Progressive Statistics for Studies in Sports Medicine and Exercise Science
Прогрессивная статистика для исследований в спортивной медицине и науках о физической нагрузке
2008-12-24
SCID: 54.1/btscbxz2
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precision of estimationpublic raw data accessregression validity statisticssample size justificationstandard deviation reporting
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Abstract (AI)
Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta-analyses, and case studies in sports medicine and exercise science. We offer forthright advice on the following controversial or novel issues: using precision of estimation for inferences about population effects in preference to null-hypothesis testing, which is inadequate for assessing clinical or practical importance; justifying sample size via acceptable precision or confidence for clinical decisions rather than via adequate power for statistical significance; showing SD rather than SEM, to better communicate the magnitude of differences in means and nonuniformity of error; avoiding purely nonparametric analyses, which cannot provide inferences about magnitude and are unnecessary; using regression statistics in validity studies, in preference to the impractical and biased limits of agreement; making greater use of qualitative methods to enrich sample-based quantitative projects; and seeking ethics approval for public access to the depersonalized raw data of a study, to address the need for more scrutiny of research and better meta-analyses. Advice on less contentious issues includes the following: using covariates in linear models to adjust for confounders, to account for individual differences, and to identify potential mechanisms of an effect; using log transformation to deal with nonuniformity of effects and error; identifying and deleting outliers; presenting descriptive, effect, and inferential statistics in appropriate formats; and contending with bias arising from problems with sampling, assignment, blinding, measurement error, and researchers' prejudices. This article should advance the field by stimulating debate, promoting innovative approaches, and serving as a useful checklist for authors, reviewers, and editors.
Key Findings
1
Avoid purely nonparametric analyses because they cannot provide inferences about magnitude and are unnecessary for many studies.
2
Justify sample size based on acceptable precision or confidence for clinical decisions rather than power for statistical significance.
3
Prefer precision of estimation (confidence intervals) over null-hypothesis significance testing for inferences about population effects and clinical/practical importance.
4
Report standard deviations (SD) instead of standard errors (SEM) to better communicate magnitude of differences and nonuniform error.
5
Use regression statistics for validity studies instead of limits of agreement, and increase use of qualitative methods and public depersonalized raw data with ethics approval to improve scrutiny and meta-analyses.
Research Object
Sample-based studies, meta-analyses, and case studies in sports medicine and exercise science
Research Subject
Statistical practices and guidelines for analysis, reporting, and inference (including precision estimation vs NHST, sample-size justification by precision, choice of summary statistics, use of regression, handling nonuniformity, qualitative augmentation, data-sharing ethics, and bias control) applied to those studies
Publication Details
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2008-12-24
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