An empirical comparison of methods for analyzing over-dispersed zero-inflated count data from stratified cluster randomized trials

Sayem Borhan, Courtney Kennedy, George Ioannidis, Alexandra Papaioannou, Jonathan Adachi, Lehana Thabane, Sayem Borhan, Courtney Kennedy, George Ioannidis, Alexandra Papaioannou, Jonathan Adachi, Lehana Thabane

Abstract

Background: The assessment of methods for analyzing over-dispersed zero inflated count outcome has received very little or no attention in stratified cluster randomized trials. In this study, we performed sensitivity analyses to empirically compare eight methods for analyzing zero inflated over-dispersed count outcome from the Vitamin D and Osteoporosis Study (ViDOS) - originally designed to assess the feasibility of a knowledge translation intervention in long-term care home setting.

Method: Forty long-term care (LTC) homes were stratified and then randomized into knowledge translation (KT) intervention (19 homes) and control (21 homes) groups. The homes/clusters were stratified by home size (<250/> = 250) and profit status (profit/non-profit). The outcome of this study was number of falls measured at 6-month post-intervention. The following methods were used to assess the effect of KT intervention on number of falls: i) standard Poisson and negative binomial regression; ii) mixed-effects method with Poisson and negative binomial distribution; iii) generalized estimating equation (GEE) with Poisson and negative binomial; iv) zero inflated Poisson and negative binomial - with the latter used as a primary approach. All these methods were compared with or without adjusting for stratification.

Results: A total of 5,478 older people from 40 LTC homes were included in this study. The mean (=1) of the number of falls was smaller than the variance (=6). Also 72% and 46% of the number of falls were zero in the control and intervention groups, respectively. The direction of the estimated incidence rate ratios (IRRs) was similar for all methods. The zero inflated negative binomial yielded the lowest IRRs and narrowest 95% confidence intervals when adjusted for stratification compared to GEE and mixed-effect methods. Further, the widths of the 95% confidence intervals were narrower when the methods adjusted for stratification compared to the same method not adjusted for stratification.

Conclusion: The overall conclusion from the GEE, mixed-effect and zero inflated methods were similar. However, these methods differ in terms of effect estimate and widths of the confidence interval.

Trial registration: ClinicalTrials.gov: NCT01398527. Registered: 19 July 2011.

Keywords: Cluster randomized trial; Count; Overdispersed; Sensitivity; Stratification; Zero inflated.

© 2020 The Author(s).

Figures

Fig. 1
Fig. 1
Results of ITT analysis using different methods with/without adjusted for stratification.
Fig. 2
Fig. 2
Results of missing data analysis using different methods with/without adjusted for stratification.

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