Epidemiology Flashcards
Regression to the Mean
Natural tendency for a variable to change with time and return towards population average.
Prevalence
% of people at one iven time that have the disease.
TP + FN) / (sample/population
or
Incidence x Duration (average)
Sensitivity
Proportion of those with the disease who have a positive test.
TP / (TP+FN)
SnNOUT:
If sensitivity is high and patient has a NEGATIVE test you can rule OUT the disease
Specificity
Proportion of those WITHOUT disease who have a negative test
TN/ (TN+FP)
SpPIN:
If Specificity is high and the patient has a POSITIVE test you can rule IN the disease
Positive Predictive Value
Closely related to PREVALENCE
The proportion of those with a POSITIVE test who actually have the disease.
TP/ (TP+FP)
Test the likely hood of a true result
Negative Predictive Value
The proportion of those with a NEGATIVE test who do NOT have the disease.
TN / (TN+FN)
Likelihood Ratios
How likely a test is to be positive among those with disease as opposed to those without.
LR+ = Sensitivity / 1-Specificity LR- = 1-Sensitivity / Specificity
Parallel Testing
Several tests performed at once
Used for rapid assessment situations
Maximized Sensitivity and Negative Predictive Value
(captures all including false positives)
Serial Testing
Order nest test on basis of prior test results
Useful in clinical situations
Maximizes Specificity and Positive Predictive Value
(more sure the patient has the disease)
Point Prevalence
Existing cases at a point in time.
Incidence
Proportion of a population, initially free of outcome, that develops the condition over a given period of time.
measured by cumulative incidence or incidence density.
Cumulative Incidence
aka RISK
Probability of an individual developing the disease during a specific period of time.
Number new cases of disease during a given time period DIVIDED BY Total persons initially AT RISK for the same time period.
Incidence Density
Number of new cases of disease during a given time DIVIDED BY population (TOTAL person time)
Risk Factors
Characteristics that are either directly related or likely lead to the target condition.
Relative importance is assessed by frequency and magnitude.
Criteria for causation argument
Statistically significant association and:
- exposure preceding disease
- strength of association (high relative risk, RR)
- dose-response relationship
- consistency from study to study
- cofounders considered
Impairments:
same effect from other causes
more than one causal factors to produce effect.
long interval between cause and effect.
Case Report
Experience of a single patient with a unique finding
Case series
experience of a group of patients with similar diagnosis
Cross-sectional studies
Prevalence study
Relationship between exposure and disease prevalence at a single point in time.
Strengths
• May generate new etiologic hypotheses
• Relatively easy and inexpensive
• Can use data collected for other purposes
Weaknesses
• No cause-effect
• Measures at one point in time; No temporality
• Prevalent cases are survivors
(see notes for set-up)
Case-Control sudies
Key comparison: Disease vs. No Disease
Measure the risk of exposure in the Disease group vs. the No Disease group.
*looking at those with disease and without and trying to determine risk factors, exposures, etc.
Strengths:
- Smaller in size
- Useful for rare or unusual diseases
- Can evaluate multiple risk factors at once.
Weaknesses:
- Cannot determine incidence directly (estimated RR)
- Uncertainty in exposure-disease time relationship
Cohort Studies
Key comparison: Risk Factor or No Risk Factor
Longitudinal study
Group of people who have something in common; observed over time to see what happens to them
Strengths:
- directly determines incidence & risk
- reduced bias by measuring exposure first.
Weaknesses:
- requires large sample size
- long time to complete
- expensive
Cross-sectional Analysis
Point Estimate,
Confidence intervals,
P-value (less valuable than CI)
Cohort Analysis
Risk Ratio
Abolute Risk
Confidence Interval,
Case Control Analysis
Odds Ratio
Confidence Interal
Relative Risk
Used in Cohort Studies
Risk Ratio [a/(a+b)] / [c/(c+d)] = 1- no association, >1 increased risk, <1 decreased risk
Absolute Risk
Used in Cohort Studies
(exposure - no exposure) or [a/(a+b)] - [c/(c+d)] = 0 - no association, >0 excess amount of disease in exposed group, <0 decreased amount of disease.
Odds Ratio
Used in Case control studies
(ad/bc) = >1 - increased risk of disease,
<1 - decreased risk of disease, 1 - no association
Confounding Bias
Occurs when two factors are associated with each other and the effect of a third factor confuses the association.
Controlled in the study via randomization, restriction of inclusion criteria, and control matching.
Controlled in analysis via stratification, multivariate analysis
Relative Risk Reduction
Used in experimental studies
Describes the magnitude of the effect. % Reduction in risk of studied outcome.
may overestimate clinical relevance, varies across populations.
[(control event rate - experimental event rate)/ control event rate]
Absolute Risk Reduction
Describes the risk difference in outcomes between patients who have undergone one therapy and those who have not.
How many patients are spared the adverse outcome, varies with underlying risk
[control event rate - experimental event rate]
Number Needed to Treat
Describes how many patients need to be treated to prevent one of them from experiencing the outcome
[1 / (control - experimental event rate)]
If the absolute risk reduction is large few patients are needed to observe benefit.
Intention-to-Treat analysis
analysis according to group assigned (randomization) regardless of treatment received.
Tests efficacy (desired effect) as well as Effectiveness (does it work in usual practice)
Explanatory analysis
Analysis according to treatment actually received regardless of randomization.
Tests efficacy
Randomized Clinical trials
Strengths:
- control over study situation
- control of exposure
- random assignment (reduced bias)
Weaknesses:
- external validity (real world application?)
- adherence to protocols
- ethics
Experimental Event Rate
Risk of outcome event in experimental group
= a/(a+b)
Control Event Rate
Risk of outcome event in control group
= c/ (c+d)
Survival Curves
Area under cure = number of person-years lived by the population studied.
A death at time t causes curve to drop by the following amount:
(current height) x (N-1) / N [N=no. of survivors]
Crude mortality rate
new deaths / total population (in given time)
Age specific mortality
No. deaths in specified age / population in that age group (in a given time, usually 1 year)
Disease specific morality
No. death of specified cause/ Total population
Proportionate morality rate
No. deaths from specific disease / total number of deaths during same time period
Case fatality rate
no. who died from disease / no. who have the disease
