EBP II Exam 1.txt

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primo1289
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EBP II Exam 1.txt
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2012-11-12 21:41:36
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EBP II Study Guide UWS
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EBP II Study Guide
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  1. P-value:
    probability of false positives in the study result
  2. Null Hypothesis:
    will be 0 for the difference in groups; while the null will be 1 for OR, HR, RR
  3. Mean:
    the average, this will be the middle of the bell shaped curve
  4. Median:
    the value that divides a series of numbers in half when they are listed in order, this will be used for skewed data that does not conform to the bell shaped curve
  5. Mode:
    the most frequently occurring number in a series
  6. Standard deviation:
    found using the patient population, square root of the variance, (the average sum squared difference from the mean); measurement of participant variability, measurement in the variability of data, how spread out it is
  7. Interquartile Range:  
    the interquartile range (IQR), also called the mid-spread or middle fifty, is a measure of statistical dispersion, being equal to the difference between the third and first quartiles. IQR = Q3 − Q1
  8. Skewed distribution:
    non-normal bell curve
  9. Normal distribution:
    normal bell curve, symmetric around the mean with the mean as the peak of the curve going out toward but never reaching 0
  10. Standard error of the mean:
    found with the clinical data, the spread of the sampled means for the data gathered, the measure of the precision/variability of the measurement (SD)
  11. Confidence Interval:
    “the neighborhood of the truth” An estimated range of values around a point estimate. Example: The 95% confidence interval says there is a 95% chance that the actual value found is within the C.I. Precision of study results, range of values that is likely to contain the population parameter
  12. **Type 1 error is considered worse because….?
    it would lead to unnecessary treatment of patients**
  13. Type I error:
    stating there is a difference in groups when there isn’t one , incorrectly rejecting the null hypothesis AKA failure to accept the null, will result in false positives
  14. Type II error:
    stating that there is no difference in groups where there is one, incorrectly accepting the null hypothesis AKA failure to reject the null, will result in false negatives
  15. Power:
    the probability to correctly reject the null hypothesis when you should. Mathematically defined as 1-Type II error rate, depends on: sample size, difference between groups and type 1 error
  16. Clinical significance:
    Is the study result of practical interest? Do other findings matter?
  17. Chi-square test:
    comparison of categorical data for large sample chosen, may be used to compare groups…test whether observed frequencies are different from expected frequencies in a data table
  18. Fisher's exact test:
    categorical for small sample chosen (>5 subjects)
  19. The t-test:
    a statistical test used to detect the difference in two means, two groups, and factor in variability in data commonly used for continuous data, comparison of 2 different groups
  20. The paired t-test:
    a t-test for used when comparing two means that are within the same group Ex. The mean at the beginning of a study and the mean at the end of the study, comparison of dependent groups
  21. Wilcoxon test:
    a test for statistical significance of data that is not on a normal bell curve distribution (non-parametric), used for paired data–used for group comparisons, rank testing
  22. Mann-Whitney U test:
    a test for statistical significance of data that is not on a normal bell curve distribution (non-parametric), used for unpaired data –used for group comparisons, rank testing
  23. ANOVA (analysis of variance):
    A way to analyze groups of means to see if they are equivalent or not; if the ANOVA model fits the data well, and if a statistically significant difference is detected then post-testing is conducted
  24. Post-hoc testing:
    • compare the group pairs, done in the second stage of statistical analysis…three types:
    • • Tukey-used if the groups are unequal in size
    • • Bonferroni-for both equal and unequal groups
    • • Scheffé-very conservative to minimize type 1 error
  25. Linear regression:
    explains the differences in means. A calculation of the line of best fit passing through a set of data, which will allow for prediction about direction and amount variables change
  26. Multiple linear regression:
    explains the differences in means, in addition to explaining the differences in groups it can also be adjusted for age, gender, smoking, cancer etc…
  27. Logistic regression:
    allows for comparison of differences in odds between groups, results are an odds ratio which is a slight over estimate of relative risk
  28. Multiple logistic regression:
    in addition to explaining differences in OR between groups, they also adjust for age, gender, smoking, cancer etc…
  29. Parametric tests:
    t-test, ANOVA, regression
  30. Non-parametric tests:
    for ranking: Wilcoxon, Mann-Whitney; for categorical data: Chi-square, Fisher’s exact
  31. Temporality:
    cause must come before effect
  32. Repeatability:
    the effects must be repeatable
  33. Biological Gradient:
    the does response effect—small dose and small response v. big dose and bigger response
  34. Reversibility:
    de-challenge v. re-challenge aka the interventional effect
  35. Plausibility:
    Does what’s happening makes sense according to biological knowledge at the time
  36. Systemic error:
    bias
  37. Random error:
    chance
  38. Numbers Needed to Treat (NNT):
    the number of patients who would need to be treated in order to prevent one additional bad outcome
  39. Numbers Needed to Harm (NNH):
    the number of patients who would need to be treated in order for one bad outcome to occur
  40. Absolute Risk (AR):
    • •Mainly used with RCT
    • •Probability of disease in the exposed group minus the probability of disease in the unexposed group
    • •Represents the excess risk due to exposure to the factor under investigation
  41. Interpretation of Relative Risks (RR), their confidence intervals, and the type of study that reports them:
    • • RR is used to assess the influence of treatment/prevention strategies of potential hazards upon the prevalence of a given condition in a given population
    • • RR will typically be used in cohort studies
    • • RR is the probability of disease in the exposed group divided by the probability of disease in the unexposed group
    • • RR >1 there is a positive association with the risk of disease
    • • RR<1 there is a negative risk association with the risk of disease
    • • RR=1 there is no association with the risk of disease
  42. Interpretation of Odds Ratios (OR), their confidence intervals, and the type of study that reports them:
    • • OR is used to assess how exposure to something effects disease
    • • Used with case control studies
    • • OR >1 those who are exposed are more likely to be diseased
    • • OR<1 those who are exposed are less likely to be diseased
    • • OR=1 there is no association with exposure and disease
    • • If 1 is included within the confidence interval then the results are not considered statistically significant
  43. Interpretation of Hazard Ratios (HR), their confidence intervals, and the type of study that reports them:
    • • An estimation of harm given an exposure to a specific hazard
    • • RR is the hazard of disease occurrence in the exposed group divided by the hazard of disease in the unexposed group over time (RR/time)
    • • Used to assess the potential hazards of upon the nearness of an event
    • • Used in studies looking for longer survivals due to a harmful or beneficial exposure
    • • HR >1 there is a positive association
    • • HR<1 there is a negative risk association
    • • HR=1 there is no association
  44. Cross-Sectional Design:
    assess health status and exposure level of subjects at a point in time
  45. Case Control:
    retrospective observational study comparing diseased and non-diseased groups
  46. Cohort:
    prospective observational study comparing diseased and non-diseased groups
  47. RCT:
    prospective experimental design where the sample is broken into 2+ groups who are then put into categories such as treatment, placebo, alternative treatment, double dosage etc.
  48. What Kind of Study is good for Diagnosis:
    Cross-Sectional analytic study
  49. What Kind of Study is good for Harm:
    Cohort Study, population based case control
  50. What Kind of Study is good for Prognosis:
    Cohort study
  51. What Kind of Study is good for Treatment:
    RCT, systematic review

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