The association between each health condition and dementia progression was evaluated using univariate and multivariate (age, gender and education) logistic regression models (weighted for attrition) in the NCI, MCI and OCIND groups.
In sensitivity analyses, a similar pattern of attenuation of regional NO2 effect estimates was observed in models weighted for time at school and home (results not shown), and in models adjusted for total (freeway plus nonfreeway) TRP exposure at school and home [Supplemental Material, Table E-2 (doi:10.1289/ehp.0901232)].
The FHP average treatment effect estimates obtained with the propensity score matching analysis and the FHP rate ratio estimates obtained with the fixed effects negative binomial regression models weighted for the propensity score performed as further analyses of the robustness of the results—are similar and confirm the findings of the study.
Also, adjustment for a large number of covariates via a regression model weighted for a propensity score did not substantially attenuate this finding (2.25, 1.11 to 4.54, P=0.024).
All models were weighted for national representation, corrected for clustering on our primary sampling unit (schools) and controlled for continuous neighbourhood-level education and population density dichotomised into urbanicity-specific quantiles.
Using a model weighted for human mutations, we obtained performance accuracies that outperformed traditional prediction methods (i.e., SIFT, PolyPhen, and PANTHER) on two separate benchmarks.
Using a model weighted for human mutations, we obtained performance accuracies that outperformed traditional prediction methods SIFT, PolyPhen, and PANTHER on two separate benchmarks.
In our previous work, we developed the Functional Analysis through Hidden Markov Models (FATHMM) software and, using a model weighted for inherited disease mutations, observed improved performances over alternative computational prediction algorithms.
In our previous work, we developed the Functional Analysis through Hidden Markov Models (FATHMM) algorithm and, using a model weighted for inherited disease mutations, observed improved performance accuracies over alternative computational prediction methods using the same benchmark (Shihab et al., 2013).
Using a model weighted for cancer-associated mutations, we observe performance accuracies, which outperform alternative computational prediction algorithms (Adzhubei et al., 2010; Capriotti and Altman, 2011; Ng and Henikoff, 2001; Reva et al., 2011) when distinguishing between driver and other germ line mutations (both disease-causing and neutral polymorphisms).
Because growth rates among healthy and cryptorchid boys may differ, complex samples general linear model weighted for sampling fractions of boys with (121/55) and without (5677/55) cryptorchidism from the total cohort was used to study the association between placenta OTC concentrations and different anthropometric parameters.
