EBM

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Author:
kjp19
ID:
250451
Filename:
EBM
Updated:
2013-12-03 14:05:48
Tags:
evidence based medicine
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Description:
EVIDENCE BASED MED
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  1. : reports on the treatment of individual
    patients and uses no control groups to compare outcomes (little statistically
    validity)
    Case series/case reports
  2. ·        
    patients
    who already have a specific condition are compared with people who do not have
    the condition. The researcher looks back to identify factors or exposures that
    might be associated with the illness
    Case control studies
  3. ·        
    identify
    a group of patients who are already taking a particular treatment or have an
    exposure, follow them forward over time, and then compare their outcomes with a
    similar group that has not been affect by the treatment or exposure being
    studied
    Cohort studies
  4. -  
    uses
    information that has been collected in the past and kept in files or databases.
    Patients are identified for exposure or non-exposure and the data is followed
    forward to an effect or outcome of interest
    Retrospective cohort (or historical cohort):
  5. focus on a clinical topic and answer a specific
    question. Summarizes results
    Systematic reviews
  6. mathematically combines the results and
    reports them as if they were one large study
    Meta-analysis
  7. ·        
    describes
    the relationship between disease and other factors at one point in time in a
    defined population. They lack any information on timing of exposure and outcome
    relationships and include only prevalent cases and are often used for comparing
    diagnostic tests
    • Cross-sectional studies (aka: prospective, blind
    • comparison to a gold standard):
  8. ·        
    describe,
    explore and explain the health-related phenomena being studies
    Qualitative research
  9. Key issues in diagnostic studies
    Diagnostic uncertainty

    Blind comparison to gold standard

    Each patient gets both tests

    Patients should be included which have high, medium, low probability of disease
  10. Key issues in prognostic studies
    • Well-defined sample
    •      
    • Similar prognosis

    • ·        
    • Complete
    • follow-up

    Objective and unbiased criteria

    • Prognostic results can be presented in the following ways
    • 1Absolute terms: 5 year survival rate
    • 2Relative terms: risk from prognostic factor
    • 2Survival curves: cumulative events over time
  11. Key issues for harm studies
    Similarity of comparison group

    Outcomes and exposures measured same for both groups

    Follow-up sufficient length (>80%)

    For cohort studies: detection biases

    • For case control studies: exposure is critical and blinding interviewer/using
    • objective data can help eliminate bias
  12. Key issues for systematic reviews
    Focused question

    Thorough literature research

    Include validated studies

    Selection of studies reproducible
  13. Types of outcomes
    • Dichotomous:
    • yes or no

    • Categorical:
    • complete, partial, no, etc (commonly represented as frequencies in tables)

    • Continuous:
    • lipid levels, A1c (but remember these can still be categorized!)
  14. Comparing continuous variables

    compares differences in means and assumes that you have a bell curve, independent
    observations, large sample size (how likely is the differences between means
    due to chance?)
    t test
  15. Comparing continuous variables

    compares observations that are not independent (ex: measurements before/after treatment, matched studies, parent-child measurements)
    Paired t test
  16. Comparing continuous variables

    assumed that you don’t have normal distribution
    Nonparametric tests
  17. Comparing continuous variables

    Nonparametric tests
       Unpaired data
    Mann-Whitney U test and Wilcoxon Rank Sum test
  18. Comparing continuous variables

    Nonparametric tests  
        Paired data
    Wilcoxon Signed Rank test
  19. Comparing continuous variables

    Nonparametric tests      
           ANOVA (analysis of variance):
    • used to compare more than 2 means (ex: clinical trial with 3 arms or multiple
    • measurements in 2 groups)

    • Kruskal-Wallis test: nonparametric counterpart to ANOVA and is commonly used with small
    • samples
  20. large sample comparing continuous

    unpaired data?

    Paired data?

    >2 groups being compared?
    Unpaired data: T test

    Paired data: paired t test

    >2 groups being compared: ANOVA
  21. small sample comparing continuous
    normal distrubution

    unpaired data?

    paired data?

    >2 groups being compared?
    unpaired data: Mann-Whitney U test, Wilcoxon Rank Sum test

    paired data: wilcoxon signed rank test

    >2 groups being compared: Kurskal-wallis test
  22. comparing categorical variables

    comparison of observed vs expected frequencies?

    used when expected frequencies in cells are small?

    tests for linear association between row and column variables?
    chi squared test: comparison of observed vs expected frequencies

    fisher's exact test: used when expected frequencies in cells are small

    mantel haenszel: tests for linear association between row and column vairiables


  23. Experimental Group Risk (EGR)?
    Control Group Risk (CGR)?
    ExperimentalGroup Risk (EGR) = A/T1

    and

    Control Group Risk (CGR) = C/T2

  24. Absolute risk reduction (ARR) or risk difference?
    • : CGR-EGR
    •  Control Group Risk (CGR) - Experimental Group Risk (EGR)
  25. Relative risk (cumulative incidence ratio, risk ratio): the risk of exposed/risk of unexposed  *commonly used in RCT or prospective cohort studies
    (example: the outcome occurs 3 time more in those exposed versus those
    unexposed)
    [EGR/CGR]

    Experimental Group Risk (EGR)/Control Group Risk (CGR)
  26. Relative risk reduction:
    1- [EGR/CGR] x 100 (the increase in the ratio the more effective the therapy)
  27. Relative benefit increase
    :[EGR-CGR]/CGR
  28. Hazard ratio
    relative risk over a period of time
  29. Odds ratio
    • -  
    • odds exposed cases/odds exposed
    • controls = [A/C]/ [B/D]*commonly
    • used in case control or retrospective studies (example: cases were 3 times more
    • likely to have been exposed than were control patients)
  30. P-value < 0.05
    means recect the null and the differences between the groups is not due to chance
  31. Confidence intervals

    differences in means: cannon include __
    differences in proportions: cannot include __
    differences in ratio measure (RR, OR, HR, PR) cannot include __
    increase in confidence interval with decrease in sample size (less precise)

    • differences in means: cannon include 0
    • differences in proportions: cannot include 0
    • differences in ratio measure (RR, OR, HR, PR) cannot include 1

  32. sensitivity=?
    specificity=?
    • Sensitivity = true positive / all disease
    • positives (probability that a person with the disease will have a positive
    • result)
    •        
    • Specificity = true negative / all disease
    • negatives (probability that a person without the disease will have a
    • negative result)
  33. Likelihood ratio (LR)
    (LR +) =

    (LR-) =
    • positive test in patients with disease (true +) / positive test in patients without disease (false +)
    • negative test in patients with disease (false -) / negative test in patients
    • without disease (true -)
  34. LR + =

    LR - =
    Increases odds of having the disease after a positive test result

    Decreases the odds of having the disease after a negative test result
  35. confounding
    Definition:
    Addresses in 2 ways:
    the association between the two groups may be due to differences in the control and experimental groups

    randomization and multivariate analysis
  36. Confounding

    Regression analysis
    good for controlling for multiple variables

    Logistic regression: used for dichotomous outcomes and generates an odds ratio

    Survival analysis/proportional hazards modeling/cox proportional hazard model: takes into account time to event
  37. confounding

    log rank test

    cox proportional hazard model and log rank test
    compares kaplan meier curves (small p value indicates that the curves are statistically different)

    compare exposure groups but do not take into account other variables

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