BEHAVIORAL SCIENCE Flashcards
Cross-sectional study
Type, Design, Measures & Examples
Observaional
Design: 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?”
Measures/Examples:
Disease Prevalance
Can show risk factor association with disease, but does not establish causality.
Case-control study
Type, Design, Measures & Examples
Observational and retrospective
Design: Compares a group of people with disease to a group without disease. Looks for prior exposure or risk factor. Asks, “What happened?”
Measures/Example:
Odds ratio (OR).
“Patients with COPD had higher odds of a history of smoking than those without COPD had.”
Cohort Study
Type, Design, Measures & Examples
Observaional and prospective or retrospective
Design: Compares a group with a given exposure or risk factor to a group without such exposure. Looks to see if exposure the likelihood of disease. Can be prospective (asks, “Who will develop disease?”) or retrospective (asks, “Who developed the disease [exposed vs. nonexposed]?”).
Measures/Example: Relative risk (RR). “Smokers had a higher risk of developing COPD than nonsmokers had.”
Twin concordance study
Design: Compares the frequency with which both monozygotic twins or both dizygotic twins develop same disease.
Measures/Examples:
Measures heritability and influence of environmental factors (“nature vs. nurture”).
Adoption study
Design: Compares siblings raised by biological vs. adoptive parents.
Measures/Example:
Mesures 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.
How can one improve the quality of clinical trials?
Study quality improves when study is randomized, controlled, and double-blinded (i.e., neither patient nor doctor knows whether the patient is in the treatment or control group).
What is triple-blind?
Triple-blind refers to the additional blinding of the researchers analyzing the data.
Drug Trial Phases; study sample; and purpose
Phase I - Small number of healthy volunteers.
Purpose: “Is it safe?” Assesses safety, toxicity, and pharmacokinetics.
Phase II - Small number of patients with disease of interest.
Purpose: “Does it work?” Assesses treatment efficacy, optimal dosing, and adverse effects.
Phase III - Large number of patients randomly assigned either to the treatment under investigation or to the best available treatment (or placebo).
Purpose: “Is it as good or better?” Compares the new treatment to the current standard of care
Phase IV - Postmarketing surveillance trial of patients after approval.
Purpose: “Can it stay?” Detects rare or long-term adverse effects. Can result in a drug being withdrawn from market.
Evaluation of diagnostic tests
Uses 2 × 2 table comparing test results with the actual presence of disease.
TP = true positive; (Test +, Disease +)
FP = false positive; (Tes+, Disease -)
TN = true negative;
FN = false negative.
Sensitivity and specificity are fixed properties of a test
Define Sensitivity
True-positive Rate
Proportion of all people with disease who test positive, or the probability that a test detects disease when disease is present.
What does a high sensitivy % indicate?
Value approaching 100% is desirable for ruling out disease and indicates a low false-negative rate.
what is high sensitivity test used for?
High sensitivity test used for screening in diseases with low prevalence.
Sensitivity formula
= TP / (TP + FN)
= 1 – false-negative rate
If sensitivity is 100%, TP / (TP + FN) = 1, FN = 0, and all negatives must be TNs
Define Specificiy
True-negative rate
Proportion of all people without disease who test negative, or the probability that a test indicates non-disease when disease is absent.
What does a high Specificity indicate and what is it used for?
Value approaching 100% is desirable for ruling in disease and indicates a low falsepositive rate.
High specificity test used for confirmation after a positive screening test.
Specificity formula
= TN / (TN + FP)
= 1 – false-positive rate
(SP-P-IN = highly SPecific test, when Positive, rules IN disease )
If specificity is 100%, TN / (TN + FP) = 1, FP = 0, and all positives must be TPs
Define Positive predictive value (PPV)
Proportion of positive test results that are true positive
. Probability that person actually has the disease given a positive test result.
PPV Formula
= TP/(TP+FP)
How does PPV relate with prevalance?
PPV varies directly with prevalence or pretest probability:
high pretest probability –> high PPV
Define Negative predictive value (NPV)
Proportion of negative test results that are true negative.
Probability that person actually is disease free given a negative test result.
NPV Formula
= TN/ (FN+TN)
How does NPV relate to prevalance?
NPV varies inversely with prevalence or pretest probability:
high pretest probability –> low NPV
Incidence Rate formula
Incidence rate =
of new cases in a specified time period
________________________________
Population at risk during same time period
WHat does incincidence look at?
NEW CASES (INCIDENTS)
Prevalance formulas
Prevalence =
# of existing cases
__________________
Population at risk
Prevalence ≈ incidence rate × average disease duration.
What does prevalance look at?
Prevalence looks at all current cases.
Odds ratio (OR)
Typically used in case-control studies.
Odds that the group with the disease (cases) was exposed to a risk factor (a/c) divided by the odds that the group without the disease (controls) was exposed (b/d).
OR= a/c / b/d = ad / bc
Relative Risk (RR)
The proportion of risk reduction attributable to the intervention as compared to a control. RRR = 1 – RR (e.g., if 2% of patients who receive a flu shot develop flu, while 8% of unvaccinated patients develop the flu, then RR = 2/8 = 0.25, and RRR = 1 – RR = 0.75).
Attributable Risk (AR)
The difference in risk between exposed and unexposed groups, or the proportion of disease occurrences that are attributable to the exposure
(e.g., if risk of lung cancer in smokers is 21% and risk in nonsmokers is 1%, then 20% (or .20) of the 21% risk of lung cancer in smokers is attributable to smoking).
AR Formula
AR = a/(a+b) - c/(c+d)
Absolute risk reduction (ARR)
The difference in risk (not the proportion) attributable to the intervention as compared to a control
(e.g., if 8% of people who receive a placebo vaccine develop flu vs. 2% of people who receive a flu vaccine, then ARR = 8% - 2% = 6% = .06).
(Risk factor +, Disease + = a)
(Risk factor +, Disease - = b)
(Risk factor - , Disease + = c)
(Risk factor -, Disease + = d)
needed to treat and how to calculae
Number of patients who need to be treated for 1 patient to benefit.
Calculated as 1/ARR.
Needed top harm and how to calculate
Number of patients who need to be exposed to a risk factor for 1 patient to be harmed.
Calculated as 1/AR.
Precision vs. Accuracy
Precision: The consistency and reproducibility of a test (reliability).
The absence of random variation in a test.
(more precision –> less standard deviation)
Accuracy: The trueness of test measurements (validity).
The absence of systematic error or bias in a test.
What reduces precision in a test?
Random error
What reduces accuracy in a test?
Systemic error
Selection bias: What is it? Examples? Strategy to reduce bias?
Nonrandom assignment to participate in a study group. Most commonly a sampling bias.
Examples include:
Berkson bias
A study looking only at inpatients
Loss to follow-up
Studying a disease with early mortality
Healthy worker and volunteer biases
Study populations are healthier than the general population
Reduce bias by:
Randomization
Ensure the choice of the right comparison/reference group
