期刊
BIOMETRICAL JOURNAL
卷 60, 期 1, 页码 66-78出版社
WILEY
DOI: 10.1002/bimj.201700103
关键词
baseline counts; negative binomial; regression; simulations
资金
- National Institute for Health Research [RDA/02/06/41]
- Care South West Peninsula
- National Institute for Health Research [RDA/02/06/41] Funding Source: researchfish
- National Institutes of Health Research (NIHR) [RDA/02/06/41] Funding Source: National Institutes of Health Research (NIHR)
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 Weis conditional negative binomial (CNB) model replicates the underlying process generating the data. In our motivating dataset, a staistically 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 lit le 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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