期刊
COMPUTATIONAL STATISTICS & DATA ANALYSIS
卷 51, 期 12, 页码 6582-6595出版社
ELSEVIER
DOI: 10.1016/j.csda.2007.03.007
关键词
antiviral agent; data augmentation; EM algorithm; infectious disease; intervention efficacy; linear model; MLE
资金
- NIAID NIH HHS [R01 AI032042, R01 AI032042-14] Funding Source: Medline
- NIGMS NIH HHS [U01 GM070749-01, U01 GM070749] Funding Source: Medline
A broad range of studies of preventive measures in infectious diseases gives rise to incidence data from close contact groups. Parameters of common interest in such studies include transmission probabilities and efficacies of preventive or therapeutic interventions. We estimate these parameters using discrete-time likelihood models. We augment the data with unobserved pairwise transmission outcomes and fit the model using the EM algorithm. A linear model derived from the likelihood based on the augmented data and fitted with the iteratively reweighted least squares method is also discussed. Using simulations, we demonstrate the comparable accuracy and lower sensitivity to initial estimates of the proposed methods with data augmentation relative to the likelihood model based solely on the observed data. Two randomized household-based trials of zanamivir, an influenza antiviral agent, are analyzed using the proposed methods. (c) 2007 Elsevier B.V. All rights reserved.
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