Penn EPID 521 - Sample Size Estimation (37 pages)

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Sample Size Estimation



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Sample Size Estimation

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Pages:
37
School:
University of Pennsylvania
Course:
Epid 521 - Statistical Methods for Epidemiologic Research

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EP 521 Spring 2007 Vol I Part 5 3 1 Sample Size Estimation A key to study design are sample size or power calculations Required of ever grant proposal In this section 1 we begin with theory behind power calculations and demonstrate how simple formulae for power and sample sizes are derived 2 Next show unified treatment of power for RD OR RR based on this theory 3 Then describe how varying the question being asked can have substantial effect on the required sample sizes 4 Brief explanation of the information needed for power calculations for matched pair studies 5 Some demonstrations on how to use and interpret software for power calculations Goals To be able to understand what affects power how to define the problem and how to get the computer to give you the answer you need Copyright 2006 Trustees of the University of Pennsylvania EP 521 Spring 2007 Vol I Part 5 3 1 Power In General Sample Size Estimation Terminology Review Null hypothesis Ho specified value for a parameter OR RR RD IRR IRD for example Alternative hypothesis Ha specified alternative value for a parameter Type I error Pr Reject Ho Ho is true Type II error Pr fail to reject Ho Ha is true Pr fail to reject Ho Ho is false Power Pr reject Ho Ha is true 1 1 Pr signifies probability over repetitions of the study References Woodward chap 8 Rothman and Greenland pp 184 8 Copyright 2006 Trustees of the University of Pennsylvania 2 EP 521 Spring 2007 Vol I Part 5 3 Notes 1 level is not a p value P value is a quantity computed from and varying with data is fixed and is specified without seeing the data 2 p value is not the Pr Ho vs Ha Is loosely defined Pr observed result or more extreme than observed Ho true 3 p value is not Pr data Ho That is the likelihood Likelihood is usually much smaller than the pvalue because p value includes not only Pr data Ho but also the Pr all other more extreme data configurations Ho 4 Absence of evidence is not evidence of absence Failing to reject Ho accept Ho as true 5 Studies



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