Compared to other study designs, cohort studies are somewhat more difficult to analyze because of the presence of time-dependent variables, including calendar period, age, time since first exposure, length of exposure, and cumulative exposure. Since individuals typically pass across several categories of these variables, the primary pre-analytic step is to calculate person-time at risk of observing events of interest to the correct categories of those variables. This usually amounts to create a new dataset with many records per subject and then to proceed with statistical analysis by calculating one or more frequency and association measures, including incidence/mortality rates, rate ratios (RR) with Poisson regression models, standardized mortality ratios (SMR), or hazard ratios (HR) with Cox regression models. In calculating cumulative exposure (lagged or unlagged, or within a specified time-window) an added complication is the necessity to link individual data with external exposure data (exposure matrices).
The purposes of the course are to give:
1) a theoretical overview of the analysis of cohort data;
2) practical information on how to do it with available software.
Learning objectives are:
1) to deal with time-dependent variables, with a focus on exposure matrices and cumulative exposure;
2) to work with time and dates in a statistical software;
3) to calculate person-time at risk in a correct manner, while avoiding mistakes such as immune/immortal person-time;
4) to illustrate specific commands for person-time calculation and cohort analysis available in statistical software like Stata and R.