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      Individualized Prediction of Changes in 6-Minute Walk Distance for Patients with Duchenne Muscular Dystrophy

      research-article
      1 , * , 1 , 2 , 3 , 2 , 2 , Collaborative Trajectory Analysis Project (cTAP)
      PLoS ONE
      Public Library of Science

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          Abstract

          Background

          Deficits in ambulatory function progress at heterogeneous rates among individuals with Duchenne muscular dystrophy (DMD). The resulting inherent variability in ambulatory outcomes has complicated the design of drug efficacy trials and clouded the interpretation of trial results. We developed a prediction model for 1-year change in the six minute walk distance (6MWD) among DMD patients, and compared its predictive value to that of commonly used prognostic factors (age, baseline 6MWD, and steroid use).

          Methods

          Natural history data were collected from DMD patients at routine follow up visits approximately every 6 months over the course of 2–5 years. Assessments included ambulatory function and steroid use. The annualized change in 6MWD (Δ6MWD) was studied between all pairs of visits separated by 8–16 months. Prediction models were developed using multivariable regression for repeated measures, and evaluated using cross-validation.

          Results

          Among n = 191 follow-up intervals (n = 39 boys), mean starting age was 9.4 years, mean starting 6MWD was 351.8 meters, and 75% had received steroids for at least one year. Over the subsequent 8–16 months, mean Δ6MWD was -37.0 meters with a standard deviation (SD) of 93.7 meters. Predictions based on a composite of age, baseline 6MWD, and steroid use explained 28% of variation in Δ6MWD (R 2 = 0.28, residual SD = 79.4 meters). A broadened prognostic model, adding timed 10-meter walk/run, 4-stair climb, and rise from supine, as well as height and weight, significantly improved prediction, explaining 59% of variation in Δ6MWD after cross-validation (R 2 = 0.59, residual SD = 59.7 meters).

          Conclusions

          A prognostic model incorporating timed function tests significantly improved prediction of 1-year changes in 6MWD. Explained variation was more than doubled compared to predictions based only on age, baseline 6MWD, and steroid use. There is significant potential for composite prognostic models to inform DMD clinical trials and clinical practice.

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          Most cited references26

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          The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies

          Background Adjustment for prognostic covariates can lead to increased power in the analysis of randomized trials. However, adjusted analyses are not often performed in practice. Methods We used simulation to examine the impact of covariate adjustment on 12 outcomes from 8 studies across a range of therapeutic areas. We assessed (1) how large an increase in power can be expected in practice; and (2) the impact of adjustment for covariates that are not prognostic. Results Adjustment for known prognostic covariates led to large increases in power for most outcomes. When power was set to 80% based on an unadjusted analysis, covariate adjustment led to a median increase in power to 92.6% across the 12 outcomes (range 80.6 to 99.4%). Power was increased to over 85% for 8 of 12 outcomes, and to over 95% for 5 of 12 outcomes. Conversely, the largest decrease in power from adjustment for covariates that were not prognostic was from 80% to 78.5%. Conclusions Adjustment for known prognostic covariates can lead to substantial increases in power, and should be routinely incorporated into the analysis of randomized trials. The potential benefits of adjusting for a small number of possibly prognostic covariates in trials with moderate or large sample sizes far outweigh the risks of doing so, and so should also be considered.
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            Stratified randomization for clinical trials.

            Trialists argue about the usefulness of stratified randomization. For investigators designing trials and readers who use them, the argument has created uncertainty regarding the importance of stratification. In this paper, we review stratified randomization to summarize its purpose, indications, accomplishments, and alternatives. In order to identify research papers, we performed a Medline search for 1966-1997. The search yielded 33 articles that included original research on stratification or included stratification as the major focus. Additional resources included textbooks. Stratified randomization prevents imbalance between treatment groups for known factors that influence prognosis or treatment responsiveness. As a result, stratification may prevent type I error and improve power for small trials (<400 patients), but only when the stratification factors have a large effect on prognosis. Stratification has an important effect on sample size for active control equivalence trials, but not for superiority trials. Theoretical benefits include facilitation of subgroup analysis and interim analysis. The maximum desirable number of strata is unknown, but experts argue for keeping it small. Stratified randomization is important only for small trials in which treatment outcome may be affected by known clinical factors that have a large effect on prognosis, large trials when interim analyses are planned with small numbers of patients, and trials designed to show the equivalence of two therapies. Once the decision to stratify is made, investigators need to chose factors carefully and account for them in the analysis.
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              Measurement in neurological rehabilitation.

              The measurement of impairment and disability can improve patient care and is now essential in clinical audit. Practical, useful measures are slowly being developed, both for use in specific diseases and for more general use. This review discusses both new measures and new work on more well-established measures.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                13 October 2016
                2016
                : 11
                : 10
                : e0164684
                Affiliations
                [1 ]University Hospitals Leuven, Child Neurology, Leuven, Belgium
                [2 ]Analysis Group, Inc., 111 Huntington Ave, 14 th floor, Boston, Massachusetts, United States of America
                [3 ]The Trajectory Analysis Project (TAP) Collaboration, One Broadway, 14 th floor, Cambridge, Massachusetts, United States of America
                Cincinnati Children's Hospital Medical Center, UNITED STATES
                Author notes

                Competing Interests: JES, ES and JS are employees of Analysis Group, Inc., which received funding via the Collaborative Trajectory Anlaysis Project (cTAP) for this research. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

                • Conceptualization: NG JaS.

                • Data curation: NG MvH.

                • Formal analysis: JaS ES JiS.

                • Funding acquisition: NG JaS.

                • Methodology: JaS ES JiS.

                • Project administration: JaS.

                • Writing – original draft: NG JaS ES JiS.

                • Writing – review & editing: NG JaS ES JiS.

                ¶ Membership of the cTAP is provided in the Acknowledgments.

                Article
                PONE-D-16-23083
                10.1371/journal.pone.0164684
                5063281
                27737016
                d1d1820d-4e23-44f6-92e1-db8771c1486b
                © 2016 Goemans et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 8 June 2016
                : 29 September 2016
                Page count
                Figures: 3, Tables: 3, Pages: 15
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100004319, Pfizer;
                Funded by: Shire Pharmaceuticals
                Funded by: funder-id http://dx.doi.org/10.13039/100008484, BioMarin Pharmaceutical;
                Funded by: PTC Therapeutics
                Funded by: Sarepta Therapeutics
                Funded by: Fonds Spierzieke Kinderen
                Award Recipient :
                This study was co-funded by Biomarin, Pfizer, PTC Therapeutics, Sarepta Therapeutics, and Shire as part of the Collaborative Trajectory Anlaysis Project (cTAP). MvdH received funding for physical function testing from Fonds Spierzieke Kinderen. The study sponsors were involved in the research design and review of the manuscript.
                Categories
                Research Article
                Physical Sciences
                Chemistry
                Chemical Compounds
                Organic Compounds
                Steroids
                Physical Sciences
                Chemistry
                Organic Chemistry
                Organic Compounds
                Steroids
                Biology and Life Sciences
                Biomechanics
                Biological Locomotion
                Walking
                Biology and Life Sciences
                Physiology
                Biological Locomotion
                Walking
                Medicine and Health Sciences
                Physiology
                Biological Locomotion
                Walking
                Medicine and Health Sciences
                Diagnostic Medicine
                Prognosis
                Medicine and Health Sciences
                Neurology
                Muscular Dystrophies
                Duchenne Muscular Dystrophy
                Medicine and Health Sciences
                Clinical Genetics
                X-Linked Traits
                Duchenne Muscular Dystrophy
                Biology and Life Sciences
                Genetics
                Heredity
                Genetic Linkage
                Sex Linkage
                X-Linked Traits
                Duchenne Muscular Dystrophy
                Medicine and Health Sciences
                Epidemiology
                Natural History of Disease
                Medicine and Health Sciences
                Clinical Medicine
                Clinical Trials
                Medicine and Health Sciences
                Pharmacology
                Drug Research and Development
                Clinical Trials
                Research and Analysis Methods
                Clinical Trials
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Forecasting
                Biology and Life Sciences
                Biochemistry
                Proteins
                Cytoskeletal Proteins
                Dystrophin
                Custom metadata
                All relevant data are within the paper and its Supporting Information files. Individual data are available via data use agreement with UZ Leuven. Please contact ctc@ 123456uzleuven.be .

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