Abstract Background When performed in an observational setting, treatment effect modification analyses should account for all confounding, where possible. Often, such studies only consider confounding between the exposure and outcome. However, there is scope for misspecification of the confounding adjustment when estimating moderation as the effects of the confounders may themselves be influenced by the moderator. The aim of this study was to investigate bias in estimates of treatment effect modification resulting from failure to account for an interaction between a binary moderator and a confounder on either treatment receipt or the outcome, and to assess the performance of different approaches to account for such interactions. Methods The theory behind the reason for bias and factors that impact the magnitude of bias is explained. Monte Carlo simulations were used to assess the performance of different propensity scores adjustment methods and regression adjustment where the adjustment 1) did not account for any moderator-confounder interactions, 2) included moderator-confounder interactions, and 3) was estimated separately in each moderator subgroup. A real-world observational dataset was used to demonstrate this issue. Results Regression adjustment and propensity score covariate adjustment were sensitive to the presence of moderator-confounder interactions on outcome, whilst propensity score weighting and matching were more sensitive to the presence of moderator-confounder interactions on treatment receipt. Including the relevant moderator-confounder interactions in the propensity score (for methods using this) or the outcome model (for regression adjustment) rectified this for all methods except propensity score covariate adjustment. For the latter, subgroup-specific propensity scores were required. Analysis of the real-world dataset showed that accounting for a moderator-confounder interaction can change the estimate of effect modification. Conclusions When estimating treatment effect modification whilst adjusting for confounders, moderator-confounder interactions on outcome or treatment receipt should be accounted for.
Worldwide evidence suggests face-to-face diabetes prevention programmes are effective in preventing and delaying the onset of type 2 diabetes by encouraging behaviour change towards weight loss, healthy eating, and increased exercise. There is an absence of evidence on whether digital delivery is as effective as face-to-face. During 2017-18 patients in England were offered the National Health Service Diabetes Prevention Programme as group-based face-to-face delivery, digital delivery ('digital-only') or a choice between digital and face-to-face ('digital-choice'). The contemporaneous delivery allowed for a robust non-inferiority study, comparing face-to-face with digital only and digital choice cohorts. Changes in weight at 6 months were missing for around half of participants. Here we take a novel approach, estimating the average effect in all 65,741 individuals who enrolled in the programme, by making a range of plausible assumptions about weight change in individuals who did not provide outcome data. The benefit of this approach is that it includes everyone who enrolled in the programme, not restricted to those who completed. We analysed the data using multiple linear regression models. Under all scenarios explored, enrolment in the digital diabetes prevention programme was associated with clinically significant reductions in weight which were at least equivalent to weight loss in the face-to-face programme. Digital services can be just as effective as face-to-face in delivering a population-based approach to the prevention of type 2 diabetes. Imputation of plausible outcomes is a feasible methodological approach, suitable for analysis of routine data in settings where outcomes are missing for non-attenders.
The ‘treat to target’ paradigm improves outcomes and reduces costs in chronic disease management but is not yet established in psoriasis. To identify treatment targets in psoriasis using two common measures of disease activity: Psoriasis Area and Severity Index (PASI) and Physician's Global Assessment (PGA). Data from a multicentre longitudinal U.K. cohort of patients with psoriasis receiving systemic or biologic therapies (British Association of Dermatologists Biologics and Immunomodulators Register, BADBIR) were used to identify absolute PASI thresholds for 90% (PASI 90) and 75% (PASI 75) improvements in baseline disease activity, using receiver operating characteristic curves. The relationship between PGA (clear, almost clear, mild, moderate, moderate–severe, severe) and PASI (range 0–72) was described, and the concordance between absolute and relative definitions of response was determined. The same approach was used to establish treatment response and eligibility definitions based on PGA. Data from 13 422 patients were available (58% male, 91% white ethnicity, mean age 44·9 years), including over 23 000 longitudinal PASI and PGA scores. An absolute PASI ≤ 2 was concordant with PASI 90 and an absolute PASI ≤ 4 was concordant with PASI 75 in 90% and 88% of cases, respectively. These findings were robust to subgroups of timing of assessment, baseline disease severity and treatment modality. PASI and PGA were strongly correlated (Spearman's rank correlation coefficient 0·92). The median PASI increased from 0 (interquartile range 0–0, range 0–23) to 19 (interquartile range 15–25, range 0–64) for PGA clear to severe, respectively. PGA clear/almost clear was concordant with PASI ≤ 2 in 90% of cases, and PGA moderate–severe severe was concordant with the National Institute for Health and Care Excellence PASI eligibility criteria for biologics in 81% of cases. An absolute PASI ≤ 2 and PGA clear/almost clear represent relevant disease end points to inform treat‐to‐target management strategies in psoriasis. What's already known about this topic? The most commonly used relative disease activity measure in psoriasis is ≥ 90% improvement in Psoriasis Area and Severity Index (PASI 90); however, it has several limitations including dependency on a baseline severity assessment. Defining an absolute target disease activity end point in psoriasis has the potential to improve patient outcomes and reduce costs, as demonstrated by treat‐to‐target approaches in other chronic diseases such as hypertension and diabetes. The Physician's Global Assessment (PGA) is a popular alternative measure of psoriasis severity in daily practice; however, its utility has not been formally assessed with respect to PASI. What does this study add? An absolute PASI ≤ 2 corresponds with PASI 90 response and is a relevant disease end point for treat‐to‐target approaches in psoriasis. There is a strong correlation between PASI and PGA. PGA moderate–severe/severe may serve as an alternative eligibility criterion for biologics to PASI‐based definitions, and PGA clear/almost clear is an appropriate alternative absolute treatment end point. What are the clinical implications of this work? Absolute PASI ≤ 2 and PGA clear/almost clear represent relevant disease end points to inform treat‐to‐target management strategies in psoriasis.
Work problems are common in people with inflammatory arthritis. Up to 50% stop work within 10 years due to their condition and up to 67% report presenteeism (i.e. reduced work productivity), even amongst those with low disease activity. Job retention vocational rehabilitation (JRVR) may help prevent or postpone job loss and reduce presenteeism through work assessment, work-related rehabilitation and enabling job accommodations. This aims to create a better match between the person's abilities and their job demands. The objectives of the Workwell trial are to test the overall effectiveness and cost-effectiveness of JRVR (WORKWELL) provided by additionally trained National Health Service (NHS) occupational therapists compared to a control group who receive self-help information both in addition to usual care.Based on the learning from a feasibility trial (the WORK-IA trial: ISRCTN76777720 ), the WORKWELL trial is a multi-centre, pragmatic, individually-randomised parallel group superiority trial, including economic evaluation, contextual factors analysis and process evaluation. Two hundred forty employed adults with rheumatoid arthritis, undifferentiated inflammatory arthritis or psoriatic arthritis (in secondary care), aged 18 years or older with work instability will be randomised to one of two groups: a self-help written work advice pack plus usual care (control intervention); or WORKWELL JRVR plus a self-help written work advice pack and usual care. WORKWELL will be delivered by occupational therapists provided with additional JRVR training from the research team. The primary outcome is presenteeism as measured using the Work Limitations Questionnaire-25. A comprehensive range of secondary outcomes of work, health, contextual factors and health resource use are included. Outcomes are measured at 6- and 12- months (with 12-months as the primary end-point). A multi-perspective within-trial cost-effectiveness analyses will also be conducted.This trial will contribute to the evidence base for provision of JRVR to people with inflammatory arthritis. If JRVR is found to be effective in enabling people to keep working, the findings will support decision-making about provision of JRVR by rheumatology teams, therapy services and healthcare commissioners, and providing evidence of the effectiveness of JRVR and the economic impact of its implementation.Clinical Trials.Gov: NCT03942783 . Registered 08/05/2019 ( https://clinicaltrials.gov/ct2/show/NCT03942783 ); ISRCTN Registry: ISRCTN61762297 . Registered:13/05/2019 ( http://www.isrctn.com/ISRCTN61762297 ). Retrospectively registered.
Abstract Background: The review aimed to investigate factors which influence receipt of systemic anti-cancer therapies (SACT) for women with secondary (metastatic) breast cancer (SBC). We aimed to identify and examine individual, clinical and contextual factors related to geographical location and health care systems which may act as barriers and enabling factors. Methods: Studies were included which reported factors associated with receipt of treatment with SACT for women >18 years with an SBC diagnosis. Information sources searched were EBSCO CINAHL Plus, Ovid MEDLINE, Ovid EMBASE, PsychINFO and the Cochrane Library and Joanna Briggs Institute (JBI) database. Assessment of methodological quality was undertaken using the using the JBI method and findings were synthesised using a narrative synthesis approach. Results: Fifteen studies published between 2009 and 2021 were included in the review. Overall treatment receipt ranged from 4% for immunotherapies to 83% for unspecified systemic anti-cancer therapies. Time to treatment ranged from median 54 days to 95 days with 81% of patients received treatment <60 days. Younger women and women of white origin with a higher socioeconomic status had an increased likelihood of timely treatment receipt. Treatment receipt varied by geographical location and place of care was associated with variation in treatment receipt with women treated at teaching, research and private institutions being more likely to receive treatment in a timely manner. Conclusions: Our review was to our knowledge the first of its kind to identify and investigate factors associated with timely receipt of SACT for women with SBC. We identified a potential interaction between geographical location and place of care which adds to the existing literature. Findings should however be interpreted with a degree of caution due to the limitations identified. Further research is required to address these limitations. Our review findings have practical implications for the development and piloting of targeted interventions to address specific barriers in a socio-culturally sensitive manner. Addressing geographical variation and place of care may require intervention at a commissioning policy level. Further qualitative research is required to understand the experience and of women and clinicians. Other: The review was undertaken as part of a PhD fellowship funded by The Christie Hospital NHS Foundation in collaboration with the Manchester Cancer Research Centre (MCRC) at The University of Manchester. The review protocol was registered in PROSPERO CRD42020196490.
Objective To investigate whether hearing difficulties exacerbate the damaging effects of enforced social distancing due to the COVID-19 pandemic on isolation and loneliness, and lead to accelerated mental health issues and cognitive dysfunction.
ABSTRACT Introduction Face-to-face group-based diabetes prevention programmes have been shown to be effective in many settings. Digital delivery may suit some patients, but research comparing the effectiveness of digital with face-to-face delivery is scarce. The aim was to assess if digital delivery of the English National Health Service Diabetes Prevention Programme (NHS DPP) is non-inferior to group-based face-to-face delivery in terms of weight change, and evaluate factors associated with differential change. The study included those recruited to the NHS DPP in 2017-2018. Research design and methods Individual-level data from a face-to-face cohort was compared to two cohorts on a digital pilot who (i) were offered no choice of delivery mode, or (ii) chose digital over face-to-face. Changes in weight at 6 and 12 months were analysed using mixed effects linear regression, having matched participants from the digital pilot to similar participants from face-to-face. Results Weight change on the digital pilot was non-inferior to face-to-face at both time points: it was similar in the comparison of those with no choice (difference in weight change: −0.284kg [95% CI: −0.712, 0.144] at 6 months) and greater in digital when participants were offered a choice (−1.165kg [95% CI: −1.841, −0.489]). Interactions between delivery mode and sex, ethnicity, age and deprivation were observed. Conclusions Digital delivery of the NHS DPP achieved weight loss at least as good as face-to-face. Patients who were offered a choice and opted for digital experienced better weight loss, compared to patients offered face-to-face only.
Biologic therapies have revolutionized the treatment of moderate‐to‐severe psoriasis. However, for reasons largely unknown, many patients do not respond or lose response to these drugs. To evaluate demographic, social and clinical factors that could be used to predict effectiveness and stratify response to biologic therapies in psoriasis. Using a multicentre, observational, prospective pharmacovigilance study (BADBIR), we identified biologic‐naive patients starting biologics with outcome data at 6 (n = 3079) and 12 (n = 3110) months. Associations between 31 putative predictors and outcomes were investigated in univariate and multivariable regression analyses. Potential stratifiers of treatment response were investigated with statistical interactions. Eight factors associated with reduced odds of achieving ≥ 90% improvement in Psoriasis Area and Severity Index (PASI 90) at 6 months were identified (described as odds ratio and 95% confidence interval): demographic (female sex, 0·78, 0·66–0·93); social (unemployment, 0·67, 0·45–0·99); unemployment due to ill health (0·62, 0·48–0·82); ex‐ and current smoking (0·81, 0·66–0·99 and 0·79, 0·63–0·99, respectively); clinical factors (high weight, 0·99, 0·99–0·99); psoriasis of the palms and/or soles (0·75, 0·61–0·91); and presence of small plaques only compared with small and large plaques (0·78, 0·62–0·96). White ethnicity (1·48, 1·12–1·97) and higher baseline PASI (1·04, 1·03–1·04) were associated with increased odds of achieving PASI 90. The findings were largely consistent at 12 months. There was little evidence for predictors of differential treatment response. Psoriasis phenotype and potentially modifiable factors are associated with poor outcomes with biologics, underscoring the need for lifestyle management. Effect sizes suggest that these factors alone cannot inform treatment selection.
Abstract Introduction Electronic Health Records (EHRs) are vital repositories of patient information for medical research, but the prevalence of missing data presents an obstacle to the validity and reliability of research. This study aimed to review and category ise methods for handling missing data in EHRs, to help researchers better understand and address the challenges related to missing data in EHRs. Materials and Methods This study employed scoping review methodology. Through systematic searches on EMBASE up to October 2023, including review articles and original studies, relevant literature was identified. After removing duplicates, titles and abstracts were screened against inclusion criteria, followed by full-text assessment. Additional manual searches and reference list screenings were conducted. Data extraction focused on imputation techniques, dataset characteristics, assumptions about missing data, and article types. Additionally, we explored the availability of code within widely used software applications. Results We reviewed 101 articles, with two exclusions as duplicates. Of the 99 remaining documents, 21 underwent full-text screening, with nine deemed eligible for data extraction. These articles introduced 31 imputation approaches classified into ten distinct methods, ranging from simple techniques like Complete Case Analysis to more complex methods like Multiple Imputation, Maximum Likelihood, and Expectation-Maximization algorithm. Additionally, machine learning methods were explored. The different imputation methods, present varying reliability. We identified a total of 32 packages across the four software platforms (R, Python, SAS, and Stata) for imputation methods. However, it’s significant that machine learning methods for imputation were not found in specific packages for SAS and Stata. Out of the 9 imputation methods we investigated, package implementations were available for 7 methods in all four software platforms. Conclusions Several methods to handle missing data in EHRs are available. These methods range in complexity and make different assumptions about the missing data mechanisms. Knowledge gaps remain, notably in handling non-monotone missing data patterns and implementing imputation methods in real-world healthcare settings under the Missing Not at Random assumption. Future research should prioritize refining and directly comparing existing methods.
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