IntroductionLarge population-based data sets present similar analytic issues across such fields as: population health, clinical epidemiology, education, justice, and children’s services. Step-wise approaches and generalized tools can bring together several pillars: big (typically administrative) data, programming, and study design/analysis. How can we improve efficiency and explore alternative designs?
 Objectives and ApproachLinked data sets typically contain: 1) files presenting longitudinal histories 2) substantive files noting various events (concussions, burns, loss of a loved one, public housing entry) and several possible covariates and outcomes.
 Step-wise approaches enable automating tasks by developing general tools (decreasing programmer input) and facilitating alternative designs. Macros improve upon the classic ‘one design, one data set’ perspective. Two case studies highlight tradeoffs in retrospective cohort studies (quasi-experiments) among sample size, length of follow-up, and the number of time periods.
 ResultsStudy 1: Step 1 calculated the number of mothers with a child placed in care during various index years. Taking 1 year before and after plac
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