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Both random and conditional fixed effects were estimated, and we report the results for the random effects analysis.
By applying a conditional, fixed-effects, logistic regression model that matched each patient with him/herself throughout the observation period we sought to minimize patient introduced variation.
To determine an odd's ratio describing the likelihood for detectable viremia during the RT-PCR vs. bDNA periods, we utilized a conditional, fixed-effects, logistic regression model that accounted for correlation between an individual patient's multiple samples (Stata version 9, StataCorpLP, College Station, TX).
Data was analysed using conditional (fixed-effects) logistic regression.
Analyses were undertaken using conditional, fixed-effects, Poisson regression.
Technically, to implement this we used conditional fixed effects Poisson models using Stata's xtpoisson command.
Conditional, fixed effects, negative binomial regression models seemed to be the most appropriate for our analysis.
The data were analysed using conditional (fixed-effects) logistic regression in STATA.
Conditional fixed effects negative binomial regression was estimated in Stata version 10 to control for these time invariant factors.
Hausman's test was used to choose among conditional fixed-effects and random - effects logistic regression model for the panel data analysis.
Analyses were made of longitudinal changes in counts of road injuries within each of 119 029 road segments with at least one casualty with conditional fixed effects Poisson models.
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