1 / 29

Case-Control Study Design

Case-Control Study Design. Two groups are selected, one of people with the disease ( cases ), and the other of people with the same general characteristics but without the disease ( controls ) Compare the past exposures of both groups. Case Control Study Design. Exposed. Diseased (Cases).

fay
Télécharger la présentation

Case-Control Study Design

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Case-Control Study Design • Two groups are selected, one of people with the disease (cases), and the other of people with the same general characteristics but without the disease(controls) • Compare the past exposures of both groups

  2. Case Control Study Design Exposed Diseased (Cases) Not Exposed Target Population Exposed Not Diseased (Controls) Not Exposed

  3. Case-Control Study Design Limitations: • Cannot yield incidence rates because subjects are selected on outcome • An estimateof the ratio of incidence rates or risks (RR) is obtained by calculating an odds ratio (OR)

  4. Odds Ratio Calculation Outcome Exposure Cases Controls Exposed A B Not Exposed C D Odds of exposure for cases A / C = Odds Ratio Odds of exposure for controls B / D (estimates the relative risk)

  5. Comparing Odds Ratios and Relative Risks Outcome Exposure Cases Controls Exposed 70 300 370 Not Exposed 30 700 730 100 1000 1100 OR = AD/BC = 5.44 RR = Ie/In = 4.41

  6. Stating your results • OR = 5.44 Those with the disease are 5.44 times as likely to have had the exposure compared to those without the disease • RR = 4.41 Those with the exposure are 4.41 times as likely to develop the disease compared to those without the exposure

  7. Summary of Strengths and Limitations of Prospective Cohort and Case-Control Studies Prospective Cohort Case-Control Strengths: • Opportunity to measure risk factors before disease occurs • Can study multiple disease outcomes • Can yield incidence rates as well as relative risk estimates Strengths: • Useful for rare disease • Relatively inexpensive • Relatively quick results Limitations: • Possible bias in measuring risk factors after disease has occurred • Possible bias in selecting control group • Identified cases may not represent exposure of all cases Limitations: • Useful for rare disease • Relatively inexpensive • Relatively quick results

  8. Randomized Clinical Trials(RCT) The Gold Standard Cohort Study

  9. Schematic diagram of a clinical trial Study Population Non-participants Participants Randomization Treatment arm Control arm Intervention or new treatment Control Improved Not Improved Not Improved Improved

  10. Crossover Design • Subjects are randomized to a sequence of two or more treatments • Each subject serves as his own control

  11. Factorial Design • Two or more treatments are evaluated simultaneously in the same set of subjects using varying combinations of treatments Randomization Placebo Treatment A Treatment B Placebo Treatment B Placebo

  12. How do we evaluate whether cancer studies are valid? Understand bias and confounding

  13. Testing for a true association • Examine the methodology for bias • Examine the analysis for confounding • Examine the results for statistical significance

  14. Examine the study design for Bias • Selection Bias • Errors in the process of identifying the study population and selecting the subjects • Information/Observation Bias • Errors in measurements of exposure or disease status

  15. Confounding • Confounding is an apparent association between disease and exposure caused by a third factor not taken into consideration

  16. Examples of Confounders • Study A found an association between gambling and lung cancer. The study may be confounded by smoking. • Study B found a larger crude death rate in Florida than in Alaska. The rate may be confounded by differences in the population age structure.

  17. Testing for Confounding • Calculate the crude rate • Calculate a rate adjusted for the confounding variable • Compare the two measures • The two measures will be different if the variable is a confounder (in practice, when the crude and adjusted measures differ by at least 10%)

  18. 1980 U.S. Standard Population Expected Number of Deaths Population at risk Age Cancer Deaths ASR (1) / (2) = (3) (3) x (4) = (5) (1) (2) (4) 0-18 5 5,000 1.00 per 1000 60,500,000 60,500 0.40 per 1000 19-64 10 25,000 140,300,000 56,120 65+ 100 15,000 6.67 per 1000 25,700,000 171,419 Total 115 45,000 xxx 288,039 226,500,000 Crude Rate (115 / 45,000) x 1000 2.56 per 1,000 Age-Adjusted Rate (288,039 / 226,500,000) x 1000 1.27 per 1,000 Not equal AGE IS A CONFOUNDER FOR DEATH FROM CANCER

  19. Evaluating Statistical Significance • The probability that you would get your results by chance alone is the p-value • A low p-value ( < 0.05 ) says that chance is not likely to explain your results • A 95% confidence interval (CI) is the range of values in which the true value will be found 95% of the time • Large samples yield small confidence intervals • Small samples yield large confidence intervals

  20. Evaluating Results • RR = 1: No difference in disease between exposed and unexposed groups • OR = 1: No difference in exposure between cases and controls • Examples: • RR = 1.8 (1.6, 2.0) is statistically significant • RR = 1.8 (0.8, 2.9) is not statistically significant • OR = 0.7 (0.6, 0.8) is statistically significant • OR = 0.7 (0.4, 1.2) is not statistically significant

  21. How do we assess whether associations between cancer and risk factors are causal? Understand criteria for causality

  22. To Show Cause • Chronic disease and complex conditions require the use of Hill’s Postulates • Strength of association • Consistency of association • Specificity of association • Temporality • Biologic gradient • Plausibility • Coherence • Experiment • Analogy

  23. How much of the morbidity and mortality from cancer might be prevented by interventions? Understand the impacts of education and screening programs

  24. Principles of Screening • Validity • Sensitivity: correctly identify those with disease • Specificity: correctly identify those without disease • + Predictive Value: proportion of correct positive tests • - Predictive Value: proportion of correct negative tests • Reliability: ability of test to give consistent results • Yield: amount of unrecognized disease brought to treatment due to screening

  25. Calculating Measures of Validity True Diagnosis Test Result Disease Total No Disease Positive a b a+b c Negative d c+d Total a+c b+d a+b+c+d Sensitivity = a/(a+c) Specificity = d/(b+d) Positive Predictive Value = a/(a+b) Negative Predictive Value = d/(c+d)

  26. Example: Breast Cancer Screening Mammogram Results Disease No Disease Total Positive 132 983 1,115 Negative 45 63,650 63,695 Total 177 64,633 64,810 Sensitivity = 132/177 = 74.6% Specificity = 63,650/64,633 = 98.5% Positive Predictive Value = 132/1,115 = 11.8% Negative Predictive Value = 63,650/63,695 = 99.9%

  27. Keys to Screening • Sensitivity: detect a sufficient number of preclinical cases to be useful • Prevalence: screen high-risk populations • Frequency: one-time screening does not allow for differences in individual risk or differences in onset • Participation: tests unacceptable to the target population will not be utilized • Follow-up: those with positive tests need to be provided with a plan of action

  28. Advice for Reading the Literature • Identify the study design • Understand how subjects are selected • Understand how exposure is defined • Evaluate potential bias and confounding • Determine if the statistical evaluation is appropriate • Make decisions about whether the outcome measures are statistically significant and/or clinically important • Use good judgment

  29. End

More Related