Block 6 Flashcards
T/F: Measures of association take into account random error
False
Statistical inference
The process of drawing conclusions about a population based on data from a sample of that population.
It allows us to deal with random error.
What allows us to deal with random error
Statistical inference.
Two methods of statistical inference in analytical epidemiology
1) Interval estimation (confidence intervals)
2) Hypothesis testing
Interval estimation
95% confidence intervals can be calculated for measures of association
Width of the 95% confidence interval is an indication of ____
the precision of the estimate
The [narrower or wider] the confidence interval, the more precise the estimate
Narrower
3 steps of hypothesis testing
1) Specify a “null” and an “alternative” hypothesis
2) Compare the results that were expected under the null hypothesis with the actual observed results (this is done with a statistical test)
3) Make a decision (this decision is based on the p-value of the statistical test)
Null hypothesis (H0)
No association between exposure and disease
Alternative hypothesis (HA)
There is an association between exposure and disease
Ecological Study Pros and Cons
Pros: Can be done quickly and inexpensively (often use existing data)
Cons: ‘Ecologic fallacy’ - relationships observed at the population level may not hold true at the individual level
Cross sectional study Pros and Cons
Pros: Fast, relatively cheap
Cons: Because presence/absence of exposure and disease are assessed at the same time in subjects, it is often not possible to determine which came first: exposure or disease?.
Identifies on prevalent (existing cases) – we might be measuring the association of the exposure with the duration of disease, and not the risk of disease.
Prevalence is a function of ____ and ____
Incidence and duration of disease
Prospective cohort study Pros (4) and Cons (5)
Pros:
1) Exposure is known to precede the outcome
2) Allow calculation of incidence (risk)
3) Facilitate study of rare exposures (actively recruit subjects with the exposure)
4) Can look at associations with multiple outcomes in the same study
Cons:
1) May have to follow large numbers of subjects for a long time
2) Can be very expensive and time consuming
3) Not good for rare disease
4) Not good for disease with a long latency
5) Differential loss to follow up can introduce bias
Retrospective cohort study Pros (5) and Cons (4)
Pros:
1) Can be done over a short time (don’t have to wait for disease to occur)
2) Generally less expensive than a prospective study
3) Allow calculation of incidence (risk)
4) Facilitate study of rare exposures (actively recruit subjects with the exposure)
5) Can look at association with multiple outcomes in the same study
Cons:
1) Temporal relationship between exposure and outcome is sometimes hard to establish (did exposure happen before outcome?)
2) Not good for rare diseases
3) Differential loss to follow up can introduce bias
4) Requires access to good medical records
Case control study pros (4) and cons (4)
Pros:
1) Can be done over a short time (disease has already occurred)
2) Relatively inexpensive
3) Facilitate study of rare diseases (actively recruit subjects with the disease)
4) Can look at associations with multiple exposures in the same study
Cons:
1) Selection of appropriate controls is essential (and often difficult)
2) Temporal relationship between exposure and outcome is sometimes hard to establish (did exposure happen before outcome?)
3) Not good for rare exposures
4) Depends upon accurate assessment of exposures that happened in the past (recall bias)
Randomized control trial pros (3) and cons (4)
Pros:
1) Well-controlled studies are essentially free of bias and confounding (random allocation, blind or double-blind)
2) Exposure is known to precede the outcome
3) Allow calculation of incidence (risk)
Cons
1) Expensive and very narrow in scope
2) Not always ethical to randomly allocate individuals to treatment
3) Not good for diseases with a long latency
4) Different loss to follow up can introduce bias
Two potential source of error in analytical studies
1) Random error
2) Systematic error
Random error (3)
1) Random error is the divergence, due to sampling variation, of the measure of association in the sample from the true measure of association in the population
2) Does not bias a study. A study with a lot of random error may be wrong but we don’t call it biased
3) Statistical inference deals with random error
Systematic error (4)
1) Error that is inherent to the study method being used, which would result in a predictable and repeatable error if the study were repeated using the same method
2) Not caused by chance
3) Biases a study
4) No formal method to deal with systematic error
T/F: there is no formal method to deal with systematic error
True
T/F: systematic error does not bias a study
False, systematic error does bias a study
Validity
Refers to the absence of systematic error in a study result
A valid measure of association
A valid measure of association will have the same value as the true measure in the source population, except for error due to random variation
