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### 55 Cards in this Set

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 Mean average Median: middle value Mode: most frequent value Endemic: localized epidemic Pandemic: world-·wide epidemic which of the following central tendency measures is sensitive to outliers? mean which of the following central tendency measures is used in a skewed population? median Variability: what are the different standard deviations and their percentage? 1 STD: 68% 2 STD: 95% 3 STD: 99% Chi square compare percentages T -test compare 2 things ANOVA compare 3+ things Skewing: shift to right (mean> median > mode) Standard Error of Mean = S/ (square root) of N => precision of mean S = standard deviation, N = sample size 1 -> 2 Question Survey: what will happen to sensitivity? NPV? specificity? and PPV? Decrease: Sn, NPV Increase: Sp, PPV PPV equation effect on prevalence example = TP / all positives (increases w / prevalence) Ex: ( +) ELISA -> Do you have HIV? NPV equation define = TN/ all negatives (probability of not having a dz if have negative test) Sensitivity = a/ a+c = TP /all diseased (people that have dz) Specificity = d/ b +d = TN/ all non-diseased (people that don't have dz) " Incidence equation = new cases/ total population Prevalence= equation example = all diseased/ total population (Ex: improved quality of care) Odds Ratio= equation what does it mean in layman's terms • OR <1 => • OR >1 => Odds Ratio= (ad+ bc) cross product (diseased are x times more likely to see risk factor) • OR <1 => protective factor • OR >1 => risk factor Correlation Coefficient: • 0: • +: • -: • 0: nothing • +:correlation • -: negative correlation CI = 95% => 95% sure it lies within the interval (cannot include 1.0) RR= equation explain in layman's terms = exposed/ unexposed (risk of getting dz w/ known exposure) define NNT p value < 0.5 = number needed to treat to change 1 life p value < 0.5 = random chance that you will be wrong 1 time out of 20 Null hypothesis => nothing's happening Power: equation explain in layman's terms 1-β = probability of detecting a true intervention what is improved by ⇧size of study power Effect size = how different two groups are α type I error (FN), P value error "too optimistic'' β: type II error (FP), Power error "too pessimistic" what is the cause of type β: type II error? small samples Prevention: 1°: 2°: 3°: Prevention: 1°: ⇩Incidence 2°: ⇩Prevalence 3°: Slows disease progression Accuracy: validity "truth" Precision: reliability "keep making the same mistake" Admission rate: hospitals have certain populations Confounding bias: define what are the 3 things that should be done to prevent this forgotten variable => use matching, restriction, randomization Lead time: time between diagnosis and treatment Hawthorne effect: the "watched" change their behavior Unacceptability: subjects lie Observer bias: the observers have knowledge about control and study samples Recall bias: inaccurate recall of past events Respondant: subjective diagnosis Sample distortion: sample does not represent population Selection who is in or out I) Clinical Trials: experimental Phase I: Phase II: Phase III: Phase IV: Phase I: Toxicity "burt pt?" Phase II: Efficacy "help pt?" Phase III: Comparison "any better?" Phase IV: Post-marketing surveillance "can they screw it up?" Cohort study: define what happens in a cohort study? what does it determine? Prospective Observe a "cohort" (group of people with similar characteristics) to see how many develop a specific disease after exposure to a risk factor. • Determines incidence: new cases of disease Cohort study: what risk it uses occurs where ex • Uses Relative Risk • Occurs in community • Ex: finds new cases of common disease Case control: type of study what happens in a case control what does it use? where does it occur? bias examples Retrospective • Select subjects with a disease, compare to controls, and study the differences • Uses Odds Ratio • Occurs in hospital • Has more selection bias • Ex: finds risk of developing rare disease Cross sectional: define what does it determine example snaphot in time • Determines prevalence: total cases of diseased • Ex: polls/ surveys Case report describe an unusual pt Case series report describe several unusual pts Consensus panel panel of experts provides a recommendation Clinical wisdom "I think .... " Meta-Analysis tries to combine data from many trials