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A Brief Introduction to Epidemiology - XI (Epidemiologic Research Designs: Experimental/Interventional Studies) PowerPoint Presentation
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A Brief Introduction to Epidemiology - XI (Epidemiologic Research Designs: Experimental/Interventional Studies)

A Brief Introduction to Epidemiology - XI (Epidemiologic Research Designs: Experimental/Interventional Studies)

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A Brief Introduction to Epidemiology - XI (Epidemiologic Research Designs: Experimental/Interventional Studies)

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  1. A Brief Introduction to Epidemiology - XI(Epidemiologic Research Designs:Experimental/Interventional Studies) Betty C. Jung, RN, MPH, CHES

  2. Learning Objectives • To understand: • What experimental studies are • The value of such studies • The basic methodology • Pros and Cons of such studies

  3. Introduction The primary purpose of research is to conduct a scientific, or, scholarly investigation into a phenomenon, or to answer a burning question. Research is defined as a systematic approach to problem solving.

  4. Epidemiological Study Designs • Observational Studies - examine associations between risk factors and outcomes (Analytical - determinants and risk of disease, and descriptive - patterns and frequency of disease) • Intervention Studies - explore the association between interventions and outcomes. (Experimental studies or clinical trials)

  5. Epidemiological Study Designs • Observational • Cross-Sectional • Case-control • Cohort • Interventional • Natural Experiment (Community Trial) • Field Trial • Experiment/Randomized Trails (ex. Clinical Trial)

  6. Examples of Experimental Epidemiologic Studies • Prophylactic vaccines tested on children populations to prove the efficacy of the vaccines in preventing the diseases (i.e., polio) • Prophylaxis with drugs in preventing disease (i.e., penicillin to prevent rheumatic fever) • Impact on health-related behavior and coronary heart disease in response to community-wide heart disease prevention intervention

  7. Value • Experiments are seen as the “Supreme Court” of epidemiologic research as they provide the strongest possible evidence of disease causation. • Experimental study designs can rule out with greater certainty factors that may confound potential cause and affect relationships. • A study’s degree of internal validity depends on the study design’s ability to determine whether an antecedent causes an effect (or outcome).

  8. Community Trials • Communities rather than individuals comprise the treatment groups • Appropriate for diseases that have their origins in social conditions that can be influenced by intervention directed at group behavior as well as individuals

  9. Limitations of Community Trials • Random allocation of communities is not practical • Only a small number of communities can be included • Other methods are needed to ensure any difference found can be attributed to the intervention rather than to any inherent differences between the communities studied

  10. Field Trials • Involve people who are disease-free but presumed to be at risk • Data collection – “in the field” – among non-institutionalized people in the general population • Used to evaluate interventions that reduce exposure without measuring the occurrence of health effects.

  11. Limitations of Field Trials • Hugh undertaking • Major logistic considerations • Major financial considerations • Think of how much work is required to randomize and allocate participants to various treatment groups!

  12. Experimental Study Design Time Treated - Improved Treated (T) Treated – Not Improved Sample of Cases Not Treated - Improved Not Treated (NT) (Control) Not Treated – Not Improved

  13. Randomized Trial Methodology • Random allocation - Each subject has an equal chance of being assigned to any group in the study, so that all groups in a study are similar in all characteristics not controlled by other methods, such as subject selection. • Random allocation can be used with matching to ensure the study groups are comparable

  14. Randomized Trial Design Time Improved R A N D O M I Z E D New Treatment Not Improved Defined Population Improved Current Treatment Not Improved

  15. Four Possibilities • The treatments do not differ and we correctly conclude they do not differ • The treatments do not differ but we conclude they do differ • The treatments differ but we conclude they do not differ • The treatments do differ and we correctly conclude that they do differ

  16. Pros • Helpful in assessing the value of new therapies to combat acute diseases in developing countries • Can evaluate a single variable in a precisely defined patient group • Prospective design • Eliminates bias by comparing two otherwise identical groups • Allows for meta-analysis

  17. Cons • Expensive and time consuming • Not always properly conducted – too few subjects, too short a time period • Influence of sponsorship • Use of surrogate endpoints may introduce “hidden bias” • Failure to randomize all eligible subjects • Failure to blind assessors to randomized status of subjects

  18. References • For Internet Resources on the topics covered in this lecture, check out my Web site: