Epidemiology Flashcards
Effect modification (interaction)
Effect modification (interaction) results when an extraneous variable (modifier) changes the direction or strength of an association between a risk factor and a disease. A modifier is associated with the disease but not the risk factor. Effect modification can sometimes be confused with:
Confounding - The bias that results when the exposure-disease relationship is obscured by the effect of an extraneous factor (confounder) associated with both exposure and disease.
Bias
Stratified analysis by the extraneous variable can help distinguish whether that variable is a confounder or an effect modifier, as follows:
Confounder: Confounding bias occurs when a perceived exposure-disease relationship is actually caused by an extraneous factor that correlates with both the exposure and the disease. There is no information suggesting that confounding occurred in this study.
The measures of association (eg, relative risk [RR] of bladder cancer among alcohol users) calculated in each of the stratified groups (eg, smokers and nonsmokers) are similar to one another (eg, nonstatistically significant RR = 0.95 [95%CI: 0.51, 1.76] and RR = 1.03 [95%CI: 0.34, 3.13]), but they differ from the measure of association calculated in the crude analysis (eg, statistically significant RR = 1.81 [95%CI: 1.06, 3.10]); stratification can remove the effects of the confounder (eg, smoking).
Effect modifier: The measures of association calculated in each of the strata are significantly different (in strength or direction) from one another; stratification can make the effects of the modifier more apparent.
Selection bias can occur with inappropriate (ie, nonrandom) assignment methods or through selective attrition of the study participants. It results in a study population that does not accurately represent the actual population, leading to erroneous conclusions regarding the exposure-disease relationship. Tx: To avoid selection bias in studies, patients are randomly assigned to treatments to minimize potential confounding variables. Many studies also perform an intention-to-treat (ITT) analysis to deal with selection bias. An ITT analysis compares the initial randomized treatment groups (the original intention) regardless of the eventual treatment to avoid counting crossover patients. Conversely, as-treated analysis compares the groups based on the actual treatment received. An as-treated analysis is performed to gauge the effectiveness of the treatment itself, with less regard for potential confounders.
Selective survival bias occurs in case-control studies when cases are selected from the entire disease population instead of just those that are newly diagnosed. For instance, a study on cancer survival that is not limited to newly diagnosed patients will contain a higher proportion of relatively benign malignancies as these patients generally live longer.
Observer bias occurs when the observer is influenced by prior knowledge or details of the study in a way that affects the results. Studies attempt to avoid this bias by blinding observers from knowing treatment assignments and by measuring objective outcomes (eg, mortality) that are less likely to be skewed by observers.
Latency
Latency is an important natural phenomenon of disease epidemiology. Most infectious diseases have relatively short latency periods (ie, the time elapsed from initial exposure to clinically apparent disease). In contrast, some disease processes (eg, cancer, heart disease) have a long latency period before clinical manifestations develop.
The concept of a latency period can also be extended to risk factors and risk reducers. Sometimes, a significant amount of time must pass before exposure to a risk modifier has a clinically evident effect on the disease process. In addition, exposure to a risk modifier may need to occur continuously over a certain period before the disease outcome is affected. In this case, at least 1 year of high-dose statin therapy was required to show a significant protective advantage over moderate-dose therapy.
Median survival is calculated in cohort studies or clinical trials, and is usually used to compare the median survival times in two or more groups of patients (e.g., receiving a new treatment or placebo).
Incidence measures (e.g., relative risk or relative rate) cannot be directly measured in case-control studies because the people being studied are those who have already developed the disease. Relative risk and relative rate are calculated in cohort studies, where people are followed over time for the occurrence of the disease.
Studies
Experimental:
Randomized controlled trial
- Random allocation into treatment & placebo groups
- Can determine efficacy of the intervention
Nonrandomized design
- Nonrandom allocation into treatment & placebo groups
- Can determine efficacy of the intervention
Observational:
Cohort
- Data gathered from the same individuals over time (longitudinal)
- Can assess risk factors or outcomes
Cross-sectional
- Data gathered at one point in time
- Can determine prevalence of an outcome in a population
Case-control
- Data gathered from individuals with the condition of interest (cases) & compared to individuals without the condition (controls)
Case
- Detailed information gathered about one individual (or a small group of individuals)
Review
Meta-analysis
Data from multiple studies are statistically combined & analyzed
Equations
ARP = (RR - 1)/RR
