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Logan Hanks

Methods Glossary

A plain-language reference for the epidemiology and population-science methods behind cancer-prevention research. Each entry gives you a direct answer you can act on — plus deeper context when you need it. New terms added as the work demands.

CDEHILNOPRS

C

  • Causal InferenceCausal inference is the set of methods and assumptions used to estimate the effect that changing an exposure would have on an outcome, as distinct from mere statistical association.
  • Collider BiasCollider bias is a spurious association induced between two variables when an analysis conditions on (stratifies, adjusts for, or selects on) a common effect of both.
  • Community-Based Participatory Research (CBPR)Community-based participatory research (CBPR) is an approach that engages community members as equal partners with researchers across all phases of a study, sharing decision-making, data, and benefits.
  • Confidence IntervalA confidence interval is a range of plausible values for a population parameter computed so that, under repeated sampling, a stated percentage (e.g. 95%) of such intervals would contain the true value.
  • Confounder AdjustmentConfounder adjustment is the use of statistical or design methods — such as stratification, regression, matching, or weighting — to remove the distortion a confounder introduces into an exposure–outcome estimate.
  • ConfoundingConfounding is a distortion of an exposure–outcome association caused by a third variable that influences both the exposure and the outcome.

D

  • Directed Acyclic Graph (DAG)A directed acyclic graph (DAG) is a diagram of variables connected by single-headed arrows with no cycles, used to encode causal assumptions and decide which variables to adjust for.

E

  • Effect Modification (Interaction)Effect modification occurs when the magnitude of an exposure's effect on an outcome differs across levels of a third variable.

H

  • Health EquityHealth equity is the principle and goal that everyone has a fair and just opportunity to attain their highest level of health, requiring the removal of avoidable, unfair differences across social groups.

I

  • Incidence vs. PrevalenceIncidence measures the rate of new cases arising over a period, while prevalence measures the proportion of a population that has the condition at a point (or interval) in time.

L

  • Logistic RegressionLogistic regression is a regression model for a binary outcome that estimates the log-odds of the outcome as a linear function of predictors, yielding adjusted odds ratios.

N

  • Number Needed to TreatThe number needed to treat (NNT) is the number of people who must receive an intervention to prevent one additional bad outcome, calculated as the reciprocal of the absolute risk reduction.

O

  • Odds RatioThe odds ratio (OR) is the ratio of the odds of an outcome in an exposed group to the odds in an unexposed group, measuring the strength of association between an exposure and an outcome.

P

  • P-valueA p-value is the probability of observing data at least as extreme as what was seen, assuming the null hypothesis is true.

R

  • Relative RiskRelative risk (RR), also called the risk ratio, is the ratio of the probability of an outcome in an exposed group to the probability in an unexposed group.
  • Risk DifferenceThe risk difference (RD), or absolute risk reduction, is the arithmetic difference in the probability of an outcome between an exposed and an unexposed group.

S

  • Screening vs. Diagnostic TestingScreening tests identify asymptomatic people who may have a condition so they can be evaluated further, whereas diagnostic tests confirm or rule out disease in people with symptoms or a positive screen.
  • Selection BiasSelection bias is a systematic error that arises when the people included in (or retained by) a study differ from the target population in ways related to both exposure and outcome.
  • Sensitivity and SpecificitySensitivity is the proportion of people with the disease who test positive; specificity is the proportion of people without the disease who test negative.
  • Social Determinants of HealthSocial determinants of health are the non-medical conditions in which people are born, grow, live, work, and age — such as income, education, housing, and access to care — that shape health outcomes.
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