Determining a risk-proportionate approach to the validation of statistical programming for clinical trials

Background: The contribution of the statistician to the design and analysis of a clinical trial is acknowledged as essential. Ability to reconstruct the statistical contribution to a trial requires rigorous and transparent documentation as evidenced by the reproducibility of results. The process of validating statistical programmes is a key requirement. While guidance relating to software development and life cycle methodologies details steps for validation by information systems developers, there is no guidance applicable to programmes written by statisticians. We aimed to develop a risk-based approach to the validation of statistical programming that would support scientific integrity and efficient resource use within clinical trials units. // Methods: The project was embedded within the Information Systems Operational Group and the Statistics Operational Group of the UK Clinical Research Collaboration Registered Clinical Trials Unit network. Members were asked to share materials relevant to validation of statistical programming. A review of the published literature, regulatory guidance and knowledge of relevant working groups was undertaken. Surveys targeting the Information Systems Operational Group and Statistics Operational Group were developed to determine current practices across the Registered Clinical Trials Unit network. A risk-based approach was drafted and used as a basis for a workshop with representation from statisticians, information systems developers and quality assurance managers (n = 15). The approach was subsequently modified and presented at a second, larger scale workshop (n = 47) to gain a wider perspective, with discussion of content and implications for delivery. The approach was revised based on the discussions and suggestions made. The workshop was attended by a member of the Medicines for Healthcare products Regulatory Agency Inspectorate who also provided comments on the revised draft. // Results: Types of statistical programming were identified and categorised into six areas: generation of randomisation lists; programmes to explore/understand the data; data cleaning, including complex checks; derivations including data transformations; data monitoring; or interim and final analysis. The risk-based approach considers each category of statistical programme against the impact of an error and its likelihood, whether the programming can be fully prespecified, the need for repeated use and the need for reproducibility. Approaches to the validation of programming within each category are proposed. // Conclusion: We have developed a risk-based approach to the validation of statistical programming. It endeavours to facilitate the implementation of targeted quality assurance measures while making efficient use of limited resources

Post stroke intervention trial in fatigue (POSITIF):Randomised multicentre feasibility trial

OBJECTIVE: To test the feasibility of a telephone delivered intervention, informed by cognitive behavioural principles, for post-stroke fatigue, and estimated its effect on fatigue and other outcomes. DESIGN: Randomised controlled parallel group trial. SETTING: Three Scottish stroke services. SUBJECTS: Stroke survivors with fatigue three months to two years post-stroke onset. INTERVENTIONS: Seven telephone calls (fortnightly then a ‘booster session’ at 16 weeks) of a manualised intervention, plus information about fatigue, versus information only. MAIN MEASURES: Feasibility of trial methods, and collected outcome measures (fatigue, mood, anxiety, social participation, quality of life, return to work) just before randomisation, at the end of treatment (four months after randomisation) and at six months after randomisation. RESULTS: Between October 2018 and January 2020, we invited 886 stroke survivors to participate in postal screening: 188/886 (21%) returned questionnaires and consented, of whom 76/188 (40%) were eligible and returned baseline forms; 64/76 (84%) returned six month follow-up questionnaires. Of the 39 allocated the intervention, 23 (59%) attended at least four sessions. At six months, there were no significant differences between the groups (adjusted mean differences in Fatigue Assessment Scale −0.619 (95% CI −4.9631, 3.694; p = 0.768), the Generalised Anxiety Disorder 7 −0.178 (95% CI −3.823, 3.467, p = 0.92), and the Patient Health Questionnaire −0.247 (95% CI −2.935, 2.442, p = 0.851). There were no between-group differences in quality of life, social participation or return to work. CONCLUSION: Patients can be recruited to a trial of this design. These data will inform the design of further trials in post-stroke fatigue

Current recommendations/practices for anonymising data from clinical trials in order to make it available for sharing:A scoping review

BACKGROUND/AIMS: There are increasing pressures for anonymised datasets from clinical trials to be shared across the scientific community, and differing recommendations exist on how to perform anonymisation prior to sharing. We aimed to systematically identify, describe and synthesise existing recommendations for anonymising clinical trial datasets to prepare for data sharing. METHODS: We systematically searched MEDLINE(®), EMBASE and Web of Science from inception to 8 February 2021. We also searched other resources to ensure the comprehensiveness of our search. Any publication reporting recommendations on anonymisation to enable data sharing from clinical trials was included. Two reviewers independently screened titles, abstracts and full text for eligibility. One reviewer extracted data from included papers using thematic synthesis, which then was sense-checked by a second reviewer. Results were summarised by narrative analysis. RESULTS: Fifty-nine articles (from 43 studies) were eligible for inclusion. Three distinct themes are emerging: anonymisation, de-identification and pseudonymisation. The most commonly used anonymisation techniques are: removal of direct patient identifiers; and careful evaluation and modification of indirect identifiers to minimise the risk of identification. Anonymised datasets joined with controlled access was the preferred method for data sharing. CONCLUSIONS: There is no single standardised set of recommendations on how to anonymise clinical trial datasets for sharing. However, this systematic review shows a developing consensus on techniques used to achieve anonymisation. Researchers in clinical trials still consider that anonymisation techniques by themselves are insufficient to protect patient privacy, and they need to be paired with controlled access

Physiological deterioration in the Emergency Department: the SNAP40-ED study

Continuous novel ambulatory monitoring may detect deterioration in Emergency Department (ED) patients more rapidly, prompting treatment and preventing adverse events. Single-centre, open-label, prospective, observational cohort study recruiting high/medium acuity (Manchester triage category 2 and 3) participants, aged over 16 years, presenting to ED. Participants were fitted with a novel wearable monitoring device alongside standard clinical care (wired monitoring and/or manual clinical staff vital sign recording) and observed for up to 4 hours in the ED. Primary outcome was time to detection of deterioration. Two-hundred and fifty (250) patients were enrolled. In 82 patients (32.8%) with standard monitoring (wired monitoring and/or manual clinical staff vital sign recording), deterioration in at least one vital sign was noted during their four-hour ED stay. Overall, the novel device detected deterioration a median of 34 minutes earlier than wired monitoring (Q1, Q3 67,194; n=73, mean difference 39.48, p<0.0001). The novel device detected deterioration a median of 24 minutes (Q1, Q3 2,43; n=42) earlier than wired monitoring and 65 minutes (Q1, Q3 28,114; n=31) earlier than manual vital signs. Deterioration in physiology was common in ED patients. ED staff spent a significant amount of time performing observations and responding to alarms, with many not escalated. The novel device detected deterioration significantly earlier than standard care