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Objective To compare improvement in pain and physical function for patients treated with baricitinib, adalimumab, tocilizumab and tofacitinib monotherapy from randomised, methotrexate (MTX)-controlled trials in conventional synthetic disease-modifying antirheumatic drugs (csDMARDs)/biologic (bDMARD)-naïve RA patients using matching-adjusted indirect comparisons (MAICs).Methods Data were from Phase III trials on patients receiving monotherapy baricitinib, tocilizumab, adalimumab, tofacitinib or MTX. Pain was assessed using a visual analogue scale (0–100 mm) and physical function using the Health Assessment Questionnaire-Disability Index (HAQ-DI). An MAIC based on treatment-arm matching, an MAIC with study-level matching and Bucher’s method without matching compared change in outcomes between therapies. Matching variables included age, gender, baseline disease activity and baseline value of outcome measure.Results With all methods, greater improvements were observed in pain and HAQ-DI at 6 months for baricitinib compared with adalimumab and tocilizumab (p

Abstract Introduction Given the chronic nature of psoriasis (PsO), more studies are needed that directly compare the effectiveness of different biologics over long observation periods. This study compares the effectiveness and durability through 12 months of anti-interleukin (IL)-17A biologics relative to other approved biologics in patients with moderate-to-severe psoriasis in a real-world setting. Methods The Psoriasis Study of Health Outcomes (PSoHO) is an ongoing 3-year, prospective, non-interventional cohort study of 1981 adults with chronic moderate-to-severe plaque psoriasis initiating or switching to a new biologic. The study compares the effectiveness of anti-IL-17A biologics with other approved biologics and provides pairwise comparisons of seven individual biologics versus ixekizumab. The primary outcome was defined as the proportion of patients who had at least a 90% improvement in Psoriasis Area and Severity Index score (PASI90) and/or a score of 0 or 1 in static Physician Global Assessment (sPGA). Secondary objective comparisons included the proportion of patients who achieved PASI90, PASI100, a Dermatology Life Quality Index (DLQI) score of 0 or 1, and three different measures of durability of treatment response. Unadjusted response rates are presented alongside the primary analysis, which uses frequentist model averaging (FMA) to evaluate the adjusted comparative effectiveness. Results Compared to the other biologics cohort, the anti-IL-17A cohort had a higher response rate (68.0% vs. 65.1%) and significantly higher odds of achieving the primary outcome at month 12. The two cohorts had similar response rates for PASI100 (40.5% and 37.1%) and PASI90 (53.9% and 51.7%) at month 12, with no significant differences between the cohorts in the adjusted analyses. At month 12, the response rates across the individual biologics were 53.5–72.6% for the primary outcome, 27.6–48.3% for PASI100, and 41.7–61.4% for PASI90. Conclusions These results show the comparative effectiveness of biologics at 6 and 12 months in the real-world setting.

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.

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., Competing Interests: This work was funded by Eli Lilly and Company and all authors are employees of Eli Lilly and Company. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Alan J.M. Brnabic was involved with the conceptualization, methodology, investigation and formal analysis of the data for the work and contributed to the original draft preparation, review and editing of the manuscript. Sarah E. Curtis was involved with the conceptualization, methodology, investigation and formal analysis of the data for the work and contributed to the review and editing of the manuscript. Joseph A. Johnston was involved with the conceptualization, methodology and investigation of the data for the work, and contributed to the original draft preparation, review and editing of the manuscript. Albert contributed to the review and editing of the manuscript. Anthony J. Zagar was involved with the methodology and investigation of the data for the work and contributed to the original draft preparation of the manuscript. Ilya Lipkovich was involved with the methodology and validation of the data for the work and contributed to the original draft preparation of the manuscript. Zbigniew Kadziola was involved with the formal analysis of the data for the work and contributed to the review and editing of the manuscript. Megan H. Murray was involved with the investigation, methodology and formal analysis of the data for the work and contributed to the original draft preparation of the manuscript. Timothy Ryan was involved with the conceptualization and investigation of the data for the work and contributed to the original draft preparation, review and editing of the manuscript. All authors have participated sufficiently in the work to agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All authors give final approval of the manuscript to be published., (Copyright: © 2024 Brnabic et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Medication use trends among patients with type 2 diabetes from 2015 to 2019 were investigated in relation to the clinical group-specific recommendations from the 2018 American Diabetes Association (ADA)/European Association for the Study of Diabetes (EASD) consensus report. Data were drawn from a large health insurance claims database representing Commercial (total patient-year count: 2,379,704) and Medicare (total patient-year count: 845,823) insurance programmes (IBM® MarketScan®). The utilization of sodium-glucose co-transporter-2 inhibitors or glucagon-like peptide-1 receptor agonists increased over time but was lower in the Medicare cohort in every year evaluated. Patients diagnosed with obesity received recommended therapies at higher rates than those without obesity. Differences were more modest between those with versus without atherosclerotic cardiovascular disease (ASCVD) or chronic kidney disease, with greater treatment adoption in those without ASCVD in the Medicare cohort. Utilization of recommended treatments was paradoxically lower in those with versus without heart failure, and worse in the Medicare than in the Commercial cohort. Utilization of sulphonylureas was not different in those with versus without severe hypoglycaemia history. In conclusion, utilization of therapies recommended in the guidelines is increasing overall, which is not preferentially guided by ADA/EASD-defined clinical groups, and there exists a persistent gap in utilization between Commercial and Medicare populations., (© 2022 Eli Lilly and Company. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

Unlike in randomized clinical trials (RCTs), confounding control is critical for estimating the causal effects from observational studies due to the lack of treatment randomization. Under the unconfoundedness assumption, matching methods are popular because they can be used to emulate an RCT that is hidden in the observational study. To ensure the key assumption hold, the effort is often made to collect a large number of possible confounders, rendering dimension reduction imperative in matching. Three matching schemes based on the propensity score (PSM), prognostic score (PGM), and double score (DSM, ie, the collection of the first two scores) have been proposed in the literature. However, a comprehensive comparison is lacking among the three matching schemes and has not made inroads into the best practices including variable selection, choice of caliper, and replacement. In this article, we explore the statistical and numerical properties of PSM, PGM, and DSM via extensive simulations. Our study supports that DSM performs favorably with, if not better than, the two single score matching in terms of bias and variance. In particular, DSM is doubly robust in the sense that the matching estimator is consistent requiring either the propensity score model or the prognostic score model is correctly specified. Variable selection on the propensity score model and matching with replacement is suggested for DSM, and we illustrate the recommendations with comprehensive simulation studies. An R package is available at https://github.com/Yunshu7/dsmatch., (© 2021 John Wiley & Sons Ltd.)

Estimating a treatment effect from observational data requires modeling treatment and outcome subject to uncertainty/misspecification. A previous research has shown that it is not possible to find a uniformly best strategy. In this article we propose a novel Frequentist Model Averaging (FMA) framework encompassing any estimation strategy and accounting for model uncertainty by computing a cross-validated estimate of Mean Squared Prediction Error (MSPE). We present a simulation study with data mimicking an observational database. Model averaging over 15+ strategies was compared with individual strategies as well as the best strategy selected by minimum MSPE. FMA showed robust performance (Bias, Mean Squared Error (MSE), and Confidence Interval (CI) coverage). Other strategies, such as linear regression, did well in simple scenarios but were inferior to the FMA in a scenario with complex confounding.

Aim: To predict optimal treatments maximizing overall survival (OS) and time to treatment discontinuation (TTD) for patients with metastatic breast cancer (MBC) using machine learning methods on electronic health records. Patients/methods: Adult females with HR+/HER2- MBC on first- or second-line systemic therapy were eligible. Random survival forest (RSF) models were used to predict optimal regimen classes for individual patients and each line of therapy based on baseline characteristics. Results: RSF models suggested greater use of CDK4 & 6 inhibitor-based therapies may maximize OS and TTD. RSF-predicted optimal treatments demonstrated longer OS and TTD compared with nonoptimal treatments across line of therapy (hazard ratios = 0.44∼0.79). Conclusion: RSF may help inform optimal treatment choices and improve outcomes for patients with HR+/HER2- MBC.

In health technology assessment (HTA), beside network meta-analysis (NMA), indirect comparisons (IC) have become an important tool used to provide evidence between two treatments when no head-to-head data are available. Researchers may use the adjusted indirect comparison based on the Bucher method (AIC) or the matching-adjusted indirect comparison (MAIC). While the Bucher method may provide biased results when included trials differ in baseline characteristics that influence the treatment outcome (treatment effect modifier), this issue may be addressed by applying the MAIC method if individual patient data (IPD) for at least one part of the AIC is available. Here, IPD is reweighted to match baseline characteristics and/or treatment effect modifiers of published data. However, the MAIC method does not provide a solution for situations when several common comparators are available. In these situations, assuming that the indirect comparison via the different common comparators is homogeneous, we propose merging these results by using meta-analysis methodology to provide a single, potentially more precise, treatment effect estimate. This paper introduces the method to combine several MAIC networks using classic meta-analysis techniques, it discusses the advantages and limitations of this approach, as well as demonstrates a practical application to combine several (M)AIC networks using data from Phase III psoriasis randomized control trials (RCT)., (© 2020 John Wiley & Sons Ltd.)

Standard network meta-analysis (NMA) and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any effect modifiers are balanced across populations. Population adjustment methods relax this assumption using individual patient data from one or more studies. However, current matching-adjusted indirect comparison and simulated treatment comparison methods are limited to pairwise indirect comparisons and cannot predict into a specified target population. Existing meta-regression approaches incur aggregation bias. We propose a new method extending the standard NMA framework. An individual level regression model is defined, and aggregate data are fitted by integrating over the covariate distribution to form the likelihood. Motivated by the complexity of the closed form integration, we propose a general numerical approach using quasi-Monte-Carlo integration. Covariate correlation structures are accounted for by using copulas. Crucially for decision making, comparisons may be provided in any target population with a given covariate distribution. We illustrate the method with a network of plaque psoriasis treatments. Estimated population-average treatment effects are similar across study populations, as differences in the distributions of effect modifiers are small. A better fit is achieved than a random effects NMA, uncertainty is substantially reduced by explaining within- and between-study variation, and estimates are more interpretable., (© 2020 The Authors, Journal of the Royal Statistical Society: Series A (Statistics in Society) Published by John Wiley & Sons Ltd on behalf of the Royal Statistical Society.)

Background: Pharmacogenetic (PGx) testing identifies pharmacotherapeutic risks to permit personalized therapy. Identifying the genetic profile of patients with acute coronary syndrome (ACS) who are considered for therapy with clopidogrel (P2Y 12 receptor blockers) and acetylsalicylic acid (ASA) contributes to the treatment paradigm. Patient preferences would inform a collaborative framework and by extension inform healthcare policy formulation., Purpose: To quantify stated preferences (willingness to pay) for attributes of a novel point-of-care PGx ( CYP2C19 ) test using a discrete choice experiment (DCE) from the general public in Ontario, Canada, and to identify starting point bias of the cost attribute., Methods: A web survey was created and included a questionnaire, decision board, and a DCE. DCE choice sets include the following attributes (levels): sample collection (blood, finger prick, and cheek swab), turnaround time for results (1 hr, 3 days, and 1 week), and cost in additional insurance premiums. The presence of starting point bias (cost attribute levels of $0, $1, $5 or $0, $2, $10) in the estimation of willingness to pay (WTP) was tested., Results: Estimates for turnaround time and cost attributes were statistically significant. Coefficients related to the starting point bias were also significant. Approximately 67% of survey participants chose the PGx test compared to status quo treatment options. WTP for a 1 hr turnaround time compared to a 1-week turnaround time was $10.77 (95% CI 9.58 -12.25)., Conclusion: This translational study shows preference for a point of care PGx test., Competing Interests: Dr. So has received unrestricted grant support (physician-initiated grant) from Eli Lilly Canada; is a member of the advisory board and has received honoraria from AstraZeneca Canada; is a member of the advisory board for Bayer Canada; has received unrestricted grant support (physician-initiated grant) from Spartan Biosciences; has received unrestricted grant support (physician-initiated grant) from Aggredyne; has received unrestricted grant support (physician-initiated grant) from Diapharma/Roche Diagnostics; and has received honoraria from Abbott Vascular, Canada. Dr. Emmanuel Papadimitropoulos is an employee of Eli Lilly Canada Inc, outside the submitted work. The authors report no other conflicts of interest in this work., (© 2020 Bereza et al.)

Background: Adjusted indirect comparisons (anchored via a common comparator) are an integral part of health technology assessment. These methods are challenged when differences between studies exist, including inclusion/exclusion criteria, outcome definitions, patient characteristics, as well as ensuring the choice of a common comparator., Objectives: Matching-adjusted indirect comparison (MAIC) can address these challenges, but the appropriate application of MAICs is uncertain. Examples include whether to match between individual-level data and aggregate-level data studies separately for treatment arms or to combine the arms, which matching algorithm should be used, and whether to include the control treatment outcome and/or covariates present in individual-level data., Results: Results from seven matching approaches applied to a continuous outcome in six simulated scenarios demonstrated that when no effect modifiers were present, the matching methods were equivalent to the unmatched Bucher approach. When effect modifiers were present, matching methods (regardless of approach) outperformed the Bucher method. Matching on arms separately produced more precise estimates compared with matching on total moments, and for certain scenarios, matching including the control treatment outcome did not produce the expected effect size. The entropy balancing approach was used to determine whether there were any notable advantages over the method proposed by Signorovitch et al. When unmeasured effect modifiers were present, no approach was able to estimate the true treatment effect., Conclusions: Compared with the Bucher approach (no matching), the MAICs examined demonstrated more accurate estimates, but further research is required to understand these methods across an array of situations., (Copyright © 2019 ISPOR–The Professional Society for Health Economics and Outcomes Research. Published by Elsevier Inc. All rights reserved.)

Since the introduction of the propensity score (PS), methods for estimating treatment effects with observational data have received growing attention in the literature. Recent research has added substantially to the number of available statistical approaches for controlling confounding in such analyses. However, researchers need guidance to decide on the optimal analytic strategy for any given scenario. To address this gap, we conducted simulations evaluating both well-established methods (regression, PS weighting, stratification, and matching) and more recently proposed approaches (tree-based methods, local control, entropy balancing, genetic matching, prognostic scoring). The simulation scenarios included tree-based and smooth regression models as true data-generation mechanisms. We evaluated an extensive number of analysis strategies combining different treatment choices and outcome models. Key findings include 1) the lack of a single best strategy across all potential scenarios; 2) the importance of appropriately addressing interactions in the treatment choice model and/or outcome model; and 3) a tree-structured treatment choice model and a polynomial outcome model with second-order interactions performed well. One limitation to this initial assessment is the lack of heterogeneous simulation scenarios allowing treatment effects to vary by patient.

In this article, we develop new methods for estimating average treatment effects in observational studies, in settings with more than two treatment levels, assuming unconfoundedness given pretreatment variables. We emphasize propensity score subclassification and matching methods which have been among the most popular methods in the binary treatment literature. Whereas the literature has suggested that these particular propensity-based methods do not naturally extend to the multi-level treatment case, we show, using the concept of weak unconfoundedness and the notion of the generalized propensity score, that adjusting for a scalar function of the pretreatment variables removes all biases associated with observed pretreatment variables. We apply the proposed methods to an analysis of the effect of treatments for fibromyalgia. We also carry out a simulation study to assess the finite sample performance of the methods relative to previously proposed methods., (© 2016, The International Biometric Society.)

Purpose: The objectives of this study were to estimate the incidence, cumulative incidence, and economic burden of Alzheimer's disease (AD) in Taiwan, using data from the National Health Insurance Research Database (NHIRD)., Materials and Methods: This was a retrospective, longitudinal, observational study using data from the Longitudinal Health Insurance Database of the NHIRD. Patients were included in this study if they were 50 years of age or older and their records included a primary or secondary diagnosis of AD. New patients who met inclusion criteria were followed up longitudinally from 2005 to 2010. Costs were calculated for the first year following the diagnosis of AD., Results: Overall, a higher percentage of women than men were diagnosed with AD (54% vs 46%, respectively). The first AD diagnosis occurred most frequently in the age of 75-84 years. The person-year incidence rate increased from 5.63/1,000 persons (95% CI, 5.32-5.94) in 2005 to 8.17/1,000 persons (95% CI, 7.78-8.57) in 2010. The cumulative incidence rate was 33.54/1,000 persons (95% CI, 32.76-34.33) in 2005-2010. The total mean inflated annual costs per patient in new Taiwan dollars (NT$) in the first year of diagnosis ranged from NT$205,413 (2009) to NT$227,110 (2005), with hospitalization representing the largest component., Conclusion: AD represents a substantial burden in Taiwan, and based on the observed increase in incidence rate over time, it is likely that this burden will continue to increase. The findings reported here are consistent with previous research. The NHIRD contains extensive real-world information that can be used to conduct research, allowing us to expand our understanding of the incidence, prevalence, and burden of disease in Taiwan.

Aims: Association between body mass index (BMI) and glycemic control, comorbidities/complications, and health-related quality of life (HRQoL) was assessed in Chinese patients with type 2 diabetes mellitus (T2DM) enrolled in the Diabetes Disease Specific Programme., Methods: Surveys of 200 physicians and 2052 patients with T2DM captured demographic, clinical, and HRQoL information. Adjusted and unadjusted analyses were conducted across 3 BMI groups; normal (18.5-<24.0, n=998), overweight (24.0-<28.0, n=822), and obese (≥28.0, n=212)., Results: There were no between group differences in the achievement of glycated hemoglobin (HbA1c) <7.0% (48mmol/mol); however, compared with the normal BMI group, more obese patients had an HbA1c >9.0% (75mmol/mol; 4.3% vs 10.2%, P=0.002). More obese patients compared with normal BMI patients had hypertension (48.6% vs 35.3%, P<0.001), dyslipidemia (35.4% vs 18.8%, P<0.001), or both hypertension and dyslipidemia (24.1% vs 13.9%, P<0.001). Patients in the obese group reported worse HRQoL and greater effects of diabetes on their daily living., Conclusions: Obesity in Chinese patients with T2DM results in poor glycemic control, more comorbidities, and worse HRQoL. Management of these patients should include efforts to reduce weight. Selection of weight-neutral or weight-reducing anti-diabetic medications maybe useful in these patients., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Purpose: The aim of this study was to investigate the correlation between changes in symptoms and changes in self-reported quality of life among Chinese patients with schizophrenia who were switched from a typical antipsychotic to olanzapine during usual outpatient care., Patients and Methods: This post hoc analysis was conducted using data from the Chinese subgroup (n=475) of a multicountry, 12-month, prospective, noninterventional, observational study. The primary publication previously reported the efficacy, safety, and quality of life among patients who switched from a typical antipsychotic to olanzapine. Patients with schizophrenia were included if their symptoms were inadequately controlled with a typical antipsychotic and they were switched to olanzapine. Symptom severity was measured using the Brief Psychiatric Rating Scale (BPRS) and the Clinical Global Impressions-Severity scale (CGI-S). Health-Related Quality of Life (HRQOL) was assessed using the World Health Organization Quality of Life-Abbreviated (WHOQOL-BREF). Paired t-tests were performed to assess changes from baseline to endpoint. Pearson's correlation coefficients (r) were used to assess the correlations between change in symptoms (BPRS and CGI-S scores) and change in HRQOL (WHOQOL-BREF scores)., Results: Symptoms and HRQOL both improved significantly over the 12 months of treatment (P<0.001). Significant correlations were observed between changes from baseline to end of study on the BPRS and the CGI-S and each of the WHOQOL-BREF four domain scores and two overall quality-of-life questions. The correlation coefficients ranged from r=-0.45 to r=-0.53 for the BPRS and WHOQOL-BREF. The correlation coefficients were slightly smaller between the CGI-S and WHOQOL-BREF, ranging from r=-0.33 to r=-0.40., Conclusion: For patients with schizophrenia, assessing quality of life has the potential to add valuable information to the clinical assessment that takes into account the patient's own perspective of well-being.