Epidemiology

1. Epidemiology Why is it so damn confusing?

2. Disease or Outcome + - a b + Exposure - c d n

3. Basic Measures • Prevalence • Estimate of population burden • Multiple study types • (Number of existing disease cases/total population) at a point in time • Ex-20% in 2013 Cases are included in denominator • Incidence • Estimate of risk • Need a cohort study (follow-up) • Cumulative • Number of new cases in a period of time/total population at risk • Ex-5% over 5 years • Rate • Number of new cases in a period of time/total person time of observation • Ex-8/127 person-years • Most precise measurement Cases and diseased time are not included in the denominator

4. Study Design • Case-Control • Population is selected on outcome • Look back for exposures • Can’t measure incidence • Only OR not RR • Multiple exposures can be evaluated • Always retrospective (increased risk of bias) • Useful for rare diseases • Cohort • Population is selected on exposure • Followed forward for outcomes • Can measure incidence • RR or OR, RR preferred but limited by type of analyses • Multiple outcomes can be evaluated • Types • 1) retrospective • 2) prospective • 3) RCT

5. Study Design • Case-Control • Cohort

6. Study Design: RCT • Intention to Treat • Patients are randomized to different treatment groups • If a patient is non-adherent or has side effects – remain in the treatment group for analysis • Determines effectiveness– how well the drug works in the real world • Traditional RCT • Patients are randomized to different treatment groups • If a patient is non-adherent or has side effects – usually removed from the analysis or to the control group for analysis • Determines efficacy – how well the drug works in a perfect environment

7. Study Design Ø = • Bias • Bias is a systematic error inherent to the study design that obscures results • Bias can’t be adjusted for during statistical analysis • Introduced by the investigators • A bias towards the null (no association) is more acceptable (underestimating a relationship) than a bias towards an association • Confounding • A confounder is a 3rd variable related to the exposure and the outcome that obscures results • Confounders can be adjusted for during statistical analysis (multivariate analysis) • Result of complicated relationships between exposures and disease • You can only adjust for confounders if you measure them and know they exist Bias and Confounding are present, to some extent, in all studies, even RCTs. The key issue is how you and the authors interpret the results with bias and confounding in mind

8. Results • RR - relative risk • Risk an exposed patient will become a case (develop disease) • Incidence in exposed/incidence in unexposed • [a/(a+b)]/[c/(c+b)] • Requires a cohort study • OR - odds ratios • Odds of exposure in a case (disease) compared to a control • odds of exposure in cases/odds of exposure in controls • (a/c)/(b/d) = ad/bc • Used to estimate RR in a case-control study (slightly overestimates unless disease is rare) • Only option for multivariate logistic regression

9. Results • PPV/NPV • Completely depends on the prevalence of disease in the population • The prevalence should always be given when presenting PPV or NPV • Positive Predictive Value • True positives/(true positive + false positive) = a/(a+b) • Negative Predictive Value • True negatives/(true negative + false negative) = d/(d+c) • Sensitivity/Specificity • Inherent to the test and the cut offs used to determine a positive test • Sensitivity • True positives/(true positive + false negative) = a/(a+c) • A negative test rules out disease, positive may not be helpful • Sensitive test has more false positives • Specificity • True negatives/(true negative + false positive) = d/(d+b) • A positive test rules in disease, negative may not be helpful • Specific test has more false negatives

11. N=1000 90% Sensitivity 90% Specificity Prevalence = 1% Prevalence = 10% Disease or Outcome + - A 9 90 B 99 90 108 180 + Test - C 1 10 D 891 810 892 820 N = 1000 10 100 990 900 PPV = 8%, 50%

12. Guide to Quickly-ish Reading Research Papers • Look at who is writing the paper – conflicts of interest, specialty, associations • How could the authors bias the study? • they may not, but its helpful to consider before reading the paper • Abstract • Population – inclusion and exclusion criteria, disease severity • How does the study population compare to your patients? • Are the results generalizable? Were only the healthiest patients included? • Results – always read all of the results • If no association is demonstrated, it doesn’t mean one doesn’t exist. Power and study design are important factors. • Conclusion/discussion • Do the authors’ conclusions reflect the study results? Some journals want earth-shattering recommendations and conclusions that may be a bit of a reach. • Do they discuss bias? All discussions should include this. • If concerns come up in the discussion, read detailed methods Even though a paper is peer reviewed and published in a journal it does not mean it is peer reviewed by an epidemiologist. I have had journals request things that are completely inappropriate.