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A comparison of different ways of including baseline counts in negative binomial models for data from falls prevention trials

A comparison of different ways of including baseline counts in negative binomial models for data from falls prevention trials
A comparison of different ways of including baseline counts in negative binomial models for data from falls prevention trials
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.
Baseline counts; Negative binomial; Regression; Simulations
0323-3847
66-78
Zheng, Han
8e8dc7c4-565c-46a0-b41d-3ed7bba5ea7b
Kimber, Alan
40ba3a19-bbe3-47b6-9a8d-68ebf4cea774
Goodwin, Victoria A.
b0c94379-0aaf-4ca2-a64d-1501954ffe7f
Pickering, Ruth M.
4a828314-7ddf-4f96-abed-3407017d4c90
Zheng, Han
8e8dc7c4-565c-46a0-b41d-3ed7bba5ea7b
Kimber, Alan
40ba3a19-bbe3-47b6-9a8d-68ebf4cea774
Goodwin, Victoria A.
b0c94379-0aaf-4ca2-a64d-1501954ffe7f
Pickering, Ruth M.
4a828314-7ddf-4f96-abed-3407017d4c90

Zheng, Han, Kimber, Alan, Goodwin, Victoria A. and Pickering, Ruth M. (2018) A comparison of different ways of including baseline counts in negative binomial models for data from falls prevention trials. Biometrical Journal, 60 (2018), 66-78. (doi:10.1002/bimj.201700103).

Record type: Article

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.

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Accepted/In Press date: 6 September 2017
e-pub ahead of print date: 25 October 2017
Published date: 2018
Keywords: Baseline counts; Negative binomial; Regression; Simulations

Identifiers

Local EPrints ID: 414170
URI: http://eprints.soton.ac.uk/id/eprint/414170
ISSN: 0323-3847
PURE UUID: 00c096a3-b08e-4e8e-a4a5-65c001fdbcf6
ORCID for Han Zheng: ORCID iD orcid.org/0000-0003-3134-6947

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Date deposited: 15 Sep 2017 16:30
Last modified: 17 Dec 2019 05:54

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