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Activity Number: 400
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract #313144 View Presentation
Title: Improved Interval Estimation of Comparative Treatment Effects
Author(s): Ryne VanKrevelen*+
Companies:
Keywords: randomized experiment ; randomization-based confidence interval ; treatment effect ; Neyman vs Fisher randomization-test
Abstract:

Comparative experiments, in which subjects are randomized to one of two treatments, are performed often. There is no shortage of papers testing whether a treatment effect exists and providing confidence intervals for the magnitude of this effect. While it is well understood that the object and scope of inference for an experiment will depend on what assumptions are made, these entities are not always clearly presented. This presentation will describe one possible method, based on ideas of Jerzy Neyman, for constructing confidence intervals of treatment effects that can be widely. Special care is taken to note which assumptions are made and what object and scope of inference are being investigated. This method is compared to a Fisher-type randomization interval and to a t interval in a real data example. A small-scale simulation study investigates the coverage properties of these intervals. The Neyman method is shown to be an attractive alternative to the Fisher-type randomization confidence interval. In contrast to the Fisher-type confidence interval, the Neyman interval is applicable for making inference about the population when a random sample is taken.


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