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29 Cards in this Set
- Front
- Back
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Describe selection bias.
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Different criteria for cases vs controls.
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Describe observational bias
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Info on disease outcome obtained in a noon-comparable manner btwn exposed & unexposed.
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Describe confounding bias.
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Failure to take into account other variables associated with both disease & the exposure. i.e. smoking
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Describe case-control studies
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Retrospective. Identify a group of workers w/ disease & examine their work history for potential exposures.
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Describe cohort studies
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Prospective. Identify a workers by exposure, watch for disease (most powerful)
Retrospective cohort: go back in time and Identify people by exposure and follow them to the present time looking fir the development of a disease. |
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Incidence Rate
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Number of new cases of a disease per worker per unit time.
Focused only on new cases |
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Prevalence Rate
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Total number of cases in a worker population at a specfic time.
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Relative Risk
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Comparison of disease prevalence between exposed and unexposed worker population.
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Attributable Risk
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Difference in risk between and exposed group and an unexposed group
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Odds Ratio
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Comparison of the odds of getting a diease in an exposed group to the odds of getting a disease in an unexposed group.
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Case Control Study
Prospective or retrospective? Cases are identified from ? How are controls selected? Cases and controls compare what? End results is ? |
Case Control Study
Retrospective- looking at people who already have disease Cases are identified from a specific population. Controls are people from same population that do not have disease. Compare exposure btwn cases and controls. End result is an odds ratio. |
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What results is given in a case control study
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odds ratio
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Cohort Study
Prospective or retrospective? How are cohort studies structured? |
Cohort Study
Prospective. Identify a population prospectively and follow them over time. Measure exposure and disease over time. Most power type of study other than clinical trials. |
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What study is the most powerful type other than clinical trials?
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Cohort studies
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Cross-Sectional Study
Population based on? How and when are disease and exposure measure over time? AKA? |
Cross-Sectional Study
Study of population regardless of exposure or disease status. Take a cross-section of population. Diease and exposure measured at the same time. AKA - prevalence study |
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Mortality Studies
Comparison of ? What is SMR? An SMR of 100 = ? Incorrect to compare SMR across? |
Compare mortality rate of a population relative to the standard population.
SMR: standard mortality rate SMR = 100 indicates no relationship |
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A study followed a group of electrical utility workers over time. The researchers studied their exposure to EMF and the development of prostate cancer. What type of study is this?
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Cohort study
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What is the null hypothesis?
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The null hypothesis is the belief that no exposure:disease relationship exists.
Often states Ho: - Smoking is not associated with lung cancer. -Benzene is not asssociated with leukemia. |
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What is the alternate hypothesis?
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The alternate hypothesis is that there IS an exposure:disease relationship.
Often stated H1 or Ha: -Smoking is associated with lung cancer. |
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Type 1 error
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Rejection of the null hypothesis when the null hypothesis is true.
-Believing there is a disease exposure relationship when no relationship exists. |
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Type II error
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Accepting the null hypothesis when the null hypothesis is not true.
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Which type of error?
Smoking is not associated w cancer? |
Type II error.
Accepting the null hypothesis when the null hypothesis is not true. |
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Which type of error?
Saccharin is a human carcinogen |
Type I error.
Rejection of the null hypothesis when the null is true. (Believing there is a diease exposure relationship when no relationship exists) |
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Which typeof error?
Ethanol consumption is not associated with liver disease? |
Type II error.
Accepting the null hypothesis when the null hypothesis is not true. |
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Alpha (α)
Beta (β) |
α = the probability of making a random type I error.
β = probability of committing type II error. |
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α = ?
Confidence = ? |
α is probably of a random type 1 error.
α = 0.05 Confidence = 1 - α Typically 95% |
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p-value
p-value is the probably of ? |
p-value is the probably of committing a random type 1 error.
If p-value < α, then results is declared statistically significant. |
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Beta (β) is the probably of ?
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β is the probably of committing type II error.
Power of test = 1 - β |
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Confidence Interval
Results from test relying on statistical methods are reported with a confidence intervale. RR and OR are reported with an interval. If the interval includes 1, then ? If the interval does not include 1, then? |
If the interval includes 1, then it is NOT statistically signficant. Eg. (0.62, 1.18)
If the interval does not include 1, then it IS statistically significant. E.g. (2.69, 5.05) |
