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We therefore used a generalised linear model for the binomial family with an identity link function.
We fitted a generalised linear model for our binary outcome (binomial distribution) with a log-link, which gives the risk ratio.
For statistically significant variables we then used a generalised linear model for a binary outcome with a log link to obtain the PR for a multivariable model.
A generalised linear model for binomial distribution with a logarithmic link function was used to calculate risk ratios (RRs) and their 95% CIs.
Comparisons between AD and GOA control cohorts were conducted using conditional logistic regression for patient characteristics and a generalised linear model for resource utilisation.
For the two secondary outcomes among HIV infected people we used a generalised linear model for the binomial family with a log link, taking into account the design effect through robust clustering owing to the small size of this subgroup.
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We chose to use logistic models for both pm and pc largely to avoid confusion with the often-assumed probit model, but any generalised linear model for a binary outcome would suffice.
A generalised linear model controlling for age, gender, doctor type and rurality.
Risk differences (RD) were calculated by primary care characteristics using a generalised linear model, accounting for patient clustering within practices.
Accordingly, all variables that tested significantly were included in an ordinal logistic regression model (through a generalised linear model) testing for the main effect of these variables on the ordinal dependent variable.
Bivariate analyses for differences in means were carried out using a two-sample Kolmogorov-Smirnov test or a generalised linear model adjusted for the effects of other variables and confounding factors.
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