Abstract Title

SAMPLE SIZE DETERMINATION IN MIXED-EFFECTS ZERO INFLATED POISSON LONGITUDINAL DATA

Presenter Name

Abhilash Vemulapalli, Sruthi Anne

Abstract

To determine sample size for a mixed-effects zero-inflated Poisson regression model via simulation. Zero inflated Poisson data was simulated first and then a mixed-effects ZIP regression model was fitted to evaluate the significance of the time trend parameter using SAS software. Sample size was estimated to test the time trend parameter. Using simulation approach we determined sample size for testing both the Binomial and Poisson component separately as well as simultaneous testing of both the parameters. The results from Likelihood-Ratio-Test (LRT) indicate that different sample size estimates are required for the Binomial and Poisson components of model.We suggest zero inflated data can be best explained using ZIP model. It is recommended to use the larger of the two estimates from Binomial or Poisson model while designing any clinical study.

Purpose (a):

To determine sample size for a mixed-effects zero-inflated Poisson regression model via simulation.

Methods (b):

Zero inflated Poisson(ZIP) data was simulated first and then a mixed-effects ZIP regression model was fitted to evaluate the significance of the time trend parameter using SAS software. Sample size was estimated to test the time trend parameter.

Results (c):

Using simulation approach we determined sample size for testing both the Binomial and Poisson component separately as well as simultaneous testing of both the parameters. The results from Likelihood-Ratio-Test (LRT) indicate that different sample size estimates are required for the Binomial and Poisson components of model.

Conclusions (d):

We suggest zero inflated data can be best explained using ZIP model. It is recommended to use the larger of the two estimates from Binomial or Poisson model while designing any clinical study.

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SAMPLE SIZE DETERMINATION IN MIXED-EFFECTS ZERO INFLATED POISSON LONGITUDINAL DATA

To determine sample size for a mixed-effects zero-inflated Poisson regression model via simulation. Zero inflated Poisson data was simulated first and then a mixed-effects ZIP regression model was fitted to evaluate the significance of the time trend parameter using SAS software. Sample size was estimated to test the time trend parameter. Using simulation approach we determined sample size for testing both the Binomial and Poisson component separately as well as simultaneous testing of both the parameters. The results from Likelihood-Ratio-Test (LRT) indicate that different sample size estimates are required for the Binomial and Poisson components of model.We suggest zero inflated data can be best explained using ZIP model. It is recommended to use the larger of the two estimates from Binomial or Poisson model while designing any clinical study.