Model Selection in Time Series Studies of Influenza-Associated Mortality

Background: Poisson regression modeling has been widely used to estimate influenza-associated disease burden, as it has the advantage of adjusting for multiple seasonal confounders. However, few studies have discussed how to judge the adequacy of confounding adjustment. This study aims to compare the performance of commonly adopted model selection criteria in terms of providing a reliable and valid estimate for the health impact of influenza. Methods: We assessed four model selection criteria: quasi Akaike information criterion (QAIC), quasi Bayesian information criterion (QBIC), partial autocorrelation functions of residuals (PACF), and generalized cross-validation (GCV), by separately applying them to select the Poisson model best fitted to the mortality datasets that were simulated under the different assumptions of seasonal confounding. The performance of these criteria was evaluated by the bias and root-mean-square error (RMSE) of estimates from the pre-determined coefficients of influenza proxy variable. These four criteria were subsequently applied to an empirical hospitalization dataset to confirm the findings of simulation study. Results: GCV consistently provided smaller biases and RMSEs for the influenza coefficient estimates than QAIC, QBIC and PACF, under the different simulation scenarios. Sensitivity analysis of different pre-determined influenza coefficients, study periods and lag weeks showed that GCV consistently outperformed the other criteria. Similar results were found in applying these selection criteria to estimate influenza-associated hospitalization. Conclusions: GCV criterion is recommended for selection of Poisson models to estimate influenza-associated mortality and morbidity burden with proper adjustment for confounding. These findings shall help standardize the Poisson modeling approach for influenza disease burden studies. © 2012 Wang et al.published_or_final_versio

Permanent Campaigning: A Meta-Analysis and Framework for Measurement

Permanent campaigning emerged as a concept in the 1970s in studies of US politics but is now recognized as a universal phenomenon. Despite its long history, there has been no attempt to build a holistic picture of the elements that constitute a permanent campaign. Generally, researchers focus on tactical elements, situating their use within an overall strategy, but there is a lack of a broader methodological framework for holistically measuring adherence to the permanent campaigning. This article presents results of a meta-analysis of relevant research to provide a framework to understand how permanent campaigning is practiced. Our study showed there were three reasonably discrete forms of campaigning activities that had been identified: those in which permanent campaign strategies are related to capacity building and strategy; a second, in which permanent campaigning relates to paid and owned media; and a third in which earned media is the main focus. In mapping these studies, we identify the common features of permanent campaigning, identifying strong and weak indicators and the extent these are employed by the government, parties, or elected representatives and within which political systems: parliamentarism or presidentialism. Our framework can be applied in future comparative research to understand trends in political communication

Consistency of semiparametric maximum likelihood estimators for two-phase, outcome dependent sampling

Semiparametric maximum likelihood estimators have recently been proposed for a class of two-phase, outcome dependent sampling models; e.g. Breslow and Holubkov (1997), Scott and Wild (1998), and Lawless, Wild, and Kalbfleisch (1999). The estimators studied by these authors are predicated on the estimates of the underlying covariate distribution being concentrated on the observed covariate values. Here we give conditions for consistency of the (restricted) maximum likelihood estimators proposed by these authors. We also consider the corresponding maximization problems in further detail and show that the unrestricted maximum likelihood estimators may have support on additional points in the covariate space. In the companion paper by Breslow, McNeney, and Wellner (2000a), efficiency and asymptotic normality of the restricted maximum likelihood estimators are also established

Supplementary Material for: Using Gene Genealogies to Detect Rare Variants Associated with Complex Traits

<b><i>Background and Objective:</i></b> Standard population genetic theory says that deleterious genetic variants are likely rare and fairly recently introduced. However, can this expectation lead to more powerful tests of association between diseases and rare genetic variation? The gene genealogy describes the relationships between haplotypes sampled from the general population. Although ancestral tree-based methods, inspired by the gene genealogy concept, have been developed for finding associations with common genetic variants, here we ask whether gene genealogies can help in identifying genomic regions containing multiple rare causal variants. <b><i>Methods:</i></b> With data simulated under several demographic models and using known gene genealogies, we developed and compared several tree-based statistics to determine which, if any, could detect the type of clustering expected with rare causal variants and whether the genealogic tree provides additional information about disease associations. <b><i>Results and Conclusions:</i></b> We found that a novel statistic based on the scaled distance between the tips of a tree performed better than other tree-based statistics. When data were simulated with mild population growth, this statistic outperformed two standard non-tree-based methods, showing that an ancestral tree-based approach has potential for rare variant discovery

Glutamate cysteine ligase catalytic subunit promoter polymorphisms and associations with type 1 diabetes age-at-onset and GAD65 autoantibody levels.

The purpose of this study was to test the hypothesis that glutamate cysteine ligase catalytic subunit (GCLC) promoter polymorphisms are susceptibility factors for type 1 diabetes (T1D), T1D age-at-onset and T1D autoantibodies. T1D patients and control subjects from the Swedish Childhood Diabetes Registry and the Swedish Diabetes Incidence Study registry were genotyped for two GCLC promoter polymorphisms; the GCLC -129 C to T single nucleotide polymorphism (GCLC -129 SNP) and the GCLC GAG trinucleotide repeat polymorphism (GCLC TNR). Glutamate decarboxylase antibody (GAD65Ab) positive T1D patients with the GCLC -129 SNP C/T genotype have increased GAD65Ab levels (p-value, <0.05) compared to the GCLC -129 SNP C/C genotype. T1D patients with an age-at-onset of 14-35 years who possess the GCLC -129 SNP T/T genotype have a higher GAD65Ab index than T1D patients with the GCLC -129 SNP C/C genotype (p-value <0.05). In addition, T1D patients with an age-at-onset of 14-35 years possess the GCLC TNR 7/8 genotype at a lower frequency than the control subjects (OR, 0.33, 95% CI, 0.13-0.82). The GCLC -129 SNP and GCLC TNR appear to be in linkage disequilibrium (p-value<0.0001). These results suggest that GCLC promoter polymorphisms may influence GAD65Ab levels and may influence the age at which T1D is diagnosed

IA-2 autoantibodies in incident type I diabetes patients are associated with a polyadenylation signal polymorphism in GIMAP5.

In a large case-control study of Swedish incident type I diabetes patients and controls, 0–34 years of age, we tested the hypothesis that the GIMAP5 gene, a key genetic factor for lymphopenia in spontaneous BioBreeding rat diabetes, is associated with type I diabetes; with islet autoantibodies in incident type I diabetes patients or with age at clinical onset in incident type I diabetes patients. Initial scans of allelic association were followed by more detailed logistic regression modeling that adjusted for known type I diabetes risk factors and potential confounding variables. The single nucleotide polymorphism (SNP) rs6598, located in a polyadenylation signal of GIMAP5, was associated with the presence of significant levels of IA-2 autoantibodies in the type I diabetes patients. Patients with the minor allele A of rs6598 had an increased prevalence of IA-2 autoantibody levels compared to patients without the minor allele (OR=2.2; Bonferroni-corrected P=0.003), after adjusting for age at clinical onset (P=8.0 times 10-13) and the numbers of HLA-DQ A1*0501-B1*0201 haplotypes (P=2.4 times 10-5) and DQ A1*0301-B1*0302 haplotypes (P=0.002). GIMAP5 polymorphism was not associated with type I diabetes or with GAD65 or insulin autoantibodies, ICA, or age at clinical onset in patients. These data suggest that the GIMAP5 gene is associated with islet autoimmunity in type I diabetes and add to recent findings implicating the same SNP in another autoimmune disease