Summary COMLEX LEVEL 3

Step 3

Biostats and Epidemiology


Mean = sum of all the values divided by the number of values
-​ 5 + 4 + 6 = = 5
-​ Highest in a positive skew (graph tail on the right)

Median = the value that is in the middle of all the values
-​ Line up the values in numerical order and alternate crossing off the lowest value and the
highest value
-​ 3, 5, 7, 9, 12, 14, 16 = 9 (number in the middle)
-​ If there is an even number of values take the average of the two remaining numbers
-​ 3, 5, 7, 9, 11, 14, 16, 17 = 9,11 = (9 + 11) / 2 = (20) / 2 = 10

Mode = the value that comes up the most often
-​ 3, 3, 5, 6, 7, 8, 8, 8, 9, 10 = 8
-​ Highest in a negative skew (graph tail on the left)

Types of Data
Nominal data
-​ Characterized by name only with no particular order
-​ Ex: blood types
Ordinal data
-​ Occurs in a particular order with no clear break points
-​ Ex: student rank list
Interval data
-​ Clear break points in the set of data points
-​ Ex: CD4 count used as a point to stop PCP prophylaxis
Ratio data
-​ Interval data that has cutoff points

Sensitivity = TP / (TP + FN) - down the left side
Specificity = TN / (TN + FP) - up the right side
Positive predictive value = TP / (TP + FP) - top row to the right
Negative predictive value = TN / (TN + FN) - bottom row to the left
False negative rate = FN / (FN + TP) - reverse of sensitivity
False positive rate = FP / (TN+ FP) - reverse of specificity

As prevalence increases, the greater the positive predictive value is

Sensitivity
-​ Likelihood a test will detect all people with the disease

, -​ A negative test will exclude that disease in a population
-​ A perfect test will have no false negative
-​ Negative test rules out disease

Specificity
-​ Likelihood that a person without a disease are correctly identified as disease negative
-​ Those with no disease will test negative
-​ A positive results rules disease in


Type I vs Type II Error
-​ Type I = false positive (Rejecting the null hypothesis when it is true)
-​ Type I is alpha
-​ Ex: Rejecting the null hypothesis when it is true, saying the drug works when it does not
-​ Type II = false negative (Not rejecting the null hypothesis when it is false)
-​ Ex: Accepting the null hypothesis, saying a drug doesn’t work when it does
-​ Type II is beta
-​ Power = 1 - beta

Factors That Decrease Errors
-​ Increased sample size
-​ Increased difference between groups (effect size)
-​ Increased precision of results (lowers standard deviation)

Relative Risk (RR) vs Relative Risk Reduction (RRR) vs Attributable Risk (AR) vs
Absolute Risk Reduction (ARR)
-​ Divide the relatives (RR) = incidence of risk among those exposed / incidence of risk
among those not exposed = (a / a + b) / (c / c + d)
-​ RRR = 1 - RR
-​ Subtract the attributes (AR) = incidence of risk among those exposed - incidence of risk
among those not exposed = (a / a + b) - (c / c + d)
-​ Absolutely backward attributes (ARR) = (c / c + d) - (a / a + b)

Number Needed to Treat vs Number Needed to Harm
-​ Treat (5 letters) vs Harm (4 letters)
-​ Treat is longer than Harm
-​ ARR is longer than AR
-​ NNT = 1 / ARR
-​ NNH = 1 / AR

Prevalence vs Incidence
-​ Prevalence is the total number at any given time (total cases)
-​ Prevalence = (A+C) / (A+B+C+D)

, -​ Incidence is the number during a specific time frame (cases per year)
-​ Incidence = Prevalence / time period

Accuracy, Precision, Reliability
Accuracy
-​ Validity
-​ Combination of sensitivity and specificity
-​ (A+D) / (A+B+C+D)
Precision
-​ Immune from randomness
-​ Data is clustered together at one point
-​ A / (A+B)
Reliability
-​ Reproducibility of the test

Odds Ratio vs Relative Risk
-​ Odds ratio used for a case-control study
-​ Odds ratio = (a + d) / (b + c)
-​ Relative risk used for cohort study
-​ RR = (a / a + b) / (c / c + d)
-​ RR < 1 = reduced risk
-​ RR > 1 = increased risk
-​ RR = 1 = no risk

Types of Studies
Case-Control Study vs Cohort Study
-​ Case-control = odds of previous exposure on the development of a disease. Starts with
those who have a disease and looks backwards to assess for risk exposure
-​ Cohort = compares those exposed to something or have a disease to those not exposed
or have the disease. Can be either prospective vs retrospective
-​ Cohort uses Relative Risk

Case Series = small collection of individual cases
-​ No control group
-​ Good for rare diseases or rare exposures
-​ Lowest level of evidence
-​ Ex: A group of researchers study a several cases Cruezfedlt-Jakob disease in a
rural town

Cross-Sectional Study = during a specific period of time to measure prevalence
-​ Measures Prevalence
-​ Ex: A group of researcher study the number of patient’s who developed
Clostridium difficile infections during the past year at a hospital

, Randomized Clinical Trial
-​ Gold standard for research for therapeutic and preventive therapies
-​ Highest level of evidence (especially if a meta-analysis of multiple trials is done)

Meta-analysis = compares the results from multiple different studies and comes to a single
conclusion




Negatives stay on the left, Positives stay on the right
-​ Falses in the overlap
Lowering the threshold = decreased FN, increased FP
-​ Sensitivity increases
-​ Specificity decreases
-​ PPV decreases
-​ NPV increases
-​ Ex: A states local health board has decided to lower the threshold of lead
exposure to determine lead toxicity in children. This means less lead is needed to
get a positive result causing the number of false positives to decrease and the
number of false positives to increase.
Increasing the threshold = increased FN, decreased FP
-​ Sensitivity decreases
-​ Specificity increases
-​ PPV increases
-​ NPV decreases

Sensitivity and NPV go together (Sensitive topics have No People Viewing)
Specificity and PPV go together (Watch a Specific PPV event)

Statistical Tests
-​ T-test = compares the MEAN of 2 sample groups (ex: comparing the weight loss among
patients who were placed on Ozempic compared to standardized diet)
-​ ANOVA = compares the MEAN of 3 OR MORE sample groups (ex: comparing the
weight loss among patients who were placed on Ozempic, standardized diet, and
standardized exercise program)
-​ Chi-square = compares CATEGORICAL data between groups to determine if groups are
related (ex: comparing vaccination status in those who contract a disease)


Z scores
-​ Based on the standard deviation (SD) around the mean
-​ Z score of one SD = 1, of two SD = 2