Step 1 - Behavioral Science 1

evolv3's version from 2015-04-30 18:49


Question Answer
Case control studiesObservational and retrospective - Compares a group of people with disease to a group without, asks - “What happened?”
Measure in Case control studiesOdds Ratio (OR)
Cohort studyObservational and prospective - compares a group with a given risk factor to a group without to assess whether the risk factor ↑ the likelihood of disease
Measure in Cohort studyRelative risk (RR)
Cross-sectional studyObservational - Collects data from a group of people to assess frequency of disease (and related risk factors) at a particular point in time - asks “what is happening”
Measure Cross-sectional studyDisease prevalence - can show risk factor association with disease but does not establish causality
Twin concordance studycompares the frequency with which both monozygotic twins or both dizygotic twins develop a disease - measures heritability
Adoption studycompares siblings raided by biologic vs. Adoptive parents - measures hertability and influence of environmental factors
Meta-analysisPools data from several studies to come to an overall conclusion. Achieves greater statistical power and integrates results of similar studies, highest echelon of clinical evidence - may be limited by quality of individual studies or bias in study selection


Question Answer
Clinical trialexperimental study involving humans, compares therapeutic benefits of 2 or more treatments or of treatment and placebo. Highest quality study when randomized, controlled and double-blinded
Phase I trial small number of patients, usually healthy volunteers - assesses safety, toxicity and pharmacokinetics
Phase II trialsmall number of patients with disease of interest - assesses treatment efficacy, optimal dosing and adverse effects
Phase III trialLarge number of patients randomly assigned either to the treatment under investigation or to the best available treatment (or placebo) - compares new treatment to the current standard of care.
Phase IV trialIs more convincing if double blinded

: detects rare/longterm adverse effects


Question Answer
SensitivityProportion of all people with disease who test positive or the ability of a test to detect a disease when it is present. - Value approaching 1 is desirable for ruling out disease and indicates a low false-negative rate. Used for screening in diseases with low prevalence.
Sensitivity equationTP / (TP+FN)
Specificity equation TN / (TN+FP)
Specificity explanationProportion of all people without disease who test negative. or the ability of a tesl to indicate non-disease when disease is not present. Value approaching 1 is desirable for ruling in disease and indicates a low false-positive rate. - Used as a confirmatory lest after a positive screening test.
PPV equationTP/(TP+FP)
NPV equationTN / (FN+TN)
Positive predictive value Proportion of positive test results that are true positive.Probability that person actually has the disease given a positive test result. (Note: If the prevalence of a disease in a population is low, even tests with high specificry or high sensitivity will have low positive predictive values!)
Negative predictive valueProportion of negative test results that are true negative. Probability that person actually is disease free given a negative test result. inverse with prevalence/pretest probability
Point prevalence equationtotal cases in population at a given time / total population at risk at a given time
Incidence equationnew cases in population over a given time period / total population at risk during that time (when calculating incidence remember that people with the disease are not in at risk population)
Prevalenceincidence x disease duration (this means prevalence > incidence for chronic diseases


Question Answer
Odds ratio (OR) for case-control studies Odds of having disease in exposed group dividcd by odds of having disease in unexposed group
Odds ratio equation(a/c) / (b/d) = ad/bc - using table where a=(+disease+risk), b=(- disease, + risk) c = (- risk, +disease) d= (-risk, - disease)
Relative risk (RR) for cohort studiesRelative probability of getting a disease in the exposed group compared to the unexposed group. Calculated as percent with disease In cxposed group divided by percent with disease in unexposed group.
Relative risk (RR) equation(a/(a+b)) /( c/(c+d))
Attributable riskThc difference in risk between exposed and unexposed groups, or the proportion of disease occurrences that are attributable to the exposure (e.g., smoking causes one-third of cases of pneumonia).
Absolute risk reductionThe reduction in risk associated with a treatment as compared to a placebo.
Number needed to treat1/absolute risk reduction
Number needed to harm1/attributable risk
Random error reducesprecision
Systematic error reducesaccuracy
PrecisionI. The consistency and reproducibility of a test (reliability) 2. The absence of random variation in a test
Accuracythe trueness of a tests measurements (validity)


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Selection biasnonrandom assignment to study group (c.g, Berkson’s bias)
Recall biasknowledge of presence of disorder alters recall by subjects
Sampling biassubjects are not representative relative to general population; therefore, results arc not generalizable
Late-look biasInformation gathered at inappropriate time-e.g., using a survey to study a fatal disease
Procedure bias subjects in different groups arc not treated the same-e.g., more attention is paid to treatment group, stimulating greater compliance
Confounding biasoccurs with 2 closed associated factors; the effect of 1 factor distorts or confuses the effect of the other
Lead-time biasearly detection confused with ↑ survival; seen with improved screening (natural history of disease is not changed, but early detection makes it seem as though survival ↑)
Pygmalion effectoccurs when a researcher's belief in the efficacy of a treatment changes the outcome of that treat
Hawthorne effectoccurs when the group being studied changes its behavior owing to the knowledge of being studied


Question Answer
Norma distributionGaussian = bell-shaped (mean = median = mode).
Bimodaldistribution simply 2 humps (2 modal peaks).
Positive skew distribution mean > median > mode. - Asymmetry with tail on right.
Negative skew distribution mean < median < mode. - Asymmetry with tail on left.
least affected by outliers in the sample.mode
Null (H0)Hypothesis of no difference (e.g there is no association between the diseases and the risk factor in the population)
Alternative (H1)Hypothesis that there is some difference (some association b/w disease and risk factor in population)
Draw study result picture56
Type 1 errorStaling that there is an effect or difference when none exists\ (to mistakenly accept the experimental hypothesis and reject the null hypothesis) P =probability of making a type I error p is judged against alpha, a preset level of significance (usually < .05). "False-positive error."
Type II errorStating that there is not an effect or difference when one exists (to fail to reject the null hypothesis when in fact H0 is false) - Beta is probability of making a type II error “false-negative error”


Question Answer
Power(1-beta)Probability of rejecting null hypothesis when it is in fact false or the likelihood of finding a difference if one in fact exists - it depends on 1. Total number of endpoints experienced by population 2. Difference in compliance between treatment groups (differences in the mean values between groups) 3. Size of expected effect
% of sample in one standard deviation68%
% of sample in two standard deviations95%
% of sample in three standard deviations99.7%
Standard error of the meanStandard deviation/ (sqrt(n))
Confidence intervalRange of values in which a specified probability of the means of repeated samples would be expected to fall - If CI for a mean of 2 variables include 0 then there is no significant difference and H0 is not rejected - if CIs b/w 2 groups overlap then these groups are not significantly different
T-test checksdifference b/w means of 2 groups
ANOVA checksdifference between means of 3+ groups
Chi squaredchecks difference between 2 or more percentages or proportions of categorical outcomes (not mean values)
Correlation coefficientr is always between -1 and +1. The closer the absolute value or r is to 1, the stronger the correlation b/w the 2 variables
Coefficient of determinationr squared (value that is usually reported)
Primary disease preventionprevent disease occurence (HPV vaccination)
Secondary disease preventionearly detection of disease (Pap smear)
Tertiary disease preventionreduce disability from disease (e.g. Chemotherapy)


Question Answer
Diabetes prevention measuresEye, foot exams; urine tests
Drug use prevention measuresHepatitis immunization; HIV, TB tests
Alcoholism prevention measuresInfluenza, pneumococcal immunizations; TB test
Overweight prevention measures blood sugar tests for diabetes
Homeless, recent immigrant inmate prevention measuresTB test
High-risk sexual behavior prevention measuresHIV, hepatitis B, syphilis, gonorrhea, chlamydia tests
Reportable diseasesHep, Hep, Hep, Hooray the SSSMMART Chick is Gone - Hep A, Hep B, Hep C, HIV, salmonella, Shigella, syphilis, Measles, Mumps, AIDS, rubella, TB, Chickenpox, Gonorrhea
Leading cause of death of infantsCongenital anomalies, short gestation/low birth weight, sudden infant death syndrome, maternal complications of pregnancy, respiratory distress syndrome
Cause of death 1-14 yoInjuries, cancer, congenital anomalies, homicide, heart disease
Cause of death 15-24 yInjuries, homicide, suicide, cancer, heart disease
Cause of death 25-64 yCancer, heart disease, injuries, suicide, stroke
Cause of death 65+Heart disease, cancer, stroke, COPD, pneumonia, influenza