https://digitalcommons.unmc.edu/cgi/viewcontent.cgi?article=1047&context=com_eye_articles

Long-term outcomes of ranibizumab therapy for diabetic macular edema: the 36-month results from two phase III trials: RISE and RIDE.

Clinical trials are the main source of information for the efficacy and safety evaluation of medical treatments. Although they are of pivotal importance in evidence-based medicine, there is a lack of usable information systems providing data-analysis and decision support capabilities for aggregate clinical trial results. This is partly caused by unavailability (i) of trial data in a structured format suitable for re-analysis, and (ii) of a complete data model for aggregate level results. In this paper, we develop a unifying data model that enables the development of evidence-based decision support in the absence of a complete data model. We describe the supported decision processes and show how these are implemented in the open source ADDIS software. ADDIS enables semi-automated construction of meta-analyses, network meta-analyses and benefit-risk decision models, and provides visualization of all results.

/pdf/addis-a-decision-support-system-for-evidence-based-medicine-3xt02o204y.pdf

ADDIS: A decision support system for evidence-based medicine

There is argument over the benefits and risks of drugs for treating chronic musculoskeletal pain. This study compared the efficacy, safety, and tolerability of diclofenac, ibuprofen, naproxen, celecoxib, and etoricoxib for patients with pain caused by osteoarthritis (OA) or rheumatoid arthritis (RA). A systematic literature review used Medline and EMBASE to identify randomised controlled trials. Efficacy outcomes assessed included: pain relief measured by visual analogue scale (VAS); Western Ontario McMaster Universities Arthritis Index (WOMAC) VAS or WOMAC Likert scale; physical functioning measured by WOMAC VAS or Likert scale; and patient global assessment (PGA) of disease severity measured on VAS or 5-point Likert scale. Safety outcomes included: Antiplatelet Trialists’ Collaboration (APTC), major cardiovascular (CV) and major upper gastrointestinal (GI) events, and withdrawals. Data for each outcome were synthesized by a Bayesian network meta-analysis (NMA). For efficacy assessments, labelled doses for OA treatment were used for the base case while labelled doses for RA treatment were also included in the sensitivity analysis. Pooled data across dose ranges were used for safety. Efficacy, safety, and tolerability data were found for 146,524 patients in 176 studies included in the NMA. Diclofenac (150 mg/day) was likely to be more effective in alleviating pain than celecoxib (200 mg/day), naproxen (1000 mg/day), and ibuprofen (2400 mg/day), and similar to etoricoxib (60 mg/day); a lower dose of diclofenac (100 mg/day) was comparable to all other treatments in alleviating pain. Improved physical function with diclofenac (100 and 150 mg/day) was mostly comparable to all other treatments. PGA with diclofenac (100 and 150 mg/day) was likely to be more effective or comparable to all other treatments. All active treatments were similar for APTC and major CV events. Major upper GI events with diclofenac were lower compared to naproxen and ibuprofen, comparable to celecoxib, and higher than etoricoxib. Risk of withdrawal with diclofenac was lower compared to ibuprofen, similar to celecoxib and naproxen, and higher than etoricoxib. The benefit-risk profile of diclofenac was comparable to other treatments used for pain relief in OA and RA; benefits and risks vary in individuals and need consideration when making treatment decisions.

https://link.springer.com/content/pdf/10.1186%2Fs13075-015-0554-0.pdf

Relative benefit-risk comparing diclofenac to other traditional non-steroidal anti-inflammatory drugs and cyclooxygenase-2 inhibitors in patients with osteoarthritis or rheumatoid arthritis: a network meta-analysis

Following a series of high-profile drug safety disasters in recent years, many countries are redoubling their efforts to ensure the safety of licensed medical products. Large-scale observational databases such as claims databases or electronic health record systems are attracting particular attention in this regard, but present significant methodological and computational concerns. In this article we show how high-performance statistical computation, including graphics processing units, relatively inexpensive highly parallel computing devices, can enable complex methods in large databases. We focus on optimization and massive parallelization of cyclic coordinate descent approaches to fit a conditioned generalized linear model involving tens of millions of observations and thousands of predictors in a Bayesian context. We find orders-of-magnitude improvement in overall run-time. Coordinate descent approaches are ubiquitous in high-dimensional statistics and the algorithms we propose open up exciting new methodological possibilities with the potential to significantly improve drug safety.

/pdf/massive-parallelization-of-serial-inference-algorithms-for-a-1pcsxsnyr2.pdf

Massive Parallelization of Serial Inference Algorithms for a Complex Generalized Linear Model

The current process of benefit–risk assessment of medicines relies primarily on intuitive expert judgment. Frameworks are needed for transparent, rational and defensible decision making that benefits patients, drug developers, and decision makers. The Benefit Risk Action Team framework is a set of processes and tools for selecting, organizing, summarizing, and interpreting data that is relevant to decisions based on benefit–risk assessments. It provides a standardized yet flexible platform for incorporating study outcomes and preference weights as well as for communicating the rationales for decisions.



Clinical Pharmacology & Therapeutics (2011) 89 2, 312–315. doi:10.1038/clpt.2010.291

Development of a framework for enhancing the transparency, reproducibility and communication of the benefit-risk balance of medicines

The BRAT Framework is a set of flexible processes and tools that provides a structured approach to pharmaceutical benefit-risk decision making in drug development and post approval settings A work in progress, it consists of six steps that produce representations of key tradeoffs, with appropriate documentation of the rationale for decisions and the assumptions made in their development This article describes insights, gained from case studies, into the Framework's performance in a variety of constructed benefit-risk scenarios, focusing on a hypothetical example of a triptan for migraine The scenarios described illustrate the challenges inherent in arriving at many of the regulatory decisions, including obtaining data for matching populations for all outcomes, finding data of consistent quality, addressing correlated outcomes (eg, elevated liver function tests and hepatitis rates), dealing with rare but serious adverse events (AEs), and understanding and making decisions based on information for many outcomes simultaneously The Framework provides a structure for organizing, interpreting, and communicating relevant information, including heterogeneity in results and the quality and level of uncertainty of data, in order to facilitate benefit-risk decisions

Application of the BRAT framework to case studies: observations and insights.

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Development of a framework for enhancing the transparency, reproducibility and communication of the benefit-risk balance of medicines

Application of the BRAT framework to case studies: observations and insights.