25 Questions
In the context of epidemiology, what is a causal relationship between an exposure E and an outcome D defined as?
A relationship where there is a significant statistical association between E and D
What should be considered before assuming a significant association between a given exposure and a given health outcome?
The presence of other confounding variables
Which of the following study types requires that exposure E precedes outcome D?
Cohort study
In causal mediation, what does a mediator M represent?
An intermediate step between exposure E and outcome D
What is the main challenge in modeling causation in epidemiology?
The complexity of natural mechanisms linking exposure to outcome
What does the term 'confounding' refer to in epidemiological research?
The influence of a third variable on the relationship between the exposure and outcome
What does it mean when a relationship between two variables is 'confounded'?
The relationship is influenced by a third variable
What is the purpose of stratifying data by sex in epidemiological analysis?
To control for the influence of gender on the exposure-outcome relationship
What does it mean to 'adjust for confounding' in epidemiological analysis?
To statistically account for the influence of confounding variables in the analysis
What does it mean when a study result is 'confounded by sex'?
The study result is influenced by differences in the male and female study populations
Why is controlling for confounding variables important in epidemiological research?
To improve accuracy and reduce bias in estimating causal effects
What does it mean to 'adjust for sex' in an epidemiological analysis?
To account for potential gender-related confounding in the analysis
What role does a mediator play in causal inference?
A mediator explains the mechanism through which the exposure influences the outcome
What is the primary purpose of causal mediation analysis?
To assess how much of the total effect operates through a mediator variable
What does it mean when a study result is 'confounded'?
The study result may be biased due to uncontrolled factors affecting the exposure-outcome relationship
What is the definition of a confounder in the context of an association between an exposure E and an outcome D?
A variable C that has an effect on D and is associated with E
What potential problem can arise when including or excluding a confounder C in the analysis?
Both a and b
What is the consequence of not dealing properly with an interaction between variables E and X?
Both c and d
In epidemiology, which are typical confounders when assessing the effect of an exposure of interest?
Sex and age
If there is an interaction between exposure E and third variable X, how should the association between E and D be described?
Describing the association for each level of X
What does it mean when a variable modifies the effect of E on D?
The variable varies among different levels of E
What could be a possible interpretation when comparing two models: one adjusted for a potential confounder and another adjusted for both the potential confounder and a modifying variable?
$\beta$A represents the expected change in the mean outcome for each 1-unit increase in the exposure, when individuals compared have the same levels of potential confounders but different levels of modifiers
In the context of a linear regression model, how is an interaction between variables handled?
By fitting separate models for each level of the interacting variable
$ ext{In a linear regression model, if we suspect that sex (S) modifies the relationship between intima media thickness (IMT) and age (A),}$ $ ext{how should we adjust for this potential modification?}$
$ ext{By fitting separate regression models for each level of sex (S)}$
$ ext{In epidemiology, when assessing the effect of an exposure on an outcome of interest, what are typical variables that need to be considered as potential confounders?}$
$ ext{Age and sex}$
Test your understanding of causality, confusion, and interaction in health sciences statistics with this quiz. Topics covered include the introduction to statistics in health sciences, causality, confusion, and interaction. This quiz is based on the work of Jose Barrera from ISGlobal Barcelona Institute for Global Health.
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
