A CTSA agenda to advance methods for comparative effectiveness research

MIFuzzy Clustering for Incomplete Longitudinal Data in Smart Health

Missing data are common in longitudinal observational and randomized controlled trials in smart health studies. Multiple-imputation based fuzzy clustering is an emerging non-parametric soft computing method, used for either semi-supervised or unsupervised learning. Multiple imputation (MI) has been widely-used in missing data analyses, but has not yet been scrutinized for unsupervised learning methods, although they are important for explaining the heterogeneity of treatment effects. Built upon our previous work on MIfuzzy clustering, this paper introduces the MIFuzzy concepts and performance, theoretically, empirically and numerically demonstrate how MI-based approach can reduce the uncertainty of clustering accuracy in comparison to non- and single-imputation based clustering approach. This paper advances our understanding of the utility and strength of MIFuzzy clustering approach to processing incomplete longitudinal behavioral intervention data.

Understanding practice-based research participation: The differing motivations of engaged vs. non-engaged clinicians in pragmatic clinical trials

Background/Aims

Pragmatic clinical trials (PCTs) represent an increasingly used strategy for “real-world” trials. Successful PCTs typically require participation of community-based practices. However, community clinicians often have limited interest or experience in clinical research. Many barriers to practice-based research have been described, but possible motivations to participate among community practices not active in research have not been well explored. The tendency is for researchers to assume similar motivations and priorities across all candidate practices. This is not necessarily the case. A better understanding of the range of reasons clinicians might see for participating in pragmatic trials could be key to promoting this type of practice-based research.

Methods

Semi-structured interviews were conducted with 30 clinicians and staff members. Half of the interviewees had experience doing practice-based clinical trials and half did not. Individuals in these two groups were also diversified in terms of their practice size and location. Participants were asked about motivations and barriers to doing practice-based research in the context of a planned osteoporosis pragmatic clinical trial. Interviews were transcribed, coded, and analyzed.

Results

Barriers identified for both experienced and not-experienced clinicians and staff members included: a lack of time, increased paperwork, disruption to work flows, and concern over practice finances. Similar findings have been reported in the US, UK, Europe, and Australia. However, regarding positive motivations of practices to participate, we found systematic differences in attitude between research-engaged and research-naïve practices that have not been previously reported. The research-experienced group offered a greater number and variety of reasons to take part than the not-experienced group. While both groups expressed motivations related to patient care, clinicians and staff members experienced in practice-based clinical trials were much more likely to cite intellectual, professional, and societal benefits not envisioned by the other group.

Conclusions

We conclude that clinicians not already participating in practice-based trials may have a narrower range of motivations than those already participating. The lack of a broader view of possible benefits to participation may also translate into more obdurate recruiting challenges. These results point to the need for recruitment, engagement, and messaging approaches differentially tailored to the needs and interests of non-participating practices.

Multiple- vs Non- or Single-Imputation based Fuzzy Clustering for Incomplete Longitudinal Behavioral Intervention Data

Disentangling patients' behavioral variations is a critical step for better understanding an intervention's effects on individual outcomes. Missing data commonly exist in longitudinal behavioral intervention studies. Multiple imputation (MI) has been well studied for missing data analyses in the statistical field, however, has not yet been scrutinized for clustering or unsupervised learning, which are important techniques for explaining the heterogeneity of treatment effects. Built upon previous work on MI fuzzy clustering, this paper theoretically, empirically and numerically demonstrate how MI-based approach can reduce the uncertainty of clustering accuracy in comparison to non-and single-imputation based clustering approach. This paper advances our understanding of the utility and strength of multiple-imputation (MI) based fuzzy clustering approach to processing incomplete longitudinal behavioral intervention data.

A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data

Web-delivered clinical trials generate big complex data. To help untangle the heterogeneity of treatment effects, unsupervised learning methods have been widely applied. However, identifying valid patterns is a priority but challenging issue for these methods. This paper, built upon our previous research on multiple imputation (MI)-based fuzzy clustering and validation, proposes a new MI-based Visualization-aided validation index (MIVOOS) to determine the optimal number of clusters for big incomplete longitudinal Web-trial data with inflated zeros. Different from a recently developed fuzzy clustering validation index, MIVOOS uses a more suitable overlap and separation measures for Web-trial data but does not depend on the choice of fuzzifiers as the widely used Xie and Beni (XB) index. Through optimizing the view angles of 3-D projections using Sammon mapping, the optimal 2-D projection-guided MIVOOS is obtained to better visualize and verify the patterns in conjunction with trajectory patterns. Compared with XB and VOS, our newly proposed MIVOOS shows its robustness in validating big Web-trial data under different missing data mechanisms using real and simulated Web-trial data.

Comparative effectiveness and practice variation in neonatal care

Comparative effectiveness research (CER) is a relatively new term for clinical research that directly assists patients, clinicians, and policymakers in making informed decisions to improve health care. In neonatology, there are similarities and differences between CER and existing clinical research and quality improvement literature. This article uses existing examples in neonatal literature to describe CER methodology and list some future directions and challenges in neonatal CER.

Role of practice-based research networks in comparative effectiveness research

Comparative effectiveness research fundamentally reorients how clinical evidence is generated and used with the goal of providing actionable information to decision-makers. To achieve this, it is vital that decision-makers and the research enterprise are engaged from research inception, to evidence generation and translation. Practice-based research networks are affiliated clinicians in diverse communities with the goal of conducting research to improve care. Practice-based research networks have the potential to advance all phases of the comparative effectiveness research cycle. The aim of this paper is to explore current and potential roles of practice-based research networks in conducting comparative effectiveness research.

A national strategy to develop pragmatic clinical trials infrastructure

An important challenge in comparative effectiveness research is the lack of infrastructure to support pragmatic clinical trials, which compare interventions in usual practice settings and subjects. These trials present challenges that differ from those of classical efficacy trials, which are conducted under ideal circumstances, in patients selected for their suitability, and with highly controlled protocols. In 2012, we launched a 1-year learning network to identify high-priority pragmatic clinical trials and to deploy research infrastructure through the NIH Clinical and Translational Science Awards Consortium that could be used to launch and sustain them. The network and infrastructure were initiated as a learning ground and shared resource for investigators and communities interested in developing pragmatic clinical trials. We followed a three-stage process of developing the network, prioritizing proposed trials, and implementing learning exercises that culminated in a 1-day network meeting at the end of the year. The year-long project resulted in five recommendations related to developing the network, enhancing community engagement, addressing regulatory challenges, advancing information technology, and developing research methods. The recommendations can be implemented within 24 months and are designed to lead toward a sustained national infrastructure for pragmatic trials.

A partnership of two U.S. research networks to improve public health

Strategic collaborations are essential in moving public health research and practice forward1 , particularly in light of escalating fiscal and environmental challenges facing the public health community. This commentary provides background and context for an emerging partnership between two national networks, Prevention Research Centers (PRCs) and Public Health Practice-Based Research Networks (PBRNs), to impact public health practice. Supported by CDC, PRCs are celebrating over 25 years of transdisciplinary applied prevention research grounded in community and stakeholder engagement. Public Health PBRNs, funded by the Robert Wood Johnson Foundation, conduct innovative public health services and systems research with public health agencies and community partners to improve public health decision-making. By utilizing each of the networks’ respective strengths and resources, collaborative ventures between PRCs and Public Health PBRNs can enhance the translation of applied prevention research to evidence-based practice and empirically investigate novel public health practices developed in the field. Three current PRC-Public Health PBRNs projects are highlighted and future research directions are discussed. Improving the interconnectedness of prevention research and public health practice is essential to improve the health of the Nation.

Translational paradigms in pharmacology and drug discovery

The translational sciences represent the core element in enabling and utilizing the output from the biomedical sciences and to improving drug discovery metrics by reducing the attrition rate as compounds move from preclinical research to clinical proof of concept. Key to understanding the basis of disease causality and to developing therapeutics is an ability to accurately diagnose the disease and to identify and develop safe and effective therapeutics for its treatment. The former requires validated biomarkers and the latter, qualified targets. Progress has been hampered by semantic issues, specifically those that define the end product, and by scientific issues that include data reliability, an overt reductionistic cultural focus and a lack of hierarchically integrated data gathering and systematic analysis. A necessary framework for these activities is represented by the discipline of pharmacology, efforts and training in which require recognition and revitalization.

The patient is in: patient involvement strategies for diagnostic error mitigation

Although healthcare quality and patient safety have longstanding international attention, the target of reducing diagnostic errors has only recently gained prominence, even though numerous patients, families and professional caregivers have suffered from diagnostic mishaps for a long time. Similarly, patients have always been involved in their own care to some extent, but only recently have patients sought more opportunities for engagement and participation in healthcare improvements. This paper brings these two promising trends together, analysing strategies for patient involvement in reducing diagnostic errors in an individual's own care, in improving the healthcare delivery system's diagnostic safety, and in contributing to research and policy development on diagnosis-related issues.

Value-of-information analysis within a stakeholder-driven research prioritization process in a US setting: an application in cancer genomics

OBJECTIVE

The objective of this study was to evaluate the feasibility and outcomes of incorporating value-of-information (VOI) analysis into a stakeholder-driven research prioritization process in a US-based setting.

METHODS

. Within a program to prioritize comparative effectiveness research areas in cancer genomics, over a period of 7 months, we developed decision-analytic models and calculated upper-bound VOI estimates for 3 previously selected genomic tests. Thirteen stakeholders representing patient advocates, payers, test developers, regulators, policy makers, and community-based oncologists ranked the tests before and after receiving VOI results. The stakeholders were surveyed about the usefulness and impact of the VOI findings.

RESULTS

The estimated upper-bound VOI ranged from $33 million to $2.8 billion for the 3 research areas. Seven stakeholders indicated the results modified their rankings, 9 stated VOI data were useful, and all indicated they would support its use in future prioritization processes. Some stakeholders indicated expected value of sampled information might be the preferred choice when evaluating specific

STUDY DESIGN

Limitations. Our study was limited by the size and the potential for selection bias in the composition of the external stakeholder group, lack of a randomized design to assess effect of VOI data on rankings, and the use of expected value of perfect information v. expected value of sample information methods.

CONCLUSIONS

Value of information analyses may have a meaningful role in research topic prioritization for comparative effectiveness research in the United States, particularly when large differences in VOI across topic areas are identified. Additional research is needed to facilitate the use of more complex value of information analyses in this setting.

Acknowledging patient heterogeneity in economic evaluation : a systematic literature review

BACKGROUND AND OBJECTIVE

Patient heterogeneity is the part of variability that can be explained by certain patient characteristics (e.g. age, disease stage). Population reimbursement decisions that acknowledge patient heterogeneity could potentially save money and increase population health. To date, however, economic evaluations pay only limited attention to patient heterogeneity. The objective of the present paper is to provide a comprehensive overview of the current knowledge regarding patient heterogeneity within economic evaluation of healthcare programmes.

METHODS

A systematic literature review was performed to identify methodological papers on the topic of patient heterogeneity in economic evaluation. Data were obtained using a keyword search of the PubMed database and manual searches. Handbooks were also included. Relevant data were extracted regarding potential sources of patient heterogeneity, in which of the input parameters of an economic evaluation these occur, methods to acknowledge patient heterogeneity and specific concerns associated with this acknowledgement.

RESULTS

A total of 20 articles and five handbooks were included. The relevant sources of patient heterogeneity (demographics, preferences and clinical characteristics) and the input parameters where they occurred (baseline risk, treatment effect, health state utility and resource utilization) were combined in a framework. Methods were derived for the design, analysis and presentation phases of an economic evaluation. Concerns related mainly to the danger of false-positive results and equity issues.

CONCLUSION

By systematically reviewing current knowledge regarding patient heterogeneity within economic evaluations of healthcare programmes, we provide guidance for future economic evaluations. Guidance is provided on which sources of patient heterogeneity to consider, how to acknowledge them in economic evaluation and potential concerns. The improved acknowledgement of patient heterogeneity in future economic evaluations may well improve the efficiency of healthcare.

[Manual for the design of non-drug trials in primary care, taking account of Good Clinical Practice (GCP) criteria]

In recent years studies not falling under the German Pharmaceutical Law ("non-drug trials") have also been increasingly expected to be conducted according to Good Clinical Practice (GCP) in order to ensure that uniform standards are maintained for data quality and patient safety. However, simple transfer of the GCP criteria is not always possible and often not useful. Given the fact that research questions regarding non-drug interventions are common in primary care (e.g., general practice), the "Network for Clinical Studies in General Practice" has developed a manual for planning and conducting non-drug trials. This manual is based on the GCP guideline, taking account of the conditions and circumstances in primary care settings. Both structure and relevant content of the manual are presented in the article. (As supplied by the authors).

Stakeholder engagement in comparative effectiveness research: how will we measure success?

Stakeholder engagement in comparative effectiveness research continues to gain national attention. While various methods are used to gather stakeholder expertise and form recommendations, evaluation of the stakeholder experience is often missing. The lack of evaluation prohibits assessing how effective and meaningful engagement practices are for enhancing research efforts and limits the ability to identify areas for future improvement. We propose that an evaluation plan of engagement processes be developed before stakeholder involvement begins and be required as part of a request for proposal or research grant where stakeholder input is being sought. Furthermore, we recommend the inclusion of six meta-criteria that represent normative goals of multiple studies: respect, trust, legitimacy, fairness, competence and accountability. To aid in the development of future evaluations, we have developed definitions for and matched specific examples of measuring each meta-criterion to serve a guide for others in the field.

Improving the efficiency and effectiveness of pragmatic clinical trials in older adults in the United States

Pragmatic clinical trials (PCTs) seek to improve the generalizability and increase the statistical power of traditional explanatory trials. They are a major tenet of comparative effectiveness research. While a powerful study design, PCTs have been limited by high cost, modest efficiency, and limited ability to fill relevant evidence gaps. Based on an American Reinvestment and Recovery Act (ARRA) supported meeting of national stakeholders, we propose several innovations and future research that could improve the efficiency and effectiveness of such studies focused in the U.S. Innovations discussed include optimizing the use of community based practices through partnership with Practice Based Research Networks (PBRNs), using information technology to simplify PCT subject recruitment, consent and randomization processes, and utilizing linkages to large administrative databases, such as Medicare, as a mechanism to capture outcomes and other important PCT variables with lower subject and research team burden. Testing and adaptation of such innovations to PCT are anticipated to improve the public health value of these increasingly important studies.

Building the evidence base for decision making in cancer genomic medicine using comparative effectiveness research

The clinical utility is uncertain for many cancer genomic applications. Comparative effectiveness research (CER) can provide evidence to clarify this uncertainty. The aim of this study was to identify approaches to help stakeholders make evidence-based decisions and to describe potential challenges and opportunities in using CER to produce evidence-based guidance. We identified general CER approaches for genomic applications through literature review, the authors' experiences, and lessons learned from a recent, seven-site CER initiative in cancer genomic medicine. Case studies illustrate the use of CER approaches. Evidence generation and synthesis approaches used in CER include comparative observational and randomized trials, patient-reported outcomes, decision modeling, and economic analysis. Significant challenges to conducting CER in cancer genomics include the rapid pace of innovation, lack of regulation, and variable definitions and evidence thresholds for clinical and personal utility. Opportunities to capitalize on CER methods in cancer genomics include improvements in the conduct of evidence synthesis, stakeholder engagement, increasing the number of comparative studies, and developing approaches to inform clinical guidelines and research prioritization. CER offers a variety of methodological approaches that can address stakeholders' needs and help ensure an effective translation of genomic discoveries.

The PBRN initiative: transforming new technologies to improve patient care

The NIDCR-supported Practice-based Research Network initiative presents dentistry with an unprecedented opportunity by providing a pathway for modifying and advancing the profession. It encourages practitioner participation in the transfer of science into practice for the improvement of patient care. PBRNs vary in infrastructure and design, and sustaining themselves in the long term may involve clinical trial validation by regulatory agencies. This paper discusses the PBRN concept in general and uses the New York University College of Dentistry's Practitioners Engaged in Applied Research and Learning (PEARL) Network as a model to improve patient outcomes. The PEARL Network is structured to ensure generalizability of results, data integrity, and to provide an infrastructure in which scientists can address clinical practitioner research interests. PEARL evaluates new technologies, conducts comparative effectiveness research, participates in multidisciplinary clinical studies, helps evaluate alternative models of healthcare, educates and trains future clinical faculty for academic positions, expands continuing education to include "benchmarking" as a form of continuous feedback to practitioners, adds value to dental schools' educational programs, and collaborates with the oral health care and pharmaceutical industries and medical PBRNs to advance the dental profession and further the integration of dental research and practice into contemporary healthcare (NCT00867997, NCT01268605).

Psychosocial interventions for patients with chronic disease

Treatment of patients with chronic diseases will be one of the main challenges of medicine in the future. This paper presents an overview of different origins, mechanism, and symptoms necessary for understanding new and different interventions that include a psychosomatic view.

In a psychosomatic therapeutic intervention there are very different targets, such as psychological symptoms, personality traits, attitudes toward disease and life, risk behaviour, and social isolation and as biological targets the change of autonomic imbalance and of the effects of the psycho-endocrinological or psycho-immunological stress responses. And there are also different psychosomatic measures that influence the individual biological, psychological and sociological targets. There is a need to give different answer to different questions in the field of psychosomatic and behavioral medicine. Comparative effectiveness research is an important strategy for solving some methodological issues. What is the target of treatment for different diseases: Symptom reduction, healing, or limiting progression to the worst case - the death of patients. We know that, the patient-physician relationship is important for every medical/therapeutic action for patients with chronic diseases.

This volume of BioPsychoSocial Medicine will present four different psychosomatic treatment studies from the clinical field in the sense of phase 2 studies: Reports of patients with obesity, anorexia nervosa, chronic somatoform pain and coronary artery disease were presented

Integrating new approaches for clinical development: translational research and relative effectiveness

Translational research and relative effectiveness are being incorporated into drug development programs to meet the demands for more robust evidence generation to support the value of new therapies. Translational research includes translating basic research into clinical practice, controlled clinical trials into potential clinical implications, evidence-based guidelines into routine clinical practice and standard practices into population health. These research concepts link with real-world outcomes, and feed into each other to improve the efficiency of research. Translational research can run into road blocks in terms of conveying the added or comparative value of research or during adoption into clinical practice. Understanding these roadblocks and developing solutions are important for success. Comparative effectiveness research can be a useful research technique to accomplish many translational medicine goals. These studies generally include heterogeneous patient populations and evaluate outcomes of relevance to payers and health technology assessors. Comparative effectiveness research can be used in drug development; different methodologies may be useful in different phases. In this article, suggestions and examples of successful use of comparative effectiveness studies are provided. Translational research and comparative effectiveness research, although clearly independent concepts, can provide a focused approach to drug development, resulting in products entering the market that bring added benefit to patients and the healthcare system overall.