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  1. Case-control study
    • Observational, retrospective
    • Design:
    • -compares a group of people with disease to a group without disease
    • -Looks for prior exposure or risk factor ("what happened?")

    • Measures/examples:
    • -Odds ratio (OR)
    • -"patients with COPD had higher odds of a history of smoking than those without COPD had"
  2. Cohort study
    Observational, prospective or retrospective

    • Design:
    • -Compares a group with a given exposure or risk factor to a group without such exposure
    • -Looks to see if exposure ↑ the likelihood of disease
    • -Prospective (who will develop disease?)
    • -Retrospective (who developed the disease?)

    • Measures/examples:
    • -Relative Risk (RR)
    • -"smokers had a higher risk of developing COPD than nonsmokers had"
  3. Cross-sectional study

    • Design:
    • -Collects data from a group of people to assess frequency of disease (and related risk factors) at a particular point in time
    • -"What is happening?"

    • Measures/examples:
    • Disease prevalence
    • -Can show risk factors associated with disease, but does not establish causality
  4. Twin concordance study
    • Design:
    • -Compares the frequency with which both monozygotic twins or both dizygotic twins develop same disease

    • Measures/example:
    • -Measures heritability
  5. Adoption study
    • Design:
    • -Compares siblings raised by biological vs. adoptive parents

    • Measures/examples:
    • -Measures heritability and influence of environmental factors
  6. Clinical trial
    Compares therapeutic benefits of 2 or more treatments, or of treatment and placebo

    • Study quality improves with:
    • -Randomization
    • -Controlled
    • -Double-blinded (neither pt nor doctor knows patient's group)
    • -Triple-blinded: additional blinding of the researchers analyzing the data
  7. Clinical trial
    Phases, sample size, purpose
    • Phase I: small number of healthy volunteers → Assesses safety, toxicity, pharmacokinetics
    • Phase II: small number of patients with disease of interest → Assesses treatment efficacy, optimal dosing, adverse effects
    • Phase III: Large number of pts, randomly assigned to investigational treatment group/best available treatment, or placebo → compares the new treatment to standard of care
    • Phase IV: Postmarketing surveillance of trial of patients after approval → Defects rare or long-term adverse reactions
  8. Evaluation of diagnostic tests
    2× 2 table
    • Compares test results with actual presence of disease
    • TP/TN = true positive/true negative
    • FP/FN = false positive/false negative
    • Image Upload 1
  9. Sensitivity/specificity vs positive/negative predictive value
    • Sensitivity and specificity are fixed properties of test
    • PPV and NPV vary with prevalence or pretest probability
  10. Sensitivity (true-positive rate)
    • - Proportion of all people with disease who test positive, or the probability that a test detects disease when disease is present
    • -Image Upload 2
    • - Sensitivity = 1 - false-negative rate
    • -Value approaching 100% is desirable for ruling out disease → low false-negative rate
    • -Used for screening in diseases with low prevalence
  11. Specificity (true-negative rate)
    • -Proportion of all people without disease who test negative, or the probability that a test indicates non-disease when disease is absent
    • -Sensitivity = TN/(TN+FP) = 1 - false-positive rate
    • -Value approaching 100% is desirable for ruling in disease → low false-positive rate
    • -Used as a confirmatory test after a positive screening test
  12. Positive predictive value (PPV)
    • Proportion of positive test results that are true positive
    • PPV = TP /(TP+FP)
    • Probability that person actually has the disease given a positive test result
    • *PPV varies directly with prevalence or pretest probability: high pretest probability → high PPV
  13. Negative predictive value (NPV)
    • Proportion of negative test results that are true negative
    • NPV = TN/(FN + TN)
    • Probability that person actually is disease free given a negative test result
    • *NPV varies inversely with prevalence or pretest probability: high pretest probability → low NPV
  14. Image Upload 3
    • A = 100% sensitivity
    • B = practical compromise between specificity and sensitivity
    • C = 100% specificity
  15. Incidence
    • Incidence looks at new incidents
    • Incidence rate = (# of new cases in a specific time period)/(population at risk during same time period)
  16. Prevalence
    • Prevalence looks at all current cases
    • Prevalence = (# of existing cases)/(Population at risk)
    • Prevalence ≃ incidence rate × average disease
    • Prevalence > incidence for chronic diseases (e.g., diabetes)
  17. Quantifying risk
    Image Upload 4
  18. Odds ratio (OR)
    • Image Upload 5
    • Uses: case-control studies
    • Odds that the group with disease (cases) was exposed to a risk factor (a/c) divided by the odds that the group without the disease (controls) was exposed (b/d)
  19. Relative risk (RR)
    • Image Upload 6
    • Uses: cohort studies
    • Risk of developing disease in the exposed group divided by risk in the unexposed group
    • If prevalence is low, RR = OR
    • (e.g., if 21% of smokers develop lung cancer vs 1% of nonsmokers, RR = 21/1 = 21)
  20. Attributable risk
    • Image Upload 7
    • The difference in risk between exposed and unexposed groups, or the proportion of disease occurrences that are attributable to the exposure
    • (e.g., if risk of lung cancer in smokers is 21% and risk in nonsmokers is 1%, then 20% of the 21% risk of lung cancer in smokers is attributable to smoking)
  21. Absolute risk reduction (ARR)
    • Absolute reduction in risk associated with a treatment as compared to a control
    • (e.g., if 8% of people who receive a placebo vaccine develop flu vs. 2% of people who receive a flu vaccine, then ARR = 8% - 2% = 6%)
  22. Number needed to treat
    • Number of patients who need to be treated for 1 patient to benefit
    • Calculated as 1/absolute risk reduction
    • Number needed to treat = 1/ARR
  23. Number needed to harm
    • Number of patients who need to be exposed to a risk factor for 1 patient to be harmed
    • Calculated as 1/attributable risk
  24. Precision
    • The consistency and reproducibility of a test (reliability)
    • Random error: reduces precision in a test
    • ↑ precision → ↓ standard deviation
    • Image Upload 8
  25. Accuracy
    • The trueness of test measurements (validity)
    • The absence of systematic error or bias in a test
    • Systematic error: reduces accuracy in a test
    • Image Upload 9
  26. Bias
    ways to reduce
    • Occurs when there is systematic error or favor in a particular direction
    • Reduce bias:
    • -Blind studies to limit influence of participants and researchers on interpretation of outcomes
    • -Placebo control groups
    • -Crossover studies (each subject acts as own control) to limit confounding bias
    • -Randomization to limit selection bias and confounding bias
    • -Matching to reduce confounding bias
  27. Selection bias
    • Nonrandom assignment to participation in a study group
    • (e.g., Berkson's bias, loss to follow-up)
  28. Recall bias
    • Knowledge of presence of disorder alters recall by subjects 
    • common in retrospective studies
  29. Sampling bias
    • Subjects are not representative of the general population
    • results are not generalizable
    • Type of selection bias
  30. Late-look bias
    • Information gathered at an inappropriate time
    • e.g., using survey to study a fatal disease (only those pts still alive will be able to answer survey)
  31. Procedure bias
    • Subjects in different groups are not treated the same
    • e.g., more attention is paid to treatment group, stimulating greater adherence
  32. Confounding bias
    • Occurs when factor is related to both exposure and outcome, but is not on the causal pathway
    • factor distorts or confuses effect of exposure on outcome
  33. Lead-time bias
    • Early detection confused with ↑ survival
    • seen with improved screening (natural history of  disease is not changed, but early detection makes it seem as though survival ↑)
  34. Observer-expectancy effect
    Occurs when a researcher's belief in the efficacy of a treatment changes the outcome of that treatment
  35. Hawthorne effect
    • Occurs when the group being studied changes its behavior owing to the knowledge of being studied
    • *Dr. Hawthorne is watching you
  36. Statistical distribution
    • Measures of central tendency = mean, median, mode
    • Measure of dispersion = standard deviation (SD), standard error of the mean (SEM), Z-score, confidence interval
  37. Image Upload 10
    • Gaussian (aka bell-shaped)
    • Mean = median = mode
  38. Standard deviation and SEM
    • σ = SD
    • n = sample size
    • Image Upload 11
    • SEM ↓ as n ↑
  39. Image Upload 12
    • Positive skew
    • Typically, mean > median > mode
    • Asymmetry with longer tail on right
    • Mode is least affected by outliers in the sample
  40. Image Upload 13
    • Negative skew
    • Typically, mean < median < mode
    • Asymmetry with longer tail on left
  41. Statistical hypotheses
    • Null (H0): hypothesis of no difference
    • -No association between the disease and the risk factor in the population
    • Alternative (H1): hypothesis of some difference
    • -There is some association between the disease and the risk factor in the population
  42. Image Upload 14
    • Type I error (α)
    • Type II error (β)
    • Image Upload 15
  43. Type I error (α)
    • Stating that there is an effect or difference when non exists
    • False positive (accepting the alternative hypothesis when the null hypothesis is true)
    • α is the probability of making a type I error
    • p is judged against a preset α level of significance (usually <.05)
    • -If p <.05 then there is less than 5% chance that the data will show something that is not really there
    • **α = you saw a difference that did not exist
  44. Type II error (β)
    • Stating that there is not an effect or difference when one exists
    • False negative (fail to reject the null hypothesis when it is false)
    • β is the probability of making a type II error
    • **β = you were blind to a difference that did exist
  45. Power (1 - β)
    • Probability of rejecting null hypothesis when it is in fact false, or the likelihood of finding a difference if one in fact exists
    • Increases with:
    • -↑ sample size
    • -↑ expected effect size
    • -↑ precision of measurement
    • *If you ↑ sample size, you ↑ power (Power in numbers)
  46. Meta-analysis
    • Pools data and integrates results from several similar studies to reach an overall conclusion
    • ↑ statistical power
    • Limited by quality of individual studies or bias in study selection
  47. Confidence interval
    • Range of values in which a specified probability of the means of repeated samples would be expected to fail
    • CI = range [mean - Z(SEM)] to [mean + Z(SEM)]
    • 95% CI corresponds to p = .05
    • 95% CI, Z = 1.96
    • 99% CI, Z = 2.58
    • *If the 95% CI for a mean difference between 2 variables includes 0, then there is no significant difference and H0 is not rejected
    • *If the 95% CI for odds ratio or relative risk includes 1, H0 is not rejected
    • *If the CI between 2 groups do not overlap → significant difference exists
    • *If the CIs between 2 groups overlap → usually no significant difference exists
  48. t-test
    • Checks difference between the means of 2 groups
    • *Mr. T is mean
  49. ANOVA
    • Checks differences between the means of 3 or more groups
    • ANOVA ANalysis OVAriance of 3 or more groups
  50. Chi-squared (Image Upload 16)
    • Test checks difference between 2 or more percentages or proportions of categorical outcomes (not mean values)
    • Image Upload 17 = compare percentages or proportions
  51. Pearson's correlation coefficient (r)
    • r is always between -1 and +1
    • Closer the absolute value of r is to 1, the stronger the linear correlation between the 2 variables
    • Coefficient of determination = Image Upload 18 (value that is usually reported)
  52. Disease prevention
    • Primary: Prevent disease occurrence (e.g., HPV vaccination)
    • Secondary: Early detection of disease (e.g., Pap smear)
    • Tertiary: Reduce disability from disease (e.g., chemotherapy)
    • PDRPrevent, Detect, Reduce disability
Card Set:
2013-03-31 17:17:29

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