@article{ref1,
title="A comparison of different ways of including baseline counts in negative binomial models for data from falls prevention trials",
journal="Biometrical journal",
year="2018",
author="Zheng, Han and Kimber, Alan and Goodwin, Victoria A. and Pickering, Ruth M.",
volume="60",
number="1",
pages="66-78",
abstract="A common design for a falls prevention trial is to assess falling at baseline, randomize participants into an intervention or control group, and ask them to record the number of falls they experience during a follow-up period of time. This paper addresses how best to include the baseline count in the analysis of the follow-up count of falls in negative binomial (NB) regression. We examine the performance of various approaches in simulated datasets where both counts are generated from a mixed Poisson distribution with shared random subject effect. Including the baseline count after log-transformation as a regressor in NB regression (NB-logged) or as an offset (NB-offset) resulted in greater power than including the untransformed baseline count (NB-unlogged). Cook and Wei's conditional negative binomial (CNB) model replicates the underlying process generating the data. In our motivating dataset, a statistically significant intervention effect resulted from the NB-logged, NB-offset, and CNB models, but not from NB-unlogged, and large, outlying baseline counts were overly influential in NB-unlogged but not in NB-logged. We conclude that there is little to lose by including the log-transformed baseline count in standard NB regression compared to CNB for moderate to larger sized datasets.<br><br>© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.<p /> <p>Language: en</p>",
language="en",
issn="0323-3847",
doi="10.1002/bimj.201700103",
url="http://dx.doi.org/10.1002/bimj.201700103"
}