FDA Guidance for Clinical Trial Sponsors
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Design and Implementation of N-Of-1 Trials: a User's Guide
Design and Implementation of N-of-1 Trials: A User’s Guide N of 1 The Agency for Healthcare Research and Quality’s (AHRQ) Effective Health Care Program conducts and supports research focused on the outcomes, effectiveness, comparative clinical effectiveness, and appropriateness of pharmaceuticals, devices, and health care services. More information on the Effective Health Care Program and electronic copies of this report can be found at www.effectivehealthcare.ahrq. gov. This report was produced under contract to AHRQ by the Brigham and Women’s Hospital DEcIDE (Developing Evidence to Inform Decisions about Effectiveness) Methods Center under Contract No. 290-2005-0016-I. The AHRQ Task Order Officer for this project was Parivash Nourjah, Ph.D. The findings and conclusions in this document are those of the authors, who are responsible for its contents; the findings and conclusions do not necessarily represent the views ofAHRQ or the U.S. Department of Health and Human Services. Therefore, no statement in this report should be construed as an official position of AHRQ or the U.S. Department of Health and Human Services. Persons using assistive technology may not be able to fully access information in this report. For assistance contact [email protected]. The following investigators acknowledge financial support: Dr. Naihua Duan was supported in part by NIH grants 1 R01 NR013938 and 7 P30 MH090322, Dr. Heather Kaplan is part of the investigator team that designed and developed the MyIBD platform, and this work was supported in part by Cincinnati Children’s Hospital Medical Center. Dr. Richard Kravitz was supported in part by National Institute of Nursing Research Grant R01 NR01393801. -
Certification Accredited Schools List
= Academic Clinical Research Programs Which Currently Meet Waiver Requirements for the Academy of Clinical Research Professional (Updated October 15, 2015) This list includes academic clinical research programs which, as of the above date, have been evaluated and found to meet the current waiver requirements established by the Academy of Clinical Research Professionals (Academy) Board of Trustees. Programs that meet the waiver requirements allow a candidate to waive 1,500 hours of hands-on experience performing the Essential Duties of the program to which the candidate has made application. This list is not to be considered exhaustive and is not designed to represent all the academic offerings available. Appearance on this list is not to be construed as an endorsement of its content or format by the Academy or a guarantee that a candidate will be eligible for certification. All individuals interested in enrolling in a program of study should evaluate any program on the basis of his or her personal needs. (* - programs marked with an asterisk MAY be accepted but acceptance will be dependent on the complete listing of courses completed) Individuals seeking additional information about each program should contact the institution directly. Academic Institution Program Name ( Click Title for Web Navigation) Academy of Applied Pharmaceutical Sciences Clinical Research Diploma Program American University of Health Sciences Master of Science in Clinical Research Anoka-Ramsey Community College Clinical Research Professional - Certificate Graduate -
Placebo-Controlled Trials of New Drugs: Ethical Considerations
Reviews/Commentaries/Position Statements COMMENTARY Placebo-Controlled Trials of New Drugs: Ethical Considerations DAVID ORENTLICHER, MD, JD als, placebo controls are not appropriate when patients’ health would be placed at significant risk (8–10). A placebo- controlled study for a new hair- uch controversy exists regarding sufficiently more effective than placebo to thickening agent could be justified; a the ethics of placebo-controlled justify its use. Finally, not all established placebo-controlled study for patients M trials in which an experimental therapies have been shown to be superior with moderate or severe hypertension therapy will compete with an already es- to placebo. If newer drugs are compared would not be acceptable (11). Similarly, if tablished treatment (or treatments). In with the unproven existing therapies, an illness causes problems when it goes such cases, argue critics, patients in the then patients may continue to receive untreated for a long period of time, a 52- control arm of the study should receive an drugs that are harmful without being week study with a placebo control is accepted therapy rather than a placebo. helpful. much more difficult to justify than a By using an active and effective drug, the Moreover, say proponents of placebo 6-week study (12). control patients would not be placed at controls, patients can be protected from When David S.H. Bell (13) explains risk for deterioration of their disease, and harm by “escape” criteria, which call for why placebo controls are unacceptable the study would generate more meaning- withdrawal from the trial if the patient for new drugs to treat type 2 diabetes, he ful results for physicians. -
Generalized Linear Models for Aggregated Data
Generalized Linear Models for Aggregated Data Avradeep Bhowmik Joydeep Ghosh Oluwasanmi Koyejo University of Texas at Austin University of Texas at Austin Stanford University [email protected] [email protected] [email protected] Abstract 1 Introduction Modern life is highly data driven. Datasets with Databases in domains such as healthcare are records at the individual level are generated every routinely released to the public in aggregated day in large volumes. This creates an opportunity form. Unfortunately, na¨ıve modeling with for researchers and policy-makers to analyze the data aggregated data may significantly diminish and examine individual level inferences. However, in the accuracy of inferences at the individ- many domains, individual records are difficult to ob- ual level. This paper addresses the scenario tain. This particularly true in the healthcare industry where features are provided at the individual where protecting the privacy of patients restricts pub- level, but the target variables are only avail- lic access to much of the sensitive data. Therefore, able as histogram aggregates or order statis- in many cases, multiple Statistical Disclosure Limita- tics. We consider a limiting case of gener- tion (SDL) techniques are applied [9]. Of these, data alized linear modeling when the target vari- aggregation is the most widely used technique [5]. ables are only known up to permutation, and explore how this relates to permutation test- It is common for agencies to report both individual ing; a standard technique for assessing statis- level information for non-sensitive attributes together tical dependency. Based on this relationship, with the aggregated information in the form of sample we propose a simple algorithm to estimate statistics. -
Generating Real-World Evidence by Strengthening Real-World Data Sources
Generating Real-World Evidence by Strengthening Real-World Data Sources Using “real-world evidence” to bring new “Every day, health care professionals are updating patients’ treatments to patients as part of the 21st electronic health records with data on clinical outcomes Century Cures Act (the “Cures Act”) is a key resulting from medical interventions used in routine clinical priority for the Department of Health and practice. As our experience with new medical products Human Services (HHS). Specifically, the Cures expands, our knowledge about how to best maximize their Act places focus on the use of real-world data benefits and minimize potential risks sharpens with each data to support regulatory decision-making, including point we gather. Every clinical use of a product produces data the approval of new indications for existing that can help better inform us about its safety and efficacy.” drugs in order to make drug development faster jacqueline corrigan-curay, md, jd and more efficient. As such, the Cures Act director of the office of medical policy in fda’s center for drug evaluation and research has tasked the Food and Drug Administration (FDA) to develop a framework and guidance for evaluating real-world evidence in the context of drug regulation.1 Under the FDA’s framework, real-world evidence (RWE) is generated by different study designs or analyses, including but not limited to, randomized trials like large simple trials, pragmatic trials, and observational studies. RWE is the clinical evidence about the use, potential benefits, and potential risks of a medical product based on an analysis of real-world data (RWD). -
Serious Adverse Event Reporting in Investigator-Initiated Clinical Trials
For debate Serious adverse event reporting in investigator-initiated clinical trials “Commonly ew drugs and medical devices offer improve- Summary ments in health care. Clinical research is under- reported SAEs Reporting adverse events (AEs) and serious AEs taken to elucidate such benefits, but also to N (SAEs) are practical steps to ensure safety for should be identify potential harms. Adverse event (AE) and serious volunteers and patients in medical research involving adopted as AE (SAE) data are crucial information in drug and device medications, treatments and devices. However, the fi universal development studies (Box 1). De nitions and re- burden and cost of reporting should be proportionate quirements for safety reporting in Australia are outlined with the public health benefit of this information. 1 endpoints to be by the international guidelines, and the National Health Unfortunately, in Australia there is clear evidence of mandated in all and Medical Research Council (NHMRC) National state- ever-increasing requirements from sponsors and ment on ethical conduct in human research 2 ethics committees to report AEs and SAEs unnec- Phase IV clinical . Further, the NHMRC has provided clarification on how AEs should essarily, leading to a decrease in the uptake of trials” be reported and who has responsibility for reviewing and research, particularly less well funded investigator- acting on them.3 Such guidelines are practical steps to initiated trials. ensure safety for participants in all research involving We believe that individual AE reports to ethics interventions, including post-marketing surveillance and committees serve no useful purpose, because in most cases the study group identity (drug exposure) Phase IV trials (Box 2) of approved medicines, treatments is not known in studies with blinded treatment arms and devices. -
Meta-Analysis Using Individual Participant Data: One-Stage and Two-Stage Approaches, and Why They May Differ Danielle L
CORE Metadata, citation and similar papers at core.ac.uk Provided by Keele Research Repository Tutorial in Biostatistics Received: 11 March 2016, Accepted: 13 September 2016 Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/sim.7141 Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ Danielle L. Burke*† Joie Ensor and Richard D. Riley Meta-analysis using individual participant data (IPD) obtains and synthesises the raw, participant-level data from a set of relevant studies. The IPD approach is becoming an increasingly popular tool as an alternative to traditional aggregate data meta-analysis, especially as it avoids reliance on published results and provides an opportunity to investigate individual-level interactions, such as treatment-effect modifiers. There are two statistical approaches for conducting an IPD meta-analysis: one-stage and two-stage. The one-stage approach analyses the IPD from all studies simultaneously, for example, in a hierarchical regression model with random effects. The two-stage approach derives aggregate data (such as effect estimates) in each study separately and then combines these in a traditional meta-analysis model. There have been numerous comparisons of the one-stage and two-stage approaches via theoretical consideration, simulation and empirical examples, yet there remains confusion regarding when each approach should be adopted, and indeed why they may differ. In this tutorial paper, we outline the key statistical methods for one-stage and two-stage IPD meta-analyses, and provide 10 key reasons why they may produce different summary results. We explain that most differences arise because of different modelling assumptions, rather than the choice of one-stage or two-stage itself. -
Studies of Staphylococcal Infections. I. Virulence of Staphy- Lococci and Characteristics of Infections in Embryonated Eggs * WILLIAM R
Journal of Clinical Investigation Vol. 43, No. 11, 1964 Studies of Staphylococcal Infections. I. Virulence of Staphy- lococci and Characteristics of Infections in Embryonated Eggs * WILLIAM R. MCCABE t (From the Research Laboratory, West Side Veterans Administration Hospital, and the Department of Medicine, Research and Educational Hospitals, University of Illinois College of Medicine, Chicago, Ill.) Many of the determinants of the pathogenesis niques still require relatively large numbers of and course of staphylococcal infections remain staphylococci to produce infection (19). Fatal imprecisely defined (1, 2) despite their increas- systemic infections have been equally difficult to ing importance (3-10). Experimental infections produce in animals and have necessitated the in- in suitable laboratory animals have been of con- jection of 107 to 109 bacteria (20-23). A few siderable assistance in clarifying the role of host strains of staphylococci have been found that are defense mechanisms and specific bacterial virulence capable of producing lethal systemic infections factors with a variety of other infectious agents. with inocula of from 102 to 103 bacteria (24) and A sensitive experimental model would be of value have excited considerable interest (25-27). The in defining the importance of these factors in virulence of these strains apparently results from staphylococcal infections, but both humans and an unusual antigenic variation (27, 28) which, the usual laboratory animals are relatively re- despite its interest, is of doubtful significance in sistant. Extremely large numbers of staphylo- human staphylococcal infection, since such strains cocci are required to produce either local or sys- have been isolated only rarely from clinical in- temic infections experimentally. -
Learning to Personalize Medicine from Aggregate Data
medRxiv preprint doi: https://doi.org/10.1101/2020.07.07.20148205; this version posted July 8, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license . Learning to Personalize Medicine from Aggregate Data Rich Colbaugh Kristin Glass Volv Global Lausanne, Switzerland Abstract There is great interest in personalized medicine, in which treatment is tailored to the individual characteristics of pa- tients. Achieving the objectives of precision healthcare will require clinically-grounded, evidence-based approaches, which in turn demands rigorous, scalable predictive analytics. Standard strategies for deriving prediction models for medicine involve acquiring ‘training’ data for large numbers of patients, labeling each patient according to the out- come of interest, and then using the labeled examples to learn to predict the outcome for new patients. Unfortunate- ly, labeling individuals is time-consuming and expertise-intensive in medical applications and thus represents a ma- jor impediment to practical personalized medicine. We overcome this obstacle with a novel machine learning algo- rithm that enables individual-level prediction models to be induced from aggregate-level labeled data, which is read- ily-available in many health domains. The utility of the proposed learning methodology is demonstrated by: i.) lev- eraging US county-level mental health statistics to create a screening tool which detects individuals suffering from depression based upon their Twitter activity; ii.) designing a decision-support system that exploits aggregate clinical trials data on multiple sclerosis (MS) treatment to predict which therapy would work best for the presenting patient; iii.) employing group-level clinical trials data to induce a model able to find those MS patients likely to be helped by an experimental therapy. -
Using Real-World Evidence to Accelerate Safe and Effective Cures Advancing Medical Innovation for a Healthier America June 2016 Leadership Senator William H
Using Real-World Evidence to Accelerate Safe and Effective Cures Advancing Medical Innovation for a Healthier America June 2016 Leadership Senator William H. Frist, MD Former U.S. Senate Majority Leader Chair, FDA: Advancing Medical Innovation Bipartisan Policy Center Representative Bart Gordon Former Member, U.S. House of Representatives Chair, FDA: Advancing Medical Innovation Bipartisan Policy Center Advisory Committee Marc M. Boutin, JD Chief Executive Officer National Health Council Mark McClellan, MD, PhD Director, Robert J. Margolis Center for Health Policy Duke University Patrick Soon-Shiong, MD Chairman and Chief Executive Officer Institute for Advanced Health Andrew von Eschenbach, MD President Samaritan Health Initiatives 1 Sta G. William Hoagland Ann Gordon Senior Vice President Writer Bipartisan Policy Center Michael Ibara, PharmD Janet M. Marchibroda Independent Consultant Director, Health Innovation Initiative and Executive Director, CEO Council on Health and Innovation Bipartisan Policy Center Tim Swope Senior Policy Analyst Bipartisan Policy Center Sam Watters Administrative Assistant Bipartisan Policy Center 2 FDA: ADVANCING MEDICAL INNOVATION EFFORT The Bipartisan Policy Center’s initiative, FDA: Advancing Medical Innovation, is developing viable policy options to advance medical innovation and reduce the time and cost associated with the discovery, development, and delivery of safe and effective drugs and devices for patients in the United States. Key areas of focus include the following: Improving the medical product development process; Increasing regulatory clarity; Strengthening the Food and Drug Administration’s (FDA) ability to carry out its mission; Using information technology to improve health and health care; and Increasing investment in medical products to address unmet and public health needs. -
Good Statistical Practices for Contemporary Meta-Analysis: Examples Based on a Systematic Review on COVID-19 in Pregnancy
Review Good Statistical Practices for Contemporary Meta-Analysis: Examples Based on a Systematic Review on COVID-19 in Pregnancy Yuxi Zhao and Lifeng Lin * Department of Statistics, Florida State University, Tallahassee, FL 32306, USA; [email protected] * Correspondence: [email protected] Abstract: Systematic reviews and meta-analyses have been increasingly used to pool research find- ings from multiple studies in medical sciences. The reliability of the synthesized evidence depends highly on the methodological quality of a systematic review and meta-analysis. In recent years, several tools have been developed to guide the reporting and evidence appraisal of systematic reviews and meta-analyses, and much statistical effort has been paid to improve their methodological quality. Nevertheless, many contemporary meta-analyses continue to employ conventional statis- tical methods, which may be suboptimal compared with several alternative methods available in the evidence synthesis literature. Based on a recent systematic review on COVID-19 in pregnancy, this article provides an overview of select good practices for performing meta-analyses from sta- tistical perspectives. Specifically, we suggest meta-analysts (1) providing sufficient information of included studies, (2) providing information for reproducibility of meta-analyses, (3) using appro- priate terminologies, (4) double-checking presented results, (5) considering alternative estimators of between-study variance, (6) considering alternative confidence intervals, (7) reporting predic- Citation: Zhao, Y.; Lin, L. Good tion intervals, (8) assessing small-study effects whenever possible, and (9) considering one-stage Statistical Practices for Contemporary methods. We use worked examples to illustrate these good practices. Relevant statistical code Meta-Analysis: Examples Based on a is also provided. -
Developing a Protocol
FACILITATOR/MENTOR GUIDE Developing a Protocol Created: 2013 Developing a Protocol. Atlanta, GA: Centers for Disease Control and Prevention (CDC), 2013. DEVELOPING A PROTOCOL Table of Contents LEARNING OBJECTIVES ................................................................................................... 3 ESTIMATED COMPLETION TIME ........................................................................................ 3 TARGET AUDIENCE ......................................................................................................... 3 PRE-WORK AND PREREQUISITES ..................................................................................... 3 MATERIALS .................................................................................................................... 4 OPTIONS FOR FACILITATING THIS TRAINING ....................................................................... 4 ICON GLOSSARY ............................................................................................................ 5 ACKNOWLEDGEMENTS .................................................................................................... 5 FACILITATOR RESPONSIBILITIES ...................................................................................... 7 SECTIONS 1 & 2: INTRODUCTION AND OVERVIEW .............................................................. 8 SECTION 3: WRITING A PROPOSAL OR CONCEPT PAPER ..................................................... 9 SECTION 4: WRITING A DRAFT OF THE PROTOCOL ...........................................................