Experimental Studies
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This document discusses experimental study designs, specifically randomized clinical trials. It describes key aspects of randomized trials including multiple experimental groups, blinding techniques, objectives related to public health and clinical practice, and historical examples. Randomized trials are identified as the ideal design for evaluating new interventions by comparing outcomes between randomized treatment groups to eliminate selection bias. Key aspects covered include selection and stratification of subjects, data collection on variables like treatment received, outcomes, and prognostic profiles, as well as blinding techniques.
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Experimental Studies
- 1. Experimental Studies BY DR MUHAMMAD TAUSEEF JAVED CONSULTANT MINISTRY OF HEALTH MAKKAH & IPH LAHORE
- 2. Types of Experimental Studies • Multiple experimental groups • Blinds single, double, triple
- 3. Public Health & Clinical Objectives • Modify natural history of disease and express disease prognosis Prevent or delay death or disability Improve health of patient or population • Need to use best preventive or therapeutic measures Randomized trials are ideal design to evaluate effectiveness and side effects of new forms of intervention
- 4. Historical Perspectives • Sir Francis Galton (1883) - ruminated over the influence of prayer • Joyce and Welldon (1965) found no benefit of prayer • R. C. Byrd (1988) - suggested positive benefits • Washington Post Parade article (2003) - also suggested positive benefits
- 6. Recent Perspectives • Effect of: coffee on CHD carotene on cancers hormonal therapy on breast cancer drug-lowering cholesterol on CHD
- 7. Randomized Trials • Historically, were done accidentally, in other words, “unplanned trials” Ambroise Pare (1510 - 1590) discovered new treatment for war wounds when original therapy was unavailable James Lind (1747) studying scurvy • Subjects assigned to groups using a non-biased procedure
- 8. Design of a Randomized Clinical Trial
- 9. Selection of Subjects • Well-designed • Eliminate subjectivity • Promote reliability Replicable, as with laboratory experiments Accurate
- 10. Selection of Subjects: Studies without Comparison • Question: If we administer a drug and the patient improves, can we attribute the improvement to the administration of that drug? • Answer: Results can always be improved by omitting controls. - Prof. Hugo Muensch Harvard University
- 11. Selection of Subjects: Studies with Comparison • Historical controls (comparison group from past) Data must be abstracted from records not kept for research purposes Differences may be due to quality of the data May not be able to substantiate differences Can be useful for drugs developed against fatal diseases
- 12. • Simultaneous Non-Randomized Controls May introduce bias Example - BCG vaccination study in NYC in 1975 • Investigators introduced selection bias in the experimental group and controls • A change in the study design that eliminated selection bias, although still not randomized, also eliminated differences observed in final results Selection of Subjects: Studies with Comparison (cont.)
- 15. • Best approach • Uses tables of random numbers • Must still eliminate physician bias • Can achieve non-predictability Selection of Subjects (cont.): Randomization
- 17. Effect of Comparability Not Randomized Randomized
- 18. Selection of Subjects (cont.): Stratified Randomization • Useful when concerned that certain variables may affect the outcome For example, when the prognosis may be much worse for older patients • Want two treatment groups to be comparable in terms of the variables of concern • Initially stratify (layer) the study population according to each variable of concern and then randomize participants to treatment groups within each stratum
- 19. Selection of Subjects (cont.): Stratified Randomization
- 20. Data Collection on Subjects: Potential Variables • Treatment: that was assigned that was received • Outcome Explicit criteria required Comparable measurements required • Prognostic Profile at Entry If risk factors for a bad outcome are known, assure that treatment groups are reasonably similar for these factors Data for prognostic factors obtained upon enrollment in study • Masking (Blinding)
- 21. Data Collection on Subjects (cont.): Masking (Blinding) • Attempt to eliminate biases & preconceptions • Single-blind Subject masking Use of placebo • Double-blind Subject masking and researcher masking • Data collectors and data analysts • Triple-blind Subject masking, researcher masking and study sponsor masking