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      The future is in the numbers: the power of predictive analysis in the biomedical educational environment

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          Abstract

          Biomedical programs have a potential treasure trove of data they can mine to assist admissions committees in identification of students who are likely to do well and help educational committees in the identification of students who are likely to do poorly on standardized national exams and who may need remediation. In this article, we provide a step-by-step approach that schools can utilize to generate data that are useful when predicting the future performance of current students in any given program. We discuss the use of linear regression analysis as the means of generating that data and highlight some of the limitations. Finally, we lament on how the combination of these institution-specific data sets are not being fully utilized at the national level where these data could greatly assist programs at large.

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          The predictive validity of the MCAT for medical school performance and medical board licensing examinations: a meta-analysis of the published research.

          Elizabeth Oddone Paolucci, Claudio Violato, Tyrone Donnon (2006)
          To conduct a meta-analysis of published studies to determine the predictive validity of the MCAT on medical school performance and medical board licensing examinations. The authors included all peer-reviewed published studies reporting empirical data on the relationship between MCAT scores and medical school performance or medical board licensing exam measures. Moderator variables, participant characteristics, and medical school performance/medical board licensing exam measures were extracted and reviewed separately by three reviewers using a standardized protocol. Medical school performance measures from 11 studies and medical board licensing examinations from 18 studies, for a total of 23 studies, were selected. A random-effects model meta-analysis of weighted effects sizes (r) resulted in (1) a predictive validity coefficient for the MCAT in the preclinical years of r = 0.39 (95% confidence interval [CI], 0.21-0.54) and on the USMLE Step 1 of r = 0.60 (95% CI, 0.50-0.67); and (2) the biological sciences subtest as the best predictor of medical school performance in the preclinical years (r = 0.32 95% CI, 0.21-0.42) and on the USMLE Step 1 (r = 0.48 95% CI, 0.41-0.54). The predictive validity of the MCAT ranges from small to medium for both medical school performance and medical board licensing exam measures. The medical profession is challenged to develop screening and selection criteria with improved validity that can supplement the MCAT as an important criterion for admission to medical schools.
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            The predictive validity of the MCAT exam in relation to academic performance through medical school: a national cohort study of 2001-2004 matriculants.

            Xiaohui Zhao, D L Dunleavy, Michael K Dowd … (2013)
            Most research examining the predictive validity of the Medical College Admission Test (MCAT) has focused on the relationship between MCAT scores and scores on the United States Medical Licensing Examination Step exams. This study examined whether MCAT scores predict students' unimpeded progress toward graduation (UP), which the authors defined as not withdrawing or being dismissed for academic reasons, graduating within five years of matriculation, and passing the Step 1, Step 2 Clinical Knowledge, and Step 2 Clinical Skills exams on the first attempt. Students who matriculated during 2001-2004 at 119 U.S. medical schools were included in the analyses. Logistic regression analyses were used to estimate the relationships between UP and MCAT total scores alone, undergraduate grade point averages (UGPAs) alone, and UGPAs and MCAT total scores together. All analyses were conducted at the school level and were considered together to evaluate relationships across schools. The majority of matriculants experienced UP. Together, UGPAs and MCAT total scores predicted UP well. MCAT total scores alone were a better predictor than UGPAs alone. Relationships were similar across schools; however, there was more variability across schools in the relationship between UP and UGPAs than between UP and MCAT total scores. The combination of UGPAs and MCAT total scores performs well as a predictor of UP. Both UGPAs and MCAT total scores are strong predictors of academic performance in medical school through graduation, not just the first two years. Further, these relationships generalize across medical schools.
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              A validity generalization perspective on the ability of undergraduate GPA and the medical college admission test to predict important outcomes.

              Clarence Kreiter, Yuka Kreiter (2006)
              Research on the validity of using the Medical College Admissions Test (MCAT) and undergraduate grade point average (GPA) for selection to medical school has produced conflicting interpretations. There is debate regarding the degree to which coefficients diminish over the course of educational and professional outcomes and disagreement over whether these two measures can predict clinical performance. To summarize and interpret the validity literature using validity generalization techniques that account for measurement error. Validity generalization techniques were used to summarize MCAT and undergraduate GPA validity research. A meta-analysis was performed to evaluate validity coefficients for two outcome domains across educational and professional attainment levels. The ability to predict academic performance decreases slightly for written tests. For clinical performance assessments, existing research does not allow an assessment of change across training levels. However, relevant studies suggest that MCAT and undergraduate GPA have a positive predictive relationship with clinical skills. A validity generalization perspective of the literature supports the use of MCAT and undergraduate GPA for selection to medical school.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Med Educ Online
                Journal ID (iso-abbrev): Med Educ Online
                Journal ID (publisher-id): MEO
                Title: Medical Education Online
                Publisher: Co-Action Publishing
                ISSN (Electronic): 1087-2981
                Publication date (Electronic): 01 July 2016
                Publication date Collection: 2016
                Volume: 21
                Electronic Location Identifier: 10.3402/meo.v21.32516
                Affiliations
                Office of Medical Education, Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, USA
                Author notes
                [* ]Correspondence to: Charles A. Gullo, Office of Medical Education, Joan C. Edwards School of Medicine, Marshall University, 1600 Medical Center Drive, Suite 3411, Huntington, WV 25701, USA, Email: gullo@ 123456marshall.edu

                Responsible Editor: Allison Vanderbilt, University of Toledo, USA.

                Article
                Publisher ID: 32516
                DOI: 10.3402/meo.v21.32516
                PMC ID: 4931024
                PubMed ID: 27374246
                SO-VID: 74d9d10b-ab4c-4032-bfe4-b5999302442e
                Copyright © © 2016 Charles A. Gullo
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.

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                Categories
                Subject: Short Communication

                ScienceOpen disciplines: Education
                Keywords: big data,mlr analysis,prediction analysis,national standardized examinations
                Data availability:
                ScienceOpen disciplines: Education
                Keywords: big data, mlr analysis, prediction analysis, national standardized examinations

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