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USMLE Epidemiology and Biostatistics Summary Meta-Analysis: pools data from several studies (greater power), limited by quality/bias of individual studies Clinical Trial: compares two groups in which one variable is manipulated and its effects measured Cohort (relative risk): compares group with risk factor to a group without – asks “what will happen?” (prospective). Proves cause-effect Case Control (odds ratio): compares group with disease to group without disease – asks “what happened?” (retrospective). Issues with confounding and inability to prove causation Case Series: good for rare diseases, describe clinical presentation of certain disease Cross-Sectional: data from a group to assess disease prevalence at a particular point in time – asks “what is happening?” Sensitivity (rule out – screening): proportion of people with disease who test positive: TP / (TP + FN) = 1 - FN. If 100%, then all negative tests are TN. Specificity (rule in – confirmatory): proportion of people without disease who test negative: TN / (TN + FP) = 1 - FP. If 100%, then all positive tests are TP. PPV: proportion of positive tests that are true positives: TP / (TP + FP). If disease prevalence is low, then PPV will be low. NPV: proportion of negative tests that are true negatives. TN / (TN + FN) Higher specificity -> higher PPV Higher sensitivity -> higher NPV Odds ratio (case control): odds of having disease in exposed group divided by odds in unexposed group. (a/b) / (c/d) = (ad) / (bc) Relative risk (cohort): relative probability of getting disease in exposed group versus unexposed. [a/(a+b)] / [c/(c+d)] Attributable risk: proportion of cases attributable to one risk factor. [a/(a+b)] - [c/(c+d)] Absolute risk reduction (ARR): [c/(c+d)] - [a/(a+b)] NNT = 1 / ARR Standardized mortality ratio (SMR) = observed No deaths / expected No deaths Incidence: No of new cases in a unit of time/ pop. at risk Prevalence: total No of cases at a given time / pop. at risk Prevalence = incidence * dz duration. Prevalence > incidence in chronic dz. Prevalence = incidence in acute dz Normal distribution: mean = median = mode Standard deviation: 1 (68%) – 2 (95%) – 3 (99.7%) SEM = σ / √n Positive skew (mean > median > mode), negative skew (mean < median < mode) Reliability (“precision”) – reproducibility of test. Affected by random error Validity (“accuracy”) – measures trueness of data. Affected by systematic error Correlation coefficient measures how related two values are: +1 = perfect positive correlation, -1 = perfect negative correlation, 0 = no correlation H0 (null hypothesis): no relationship between two measurements Type I (α) error: reject null when it’s true Type II (β) error: accept null when it’s false Power (1-β): probability of rejecting null when it is indeed false (increase sample size to increase power) Selection bias: nonrandom assignment of subjects Sampling bias: subjects not representative of population Recall bias: risk for retrospective studies (pts cannot remember things); knowledge of disorder presence alters recall Late-look bias: data gathered at inappropriate time Lead-time bias: early detection confused with increased survival Confounding bias: a factor is related to both exposure and outcome, but not on the causal pathway Procedure bias: subjects in different groups not treated the same - Rishi Kumar, MD @rishimd #USMLE #Epidemiology #Biostatistics #formulas #calculations #diagnosis

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Indraneel Prabhu

@IndyPrabhu

My name is Indraneel Prabhu, or Indy for short, and I am an M4 at Touro University Nevada. I was born in India and raised in the Bay Area, CA. Throughout my time in medical school, I saw the value in free open-access education at all levels, and hope to provide quality content!

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