The Prevention and Treatment of Missing Data in Clinical Trials
NATIONAL RESEARCH COUNCIL
OF THE NATIONAL ACADEMIES
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NOTICE: The project that is the subject of this report was approved by the Governing Board of the National Research Council, whose members are drawn from the councils of the National Academy of Sciences, the National Academy of Engineering, and the Institute of Medicine. The members of the committee responsible for the report were chosen for their special competences and with regard for appropriate balance.
This study was supported by contract number HHSF223200810020I, TO #1 between the National Academy of Sciences and the U.S. Food and Drug Administration. Support for the work of the Committee on National Statistics is provided by a consortium of federal agencies through a grant from the National Science Foundation (award number SES-0453930). Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the organizations or agencies that provided support for this project.
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Suggested citation: National Research Council. (2010). The Prevention and Treatment of Missing Data in Clinical Trials. Panel on Handling Missing Data in Clinical Trials. Committee on National Statistics, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.
THE NATIONAL ACADEMIES
Advisers to the Nation on Science, Engineering, and Medicine
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The National Research Council was organized by the National Academy of Sciences in 1916 to associate the broad community of science and technology with the Academy’s purposes of furthering knowledge and advising the federal government. Functioning in accordance with general policies determined by the Academy, the Council has become the principal operating agency of both the National Academy of Sciences and the National Academy of Engineering in providing services to the government, the public, and the scientific and engineering communities. The Council is administered jointly by both Academies and the Institute of Medicine. Dr. Ralph J. Cicerone and Dr. Charles M. Vest are chair and vice chair, respectively, of the National Research Council.
PANEL ON HANDLING MISSING DATA IN CLINICAL TRIALS
RODERICK J.A. LITTLE (Chair),
Department of Biostatistics, University of Michigan, Ann Arbor
Department of Mathematics and Statistics, Boston University
Department of Epidemiology, Johns Hopkins University
SCOTT S. EMERSON,
Department of Biostatistics, University of Washington, Seattle
JOHN T. FARRAR,
Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine
Department of Biostatistics, Johns Hopkins University
JOSEPH W. HOGAN,
Center for Statistical Sciences, Program in Public Health, Brown University
International Institute for Biostatistics and Statistical Bioinformatics, Universiteit Hasselt and Katholieke Universiteit Leuven, Belgium
SUSAN A. MURPHY,
Department of Statistics, University of Michigan, Ann Arbor
JAMES D. NEATON,
School of Public Health, University of Minnesota
Departmento de Economia, Universidad Torcuato Di Tella, Buenos Aires, Argentina
Department of Biostatistics, Johns Hopkins University
WEICHUNG (JOE) SHIH,
Department of Biostatistics, University of Medicine and Dentistry of New Jersey School of Public Health
JAY P. SIEGEL,
Johnson & Johnson, Radnor, Pennsylvania
Department of Statistics, University of California, Irvine
MICHAEL L. COHEN, Study Director
AGNES GASKIN, Administrative Assistant
COMMITTEE ON NATIONAL STATISTICS 2009-2010
WILLIAM F. EDDY (Chair),
Department of Statistics, Carnegie Mellon University
KATHARINE G. ABRAHAM,
Department of Economics and Joint Program in Survey Methodology, University of Maryland
Department of Statistics, Iowa State University
Phase Forward, Inc., Waltham, Massachusetts
Department of Economics, University of Maryland
V. JOSEPH HOTZ,
Department of Economics, Duke University
Department of Statistics, Indiana University
George R. Brown School of Engineering, Rice University
Heller School for Social Policy and Management, Brandeis University
Department of Sociology, Princeton University
SALLY C. MORTON,
Biostatistics Department, University of Pittsburgh
Division of Health Policy Research and Education, Harvard University
SAMUEL H. PRESTON,
Population Studies Center, University of Pennsylvania
Department of Statistics, University of California, Irvine
Joint Program in Survey Methodology, University of Maryland, and Survey Research Center, University of Michigan
Department of Health Care Policy, Harvard Medical School
CONSTANCE F. CITRO, Director
I would like to express appreciation to the following individuals who provided valuable assistance in producing this report. Particular thanks to Robert O’Neill and Tom Permutt at the U.S. Food and Drug Administration (FDA) for initiating the project, providing excellent presentations at the first meeting of the panel, and continuing support in providing timely information. We also thank Frances Gipson, FDA’s technical representative, who assisted greatly in arranging the panel’s first meeting at FDA and acquiring FDA documents throughout the study. The following FDA staff members presented invaluable information to the panel at its first meeting: Sharon Hertz, Henry Hsu, Robert O’Neill, Tom Permutt, Bruce Schneider, Norman Stockbridge, Robert Temple, Steve Winitsky, Lilly Yue, and Bram Zuckerman. At the panel’s workshop on September 9, 2009, we benefited very much from the presentations of the following knowledgeable experts: Abdel Babiker, Don Berry, James Carpenter, Christy Chuang-Stein, Susan Ellenberg, Thomas Fleming, Dean Follmann, Joseph Ibrahim, John Lachin, Andrew Leon, Craig Mallinckrodt, Devan Mehrotra, Jerry Menikoff, David Ohlssen, and Edward Vonesh.
I am particularly indebted to the members of the Panel on Handling Missing Data in Clinical Trials. They worked extremely hard and were always open to other perspectives on the complicated questions posed by missing data in clinical trials. It was a real pleasure collaborating with all of them on this project.
I also thank the staff, especially our study director, Michael L. Cohen, who converted the musings of the panel into intelligible prose, arbitrated differences in opinion with good humor, and worked very hard on writing
and improving the report. I also thank Agnes Gaskin, who performed her usual exemplary service on all administrative matters. Eugenia Grohman provided extremely useful advice on presenting the material in this report, along with careful technical editing.
This report has been reviewed in draft form by individuals chosen for their diverse perspectives and technical expertise, in accordance with procedures approved by the Report Review Committee of the National Research Council (NRC). The purpose of this independent review is to provide candid and critical comments that will assist the institution in making its published report as sound as possible and to ensure that the report meets institutional standards for objectivity, evidence, and responsiveness to the study charge. The review comments and draft manuscript remain confidential to protect the integrity of the deliberative process. We wish to thank the following individuals for their review of this report: Christy J. Chuang-Stein, Statistical Research and Consulting Center, Pfizer, Inc.; Shein-Chung Chow, Biostatistics and Bioinformatics, Duke University School of Medicine; Susan S. Ellenberg, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine; Thomas Fleming, Department of Biostatistics, School of Public Health and Community Medicine, University of Washington; Yulei He, Department of Health Care Policy, Harvard Medical School; Robin Henderson, School of Mathematics and Statistics, University of Newcastle; Devan V. Mehrotra, Clinical Biostatistics, Merck Research Laboratories; Donald B. Rubin, Department of Statistics, Harvard University; and Steve Snapinn, Global Biostatistics and Epidemiology, Amgen, Inc.
Although the reviewers listed above have provided many constructive comments and suggestions, they were not asked to endorse the conclusions or recommendations nor did they see the final draft of the report before its release. The review of this report was overseen by Gilbert S. Omenn, Center for Computational Medicine and Biology, University of Michigan Medical School and Joel B. Greenhouse, Department of Statistics, Carnegie Mellon University. Appointed by the NRC’s Report Review Committee, they were responsible for making certain that an independent examination of this report was carried out in accordance with institutional procedures and that all review comments were carefully considered. Responsibility for the final content of this report rests entirely with the authoring panel and the institution.
Finally, the panel recognizes the many federal agencies that support the Committee on National Statistics directly and through a grant from the National Science Foundation. Without their support and their commitment to improving the national statistical system, the work that is the basis of this report would not have been possible.
Roderick J.A. Little, Chair
Panel on Handling Missing Data in Clinical Trials
Active Control: In situations where the experimental therapy is to be an alternative to some existing standard of care, ethical or logistical constraints may dictate that the experimental therapy be tested against that “active” therapy that has previously shown evidence in an adequate and well-controlled clinical trial as an effective therapy. The ideal would be that patients would be randomized in a double blind fashion to either the experimental therapy or the active control, though the logistical difficulties of producing placebos for each treatment sometimes precludes a double blind study structure.
Contrasted with Placebo control: In situations where the experimental therapy is to be added to some existing standard of care, it is best to randomize subjects in a double-blind fashion to either the experimental therapy or a placebo control that is similar in appearance.
Common Analysis Estimands:
Per Protocol: In a per-protocol analysis, the analysis may be restricted to participants who had some minimum exposure to the study treatments, who met inclusion/exclusion criteria, and for whom there were no major protocol violations. The specific reasons for excluding randomized participants from a per-protocol analysis should be specified in advance of unblinding the data.
Intention to Treat: In an intention-to-treat analysis, all participants that satisfy the exclusion criteria are analyzed as belonging to the treatment arms to which they were randomized, regardless of whether they received or adhered to the allocated intervention for the full duration of the trial.
As Treated: In an as-treated analysis, the participants are grouped according to the treatment regimen that they received, which is not necessarily the treatment to which they were initially assigned.
Complier-Averaged Causal Effect (CACE): A parameter used to estimate the average effect of the treatment in the subpopulation of individuals that could remain on study or control treatments for the full length of the study.
Dropout: Treatment dropout is the result of a participant in a clinical trial discontinuing treatment; analysis dropout is the result of the failure to measure the outcome of interest for a trial participant.
Enrichment: Treatments are often only tolerated by or are only efficacious for a subset of the population. To avoid problems associated with treatment discontinuation, and to test a treatment on the subpopulation that can most benefit from it, it can be advantageous to determine whether a potential trial participant is a member of the subpopulation that can either tolerate or benefit from a treatment. This pretesting and selection of participants for trial participation prior to randomization into the treatment and control arms is called enrichment, and can include (1) selecting people with potentially responsive disease, (2) selecting people likely to have an event whose occurrence is the outcome of interest, (3) selecting people likely to adhere to the study protocol, and (4) selecting people who show an early response to the test drug.
Last Observation Carried Forward (LOCF): A single imputation technique that imputes the last measured outcome value for participants who either drop out of a clinical trial or for whom the final outcome measurement is missing. Baseline Observation Carried Forward (BOCF): A single imputation technique that imputes the baseline outcome value for participants who either drop out of a clinical trial or for whom the final outcome measurement is missing.
Noninferiority vs. Superiority Trials: A noninferiority clinical trial compares the experimental therapy to some active control with the aim of establishing that the experimental therapy is not unacceptably worse than an active control that showed evidence as an effective treatment in previously conducted adequate and well-controlled clinical trials. A noninferiority trial is often conducted in a setting in which (1) the experimental therapy, if approved, would be used in place of some existing treatment that was previously found to show evidence of effect, (2) it is not ethical or feasible to conduct a placebo controlled trial, (3) it would be clinically appropri-
ate to approve a new treatment that is only approximately equivalent to a current standard therapy with respect to some primary clinical outcome, and (4) the new experimental therapy might have other advantages such as a better adverse event profile, ease of administration, etc. Rather than rejecting a null hypothesis of equality between the experimental therapy and control treatment, a noninferiority clinical trial is designed to reject a null hypothesis that the experimental therapy is some specified amount (“the noninferiority margin”) worse than the active control. Selection of the noninferiority margin must consider such issues as the magnitude of effect estimated for the active control in prior clinical trials, any bias that might be present in those previous trials relative to the effect of the active control in the population and setting used in the noninferiority trials, the proportion of effect that must be preserved for any approved treatment, etc. A Superiority clinical trial is one in which an experimental therapy would be approved only if that therapy showed statistically credible evidence of superiority over a clinically relevant control therapy in an adequate and well-controlled clinical trial. The superiority trial is designed to reject a null hypothesis of equality between the experimental and control therapies.
Randomized Withdrawal: A clinical trial design in which all participants are initially provided the study treatment. Then, participants that have a positive response to the study treatment are randomly selected either to remain on the study treatment or to be switched to a placebo. Positive indications are when those that continue on study treatment are observed to have better outcomes than those who are switched to the placebo.
Run-In Design: Similar to an enrichment design, a run-in design is a design incorporating an initial period in which a subset of the participants are selected given indications as to their likelihood of compliance or the magnitude of their placebo effect. The key difference between a run-in design and an enrichment design is that the active treatment is not used to identify the subset of participants for study.
Titration: In opposition to a fixed dose protocol, titration is the adjustment of dosage to increase the treatment benefit and tolerability for participants during the course of a clinical trial.
Washout: (Placebo) washout is a period of time without active treatment that is scheduled before the beginning of use of study treatment, often used to eliminate any residual effects that might remain after a previous period on active treatment.