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      Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review

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          Abstract

          Missing values are common in health research and omitting participants with missing data often leads to loss of statistical power, biased estimates and, consequently, inaccurate inferences. We critically reviewed the challenges posed by missing data in medical research and approaches to address them. To achieve this more efficiently, these issues were analyzed and illustrated through a systematic review on the reporting of missing data and imputation methods (prediction of missing values through relationships within and between variables) undertaken in risk prediction studies of undiagnosed diabetes. Prevalent diabetes risk models were selected based on a recent comprehensive systematic review, supplemented by an updated search of English-language studies published between 1997 and 2014. Reporting of missing data has been limited in studies of prevalent diabetes prediction. Of the 48 articles identified, 62.5% ( n = 30) did not report any information on missing data or handling techniques. In 21 (43.8%) studies, researchers opted out of imputation, completing case-wise deletion of participants missing any predictor values. Although imputation methods are encouraged to handle missing data and ensure the accuracy of inferences, this has seldom been the case in studies of diabetes risk prediction. Hence, we elaborated on the various types and patterns of missing data, the limitations of case-wise deletion and state-of the-art methods of imputations and their challenges. This review highlights the inexperience or disregard of investigators of the effect of missing data in risk prediction research. Formal guidelines may enhance the reporting and appropriate handling of missing data in scientific journals.

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          Multivariate Data Analysis

          For graduate courses in Marketing Research, Research Design and Data Analysis. For the non-statistician, this applications-oriented introduction to multivariate analysis reduces the amount of statistical notation and terminology used while focusing on the fundamental concepts that affect the use of specific techniques.
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            Bootstrap Methods for Developing Predictive Models

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              Multivariable prognostic analysis in traumatic brain injury: results from the IMPACT study.

              We studied the prognostic value of a wide range of conventional and novel prognostic factors on admission after traumatic brain injury (TBI) using both univariate and multivariable analysis. The outcome measure was Glasgow Outcome Scale at 6 months after injury. Individual patient data were available on a cohort of 8686 patients drawn from eight randomized controlled trials and three observational studies. The most powerful independent prognostic variables were age, Glasgow Coma Scale (GCS) motor score, pupil response, and computerized tomography (CT) characteristics, including the Marshall CT classification and traumatic subarachnoid hemorrhage. Prothrombin time was also identified as a powerful independent prognostic factor, but it was only available for a limited number of patients coming from three of the relevant studies. Other important prognostic factors included hypotension, hypoxia, the eye and verbal components of the GCS, glucose, platelets, and hemoglobin. These results on prognostic factors will underpin future work on the IMPACT project, which is focused on the development of novel approaches to the design and analysis of clinical trials in TBI. In addition, the results provide pointers to future research, including further analysis of the prognostic value of prothrombin time, and the evaluation of the clinical impact of intervening aggressively to correct abnormalities in hemoglobin, glucose, and coagulation.
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                Author and article information

                Contributors
                kmasconi@gmail.com
                tandimatsha@gmail.com
                echouffotcheugui@yahoo.com
                erasmusrajiv@gmail.com
                apkengne@yahoo.com
                Journal
                EPMA J
                EPMA J
                The EPMA Journal
                BioMed Central (London )
                1878-5077
                1878-5085
                11 March 2015
                11 March 2015
                2015
                : 6
                : 1
                : 7
                Affiliations
                [ ]Division of Chemical Pathology, Faculty of Health Sciences, National Health Laboratory Service (NHLS) and University of Stellenbosch, Cape Town, South Africa
                [ ]Non-Communicable Diseases Research Unit, South African Medical Research Council, PO Box 19070, , Tygerberg, 7505 Cape Town, South Africa
                [ ]Department of Biomedical Technology, Faculty of Health and Wellness Sciences, Cape Peninsula University of Technology, Cape Town, South Africa
                [ ]Hubert Department of Public Health, Rollins School of Public Health, Emory University, Atlanta, GA USA
                [ ]Department of Medicine, MedStar Health System, Baltimore, MD USA
                [ ]Department of Medicine, University of Cape Town, Cape Town, South Africa
                Article
                28
                10.1186/s13167-015-0028-0
                4380106
                25628770
                cd4af219-aa93-4621-8c64-ca1e64451dce
                © Masconi et al.; licensee BioMed Central. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 3 October 2014
                : 7 February 2015
                Categories
                Review
                Custom metadata
                © The Author(s) 2015

                Molecular medicine
                predictive,preventive and personalized medicine,diabetes mellitus,risk,guidelines,patterns,screening,modeling,patient stratification

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