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Data Needs for a Model

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  1. Cardiovascular Epidemiology and Epidemiological Modelling Data Needs for a Model Martin O’Flaherty Simon Capewell Division of Public Health University of Liverpool

  2. Model data needs • Depend on: • Questions to be addressed • Choice of model logic • Desired outputs • Purpose: • Populate the model • Validate the model • Key issues • Availability • Format A modeller’s dilemma: Built your model around your data OR Build a model and then gather the data.

  3. A generic model of a chronic disease Healthy Disease Death

  4. A generic model addressing a generic public health question Healthy Disease Death Primary Prevention Secondary Prevention

  5. A generic model of a chronic disease What determines INCIDENCE Healthy Disease How large are the groups What Determines prognosis Death Time

  6. A summary of IMPACT data needs • Number of people in each group • In each risk factor • In each disease subgroup (eg: AMI, UA, CA, HF) • Number of deaths • What determines incidence • Risk factors effect measures (RR or b) • What determines prognosis: • Case fatality rates for each disease group (rates) • Interventions that reduce case fatality (RRR) • Uptake of those interventions (%)

  7. A summary of IMPACT data needs • Take into account time • Trends of: • Number of people in each group • Levels of risk factors • Levels of uptake of treatments • Data to validate the model • Observed mortality

  8. Some last (but not least) data need • Data to support assumptions: • Needed to fill gaps in knowledge • They are guesses, but the better the data supporting the guess the better the guess. • An example: • “English fatality rates: Scottish SLIDE data adjusted using England/Scotland SMR (see http://www.heartstats.org/temp/Tabsp1.6spweb06spup.XLS)” • Data will need: • Critical appraisal • Adaptation • Documented (appendices)

  9. The data gathering task • This phase of a modelling project is critical: • Defines feasibility • Defines the quality of the final product (the principle of “garbage IN, garbage OUT” • It is time consuming: it will take a large amount of the available resources. • But as any activity in a modelling exercise, data gathering is an ITERATION: • We start simple, and we add layers of greater complexity.

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