JSM 2005 - Toronto

Abstract #303384

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 483
Type: Contributed
Date/Time: Thursday, August 11, 2005 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #303384
Title: Sample Size and Statistical Power Assessing the Effect of Interventions in the Context of Mixture Distributions with Detection Limits
Author(s): Haitao Chu*+ and Lei Nie and Stephen R. Cole
Companies: Johns Hopkins University and University of Maryland Baltimore County and Johns Hopkins University
Address: 615 N Wolfe Street, Baltimore, MD, 21205, United States
Keywords: mixture model ; detection limit ; sample size ; power ; left censoring
Abstract:

Often in randomized clinical trials and observational cohort studies, a nonnegative, continuously distributed response variable is measured in treatment and control groups. In the presence of true zeros for the response variable, a two-part, zero-inflated, log-normal model (which assumes the data has a probability mass at zero and a continuous response for values greater than zero) usually is recommended. However, in studies of environmental health, many quantitative assays for metabolites of toxicants are subject to left-censoring due to values falling below assay detection limits. Here, a zero-inflated, log-normal mixture model often is suggested as true zeros are indistinguishable from left-censored values due to the limit of detection. Based on the expected differences between the detected values of individuals in the treatment and control groups, we derive the required sample size to assess the effect of a treatment in the context of mixture models. Methods for calculation of statistical power also are presented.

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