Stats Attack!

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  1. phase I
    • few healthy volunteers, also terminal HIV/cancer for toxic drugs, or refractory patients
    • pharmacodynamics, kinetics, toxicity, safety
  2. phase II
    • few patients with placebo if you want
    • see if the drug works and the best dose
  3. phase III
    • many patients with control being best treatment/placebo
    • is it better than the standard of care?
  4. phase IV
    • every drug on the market is in this stage
    • delayed effects
  5. sensitivity
    • NOT affected by prevalence
    • SNNNOut (Negative, rule out, increases Negative predictive value)
    • TP/(TP+FN) (looking at everyone with the disease)
  6. specificity
    • NOT affected by prevalence
    • SPPPIn (Positive, rule in, increases Positive predictive value)
    • TN/(TN+FP) (looking at everyone without the disease)
  7. positive predictive value
    • affected by prevalence!
    • probability that someone with a positive test has the disease
    • TP/(TP+FP) (looking at everyone who tested positive)
    • increases with specificity (SPPPin)
  8. negative predictive value
    • affected by prevalence!
    • probability that someone with a negative test does not have the disease
    • TN/(TN+FN)
    • (increases with sensitivity (SNNNout)
  9. positive likelihood ratio
    • NOT affected by prevalence
    • used to determine the odds of having the disease given a positive test
    • SN/(1-SP) (sensitivity on top because you have the disease, 1-SP refers to false positives b/c the test was positive)
    • use odds!
    • LR+>1 is a test that will increase the odds of having the disease if positive
    • LR+<1 a test decreases the odds of having the disease if positive
  10. negative likelihood ratio
    • NOT affected by prevalence
    • used to determine the odds of HAVING the disease despite a negative test
    • (1-SN)/SP (sensitivity on top because you have the disease, 1-SN because we are looking for false negatives)
    • use odds!
    • LR-<1 is a test that will increase the odds of having the disease if positive
    • LR+>1 a test decreases the odds of having the disease if positive
  11. odds
    • yes/no (opposed to yes/total or no/total)
    • o=p/(1-p)
    • p=o/(1+o)
  12. incidence
    • new cases/number at risk
    • can increase with a cure if the cure doesn't reduce their risk of reacquiring a disease (prevalence would be down because you are curing chronic cases)
  13. prevalence
    • number of cases/population
    • can reduce prevalence by cure or increased mortality
    • treatments that prolong your life with the disease increase the prevalence but do not increase the incidence
  14. odds ratio
    • case-control studies
    • odds that the disease group was exposed divided by the odds that the healthy group was exposed
    • approximates relative risk in rare disease assumption
  15. relative risk
    • cohort studies
    • percent of exposed group who develops the disease divided by percent of unexposed group who develops the disease
    • RR=EER/CER
    • Relative Ratio
  16. attributable risk
    • difference in percent of exposed group who develops the disease and percent of unexposed group who develops the disease
    • AR=EER-CER
    • used in number needed to hARm
  17. absolute risk reduction
    • difference in percent of untreated group who develops the disease and the percent of treated group who develops the disease
    • ARR=CER-EER (assuming the exposure is treatment)
    • used in number needed to treat (treatments deal in ABSOLUTES!)
    • used in relative risk reduction
  18. relative risk reduction
    • absolute risk reduction divided by percent of untreated group who develop the disease
    • RRR=ARR/CER (assuming the exposure is treatment)
    • 2% of vaccinated patients get flu, 8% of unvax get flu
    • ARR=8%-2%=6%
    • RRR=6%/8%=.75
  19. number needed to treat
    • how many patients are treated before one benefits who wouldn't have benefited without treatment
    • NNT=1/ARR
  20. number needed to harm
    • how many patients are exposed before one gets the disease who wouldn't have gotten the disease if unexposed
    • NNH=1/AR
    • number needed to hARm
  21. precision (aka and error)
    • reliability
    • consistency
    • reproducibility
    • repeatability
    • reduces random error to reduce the SD and increase statistical power (1-beta)
  22. accuracy aka and how it is reduced
    • validity
    • trueness
    • truthiness
    • reduced by systematic errors
  23. Berkson bias
    • type of selection bias
    • study population selected from hospital is less healthy than general population
    • reduced by choice of appropriate control group
    • reduced by randomization
  24. healthy worker effect
    • study population is healthier than general population
    • reduced by choice of appropriate control group
    • reduced by randomization
  25. non-response bias
    • those who participate are different from those who don't in a meaningful way
    • reduced by randomization
    • reduced by choice of appropriate control group
  26. recall bias
    • patients with disease more likely to recall exposure
    • reduced by decreased time from exposure to study
  27. measurement bias
    • not using good tools or methods when assessing control AND study population
    • reduced by objective, standardized, previously tested methods planned ahead of time
  28. procedure bias
    • study population or control population receive meaningfully different treatments because of participants' or researchers' bias
    • reduced by blinding, placebo
  29. observer-expectancy bias
    • researchers' belief in treatment changes the outcome, like not classifying someone as having depression because you know they are in the treatment arm
    • reduced by blinding, placebo
  30. confounding bias
    • a factor related to both the actual exposure and the disease is not responsible for causing all of the disease, like coal miners smoking more than the general population when looking at lung cancer
    • reduced by multiple/repeated studies, crossover studies, matching, restriction, randomization
  31. lead-time bias
    • detecting a disease earlier so that they "live longer with the disease" even though they don't live longer
    • reduced by measuring back end survival (survival according to disease severity)
  32. positive outliers
    • most affect mean
    • least affect mode
    • positive skew
    • Image Upload 1
  33. negative outliers
    • most affect mean
    • least affect mode
    • negative skew
    • Image Upload 2
  34. standard deviation
    • SD
    • deviation from the mean within the study
  35. standard error
    • SEM
    • deviation between the calculated mean and the actual real life mean
    • SEM=SD/(rad[n])
    • SEM decreases with a larger studied population
    • used to calculate confidence interval
  36. normal distribution SDs
    • -1 to +1 = 68%
    • -2 to +2 = 95%
    • -3 to +3 = 99.7%
    • < +1 = 84%
    • > +1 = 16%
    • < +2 = 97.7%
    • > +2 = 2.3%
  37. H0 and error
    • "null hypothesis"
    • there is no association between the disease and exposure
    • if H0 is true in reality but study indicates H1, then type I error (alpha)
    • alpha (usually .05) is chance of type 1 error
    • aka false positive error
  38. H1 and error
    • "alternative hypothesis"
    • there is an association between the disease and exposure
    • if H1 is true in reality but study indicates H0, then type II error (beta)
    • beta is related to power power=1-beta
    • beta is reduced by increase in sample size, expected effect size, and precision of measurement
  39. confidence interval
    • range in which you are X confident that the real life number exists
    • CI = +/- Z(SEM)
    • CI of 95% -> Z=1.96
    • CI of 99% -> Z=2.58
    • for absolutes, if CI includes 0, do not reject the null hypothesis
    • for ratios, if CI includes 1, do not reject the null hypothesis
    • for two groups, if CIs overlap, do not reject the null hypothesis
  40. t-test
    • looks for differences between the means of 2 groups
    • tea for 2
    • ex: comparing mean blood pressure of men and women
  41. analysis of variance
    • ANOVA
    • looks for differences between the means of 3 or more groups
    • 3 words for 3 or more groups
    • ex: comparing mean blood pressure of 3 ethnic groups
  42. chi-square
    • looks for difference between 2 or more percentages of categorical outcomes
    • chi-tegorical
    • ex: comparing percentage of members of three ethnic groups who have HTN
  43. Pearson correlation coefficient
    • r
    • between -1 and +1
    • the closer to abs(1), the stronger the linear correlation is
    • has nothing to do with the steepness of the slope
    • -1 for any negative slope
    • +1 for any positive slope
  44. coefficient of determination
    • r2
    • usually the value reported
    • the closer to 1, the stronger the linear correlation
    • always positive even for negative slopes
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Stats Attack!
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High probability of it being high yield...or is it odds?
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