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Related Experiment Video

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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The impact of missing data in a generalized integer-valued autoregression model for count data.

Mohamed Alosh¹

¹Division of Biometrics III, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA.

Journal of Biopharmaceutical Statistics

|February 26, 2010

Summary

Missing count data significantly impacts statistical models. This study shows missing completely at random (MCAR) and missing at random (MAR) mechanisms yield reliable estimates, unlike missing not at random (MNAR), which causes bias.

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Area of Science:

Statistics
Biostatistics
Longitudinal Data Analysis

Background:

The impact of missing data on continuous data models is well-studied.
Research on missing count data mechanisms and their effect on longitudinal models is limited.

Purpose of the Study:

To investigate the impact of different missing data mechanisms on parameter estimates for a generalized autoregressive model of order 1 (GAR(1)) for longitudinal count data.
To evaluate the performance of the GAR(1) model in clinical trial settings, considering its ability to model dependence and overdispersion.

Main Methods:

Introduced a generalized autoregressive model of order 1 (GAR(1)) for longitudinal count data.
Conducted a simulation experiment to assess the impact of missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) mechanisms on model parameter estimates.
Compared estimates from different missing data scenarios with those from complete data.

Main Results:

Estimates under MCAR and MAR mechanisms were close to complete data estimates.
The MNAR mechanism resulted in the greatest bias in parameter estimates.
Imputing the last observed value carried forward (LOCF) under MAR yielded results similar to MAR estimates, potentially due to the model's Markov property and high dependence.

Conclusions:

The generalized autoregressive model of order 1 (GAR(1)) is a viable tool for longitudinal count data in clinical trials.
Missing data mechanisms significantly influence parameter estimates, with MNAR posing the most substantial challenge.
LOCF imputation under MAR can be a reasonable approach for this model under specific conditions.