Research Methodology 1
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This document discusses different study designs used in public health research, including observational and interventional studies. Observational studies include cross-sectional, case-control and cohort studies. Cross-sectional studies collect data at one point in time to measure disease prevalence. Case-control studies compare exposures in cases (people with disease) versus controls (people without disease). Cohort studies follow groups over time to measure disease incidence and identify risk factors. Interventional studies, like randomized controlled trials, are considered the strongest type of evidence as they involve intervention to reduce exposure or compare alternative treatments.
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Research Methodology 1
- 1. Tamer Hifnawy MD. Dr.PH Associate Professor Public Health & Community Medicine Faculty of Medicine – BSU- Egypt College of Dentistry Taibah University- KSA Vice Dean For Quality, Development & International Affairs CertifiedTrainer for International Research Ethics
- 2. Study designs can be split into: 1. Observational: collect information on conditions the investigator has no control. 2. Interventional (experimental): intervene to reduce exposure or to allocate alternative treatments.
- 4. Did the investigator assign exposures? Yes No Experimental study Observational study Interventional Studies Comparison group? Descriptive study Analytical study Direction? Cross-sectional study Case-Control study Cohort study Yes No Clinical Trials
- 7. case report case series case-control cohort studies clinical trial randomized clinical trial blinded randomized clinical trial Best Evidence
- 8. The strongest evidence for therapeutic interventions is provided by systematic review of Randomized, Triple-blind, Controlled trials
- 9. You will need to examine the study’s methodology and results
- 11. A point in time picture of disease, health event, medical or psychosocial phenomenon. Most often utilizing a survey mechanism Cross Sectional Study
- 12. They collect information to measure prevalence at one point in time. The prevalence of the outcome can be measured without reference to exposure or the reverse (descriptive). The prevalence of disease can be measured in relation to the exposure of interest (analytical).
- 13. Population Representative sample (if not using the whole pop) Number with Disease or Condition Number without Disease or Condition Calculate prevalence of disease, co ndition TIME
- 14. No of cases of a disease diagnosed during a cross-section study ------------------------------------ X 100 All examined individuals studied
- 15. Population Representative sample (if not using the whole pop) Number with Disease and with Exposure Calculate prevalence of disease, in exposed. Number with Disease but without Exposure Number without Disease but with Exposure Number without Disease and without Exposure Calculate prevalence of disease, in unexposed TIME
- 16. Assume we are interested in the possible relationship between serum cholesterol level (Exposure) and ECG evidence of CHD (Outcome) Information about exposure and disease is collected simultaneously
- 17. Analysis of Cross-Sectional Studies dc Not Exposed baExposed No DiseaseDisease a / (a + b) Prevalence Ratio (PR) = ------------ c / (c + d)
- 18. Cross-Sectional Studies 45050 Normal/ Low 400100High No CHDCHD Cholesterol 100 / (100 + 400) 0.2 Prevalence Ratio = --------------------- = ---- = 2.0 50 / (50 + 450) 0.1
- 19. Cross-Sectional Studies 100 / (100 + 400) 0.2 Prevalence Ratio = --------------------- = ---- = 2.0 50 / (50 + 450) 0.1 Interpretation: In this study population, the prevalence of CHD is 2 times higher among those with high cholesterol, compared to the prevalence in those with normal or low cholesterol.
- 20. Cross Sectional Study Advantages Less time Less expensive Ease Provides vast information Can study interrelatedness of variables Not based on illness or medical treatment Disadvantages Rarely able to establish cause and effect Bias to individuals who complete the survey Recurrent conditions not picked up
- 22. In language this means “ A group united in some struggle” Word Cohort is used in statistics to indicate studying a group of different individuals with some common features.
- 23. “ WHAT Will HAPPEN ? ” Looking Forward in Time
- 24. Marching towards outcomes
- 28. Ate Meat Did not eat Meat ill not ill 49 49 98 2 8 10
- 30. A B C D Exposed A+B Not exposed C+D Cancer Cancer free Relative Risk = divided by risk of cancer among exposed group (A/A+B) Risk of cancer among unexposed (C/C+D)
- 31. Relative risks (RR) is a direct estimator of the exposure risk When RR value is one then the risk of disease is the same in the exposed and unexposed. This means that the studied exposure factor is not a risk factor. When RR value is more than one then the risk of disease in the exposed group is greater than the risk in the unexposed group. This means that the factor is a risk factor, or an etiological factor for disease. When RR is less than one then the risk of disease in the exposed group is less than the risk in the unexposed. This means that the factor is a protective factor.
- 32. A cohort study was done to determine the association between vegetarianism and CHD. Hypothetical results are presented in the following table.
- 33. Vegetarians Non Vegetarians CHD No-CHD 50 2450 2500 1000 9000 10000
- 36. Cancer No Cancer Total Exposed 10 100 110 Not Exposed 9 456 465 Relative risk = (A/A+B) / (C/C+D) = (10/110) / (9/465) = 4.79
- 37. PLUSES Causality Can cope with rare exposures Multiple outcomes can be studied Disease incidence is measured MINUSES High costs Long time period Losses to follow-up Residual confounding
- 40. This means collection of data for two different groups of people. The first group would be the group of concern, that we need to collect data about, this is known as cases groups. The second group are people who are chosen for comparison with the case group, this is known as control group.
- 41. It is important to know that the control group must be comparable to the case group, this is called matching. It is very difficult to match an individual to another, because if he is of the same age, he may not be of the same weight or height. Hence one to one match is difficult, we usually get two or three control individuals to each case person.
- 42. Another way is matching the whole case group characteristics by a group of similar controls. Accordingly, control group size should be at least equal to the case group, of course it may be double or triple the size of cases if two to one or three to one matching is used.
- 52. 1. Start with finding subjects cases controls 2. Look back at risk factors Back then today time Yes or No
- 53. A B C D Cases Controls Exposed Un-exposed Exposure Odds ratio = divided by odds of exposure among the cases (A/B) odds of exposure among the controls (C/D)
- 54. Exposed Not exposed Cases 119 317 Controls 68 319 Study of OC use and ovarian cancer risk Odds ratio = (A/B) / (C/D) = (119/317) / (68/319) = 1.76
- 55. Exposed Not exposed Cases 119 317 Controls 68 319 Odds ratio = (A*D) / (B*C) = (119*319) / (317*68) = 1.76
- 56. OR = 1.76 means that : The odds of exposure to Oral contraceptive among cases with ovarian cancer is 1.76 times as large the odds of exposure of OC among controls. OC could be thus a Risk factor for developingOvarian Cancer
- 57. Cannot compute directly relative risk Not suitable for rare exposure Relationship exposure-disease difficult to establish (association not causality) Biases +++ control selection recall biases when collecting data
- 58. Rare diseases Several exposures Long latency Rapidity Low cost Small sample size Available data No ethical problem