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Researchers are increasingly using insights derived from large-scale, electronic healthcare data to inform drug development and provide human validation of novel treatment pathways and aid in drug repurposing/repositioning. The objective of this study was to determine whether treatment of patients with multiple sclerosis with dimethyl fumarate, an activator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, results in a change in incidence of type 2 diabetes and its complications. This retrospective cohort study used administrative claims data to derive four cohorts of adults with multiple sclerosis initiating dimethyl fumarate, teriflunomide, glatiramer acetate or fingolimod between January 2013 and December 2018. A causal inference frequentist model averaging framework based on machine learning was used to compare the time to first occurrence of a composite endpoint of type 2 diabetes, cardiovascular disease or chronic kidney disease, as well as each individual outcome, across the four treatment cohorts. There was a statistically significantly lower risk of incidence for dimethyl fumarate versus teriflunomide for the composite endpoint (restricted hazard ratio [95% confidence interval] 0.70 [0.55, 0.90]) and type 2 diabetes (0.65 [0.49, 0.98]), myocardial infarction (0.59 [0.35, 0.97]) and chronic kidney disease (0.52 [0.28, 0.86]). No differences for other individual outcomes or for dimethyl fumarate versus the other two cohorts were observed. This study effectively demonstrated the use of an innovative statistical methodology to test a clinical hypothesis using real-world data to perform early target validation for drug discovery. Although there was a trend among patients treated with dimethyl fumarate towards a decreased incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease relative to other disease-modifying therapies–which was statistically significant for the comparison with teriflunomide–this study did not definitively support the hypothesis that Nrf2 activation provided additional metabolic disease benefit in patients with multiple sclerosis. [ABSTRACT FROM AUTHOR]

Despite the abundance of data concerning biologic treatments for patients with psoriasis, clinicians are often challenged with discerning the optimal treatment for each patient. To inform this selection, this study explored whether a patient's baseline characteristics or disease profile could predict the likelihood of achieving complete skin clearance with biologic treatment. Machine-learning and other statistical methods were applied to the substantial data collected from patients with moderate-to-severe psoriasis in the ongoing, international, prospective, observational Psoriasis Study of Health Outcomes (PSoHO). The 3 measures of complete skin clearance were a psoriasis area and severity index (PASI)100 response at (a) week 12, (b) month 12, and (c) week 12 and maintain ed at month 6 and month 12 (PASI100 durability). From these real-world data, the absence of nail psoriasis emerged as the most consistent feature that may be used by clinicians to predict high-level treatment responses with biologic treatment. Other significant predictors of skin clearance with biologic treatments were the absence of hypertension and a lower body surface area affected by psoriasis. Overall, this study evidences the substantial challenge of identifying reliable clinical markers of treatment response for patients with psoriasis and highlights the importance of regular screening for psoriatic nail involvement. [ABSTRACT FROM AUTHOR]

Background: Network meta-analysis (NMA) and indirect comparisons combine aggregate data (AgD) from multiple studies on treatments of interest but may give biased estimates if study populations differ. Population adjustment methods such as multilevel network meta-regression (ML-NMR) aim to reduce bias by adjusting for differences in study populations using individual patient data (IPD) from 1 or more studies under the conditional constancy assumption. A shared effect modifier assumption may also be necessary for identifiability. This article aims to demonstrate how the assumptions made by ML-NMR can be assessed in practice to obtain reliable treatment effect estimates in a target population. Methods: We apply ML-NMR to a network of evidence on treatments for plaque psoriasis with a mix of IPD and AgD trials reporting ordered categorical outcomes. Relative treatment effects are estimated for each trial population and for 3 external target populations represented by a registry and 2 cohort studies. We examine residual heterogeneity and inconsistency and relax the shared effect modifier assumption for each covariate in turn. Results: Estimated population-average treatment effects were similar across study populations, as differences in the distributions of effect modifiers were small. Better fit was achieved with ML-NMR than with NMA, and uncertainty was reduced by explaining within- and between-study variation. We found little evidence that the conditional constancy or shared effect modifier assumptions were invalid. Conclusions: ML-NMR extends the NMA framework and addresses issues with previous population adjustment approaches. It coherently synthesizes evidence from IPD and AgD studies in networks of any size while avoiding aggregation bias and noncollapsibility bias, allows for key assumptions to be assessed or relaxed, and can produce estimates relevant to a target population for decision-making. Highlights: Multilevel network meta-regression (ML-NMR) extends the network meta-analysis framework to synthesize evidence from networks of studies providing individual patient data or aggregate data while adjusting for differences in effect modifiers between studies (population adjustment). We apply ML-NMR to a network of treatments for plaque psoriasis with ordered categorical outcomes. We demonstrate for the first time how ML-NMR allows key assumptions to be assessed. We check for violations of conditional constancy of relative effects (such as unobserved effect modifiers) through residual heterogeneity and inconsistency and the shared effect modifier assumption by relaxing this for each covariate in turn. Crucially for decision making, population-adjusted treatment effects can be produced in any relevant target population. We produce population-average estimates for 3 external target populations, represented by the PsoBest registry and the PROSPECT and Chiricozzi 2019 cohort studies. [ABSTRACT FROM AUTHOR]

Background. Chronic pain sufferers and caregivers share the risk of higher levels of psychological distress and adverse effects on well-being. This study examined the joint impact of chronic pain and primary caregiving on older people. Methods. Data came from the New South Wales (NSW) Older People's Health Survey 1999, a state-wide general health survey of over 9000 NSW residents 65 years old or older. Using survey logistic regression modeling, we examined the relationship between chronic pain with different levels of disability, caregiving status, self-reported physical functioning, and two dependent variables--poor/fair self-rated health and psychological distress. Results. Caregivers with chronic pain reported more psychological distress and poorer self-rated health than caregivers without pain, when both were compared to noncaregivers without pain (age-adjusted and sex-adjusted odds ratios [ORs] for caregivers with pain were 3.4 and 2.8, respectively, both p < .001). Caregivers with pain and noncaregivers with pain had similar patterns of results. Physical function significantly declined for both caregivers and noncaregivers with pain when compared with noncaregivers without pain. Conclusions. Older people coping with caregiving and chronic pain are a potentially vulnerable group. Chronic pain status should be ascertained in older people who are caregivers, with particular attention to the issue of caregiver psychological distress and physical well-being. Key Words: Caregivers--Chronic pain--Well-being.