A Database is not what you think and different designs serve different purposes.
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A Database is not what you think and different designs serve different purposes.

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Lecture given at Eular Vienna 2005.

Lecture given at Eular Vienna 2005.

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A Database is not what you think and different designs serve different purposes. A Database is not what you think and different designs serve different purposes. Presentation Transcript

  • Different designs of longitudinal observational studies (databases, regist ries , cohort studies) serve different purposes Loreto Carmona Research Unit Spanish Society of Rheumatology
  • Why this talk?
    • Sometimes the only point in which investigators agree in a given project is about the need to collect data .
    • T here is indeed much misunderstanding about databases.
    • T he first principle of data collection : We should not collect data just for the sake of it, without explicit and specified objectives .
  • What is a database?
    • NOT:
      • Any set of data entered into a computer
    • YES:
      • A tool to organize the data in a study
    View slide
  • What is not a database? A dataset View slide
  • Other uses
  • What is a database?
    • Organizing
    • Hierarchical
    • Relational
  • Structure patient treatment treatment treatment treatment AE AE AE AE AE AE
  • Structure patient visit visit visit visit outcome outcome outcome outcome outcome outcome
  • Time and databases Medical databases time Longitudinal Observational Studies
  • Can we analyse data directly from a DB? wide long
  • wide
  • long
  • Types of databases
    • Depend on the purpose and design of the study they serve .
  • Types of databases
    • Registries
    • Cohorts
    • Administrative databases
    • Clinical databases
  • Registries
    • The introduction of new elements is not pre-planned (no schedule).
      • relate to the pace of patients entry
      • events entry
    • The purpose of the registry defines the types of registry:
      • Disease registry
      • Drug registry
  • Registries
    • Disease registry :
      • to estimate incidence of diseases
      • pace of new entries = occurrence of new cases
    • Drug registry :
      • The main purpose of such registries is the identification of adverse events
      • pace of entry = initiation of new treatments, of a target drug or therapeutic group, in patients who meet the inclusion criteria
  • Cohorts
    • Studies set up for testing hypotheses .
      • Sensu strictu aetiological hypotheses
      • they can also be assembled to test prognosis, or to test resource use hypothesis
    • Subjects in a cohort must be sampled in a probabilistic way ( to be representative ) .
    • Timing of data entry pre-established in visits or examinations at a given interval ( periodical ).
  • Cohorts (concepts)
    • Inception cohort
      • all new cases
      • or early
    • Nested case-control studies
      • advantage: cases and controls selected equally
  • Cohorts (concepts)
    • Open cohorts
      • Permanent incorporation
      • allow the study of the simultaneous influence of calendar time, age (duration) and cohort (onset) in demography, epidemiology, and clinical follow-up
  • Cohorts (concepts)
  • Cohorts (concepts)
    • Closed cohorts
      • specific research objective ( sample size )
      • recruitment period
      • subjects become older with follow-up
    • Permanent surveys
      • examination over time is established in repeated cross-sectional surveys
      •  large open cohorts which are monitored over such a long time ( Framingham study )
  • Cohorts (concepts)
    • Randomised controlled trials
      • cohorts in which the hypothesis is the efficacy of an intervention
      • Not a LOS ( planned intervention )
  • Cohorts (concepts)
    • Aren’t prognosis or resource use studies cohorts?
      • OK if the sample is selected in a probabilistic way
      • and the hypothesis is pre-established before the launch of the cohort :
        • link between exposure to prognostic factors (i.e. determinants) and rate of occurrence of outcome
        • link between determinants and rate of use or cost
  • Types of databases
    • Registries
    • Cohorts
    • Administrative databases
    • Clinical databases
  • Administrative databases
    • S pecific purpose : managing the economic and organization aspects
    • They allow to assess and preview :
      • the need of workforce
      • allocation of resources
      • logistic needs
      • insurance monitoring
      • costs, etc.
  • Administrative databases as surrogates
    • PROS
    • rapid response (availability)
    • large sets of population ( increases statistical power )
    • CONS
    • severe selection bias
  • Administrative databases ’ best use
    • To cross-check data in clinical databases :
      • to confirm death ,
      • work status ,
      • drug consumption ...
  • Clinical databases
    • Quite often physicians record patients characteristic and variables directly from their practice in an intent to monitor health care practices.
    • The resulting clinical databases are useful to :
      • retrieve patients’ reports
      • Retrieve individual clinical histories
      • assess the individual practice composition of a department or a clinic
  • Clinical databases ’ bias
    • The patients included have not been selected in a probabilistic way , what precludes the use of formal hypothesis testing statistical instruments.
    • Representativity
    • D ifficult y to record all variables with the expected quality of a main outcome variable if one does not know ahead of time what the main outcome variable will be ( study protocol )
  • Clinical databases ’ best use
    • To monitor practice
    • T o identify patients with a given characteristic, and to use them to draw a random sample, better if multicentre, and to test prospectively a research hypothesis
  • Conclusions
    • We should bear in mind the meaning and use of databases.
    • Different types of databases serve very different purposes .
    • A clear understanding of the different investigation designs sh ould avoid many of the databases we launch be just a lot of work and very little of science.