Dataset Information

Augmented backward elimination: a pragmatic and purposeful way to develop statistical models.

ABSTRACT: Statistical models are simple mathematical rules derived from empirical data describing the association between an outcome and several explanatory variables. In a typical modeling situation statistical analysis often involves a large number of potential explanatory variables and frequently only partial subject-matter knowledge is available. Therefore, selecting the most suitable variables for a model in an objective and practical manner is usually a non-trivial task. We briefly revisit the purposeful variable selection procedure suggested by Hosmer and Lemeshow which combines significance and change-in-estimate criteria for variable selection and critically discuss the change-in-estimate criterion. We show that using a significance-based threshold for the change-in-estimate criterion reduces to a simple significance-based selection of variables, as if the change-in-estimate criterion is not considered at all. Various extensions to the purposeful variable selection procedure are suggested. We propose to use backward elimination augmented with a standardized change-in-estimate criterion on the quantity of interest usually reported and interpreted in a model for variable selection. Augmented backward elimination has been implemented in a SAS macro for linear, logistic and Cox proportional hazards regression. The algorithm and its implementation were evaluated by means of a simulation study. Augmented backward elimination tends to select larger models than backward elimination and approximates the unselected model up to negligible differences in point estimates of the regression coefficients. On average, regression coefficients obtained after applying augmented backward elimination were less biased relative to the coefficients of correctly specified models than after backward elimination. In summary, we propose augmented backward elimination as a reproducible variable selection algorithm that gives the analyst more flexibility in adopting model selection to a specific statistical modeling situation.

SUBMITTER: Dunkler D

PROVIDER: S-EPMC4240713 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Augmented backward elimination: a pragmatic and purposeful way to develop statistical models.

Dunkler Daniela D Plischke Max M Leffondré Karen K Heinze Georg G

PloS one 20141121 11

Statistical models are simple mathematical rules derived from empirical data describing the association between an outcome and several explanatory variables. In a typical modeling situation statistical analysis often involves a large number of potential explanatory variables and frequently only partial subject-matter knowledge is available. Therefore, selecting the most suitable variables for a model in an objective and practical manner is usually a non-trivial task. We briefly revisit the purpo ...[more]

PMID: 25415265

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Project description:Strong understanding of cardiac anatomy and function are essential components of veterinary medical education; however, the heart is considered challenging to comprehend due to its complexity. This study introduced and assessed a new learning resource, the IVALA® augmented reality (AR) heart program in a cohort of pre-veterinary students. Students were randomly divided into traditional textbook learning and AR learning groups. All students underwent a pre- and post-intervention testing assessing baseline cardiac anatomy knowledge, as well as pre-intervention evaluation of inherent spatial awareness. Teaching and learning included a 60-min cadaveric learning experience guided by either traditional learning resources or the IVALA® program. All students completed a participant survey about their learning experiences. Seventy-four students (36 in the control, and 38 in the IVALA® group) participated in the research. Overall, students improved in cardiac knowledge by an average of 24.5% after intervention regardless of study methodology. No significant difference in post-test improvement was noted between the two groups. On a 20-question assessment, students in the IVALA® group improved by an average of 4.9 questions correct over their pre-intervention test, and the control group improved by an average of 4.8 questions (p = 0.9). A positive correlation was found between spatial awareness scores and post-test improvement regardless of cohort group (p = 0.03). Sixty-two individuals (83.8%) completing the participant survey reported an overwhelming preference for learning with AR compared to traditional methods. This study illustrates student preference of IVALA® program in learning anatomy of the heart and supports its use is as effective as traditional methods of teaching with the benefit of increased enthusiasm and engagement.Supplementary informationThe online version contains supplementary material available at 10.1007/s40670-021-01260-8.

Dataset Information

Augmented backward elimination: a pragmatic and purposeful way to develop statistical models.

Publications

Augmented backward elimination: a pragmatic and purposeful way to develop statistical models.

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