![]() If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.įor technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F Baum (email available below). If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. You can help adding them by using this form. We have no bibliographic references for this item. It also allows you to accept potential citations to this item that we are uncertain about. This allows to link your profile to this item. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. See general information about how to correct material in RePEc.įor technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact. When requesting a correction, please mention this item's handle: RePEc:boc:asug03:07. ![]() You can help correct errors and omissions. This involves estimating an effect size and choosing (usually 0.05) and the desired power (1 - B), often 0.80 estimate power before collecting data for some planned analyses. Suggested CitationĪll material on this site has been provided by the respective publishers and authors. Power calculations in applied research serve 3 main purposes: compute the required sample size prior to data collection. Stata programs are available for these comparisons. Results are presented for population based, family and matching schemes that have been proposed to improve power, and comparisons of the power of different designs are made. ![]() Required sample size depends on several design parameters and so the simplicity of these methods means that the efficiency of many designs can be compared over different ranges, a valuable tool at the planning stage of a study. Because of the low power of case-control studies to detect interactions, a wide range of different strategies have been proposed. We apply these methods to power and sample-size calculations for case-control studies of gene-gene and gene-environment interactions. The likelihood ratio test statistic for the hypothesis of interest is distributed as a non-central chi-squared statistic under the alternative hypothesis, and the likelihood ratio test statistic from the analysis of the exemplary data set is an approximation to the non-centrality parameter for this distribution. Following the method described by Self et al (1992) a large exemplary data set with expected risk factor frequencies among cases and controls under any alternative hypothesis is created. We use Stata's npnchi2 and nchi2 functions to calculated power and required sample size for case-control studies.
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