In Cox models, which controlled for smoking, increased lung cancer risk was observed among workers with ILO scores of 0/1 (RR = 1.17, 95% CI = 0.89-1.54), with a strong trend for increasing lung cancer risk with increasing ILO profusion score >0/0.
Statistical analyses included chi-square analysis and logistic regression models which controlled for numerous factors.
We analysed time-series data with non-parametric regression models including generalised additive models, which controlled for time trends.
* Adjusted odds ratios obtained from logistic models which controlled for age, sex, marital status, residence, cause of death and year of death.
Linear mixed-effects models which controlled for age, sex and education, showed that the level of loneliness at baseline was associated with the rate of motor decline (Estimate, -0.016; S.E. 0.006, p = 0.005).
Logistic regression models, which controlled for demographic and psychosocial factors that varied by recruitment cohort as well as experimental variables (that is, randomization status), were used to examine changes to health behaviors and advance planning, including tests for interaction effects between cohort and genetic risk status.
With this framework, the best way to account for individual heterogeneity is to include a diverse array of individual covariates in the hazard model which control for individual characteristics as well as household characteristics.
Column 4 presents results from the bivariate Tobit model, which controls for censoring in both equations.
As a base model, we create a model which controls for reported seasonal patterns in suicide occurrences and takes into account the most recent suicide statistics at our disposal.
My second specification is based on a fixed effects model, which controls for unobserved (time-constant) variables affecting both preferences and marriage decisions by taking the difference of observations in time periods before and after marriage for the same individual.
