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Lecture 8 Objective 20. Describe the elements of design of observational studies: case reports/series.

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Presentation on theme: "Lecture 8 Objective 20. Describe the elements of design of observational studies: case reports/series."— Presentation transcript:

1 Lecture 8 Objective 20. Describe the elements of design of observational studies: case reports/series.

2 Case Reports/Case Series describe the experience of a single case or group of cases. used to report a new syndrome or disease or the emergence of an epidemic. hypothesis formation but may not be used to evaluate the effectiveness of treatment.

3 Case Report/Case Series There is one exception to that in which a new treatment results in significant survival when previously most cases had died. A model for the publication of a case report is that found in the New England Journal of Medicine.

4 Lecture 8 Objective 21.Describe the elements of design of observational studies: correlational studies

5 Correlational/Ecological Studies uses data from entire populations to compare disease frequencies between different groups during the same period of time or in the same population at different points in time. Good for formulation of hypotheses.

6 Correlational/Ecological Studies e.g. the relationship between per capita daily consumption of meat and rates of colon cancer in women for a large number of countries shows a striking positive relationship. Further examples are in the time trends of the incidence of disease or number of cases of disease.

7 Asthma and Soybeans

8 Lecture 8 Objective 22. Discuss the sources of bias in epidemiological studies and limitations of these studies

9 Limitations of Epid Studies chance bias confounding

10 Chance  = risk of declaring a difference when there is none (H o is true) –Control: choice of , repeat study for verification  = risk of failing to detect a difference when there is a difference (H A is true) –Control: sample size!!!!

11 Bias Bias - systematic error –selection bias nonresponse diagnostic or surveillance volunteer loss to followup –information bias recall interviewer -

12 Prevention of Bias addressed when designing a study. selection bias - study participants should be carefully chosen. In case/control studies the two groups should be similar with respect to their willingness to participate and other factors.

13 Prevention of Bias Loss to followup - minimized by choosing a well-defined population with respect to occupation, place of employment, or some other characteristic that makes them easier to follow-up. information bias, efforts should be made to obtain information on all study subjects in a consistent manner.

14 Prevention of Bias Masking or blinding of interviewers/observers as to the disease status of the participant is one possible method. Proper training of interviewers and observers is also important to minimize bias in the way information is obtained and recorded

15 Confounding The validity of the relative risks are based on the assumption that the risk estimates are not confounded by some extraneous variable. Confounding is another form of bias. Any variable that is related to both the exposure and to the disease is a potentially confounding variable.

16 Confounding oral contraceptive and bacteriuria study the RR was 1.4, which suggested a 40% increase in the risk of bacteriuria among women who were contraceptive users. contraceptive users may be more sexually active than non-contraceptive users.

17 Confounding sexual activity is related to both contraceptive use (exposure) and to the incidence of bacteriuria (disease) and thus sexual activity is a potentially confounding variable.

18 Confounding To determine whether sexual activity is confounding examine the relative risk in two cases –1) when there was no adjustment for sexual activity, and –2) when there was adjustment for sexual activity.

19 Confounding If the RRs from these analyses differed, then we would have evidence that sexual activity is a confounding variable.

20 Control of Confounding Matching Restriction of the population Post Stratification on confounder Statistical analysis

21 Validity and Generalizability inclusion and exclusion criteria impact of study design issues. feasibility of the study –sample size, –inclusion of very ill patient (who will have minimal response) and the –need to complete the study in requisite time

22 ASSESSING CAUSE AND EFFECT  Strength of association: The size of the relative risk is a measure of the strength of association.  Coherence or biological plausibility of the association: Previous experiments establishing the relationship in animal models.

23 ASSESSING CAUSE AND EFFECT  Consistency of the association: The association is found across a number of different studies.  Temporal relationship of the association: The exposure to the factor must precede development of disease.

24 ASSESSING CAUSE AND EFFECT  Specificity of the association: One specific exposure results in disease. Dose-response: A dose response is informative but no proof of cause.

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