BACKGROUND:There is a need for more Comparative Effectiveness Research (CER) to strengthen the evidence base for clinical and policy decision-making. Effectiveness Guidance Documents (EGD) are targeted to clinical researchers. The aim of this EGD is to provide specific recommendations for the design of prospective acupuncture studies to support optimal use of resources for generating evidence that will inform stakeholder decision-making.METHODS:Document development based on multiple systematic consensus procedures (written Delphi rounds, interactive consensus workshop, international expert review). To balance aspects of internal and external validity, multiple stakeholders including patients, clinicians and payers were involved.RESULTS:Recommendations focused mainly on randomized studies and were developed for the following areas: overall research strategy, treatment protocol, expertise and setting, outcomes, study design and statistical analyses, economic evaluation, and publication.CONCLUSION:The present EGD, based on an international consensus developed with multiple stakeholder involvement, provides the first systematic methodological guidance for future CER on acupuncture.

BACKGROUND:There is a need for more Comparative Effectiveness Research (CER) on Chinese medicine (CM) to inform clinical and policy decision-making. This document aims to provide consensus advice for the design of CER trials on CM for researchers. It broadly aims to ensure more adequate design and optimal use of resources in generating evidence for CM to inform stakeholder decision-making.METHODS:The Effectiveness Guidance Document (EGD) development was based on multiple consensus procedures (survey, written Delphi rounds, interactive consensus workshop, international expert review). To balance aspects of internal and external validity, multiple stakeholders, including patients, clinicians, researchers and payers were involved in creating this document.RESULTS:Recommendations were developed for "using available data" and "future clinical studies". The recommendations for future trials focus on randomized trials and cover the following areas: designing CER studies, treatments, expertise and setting, outcomes, study design and statistical analyses, economic evaluation, and publication.CONCLUSION:The present EGD provides the first systematic methodological guidance for future CER trials on CM and can be applied to single or multi-component treatments. While CONSORT statements provide guidelines for reporting studies, EGDs provide recommendations for the design of future studies and can contribute to a more strategic use of limited research resources, as well as greater consistency in trial design.

The purpose of this study was to determine the effects of following either the Curves® Fitness and Weight Management Plan or the Weight Watchers® Momentum™ Plan on body composition and markers of health and fitness in previously sedentary obese women. Fifty-one women (age 35±8 yrs; height 163±7 cm; weight 90±1 kg; BMI 34±5 kg/m2; 47±7% body fat) were randomized to participate in the Curves® (C) or Weight Watchers® (W) weight loss programs for 16-wks. Participants in the C group (n=24) followed a 1,200 kcal/d diet for 1-wk; 1,500 kcal/d diet for 3 wks (~30%:45% CHO:PRO); and 2,000 kcals/d for 2-wks (45:30) and repeated this diet while participating in a supervised Curves® with Zumba program 3-d-wk. Remaining subjects (n=27) followed the W point-based diet program, received weekly group counseling, and were encouraged to exercise. Body composition, anthropometrics, resting energy expenditure (REE), lipid biomarkers, and hormone concentrations were assessed at 0, 4, 10, and 16 weeks. Maximal cardiopulmonary exercise capacity and upper and lower body isotonic strength and endurance were assessed at 0 and 16 weeks. Data were analyzed using multivariate analysis of variance for repeated measures. MANOVA analysis of body composition data revealed overall time (Wilks’ Lamda p=0.001) and time by diet effects (Wilks’ Lamda p=0.003). Subjects in both groups lost a similar amount of total mass (C -2.4±2.0, -4.1±3.4, -5.1±3.9; W -2.3±2.3, -4.5±3.0, -5.5±4.6 kg, p=0.78). However, subjects in the C group tended to have a greater reduction in percent body fat (C -3.3±5.2, -3.2±4.6, -4.7±5.4; W 0.6±6.7, -0.6±8.3, -1.4±8.1%, p=0.10) and body fat mass (C -3.9±5.5, -4.6±5.3, -6.4±5.9; W -0.4±5.7, -2.1±6.7, -2.9±7.8 kg, p=0.09), while maintaining FFM (C 1.5±4.3, 0.52±3.7, 1.3±4.0; W -1.8±5.4, -2.4±5.8, -2.5±5.1, p=0.01). While both groups had increases cardiovascular fitness, the C group experienced improvements in upper body muscular endurance (C 1.4±3.9; W -1.2±2.4 repetitions, p=0.006). Both groups experienced improvements in lipid biomarkers; however, only the C group experienced a moderate increase in HDL-c. Results indicate that participants following the C program experienced more favorable changes in body composition and markers of fitness and health than participants in the W program.

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LEAP and TEACCH represent two comprehensive treatment models (CTMs) that have been widely used across several decades to educate young children with autism spectrum disorders. The purpose of this quasi-experimental study was to compare high fidelity LEAP (n = 22) and TEACCH (n = 25) classrooms to each other and a control condition (n = 28), in which teachers in high quality special education programs used non-model-specific practices. A total of 198 children were included in data analysis. Across conditions, children's performances improved over time. This study raises issues of the replication of effects for CTMs, and whether having access to a high quality special education program is as beneficial as access to a specific CTM.

This paper describes and tests a largely ignored but important preliminary step for comparative effectiveness research: retrospective evidence syntheses to first establish a knowledge base of condition-based medical conditions. By aggregating and organizing what is already known about a treatment or system, gaps in knowledge can be identified and future research designed to meet those gaps.An information synthesis process may also discover that few knowledge gaps in the knowledge base yet exist, the gaps are negligible, and / or treatment effectiveness and study quality is stable across many years, but is simply not clinically important. A consistent finding of low effectiveness is evidence against more research, including exclusion of a treatment from future comparative effectiveness studies. Though proponents of weak treatments or systems may choose to proceed with further research, use of public funds or resources that eventually increase costs to the public are unwarranted.By first establishing a treatment or system knowledge base, at least three comparative effectiveness research decisions are conceivable: (1) treatment or system should be included in future comparative effectiveness trials to establish relative effectiveness for a given condition, (2) has promise but requires more research in a prospective CER trial, or (3) the treatment is less effective than others for a given condition, making future research unnecessary. Thus, a "retroactive comparative effectiveness research method," rCER, is proposed here to identify which treatments are worth including in future prospective trials and which are known to have small to modest effect sizes and are not worth the time and expense of a closer look.The rCER method herein showed that for non-surgical low back pain any treatments did not improve greatly upon the normal and natural pain trajectory for acute low back pain. Therefore, any advantage in pain reduction by any treatment of acute low back pain over back pain's normal course of resolution without care, is quite small, and as such, the incremental cost for the marginal improvement over no treatment becomes quite large. While the quality of non-surgical low back pain studies over the past 34 years has steadily increased, the effect size has not, leading to the conclusion that future research on non-surgical low back pain treatment is unwarranted.

A key element in evidence-based medicine approaches is the ability for clinicians to evaluate the scientific rigor and relevance of research evidence. In the treatment of diabetes, clinicians make increasingly difficult decisions about which drug regimens are best for their patients with limited evidence-based information. While the consensus is that metformin should be the initial drug treatment when diet and exercise are not sufficient, clinicians disagree on whether sulfonylureas should remain a suitable therapy after metformin. While this would be improved with further research investigating the comparative safety of therapeutic options, there is also need for better ways to synthesize available information to guide evidence-based decision-making in health services research. Study 1 summarizes the pre-existing evidence on the long-term safety risks associated with sulfonylurea therapy relative to other drug classes. Results from a series of meta-analyses provide some evidence that sulfonylureas are associated with elevated all-cause mortality and cardiovascular risks relative to several other medications, either as a monotherapy or in combination with metformin. Study 2 analyzes the comparative safety of second-line treatment in diabetic patients in the Veterans Health Administration to address gaps in the literature. Results suggest that second-line use of sulfonylureas is associated with increased risks compared to thiazolidinediones. Results also suggest that changes to existing metformin therapy may lead to differential hazards. Clinicians may disagree about the quality of the evidence as well as the relevancy to their own treatment population. Improvements in methods for evidence-based medicine that take this into account are needed. Study 3 applies an underutilized research method that allows for a more thoughtful synthesis of all available evidence. This framework allows clinicians to incorporate the scientific rigor and relevancy of previous study results when integrating new data into their current knowledge base. Results suggest an elevated risk in all models for sulfonylureas compared to thiazolidinediones and highlight the need to design more focused research to support clinical decision-making around medication safety. This novel application to evidence synthesis shows promise as applied to a health services research problem and has potential as a useful framework in other health services research areas. / 2017-12-09T00:00:00Z

The objective of this dissertation is to develop and evaluate quantitative models that have the potential to improve cardiovascular disease (CVD) screening and projection efforts in the U.S. Paper 1 assesses the exchangeability of a non-laboratory-based CVD risk score (predictors do not include cholesterol) with more commonly-used laboratory-based scores, such as the Framingham risk equations. Under conventional thresholds for identifying high-risk individuals, 92-96% of adults in the National Health and Nutrition Examination Survey (NHANES III) were equivalently characterized as high- or low-risk using either type of score. The 10-year CVD death results also suggest that simple CVD risk assessment could be a useful proxy for more expensive laboratory-based screening strategies in the U.S. or other resource-limited settings. Paper 2 uses micro-simulation modeling techniques to evaluate the cost effectiveness of primary cardiovascular disease (CVD) screening using staged laboratory-based and/or non-laboratory-based total CVD risk assessment. The results imply that efficient screening guidelines should include non-laboratory-based risk assessment, either as a single stage or as part of multistage screening approach. Compared to current CVD screening guidelines, fewer cholesterol tests would be administered and more adults would receive low-cost statins under cost-effective screening policies. Paper 3 examines the trends of CVD risk factors, treatment, and total risk in the U.S. from 1973-2010, and offers projections of these variables for 2015-2030. Nine waves of cross-sectional NHANES data show that the divergent, observed trends in common CVD risk factors (such as smoking, BMI, total cholesterol, and blood pressure) are expected to continue in future years. Age-adjusted CVD risk has decreased over time (during the observed and projected periods), but total risk has increased when considering the impact of aging on CVD risk. Scenario analyses suggest that strategies targeting cholesterol and blood pressure treatment have the greatest potential to reduce future CVD burden in the U.S.

Much health related research depends heavily on the analysis of a rapidly expanding universe of observational data. A challenge in analysis of such data is the lack of sound statistical methods and tools that can address multiple facets of estimating treatment or exposure effects in observational studies with a large number of covariates. We sought to advance methods to improve analysis of large observational datasets with an end goal of understanding the effect of treatments or exposures on health. First we compared existing methods for propensity score (PS) adjustment, specifically Bayesian propensity scores. This concept had previously been introduced (McCandless et al., 2009) but no rigorous evaluation had been done to evaluate the impact of feedback when fitting the joint likelihood for both the PS and outcome models. We determined that unless specific steps were taken to mitigate the impact of feedback, it has the potential to distort estimates of the treatment effect. Next, we developed a method for accounting for uncertainty in confounding adjustment in the context of multiple exposures. Our method allows us to select confounders based on their association with the joint exposure and the outcome while also accounting for the uncertainty in the confounding adjustment. Finally, we developed two methods to combine het- erogenous sources of data for effect estimation, specifically information coming from a primary data source that provides information for treatments, outcomes, and a limited set of measured confounders on a large number of people and smaller supplementary data sources containing a much richer set of covariates. Our methods avoid the need to specify the full joint distribution of all covariates.

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