Understanding and Improving Recruitment to Randomised Controlled Trials:Qualitative Research Approaches

Context The importance of evidence from randomised trials is now widely recognised, although recruitment is often difficult. Qualitative research has shown promise in identifying the key barriers to recruitment, and interventions have been developed to reduce organisational difficulties and support clinicians undertaking recruitment. Objective This article provides an introduction to qualitative research techniques and explains how this approach can be used to understand — and subsequently improve — recruitment and informed consent within a range of clinical trials. Evudence acquisition A literature search was performed using Medline, Embase, and CINAHL. All studies with qualitative research methods that focused on the recruitment activity of clinicians were included in the review. Evidence synthesis The majority of studies reported that organisational difficulties and lack of time for clinical staff were key barriers to recruitment. However, a synthesis of qualitative studies highlighted the intellectual and emotional challenges that arise when combining research with clinical roles, particularly in relation to equipoise and patient eligibility. To support recruiters to become more comfortable with the design and principles of randomised controlled trials, interventions have been developed, including the QuinteT Recruitment Intervention, which comprises in-depth investigation of recruitment obstacles in real time, followed by implementation of tailored strategies to address these challenges as the trial proceeds. Conclusions Qualitative research can provide important insights into the complexities of recruitment to trials and inform the development of interventions, and provide support and training initiatives as required. Investigators should consider implementing such methods in trials expected to be challenging or recruiting below target. Patient summary Qualitative research is a term used to describe a range of methods that can be implemented to understand participants’ perspectives and behaviours. Data are gathered from interviews, focus groups, or observations. In this review, we demonstrate how this approach can be used to understand—and improve—recruitment to clinical trials. Taken together, our review suggests that healthcare professionals can find recruiting to trials challenging and require support with this process.</p

Beyond attention: deriving biologically interpretable insights from weakly-supervised multiple-instance learning models

Recent advances in attention-based multiple instance learning (MIL) have improved our insights into the tissue regions that models rely on to make predictions in digital pathology. However, the interpretability of these approaches is still limited. In particular, they do not report whether high-attention regions are positively or negatively associated with the class labels or how well these regions correspond to previously established clinical and biological knowledge. We address this by introducing a post-training methodology to analyse MIL models. Firstly, we introduce prediction-attention-weighted (PAW) maps by combining tile-level attention and prediction scores produced by a refined encoder, allowing us to quantify the predictive contribution of high-attention regions. Secondly, we introduce a biological feature instantiation technique by integrating PAW maps with nuclei segmentation masks. This further improves interpretability by providing biologically meaningful features related to the cellular organisation of the tissue and facilitates comparisons with known clinical features. We illustrate the utility of our approach by comparing PAW maps obtained for prostate cancer diagnosis (i.e. samples containing malignant tissue, 381/516 tissue samples) and prognosis (i.e. samples from patients with biochemical recurrence following surgery, 98/663 tissue samples) in a cohort of patients from the international cancer genome consortium (ICGC UK Prostate Group). Our approach reveals that regions that are predictive of adverse prognosis do not tend to co-locate with the tumour regions, indicating that non-cancer cells should also be studied when evaluating prognosis

Investigating the prostate specific antigen, body mass index and age relationship:is an age–BMI-adjusted PSA model clinically useful?

Purpose Previous studies indicate a possible inverse relationship between prostate-specific antigen (PSA) and body mass index (BMI), and a positive relationship between PSA and age. We investigated the associations between age, BMI, PSA, and screen-detected prostate cancer to determine whether an age–BMI-adjusted PSA model would be clinically useful for detecting prostate cancer. Methods Cross-sectional analysis nested within the UK ProtecT trial of treatments for localized cancer. Of 18,238 men aged 50–69 years, 9,457 men without screen-detected prostate cancer (controls) and 1,836 men with prostate cancer (cases) met inclusion criteria: no history of prostate cancer or diabetes; PSA\10 ng/ml; BMI between 15 and 50 kg/m2. Multivariable linear regression models were used to investigate the relationship between log-PSA, age, and BMI in all men, controlling for prostate cancer status. Results In the 11,293 included men, the median PSA was 1.2 ng/ml (IQR: 0.7–2.6); mean age 61.7 years (SD 4.9); and mean BMI 26.8 kg/m2 (SD 3.7). There were a 5.1% decrease in PSA per 5 kg/m2 increase in BMI (95% CI 3.4–6.8) and a 13.6% increase in PSA per 5-year increase in age (95% CI 12.0–15.1). Interaction tests showed no evidence for different associations between age, BMI, and PSA in men above and below 3.0 ng/ml (all p for interaction [0.2). The age–BMI-adjusted PSA model performed as well as an age-adjusted model based on National Institute for Health and Care Excellence (NICE) guidelines at detecting prostate cancer. Conclusions Age and BMI were associated with small changes in PSA. An age–BMI-adjusted PSA model is no more clinically useful for detecting prostate cancer than current NICE guidelines. Future studies looking at the effect of different variables on PSA, independent of their effect on prostate cancer, may improve the discrimination of PSA for prostate cancer.</p

Training recruiters to randomized trials to facilitate recruitment and informed consent by exploring patients' treatment preferences

BACKGROUND: Patients’ treatment preferences are often cited as barriers to recruitment in randomized controlled trials (RCTs). We investigated how RCT recruiters reacted to patients’ treatment preferences and identified key strategies to improve informed decision-making and trial recruitment. METHODS: Audio-recordings of 103 RCT recruitment appointments with 96 participants in three UK multicenter pragmatic RCTs were analyzed using content and thematic analysis. Recruiters’ responses to expressed treatment preferences were assessed in one RCT (ProtecT - Prostate testing for cancer and Treatment) in which training on exploring preferences had been given, and compared with two other RCTs where this specific training had not been given. RESULTS: Recruiters elicited treatment preferences similarly in all RCTs but responses to expressed preferences differed substantially. In the ProtecT RCT, patients’ preferences were not accepted at face value but were explored and discussed at length in three key ways: eliciting and acknowledging the preference rationale, balancing treatment views, and emphasizing the need to keep an open mind and consider all treatments. By exploring preferences, recruiters enabled participants to become clearer about whether their views were robust enough to be sustained or were sufficiently weak that participation in the RCT became possible. Conversely, in the other RCTs, treatment preferences were often readily accepted without further discussion or understanding the reasoning behind them, suggesting that patients were not given the opportunity to fully consider all treatments and trial participation. CONCLUSIONS: Recruiters can be trained to elicit and address patients’ treatment preferences, enabling those who may not have considered trial participation to do so. Without specific guidance, some RCT recruiters are likely to accept initial preferences at face value, missing opportunities to promote more informed decision-making. Training interventions for recruiters that incorporate key strategies to manage treatment preferences, as in the ProtecT study, are required to facilitate recruitment and informed consent. TRIAL REGISTRATION: ProtecT RCT: Current Controlled Trials ISRCTN20141297. The other two trials are registered but have asked to be anonymized