PPT-Sample SIZe for Non-INFERIORITY Studies

Design and Endpoint Considerations Demonstrated on Head of Statistics nQuery Lead Researcher FDA Guest Speaker Guest Lecturer Webinar Host HOSTED BY Ronan Fitzpatrick AGENDA Introduction to Noninferiority Testing. Study Design and Sample Size. Ideally we are involved in a study from the beginning. As statisticians (an epidemiologist) part of our role is to ensure the study is designed to address the primary hypothesis under consideration. Jed Friedman, World . Bank. SIEF Regional Impact Evaluation Workshop. Beijing, China. July 2009. Adapted from slides by Esther . Duflo. , J-PAL. Planning Sample Size for . Randomized Evaluations. General question: . Presented by :Dr. . Reshma.S. . Moderator : Dr. . Subodh. S Gupta. Why do we need sample size calculation?. When should we calculate sample size?. Basic principles for sample size calculation. Derivation of sample size formula. 13 Sampling: quantitative and qualitative. . CONTENTS. Samples and populations. Representativeness. Sample size. Weighting. Sampling for qualitative research. Samples and populations. Population: . Total category of subjects that is the focus of attention in a particular research project (can be non-human). 2016. Youngju Pak, PhD.. Biostatistician. ypak@labiomed.org. Session . 2. Understanding Equivalence and . Noninferiority. testing. Testing Inequality . v.s. . Equality. Testing Inequality. Ha: | mean(treatment ) - mean (control ) | ≠ 0. Excel. GrowingKnowing.com © 2011. 1. GrowingKnowing.com © 2011. Sample size. How big should a sample be for a valid study?. Why phone 10,000 students if phoning 50 is enough?. Large samples take longer and cost more. Manual. GrowingKnowing.com © 2011. 1. GrowingKnowing.com © 2011. Sample size. How big should a sample be for a valid study?. Why phone 10,000 students if phoning 50 is enough?. Large samples take longer and cost more. November 17, . 2015. Allen Kunselman, MA. Division of Biostatistics and Bioinformatics. Department of Public . H. ealth Sciences. Quotes. An approximate answer to the right question is worth a good deal more than an exact answer to the wrong question.. Review of Type I & Type II Errors. When we conduct a test of any hypothesis regardless of the test used we make one of two possible decisions:. Reject the null (H. o. ) in favor of the alternative (H. artbin. ). Ella Marley-Zagar, Ian . White. , . Mahesh . Parmar, Patrick Royston, . Abdel Babiker. e.marley-zagar@ucl.ac.uk. MRC Clinical Trials Unit at UCL. “London” Stata Conference. 9. . September . & . specificities. for epidemic diseases in LMICs. P. . . Olliaro. WHO/TDR & . University of Oxford. Regulatory. Clinical studies. (Phase I, II, III). First In . Humans. Discovery. & Pre-clinical. Design considerations and sample size. Demonstrated on . Head of Statistics. nQuery. Lead Researcher. FDA Guest Speaker. Guest Lecturer. Webinar Host. HOSTED BY: . Ronan Fitzpatrick. AGENDA. Background. : . Grant . Writing for Cancer Studies . Masha Kocherginsky, PhD. Professor of Biostatistics, Departments of Preventive Medicine. Director, Quantitative Data Sciences Core, Lurie Cancer Center. Northwestern University. Binary, Ordinal and . Contingency Table Data. Ronan Fitzpatrick. Lead Statistician. nQuery. Webinar. Host. Agenda. Categorical Data Overview. Binary Data Methods & Sample Size. Ordinal Data Methods & Sample Size.