Differences in age and sex between the 2 study populations were analyzed by using a general linear model (PROC GLM; CLASS population; MODEL age = population; MEANS population) and a Mantel-Haenszel χ2 test, respectively.
The fraction of the variance in height explained by the haplotypes were estimated by analysis of variance using a generalized linear model including sex, age, population affinity (VB or NB) and haplotype status (carrying or not carrying the negatively associated allele).
The following variables identified a priori were included in the logistic regression model: age, parasite density, study population (asymptomatic or symptomatic) and molecular markers.
In the Poisson model, age-sex-specific population figures were used as the offset population.
The stability of the steady state of the age population balance model under conditions that allow oscillatory dynamics was not resolved.
A scenario tree model which accounted for administrative region, age, population immunity, the likelihood of AFP, and the probability of notification and stool sampling was used to assess the sensitivity of AFP surveillance for wild poliovirus infection among children aged less than 15 years in Australia.
The primary association analyses were adjusted for sex, age, population stratification, and cohort (Model A) and sex, age, population stratification, cohort, and APOE-ɛ4 (Model B).
We used three levels of adjustment: (i) adjusted for age, population and attending examination in a clinic (Model 1); (ii) adjusted for age, population, attending examination in a clinic and socioeconomic status indicators (Model 2) and (iii) adjusted for age, population, attending examination in a clinic, socioeconomic status indicators and health behaviours (Model 3).
a-f Models a-f Modelsor sex, adjustedulation group, smoking habits, occupational exposure history to dust/chemical foresexnd mage residential heating/cooking fuel typopulation
In our vaccination model, age-dependent fractions of the population are either vaccinated with TIV or with LAIV during October and November.
In the above age-structured predator prey population models, age structure is introduced into interactions of multi-species, and population models can quickly become remarkably complex [7, 13].
