Printer Friendly

Update: sample-size/meta-analysis tool.

To estimate sample size for a crossover or parallel-groups controlled trial, you need an estimate of the typical error (aka error of measurement or within-subject standard deviation) of the dependent variable over a period of time and with subjects similar to those in your intended study. Finding such an estimate in published reliability studies is difficult, especially if you are planning a long-term intervention, because reliability studies generally don't go beyond a week between trials and the subjects may be quite different (sedentary vs active vs competitive). However, you can usually find another intervention with a time frame and subjects similar to yours, so I have worked out how to extract the typical error from such studies. The authors will need to have provided not only the magnitude of their effect and the sample size (in the control and experimental groups for a parallel-groups trial), but also inferential information in the form of either an exact p value or confidence limits. The design doesn't have to be exactly the same: you can use data from a controlled trial for an intended crossover or vice versa.

It doesn't matter that the intervention was different from what you intend to use. Any intervention may be better than a reliability study, because any individual responses to the intervention will increase the typical error. Hence if your intervention produces individual responses, the estimate of sample size based on a reliability study will be too low. Of course it's not quite that simple, because the individual responses to different interventions will differ, and if there are individual responses there is probably a substantial mean effect, in which case the sample size in your study can be smaller. But estimates of sample size are always approximate, and the approach I am presenting here is, in my opinion, as good as it gets. I have updated the spreadsheet and article on sample-size estimation accordingly.

You can also use this approach when meta-analyzing crossovers or controlled trials when the authors of a given study have not provided enough inferential information to derive the standard error for their estimate of the effect. In previous meta-analyses (e.g., Vandenbogaerde and Hopkins, 2009) we have imputed the standard error in what we believe is the best way possible: from the typical error in comparable studies. The relevant cells in the sample-size spreadsheet now provide you with the typical error. You will have to convert it to a standard error yourself by dividing by [square root of n] for a crossover or multiplying by [square root of (1/[n.sub.1] + 1/[n.sub.2])] for a parallel-groups trial, where n, [n.sub.1] and [n.sub.2] are appropriate sample sizes in the study for which you are imputing the standard error.

Vandenbogaerde TJ, Hopkins WG (2011). Effects of acute carbohydrate supplementation on endurance performance: a meta-analysis. Sports Medicine 41, 773-792

Will G Hopkins, Sport and Recreation, AUT University, Auckland, New Zealand. Email. Sportscience 17, i-ii, 2013 ( Reviewer: Alan M Batterham, University of Teesside, Middlesbrough, UK. Published July 2013.

COPYRIGHT 2013 Internet Society for Sport Science
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2013 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Hopkins, Will G.
Article Type:Report
Date:Jan 1, 2013
Previous Article:Mixed linear modeling: a very short introduction.
Next Article:Update: meta-analysis slideshow.

Terms of use | Privacy policy | Copyright © 2018 Farlex, Inc. | Feedback | For webmasters