EPID 623-88 Introduction to Analysis and Interpretation of HIV/STD Data

1. EPID 623-88Introduction to Analysis and Interpretation of HIV/STD Data Epidemiologic Study Designs—a review Manya Magnus, Ph.D. Summer 2001

2. Objectives • To review epidemiologic study designs • To review measures of association used in epidemiologic studies • To discuss interpretation of study results

3. Basic study designs

4. Case reports/case series (1) • Description of unique, unusual, rare events in one or several individuals • Often used by physicians • May stimulate awareness of problem (note: PCP, DES/vaginal clear cell adenocarcinoma), hypothesis generating

5. Case reports/case series (2) • Analysis of case series: • Might focus on incidence or prevalence in general (or target) population • Raw data, small numbers • If more than a few, proportions (5/10=50%) • Narrative as essence of report, plus communication of context, why interesting

6. Birth cohorts • Allows exploration of trends based on age or period effects • Allows exploration of cumulative effects of exposure, latency periods, timing of exposure • Explore cohort effects, age effects, period effects • Interaction between time and calendar age • Looking graphically at cross-sectional data by age group

7. Ecologic studies • Aggregate measures • Environmental measures • Global measures • Units of observation generally geographic region, area • Looking at number of outcome events and predictors of interest on aggregate level • “Ecologic fallacy” issues • Evaluation of association: plotting, comparison of rates, prevalence, adjusted-measures, etc.

8. Cross-sectional studies • Individual-level data • “Snapshot” of exposure and outcome • No temporality provided • No causation can be inferred • Useful in hypothesis generating • Analysis: frequencies, cross-tabs, point prevalence rate ratio, odds ratio, comparison of rates, etc.

9. Cohort studies • Concurrent or non-concurrent • “gold standard” for observational studies • Basis for other designs • Disease-free at baseline • Follow for outcome • Analysis: frequencies, cross-tabs, relative risk, hazard ratio, attributable risk, etc.

10. Case-control studies • Look at those with/without outcome of interest and evaluate exposures • Good for rare diseases • Many variations (nested, case-cohort, etc.) • Analysis: frequencies, cross-tabs, odds ratio, attributable risk, etc.

11. Randomized controlled clinical trials • Cohort studies based on model of RCT • Randomize subjects to receive intervention and follow for outcome(s) of interest • Intent-to-treat analysis • Analysis: frequencies, cross-tabs, relative risk, hazard ratio, etc.

12. Community-based interventions • Similar to RCT, but randomizing communities • Unit of analysiscommunity (note power issues)

13. Reminders (1)

14. Reminders (2) Relative risk (RR) formula [A/(A+B)]/[C/(C+D)]

15. Reminders (3) Odds ratio (OR) formula AD/BC

16. Interpreting published results

17. Steps to understanding published tables • What is the study design?

18. Steps to understanding published tables • What is the study design? • What is unit of analysis?

19. Steps to understanding published tables • What is the study design? • What is unit of analysis? • What are predictors of interest?

20. Steps to understanding published tables • What is study design? • What is unit of analysis? • What are predictors of interest? • What are outcomes of interest?

21. Steps to understanding published tables • What is study design? • What is unit of analysis? • What are predictors of interest? • What are outcomes of interest? • In tables, what is n?

22. Steps to understanding published tables • What is study design? • What is unit of analysis? • What are predictors of interest? • What are outcomes of interest? • In tables, what is n? • Is the table referring to subset or whole sample?

23. Steps to understanding published tables • What is study design? • What is unit of analysis? • What are predictors of interest? • What are outcomes of interest? • In tables, what is n? • Is the table referring to subset or whole sample? • What is being presented, compared?

24. Steps to understanding published tables • What is study design? • What is unit of analysis? • What are predictors of interest? • What are outcomes of interest? • In tables, what is n? • Is the table referring to subset or whole sample? • What is being presented, compared? • What are the denominators? Do they differ by column?

25. Steps to understanding published tables • What is study design? • What is unit of analysis? • What are predictors of interest? • What are outcomes of interest? • In tables, what is n? • Is the table referring to subset or whole sample? • What is being presented, compared? • What are the denominators? Do they differ by column? • Can you add up data from text?

26. Steps to understanding published tables • What is study design? • What is unit of analysis? • What are predictors of interest? • What are outcomes of interest? • In tables, what is n? • Is the table referring to subset or whole sample? • What is being presented, compared? • What are the denominators? Do they differ by column? • Can you add up data from text? • Can you calculate measures of association? Do they agree with the authors’?

27. Steps to understanding published tables • What is study design? • What is unit of analysis? • What are predictors of interest? • What are outcomes of interest? • In tables, what is n? • Is the table referring to subset or whole sample? • What is being presented, compared? • What are the denominators? Do they differ by column? • Can you add up data from text? • Can you calculate measures of association? Do they agree with the authors’? • Are the authors’ conclusions correct?