to an active control in a clinical trial, an ideal design is to include a placebo arm to ensure both the experimental product and the active control is superior to placebo. We aim to identify methodology to control Type I error rate and maintain adequate power in a superiority/non-inferiority seamless clinical trial defined as: 1. selecting the best experimental treatment dose vs. placebo out of multiple treatment doses in Stage I; and 2. assessing non-inferiority of the chosen experimental dose to an active control, after adding subjects to yield adequate power for non-inferiority, in Stage II. The trial design here is an antihypertensive trial with change in systolic blood pressure as the outcome. The trial has three experimental treatment doses arms of experimental, a placebo control arm, and an active control arm. A simulation study of 20,000 such trials was conducted. We apply multiple comparison methodologies in Stage I to detect the most beneficial experimental treatment dose versus placebo, and test non-inferiority of the selected experimental dose to the active control in Stage II. Simulated Type I error rate and power for claiming non-inferiority are calculated for various dose-response trends. The need to adjust alpha to control Type I error either stage is assessed, seeking the optimal approach for doing so. Next, type I error and power for various fixed and variable non-inferiority margins are evaluated, exploring a range of margins informed by the first stage results of the study. A variable non-inferiority margin informed completely by the first stage of the trial approach results in inflated error rate which cannot be controlled by suggested multiplicity adjustments. We assess a synthesis approach between the fixed and variable margins, to both control the family-wise error rates and reach adequate power, depending on a tuning parameter defined in our work. We conclude that well-designed and adequately controlled seamless superiority/non-inferiority trials are possible with appropriate multiple comparisons adjustments and could result in less development time and fewer subjects needed to assess efficacy than separate trials.

Yes / Randomised controlled trials of non-pharmacological interventions in children's therapy are rare. This is, in part, due to the challenges of the acceptability of common trial designs to therapists and service users. This study investigated the acceptability of participation in cluster randomised controlled trials to therapists and service users. A national electronic survey of UK occupational therapists, physiotherapists, speech and language therapists, service managers, and parents of children who use their services. Participants were recruited by NHS Trusts sharing a link to an online questionnaire with children's therapists in their Trust and with parents via Trust social media channels. National professional and parent networks also recruited to the survey. We aimed for a sample size of 325 therapists, 30 service managers, and 60 parents. Trial participation was operationalised as three behaviours undertaken by both therapists and parents: agreeing to take part in a trial, discussing a trial, and sharing information with a research team. Acceptability of the behaviours was measured using an online questionnaire based on the Theoretical Framework of Acceptability constructs: affective attitude, self-efficacy, and burden. The general acceptability of trials was measured using the acceptability constructs of intervention coherence and perceived effectiveness. Data were collected from June to September 2020. Numerical data were analysed using descriptive statistics and textual data by descriptive summary. A total of 345 survey responses were recorded. Following exclusions, 249 therapists and 40 parents provided data which was 69.6% (289/415) of the target sample size. It was not possible to track the number of people invited to take the survey nor those who viewed, but did not complete, the online questionnaire for calculation of response rates. A completion rate (participants who completed the last page of the survey divided by the participants who completed the first, mandatory, page of the survey) of 42.9% was achieved. Of the three specified trial behaviours, 140/249 (56.2%) therapists were least confident about agreeing to take part in a trial. Therapists (135/249, 52.6%) reported some confidence they could discuss a trial with a parent and child at an appointment. One hundred twenty of 249 (48.2%) therapists reported confidence in sharing information with a research team through questionnaires and interviews or sharing routine health data. Therapists (140/249, 56.2%) felt that taking part in the trial would take a lot of effort and resources. Support and resources, confidence with intervention allocation, and sense of control and professional autonomy over clinical practice were factors that positively affected the acceptability of trials. Of the 40 parents, twelve provided complete data. Most parents (18/40, 45%) agreed that it was clear how trials improve children's therapies and outcomes and that a cluster randomised trial made sense to them in their therapy situation (12/29, 30%). Using trials to evaluate therapy interventions is, in principle, acceptable to therapists, but their willingness to participate in trials is variable. The willingness to participate may be particularly influenced by their views related to the burden associated with trials, intervention allocation, and professional autonomy. / The study was funded and supported by the UK Occupational Therapy Research Foundation, a division of the Royal College of Occupational Therapists, as part of the 2018–2021 Research Priority Grant.

<p>Two different principles were studied. 1st - statistical analysis techniques were used to obtain medical results from a patient population. 2nd - the patient population was used to study the statistical analysis techniques. </p><p>Medical conclusions: latanoprost and timolol treatment showed a statistically significant and clinically useful mean IOP-reduction in a typical worldwide clinical trial population. Latanoprost reduced the IOP 1.6 mm Hg more than timolol. The IOP-reduction was maintained with timolol and slightly enforced with latanoprost up to 6 months of treatment. The mean IOP-reduction was maintained during 2 years of latanoprost treatment. The overall risk of withdrawal due to insufficient IOP-reduction with latanoprost was 8%. </p><p>The statistical methodological issues are of a general and reoccurring character in trial design of the IOP-reduction: should the statistical hypothesis testing be based on the mean intraocular pressure (IOP) or the proportion of patients who reach a specific IOP level, should the estimate of the IOP or IOP-reduction be based on single eyes, mean of bilaterally eligible and identically treated eyes or the difference between an eye with active treatment and a placebo treated contralateral eye, and is mean of replicated recordings useful? Statistical methodological conclusions: the most effective response variable varies with the selected patient population. Therefore, the trial design process should include a comparison of the variability, test power and required sample size for the possible response variables in a sample of the target population. At minimum a statistical consideration should be done.</p>

Two different principles were studied. 1st - statistical analysis techniques were used to obtain medical results from a patient population. 2nd - the patient population was used to study the statistical analysis techniques. Medical conclusions: latanoprost and timolol treatment showed a statistically significant and clinically useful mean IOP-reduction in a typical worldwide clinical trial population. Latanoprost reduced the IOP 1.6 mm Hg more than timolol. The IOP-reduction was maintained with timolol and slightly enforced with latanoprost up to 6 months of treatment. The mean IOP-reduction was maintained during 2 years of latanoprost treatment. The overall risk of withdrawal due to insufficient IOP-reduction with latanoprost was 8%. The statistical methodological issues are of a general and reoccurring character in trial design of the IOP-reduction: should the statistical hypothesis testing be based on the mean intraocular pressure (IOP) or the proportion of patients who reach a specific IOP level, should the estimate of the IOP or IOP-reduction be based on single eyes, mean of bilaterally eligible and identically treated eyes or the difference between an eye with active treatment and a placebo treated contralateral eye, and is mean of replicated recordings useful? Statistical methodological conclusions: the most effective response variable varies with the selected patient population. Therefore, the trial design process should include a comparison of the variability, test power and required sample size for the possible response variables in a sample of the target population. At minimum a statistical consideration should be done.

The potential that telemedicine interventions may have in effectively delivering remote specialized cardiovascular care to large numbers of patients with heart diseases has recently come under question. In the first phase of this thesis, a systematic review and meta-analysis was conducted to compare the impact of a basic form of telemedicine that is regular patient follow-up by telephone, with usual care for individuals with coronary artery disease following their discharge. In the second phase of this thesis, a network meta-analysis, using Bayesian methods for multiple treatment comparisons, was conducted to compare the more complex forms of telemedicine for patients with heart failure. In the third and final phase of this thesis, a randomized controlled trial was designed to compare the impact of two forms of telemedicine, identified in the earlier two phases as being the most promising, on clinical outcomes, cardiac risk factors and patient reported outcomes.

The potential that telemedicine interventions may have in effectively delivering remote specialized cardiovascular care to large numbers of patients with heart diseases has recently come under question. In the first phase of this thesis, a systematic review and meta-analysis was conducted to compare the impact of a basic form of telemedicine that is regular patient follow-up by telephone, with usual care for individuals with coronary artery disease following their discharge. In the second phase of this thesis, a network meta-analysis, using Bayesian methods for multiple treatment comparisons, was conducted to compare the more complex forms of telemedicine for patients with heart failure. In the third and final phase of this thesis, a randomized controlled trial was designed to compare the impact of two forms of telemedicine, identified in the earlier two phases as being the most promising, on clinical outcomes, cardiac risk factors and patient reported outcomes.