First Aid-Behavioral Science Flashcards
Cohort study
Observational and prospective. Compares a group with a given exposure or risk factor to a group without.
Looks to see if exposure increases the likelihood of disease.
Asks, “What will happen?”
Measure: Relative risk (RR)
Case-control study
Observational and retrospective; Compares a group of people with disease to a group without. Looks for prior exposure or risk factor.
Asks, “what happened?”
Measure: Odds ratio (OR)
Cross-Sectional Study
Observational. 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: Disease prevalence
Can show risk factor association with disease, but does not establish causality.
Twin concordance study
Compares the frequency with which both monozygotic twins or both dizygotic twins develop a disease. Measures heritability.
Adoption Study
Compares siblings raised by biologic vs. adoptive parents.
Measures heritability and influence of environmental factors.
Clinical trial
experimental 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 (neither patient nor doctor knows if the patient is in the treatment or control group).
Phase I (Clinical Trial)
Small number of patients, usually healthy volunteers. Assesses safety, toxicity, and pharmacokinetics.
Phase II (Clinical Trial)
Small number of patients with disease of interest. Assesses treatment efficacy, optimal dosing, and adverse effects.
Phase III (Clinical Trial)
Large number of patients randomly assigned either to the treatment under investigation or to the best available treatment (or placebo). Compares the new treatment to the current standard of care.
Phase IV (Clinical Trial)
Postmarketing surveillance trial of patients after approval. Detects rare or long-term adverse effects.
Meta-analysis
Pools 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.
Evaluation of diagnostic tests
Uses 2 x 2 table comparing test results with the actual presence of disease. TP = true positive FP = False positive TN = True negative FN = False negative
Sensitivity
proportion of all people with disease who test positive, or the ability of a test to detect a disease when it is present.
= TP / (TP + FN)
= 1 - false-negative rate
SNOUT = SeNsitivity rules OUT
Value approaching 1 is desirable for ruling out disease and indicates a low false-negative rate. Used for screening in diseases with low prevalence.
If 100% sensitive, TP / (TP + FN) = 1, FN = 0, and all negatives must be TNs.
Specificity
Proportion of al people without disease who test negative, or the ability of a test to indicate non-disease when disease is not present.
= TN / (TN + FP)
= 1 false-positive rate
Value approaching 1 is desirable for ruling in disease and indicates a low false-positive rate. Used as a confirmatory test after a positive screening test.
SPIN = SPecificity rules IN.
Positive Predictive Value (PPV)
proportion of positive test results that are true positive.
= TP / (TP + FP)
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 specificity or high sensitivity will have low positive predictive values!)
Negative predictive value (NPV)
Proportion of negative test results that are true negative.
= TN / (FN + TN)
Probability that person actually is disease free given a negative test result
Point prevalence
= (total cases in population at a given time) / (total population at a given time)
Incidence
= (new cases in population over a given time period) / (total population at risk during that time period)
Incidence is new incidents
when calculating incidence, don’t forget that people currently with the disease, or those previously positive for it, are not considered at risk.
Prevalence
= incidence x disease duration
prevalence > incidence for chronic diseases (e.g., diabetes)
prevalence = incidence for acute disease (e.g., common cold)
Odds ratio (OR)
for case-control studies. Odds of having disease in exposed group divided by odds of having disease in unexposed group. Approximates relative risk if prevalence of disease is not to high.
OR, RR, AR picture
UPLOAD IN THE FUTURE (location 1752)
Relative Risk (RR)
for cohort studies. Relative probability of getting a disease in the exposed group compared to the unexposed group. Calculated as the percent with disease in exposed group divided by percent with disease in unexposed group
Attributable risk
the 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 reduction
the reduction in risk associated with a treatment as compared to a placebo.
Number needed to treat
1/absolute risk reduction
Number needed to harm
1/attributable risk.
Precision
Precision is:
- the consistency and reproducibility of a test (reliability)
- the absence of random variation in a test. Random error-reduced precision in a test
Accuracy
is the trueness of test measurements (validity).
Systemic error-reduced accuracy in a test
Bias
occurs when one outcome is systematically favored over another. Systematic errors.
Selection bias
nonrandom assignment to study group (e.g. Berkson’s bias)
1. Blind studies (double blind is better)
Recall bias
knowledge of presence of disorder alters recall by subjects
2. Placebo responses
Samling bias
subjects are not representative relative to general population; therefore results are not generalizable.
Late-look bias
information gathered at an inappropriate time—e.g., using a survey to study a fatal disease (only those patients still alive will be able to answer survey)
Procedure bias
subjects in different groups are not treated the same—e.g., more attention is paid to treatment group, stimulating greater compliance
Confounding bias
occurs with 2 closely associated factors; the effect of 1 factor distorts or confuses the effect of the other
Lead-time bias
early 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 effect
occurs when a researcher’s belief in the efficacy of a treatment changes the outcome of that treatment
Hawthorne effect
occurs when the group being studied changes its behavior owing to the knowledge of being studied
Normal distribution
≈ Gaussian ≈ bell-shaped (mean = median = mode).
Bimodal
s simply 2 humps (2 modal peaks).
Positive skew
mean > median > mode. Asymmetry with tail on right.
Negative skew
mean < median < mode. Asymmetry with tail on left.
Null hypothesis (H₀)
Hypothesis of no difference (e.g., there is no association between the disease and the risk factor in the population).
Alternative hypothesis (H₁)
Hypothesis that there is some difference (e.g., there is some association between the disease and the risk factor in the population).
Type I error (α)
Stating 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 α, a preset level of significance (usually
< .05). “False-positive error.”
Type II error (β)
Stating that there is not an effect or difference when one exists (to fail to reject the null hypothesis when in fact H0 is false).
β is the probability of making a type II error. “False-negative error.”
p
If p < .05, then there is less than a 5% chance that the data will show something that is not really there.
α
α = you “saw” a difference that did not exist—for example, convicting an innocent man.
β
β = you did not “see” a difference that does exist— for example, setting a guilty man free.
Power (1 - β)
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:
- Total number of end points experienced by population
- Difference in compliance between treatment groups (differences in the mean values between groups)
- Size of expected effect
If you ↑ sample size, you ↑ power. There is power in numbers.
Power = 1 - β.