Priorities for methodological research on patient and public involvement in clinical trials A modified Delphi process

Background Despite increasing international interest, there is a lack of evidence about the most efficient, effective and acceptable ways to implement patient and public involvement (PPI) in clinical trials. Objective To identify the priorities of UK PPI stakeholders for methodological research to help resolve uncertainties about PPI in clinical trials. Design A modified Delphi process including a two round online survey and a stakeholder consensus meeting. Participants In total, 237 people registered of whom 219 (92%) completed the first round. One hundred and eighty-seven of 219 (85%) completed the second; 25 stakeholders attended the consensus meeting. Results Round 1 of the survey comprised 36 topics; 42 topics were considered in round 2 and at the consensus meeting. Approximately 96% of meeting participants rated the top three topics as equally important. These were as follows: developing strong and productive working relationships between researchers and PPI contributors; exploring PPI practices in selecting trial outcomes of importance to patients; and a systematic review of PPI activity to improve the accessibility and usefulness of trial information (eg participant information sheets) for participants. Conclusions The prioritized methodological research topics indicate important areas of uncertainty about PPI in trials. Addressing these uncertainties will be critical to enhancing PPI. Our findings should be used in the planning and funding of PPI in clinical trials to help focus research efforts and minimize waste

What are the main inefficiencies in trial conduct : a survey of UKCRC registered clinical trials units in the UK

BACKGROUND: The UK Clinical Research Collaboration (UKCRC) registered Clinical Trials Units (CTUs) Network aims to support high-quality, efficient and sustainable clinical trials research in the UK. To better understand the challenges in efficient trial conduct, and to help prioritise tackling these challenges, we surveyed CTU staff. The aim was to identify important inefficiencies during two key stages of the trial conduct life cycle: (i) from grant award to first participant, (ii) from first participant to reporting of final results. METHODS: Respondents were asked to list their top three inefficiencies from grant award to recruitment of the first participant, and from recruitment of the first participant to publication of results. Free text space allowed respondents to explain why they thought these were important. The survey was constructed using SurveyMonkey and circulated to the 45 registered CTUs in May 2013. Respondents were asked to name their unit and job title, but were otherwise anonymous. Free-text responses were coded into broad categories. RESULTS: There were 43 respondents from 25 CTUs. The top inefficiency between grant award and recruitment of first participant was reported as obtaining research and development (R&D) approvals by 23 respondents (53%), contracts by 22 (51%), and other approvals by 13 (30%). The top inefficiency from recruitment of first participant to publication of results was failure to meet recruitment targets, reported by 19 (44%) respondents. A common comment was that this reflected overoptimistic or inaccurate estimates of recruitment at site. Data management, including case report form design and delays in resolving data queries with sites, was reported as an important inefficiency by 11 (26%) respondents, and preparation and submission for publication by 9 (21%). CONCLUSIONS: Recommendations for improving the efficiency of trial conduct within the CTUs network include: further reducing unnecessary bureaucracy in approvals and contracting; improving training for site staff; realistic recruitment targets and appropriate feasibility; developing training across the network; improving the working relationships between chief investigators and units; encouraging funders to release sufficient funding to allow prompt recruitment of trial staff; and encouraging more research into how to improve the efficiency and quality of trial conduct

Comparison of sociodemographic and health-related characteristics of UK Biobank participants with the general population

UK Biobank is a population-based cohort of 500,000 participants recruited between 2006 and 2010. Approximately 9.2 million individuals aged 40-69 years who lived within 25 miles of the 22 assessment centres in England, Wales and Scotland were invited, and 5.4% participated in the baseline assessment. The representativeness of the UK Biobank cohort was investigated by comparing demographic characteristics between non-responders and responders. Sociodemographic, physical, lifestyle and health-related characteristics of the cohort were compared with nationally representative data sources. UK Biobank participants were more likely to be older, women and to live in less socioeconomically deprived areas than non-participants. Compared with the general population, participants were less likely to be obese, smoke, drink alcohol on a daily basis and had fewer self-reported health outcomes. Rates of all-cause mortality and total cancer incidence (at age 70-74 years) were 46.2% and 11.8% lower in men, and 55.5% and 18.1% lower in women, respectively, than the general population of the same age. UK Biobank is not representative of the sampling population, with evidence of a ‘healthy volunteer’ selection bias. Nonetheless, the valid assessment of exposure-disease relationships may be widely generalizable and does not require participants to be representative of the population at large

New Models for Large Prospective Studies: Is There a Better Way?

Large prospective cohort studies are critical for identifying etiologic factors for disease, but they require substantial long-term research investment. Such studies can be conducted as multisite consortia of academic medical centers, combinations of smaller ongoing studies, or a single large site such as a dominant regional health-care provider. Still another strategy relies upon centralized conduct of most or all aspects, recruiting through multiple temporary assessment centers. This is the approach used by a large-scale national resource in the United Kingdom known as the “UK Biobank,” which completed recruitment/examination of 503,000 participants between 2007 and 2010 within budget and ahead of schedule. A key lesson from UK Biobank and similar studies is that large studies are not simply small studies made large but, rather, require fundamentally different approaches in which “process” expertise is as important as scientific rigor. Embedding recruitment in a structure that facilitates outcome determination, utilizing comprehensive and flexible information technology, automating biospecimen processing, ensuring broad consent, and establishing essentially autonomous leadership with appropriate oversight are all critical to success. Whether and how these approaches may be transportable to the United States remain to be explored, but their success in studies such as UK Biobank makes a compelling case for such explorations to begin

Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis.

OBJECTIVES: 1) To use data-driven method to examine clinical codes (risk factors) of a medical condition in primary care electronic health records (EHRs) that can accurately predict a diagnosis of the condition in secondary care EHRs. 2) To develop and validate a disease phenotyping algorithm for rheumatoid arthritis using primary care EHRs. METHODS: This study linked routine primary and secondary care EHRs in Wales, UK. A machine learning based scheme was used to identify patients with rheumatoid arthritis from primary care EHRs via the following steps: i) selection of variables by comparing relative frequencies of Read codes in the primary care dataset associated with disease case compared to non-disease control (disease/non-disease based on the secondary care diagnosis); ii) reduction of predictors/associated variables using a Random Forest method, iii) induction of decision rules from decision tree model. The proposed method was then extensively validated on an independent dataset, and compared for performance with two existing deterministic algorithms for RA which had been developed using expert clinical knowledge. RESULTS: Primary care EHRs were available for 2,238,360 patients over the age of 16 and of these 20,667 were also linked in the secondary care rheumatology clinical system. In the linked dataset, 900 predictors (out of a total of 43,100 variables) in the primary care record were discovered more frequently in those with versus those without RA. These variables were reduced to 37 groups of related clinical codes, which were used to develop a decision tree model. The final algorithm identified 8 predictors related to diagnostic codes for RA, medication codes, such as those for disease modifying anti-rheumatic drugs, and absence of alternative diagnoses such as psoriatic arthritis. The proposed data-driven method performed as well as the expert clinical knowledge based methods. CONCLUSION: Data-driven scheme, such as ensemble machine learning methods, has the potential of identifying the most informative predictors in a cost-effective and rapid way to accurately and reliably classify rheumatoid arthritis or other complex medical conditions in primary care EHRs

Genetic associations with sporadic cerebral small vessel disease

Background: Cerebral small vessel disease (SVD) causes substantial cognitive, psychiatric and physical disabilities. Despite its common nature, SVD pathogenesis and molecular mechanisms remain poorly understood, and prevention and treatment are probably suboptimal. Identifying the genetic determinants of SVD will improve understanding and may help identify novel treatment targets. The aim of this thesis is to better understand genetic associations with SVD through investigating its pathological, radiological and clinical phenotypes. Methods: To unravel the genetic associations with SVD, I used three complementary approaches. First, I performed a systematic review looking at existing intracerebral haemorrhage (ICH) classification systems and their reliability, to help inform future studies of ICH genetics. Second, I performed a series of systematic reviews and meta-analyses, investigating associations between genetic polymorphisms and histopathologically confirmed cerebral amyloid angiopathy (CAA). Third, I performed meta-analyses of existing genome-wide datasets to determine associations of >1000 common single nucleotide polymorphisms (SNP) in the COL4A1/COL4A2 genomic region with clinico-radiological SVD phenotypes: ICH and its subtypes, ischaemic stroke and its subtypes, and white matter hyperintensities. Results: The reliability of existing ICH classification systems appeared excellent in eight studies conducted in specialist centres with experienced raters, although these existing systems have several limitations. In my systematic evaluation of CAA genetics, meta-analyses of 24 studies including 3520 participants showed robust evidence for a dose-dependent association between APOE ɛ4 and histopathological CAA. There was, however, no convincing association between APOE ɛ2 and presence of CAA in a meta-analysis of 11 studies including 1640 participants. Meta-analyses of five studies including 497 participants showed, contrary to an existing popular hypothesis, that while APOE 4 may increase the risk of developing severe CAA vasculopathy, there is no clear evidence to support a role of ɛ2. There were few data about the role of APOE in hereditary CAA, but in the three studies that had looked at this, there was no evidence for an association between APOE ɛ4 and CAA severity. There were too few studies and participants to draw firm conclusions about the effect of non-APOE ε2/ε3/ε4 genetic polymorphisms on CAA, but there were positive associations with TGF-β1, TOMM40 and CR1 genes in four studies. Finally, in my meta-analyses of the COL4A1/COL4A2 genomic region, three intronic SNPs in COL4A2 were associated with SVD phenotypes: significantly with deep ICH, and suggestively with lacunar ischaemic stroke and WMH. Conclusions: I have shown that while existing ICH classification systems appear to have very good reliability, further research is needed to determine their performance in different settings. For large population-based prospective studies of ICH genetics, anatomical systems are likely to be more feasible, scalable and appropriate, although they have limitations and will need to be further developed. Using systematic reviews and meta-analyses, I have confirmed a dose-related association between APOE ɛ4 and histopathological CAA, but also demonstrated that, despite popular acceptance, there is insufficient data to draw firm conclusions about the association with APOE ɛ2. I found some positive associations with CAA in other genes, which merit replication in further larger studies, and showed that there is currently insufficient data about the role of APOE in hereditary CAA. Finally, I identified a novel association between a locus in a known hereditary SVD gene – COL4A2 – and sporadic SVD. This highlights a new and successful approach for selecting candidate genes and can be expanded in future studies to include other known hereditary SVD genes

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京都大学0048新制・課程博士博士(工学)甲第13392号工博第2863号新制||工||1421(附属図書館)UT51-2007-Q793京都大学大学院工学研究科材料工学専攻(主査)教授 乾 晴行, 教授 酒井 明, 教授 田村 剛三郎学位規則第4条第1項該当Doctor of EngineeringKyoto UniversityDA

Approximate numbers of incident cases of some exemplar conditions expected to accrue during the first 20 years of follow-up in UK Biobank.

<p>Based on UK age- and sex-specific rates with adjustment for healthy cohort effects and losses to follow-up [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1001779#pmed.1001779.ref006" target="_blank">6</a>].</p><p>MI: myocardial infarction; COPD: chronic obstructive pulmonary disease or chronic bronchitis/emphysema</p><p>Approximate numbers of incident cases of some exemplar conditions expected to accrue during the first 20 years of follow-up in UK Biobank.</p