PKU79lK:refs.MYD ?OWatson, P. Hasegawa, H. Roelands, B. Piacentini, M. F. Looverie, R. Meeusen, R.2005zAcute dopamine/noradrenaline reuptake inhibition enhances human exercise performance in warm, but not temperate conditions873-883Journal of Physiology565s? Bishop, D.20080An applied research model for the sport sciences253-263Sports Medicine38? Hopkins, W G20065Estimating sample size for magnitude-based inferences63-70 Sportscience10Qconfidence limits, research design, statistical power, Type 1 error, Type 2 error0Sample-size estimation based on the traditional method of statistical significance is not appropriate for a study designed to make an inference about real-world significance, which requires interpretation of magnitude of an outcome. I present here a spreadsheet using two new methods for estimating sample size for such studies, based on acceptable uncertainty defined either by the width of the confidence interval or by error rates for a clinical or practical decision arising from the study. The spreadsheet includes a section for estimating sample size by the traditional method, which requires sample sizes three times greater than those provided by the new methods. The key issues and statistical principles underlying sample-size estimation are outlined in an accompanying slideshow and conference poster. "http://sportsci.org/2006/wghss.htm]Sport and Recreation, AUT University, Auckland 0627, New Zealand. Email: will=AT=clear.net.nz? Daly, L E2000%Confidence intervals and sample sizes139-152$Statistics with Confidence (2nd ed.).Altman, D G Machin, D Bryant, T N Gardner, M JBristol BMJ Books? Hopkins, W G2000Quantitative research design2sportsci.org/jour/0001/wghdesign.html (4318 words) Sportscience4(1)Qconfidence limits, research design, statistical power, Type 1 error, Type 2 error0Sample-size estimation based on the traditional metho d of statistical significance is not appropriate for a study designed to make an inference about real-world significance, which requires interpretation of magnitude of an outcome. I present here a spreadsheet using two new methods for estimating sample size for such studies, based on acceptable uncertainty defined either by the width of the confidence interval or by error rates for a clinical or practical decision arising from the study. The spreadsheet includes a section for estimating sample size by the traditional method, which requires sample sizes three times greater than those provided by the new methods. The key issues and statistical principles underlying sample-size estimation are outlined in an accompanying slideshow and conference poster. "http://sportsci.org/2006/wghss.htm]Sport and Recreation, AUT University, Auckland 0627, New Zealand. Email: will=AT=clear.net.nz?Batterham, A M Hopkins, W G2005%A decision tree for controlled trials33-39 Sportscience9%analysis, bias, crossover, randomizedFA controlled trial is used to estimate the effect of an intervention. We present here a decision tree for choosing the most appropriate of five kinds of con-trolled trial for numeric outcome measures. A time series or quasi-experimental design is used when there is no opportunity for a separate control group or control treatment. In this design, the weakest of the five, a series of measurements taken before the intervention serves as a baseline to estimate change resulting from the intervention. In trials with a separate control group, the usual design is a fully controlled parallel-groups trial, in which subjects are measured before and after their allocated control or experimental treatment. A posts-only design, in which subjects are measured only after their treatment, can be more efficient when poor reliability of the outcome measure over the time frame of the intervention makes large sample sizes unavoidable. Cross-over studies, in which all the subjects receive all the treatments, are an option when the effects of the treatments wash out in an acceptable time. In fully con-trolled crossovers, subjects are measured before and after each treatment, whereas measurements are taken only after each treatment in a simple cross-over. Fully controlled crossovers, arguably the best of the five designs, are more efficient if the outcome measure becomes too unreliable over the wash-out period, and they provide an assessment of the effect of the treatment on each subject. In simple crossovers, individual assessment is possible only by including a repeat of the control treatment.&http://sportsci.org/jour/05/wghamb.htmSchool of Health and Social Care, University of Teesside, Middlesbrough, UK; Sport and Recreation, AUT University, Auckland 1020, New Zealand. Email: will=AT=clear.net.nz?%Greenland, S. Pearl, J. Robins, J. M.1999*Causal diagrams for epidemiologic research37-48 Epidemiology10Y? Taubes, G.1995Epidemiology faces its limits164-169Science269? Cole, S A Hernan, M A2002(Fallibility in estimating direct effects163-165%International Journal of Epidemiology31? Shrier, I.2007PUnderstanding causal inference: the future direction in sports injury prevention220-224"Clinical Journal of Sport Medicine17? )Hernan, M A Hernandez-Diaz, S Robins, J M2004'A structural approach to selection bias615-625 Epidemiology15F? 2Hopkins, W G Marshall, S W Batterham, A M Hanin, J2009JProgressive statistics for studies in sports medicine and exercise science+Medicine and Science in Sports and Exercise (in press)?0Pigozzi, F. Spataro, A. Fagnani, F. Maffulli, N.2003Preparticipation screening for the detection of cardiovascular abnormalities that may cause sudden death in competitive athletes4-5"British Journal of Sports Medicine37;?Renstrom, P. Ljungqvist, A. Arendt, E. Beynnon, B. Fukubayashi, T. Garrett, W. Georgoulis, T. Hewett, T. E. Johnson, R. Krosshaug, T.2008jNon-contact ACL injuries in female athletes: an International Olympic Committee current concepts statement394-412"British Journal of Sports Medicine42PK8I/**refs.FRM 0B< !// !HPRIMARYyearIndex 6ByP/) idreference_type text_stylesauthoryear title pages secondary_title volume numbernumber_of_volumessecondary_authorplace_published publishersubsidiary_authoredition keywords type_of_workdate2)  abstractlabelurltertiary_titletertiary_author notes isbn custom_1 custom_2 custom_3 custom_4alternate_titleaccession_number call_number short_title custom_5 custom_6sectionoriginal_publicationH) reprint_editionreviewed_itemauthor_addressimagecaption custom_7 electronic_resource_number link_to_pdf translated_author translated_titlename_of_databasedatabase_providerresearch_notes language access_datelast_modified_date !! H!H!H! (H! 3H! >H! IH! TH!_H!jH!uH! H!H!H! H! H!H! H!H!H!H!H! H! H! H! H! %H! 0H!;H!FH! QH! \H! gH! rH!}H!H!H!H!H!H!H! H! H! H! H! H!H! H!H! 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