TY - JOUR AU - Batterham, A M AU - Hopkins, W G PY - 2005 TI - A decision tree for controlled trials SP - 33-39 JF - Sportscience VL - 9 N1 - A decision tree for controlled trials KW - confidence limits, confounding, covariate, inference, modeling N2 - Data analysis that fails to account for independent groups defined by a subject characteristic (e.g., sex) or by a design characteristic (e.g., treatment order) can result in bias, confounding, and loss of precision in the outcome. Combining the outcomes from separate analyses of the groups is a robust approach to the problem that is easily achieved with the spreadsheet presented here. Differences in the outcome between groups represent the effect of the characteristic on the outcome, while the mean of the outcomes represents the outcome adjusted appropriately for the characteristic. The spreadsheet calculates confidence limits for the differences and for the mean from the confidence limits for the outcome in each group. It also presents magnitude-based inferences for the differences and mean. There are separate cells in the spreadsheet for outcomes represented by means or other normally distributed statistics, relative rates (risk, odds and hazard ratios) or other log-normally distributed statistics, and correlation coefficients. AD - Sport and Recreation, AUT University, Auckland 0627, New Zealand. Email: will=AT=clear.net.nz UR - http://sportsci.org/2006/wghcom.htm ID - 5 ER - TY - JOUR AU - Hopkins, W G PY - 2006 TI - Spreadsheets for analysis of controlled trials, with adjustment for a subject characteristic SP - 46-50 JF - Sportscience VL - 10 N1 - Spreadsheets for analysis of controlled trials, with adjustment for a subject characteristic KW - crossover, design, inference, repeated measures, intervention, randomized, transformation, t statistic N2 - Spreadsheets previously available at this site for analysis of controlled trials have been updated to allow inclusion of one covariate representing a subject characteristic. The spreadsheets provide estimates of the effect of an intervention adjusted to any chosen value of the covariate, thereby reducing the possibility for confounding of the effect when a characteristic such as age, fitness or sex is unequal in the experimental and control groups. The pre-test value of the dependent variable can also be included as a covariate to avoid confounding by the phenomenon of regression to the mean. Graphs of change scores plotted against the covariate show visually how the treatment effect is adjusted to the chosen value of the covariate. The spreadsheets also provide an estimate of the effect of the covariate itself, representing individual responses attributable to the covariate. Other new features of the spreadsheets include plots of raw and back-transformed means with easily modified standard-deviation bars, and qualitative inferential outcomes based on interpretation of the span of the confidence interval relative to magnitude thresholds for trivial, small, moderate, large, and very large. AD - Sport and Recreation, AUT University, Auckland 0627, New Zealand. Email: will=AT=clear.net.nz UR - http://sportsci.org/2006/wghcontrial.htm ID - 1 ER - TY - JOUR AU - Hopkins, W G PY - 2006 TI - A spreadsheet for combining outcomes from several subject groups SP - 51-53 JF - Sportscience VL - 10 N1 - A spreadsheet for combining outcomes from several subject groups KW - confidence limits, confounding, covariate, inference, modeling N2 - Data analysis that fails to account for independent groups defined by a subject characteristic (e.g., sex) or by a design characteristic (e.g., treatment order) can result in bias, confounding, and loss of precision in the outcome. Combining the outcomes from separate analyses of the groups is a robust approach to the problem that is easily achieved with the spreadsheet presented here. Differences in the outcome between groups represent the effect of the characteristic on the outcome, while the mean of the outcomes represents the outcome adjusted appropriately for the characteristic. The spreadsheet calculates confidence limits for the differences and for the mean from the confidence limits for the outcome in each group. It also presents magnitude-based inferences for the differences and mean. There are separate cells in the spreadsheet for outcomes represented by means or other normally distributed statistics, relative rates (risk, odds and hazard ratios) or other log-normally distributed statistics, and correlation coefficients. AD - Sport and Recreation, AUT University, Auckland 0627, New Zealand. Email: will=AT=clear.net.nz UR - http://sportsci.org/2006/wghcom.htm ID - 4 ER - TY - JOUR AU - Hopkins, W G PY - 2006 TI - Estimating sample size for magnitude-based inferences SP - 63-70 JF - Sportscience VL - 10 N1 - Estimating sample size for magnitude-based inferences KW - confidence limits, research design, statistical power, Type 1 error, Type 2 error N2 - Sample-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. AD - Sport and Recreation, AUT University, Auckland 0627, New Zealand. Email: will=AT=clear.net.nz UR - http://sportsci.org/2006/wghss.htm ID - 9 ER - TY - JOUR AU - Hopkins, W G PY - 2010 TI - Linear models and effect magnitudes for research, clinical and practical applications SP - 49-57 JF - Sportscience VL - 14 N1 - Linear models and effect magnitudes for research, clinical and practical applications ID - 8 ER - TY - JOUR AU - Hopkins, W G PY - 2015 TI - Individual responses made easy SP - 1444-1446 JF - Journal of Applied Physiology VL - 118 N1 - Individual responses made easy M3 - 10.?1152/?japplphysiol.?00098.?2015 ID - 10 ER - TY - JOUR AU - Hopkins, W G PY - 2016 TI - SAS (and R) for mixed models SP - iii JF - Sportscience VL - 20 N1 - SAS (and R) for mixed models ID - 3 ER - TY - JOUR AU - Hopkins, W G PY - 2017 TI - Spreadsheets for analysis of controlled trials, crossovers and time series SP - 1-4 JF - Sportscience VL - 21 N1 - Spreadsheets for analysis of controlled trials, crossovers and time series ID - 11 ER - TY - JOUR AU - Hopkins, W G PY - 2017 TI - SAS programs for analyzing individual responses in controlled trials SP - 27-35 JF - Sportscience VL - 21 N1 - SAS programs for analyzing individual responses in controlled trials ID - 12 ER - TY - JOUR AU - Hopkins, W.G. AU - Batterham, A.M. PY - 2016 TI - Error rates, decisive outcomes and publication bias with several inferential methods SP - 1563-1573 JF - Sports Medicine VL - 46 N1 - Error rates, decisive outcomes and publication bias with several inferential methods ID - 6 ER - TY - JOUR AU - Malcata, R M AU - Hopkins, W G PY - 2014 TI - Variability of competitive performance of elite athletes: a systematic review SP - 1763-74 JF - Sports Medicine VL - 44 N1 - Variability of competitive performance of elite athletes: a systematic review M3 - 10.1007/s40279-014-0239-x ID - 7 ER - TY - JOUR AU - Satterthwaite, F W PY - 1946 TI - An approximate distribution of estimates of variance components SP - 110-114 JF - Biometrics Bulletin VL - 2 N1 - An approximate distribution of estimates of variance components KW - stats, degrees of freedom, confidence limits ID - 2 ER -