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Activity Number:
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149
- Advances in Modeling Multilevel Observational Data from Complex Surveys
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 31, 2017 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #322876
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Title:
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Modeling Count Data from a Complex Survey using Pseudo Maximum Likelihood
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Author(s):
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Mulugeta Gebregziabher* and Lin Dai
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Companies:
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MUSC and Medical University of South Carolina
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Keywords:
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complex survey ;
demographic health survey ;
empirical varaince ;
pseudo-likelihood ;
sampling weight
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Abstract:
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In this study we introduce a multilevel pseudo maximum likelihood (MPML) method for weighted count data from a complex survey. We conduct a simulation study to assess the finite sample performance of this estimation method under several scaling methods of the survey sampling weights. In addition, different approaches for estimating the variance of parameter estimates corresponding to covariates that are included in the model are considered and compared in the simulation studies. The simulation results show that MPML estimation with scaled sampling weight provide better performance in terms of relative bias and 95% confidence interval coverage compared to estimations by standard approaches that use sampling weight or that ignore sampling weight. Thus, we recommend MPML to analyze multilevel weighted count data. Among the four variance estimation approaches we considered, the empirical variance estimator provides values that reflect the true variance of the estimators. We demonstrate the proposed methods using real data from national surveys of three countries in sub-Saharan Africa.
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Authors who are presenting talks have a * after their name.
