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40 Cards in this Set
- Front
- Back
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Variable
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Characteristic that takes on different values in different persons, places or things. Able to quantify information
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Discrete variable
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characterized by gaps or interruptions in values that it can assume
Example: # of siblings = must be 0, 1, 2, 3, 4…… it cannot be 5.3 siblings |
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Continuous variable
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variables that can take on any value
Ordinal variables are usually considered continuous |
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Categorical Variables
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Must assign a number to, but that number in itself means nothing
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Independent Variables
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These are “manipulated” by the experimenter
Ex: In epidemiology the exposure is the independent variable and the disease or outcome is the dependent variable |
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Dependent variables
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are the outcome
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Regression is as simple as Y=mx + b
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X=independent variable; y= dependent variable
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Study Design Sequences
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used to answer research questions, and create new knowledge
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Case-series Study Design
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-A series of cases
-No control/comparison group -Cannot calculate relative risk, attributable risk or odds ratios -Cannot makes statements about preventing disease -Causation cannot be shown |
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Ecological Study Design
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-Compare groups, but do not have data on individuals
-Used to generate rather than test hypotheses -Apt to ecological fallacies |
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Cross-sectional study
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-Observational, no intervention
-Effective at providing a picture of disease, health or psychological phenomenon -effective at assessing several variables at once and associations between those variables |
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Cross-sectional study
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-Disease and exposure data collected simultaneously
-Can determine distribution of disease and exposure -Problem: Temporal sequence not always known (ex: runners/thin) |
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Case-control or retrospective study
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-Observational
-Compare the past exposures of cases and controls -Collect data after onset of disease & Look backward in time to find the exposure |
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Prospective studies or “cohort studies”
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-All subjects are “well” at the onset
-Data collected before the disease, temporal sequence known -Provides incidence data -Divide on some exposure (ex: smoking) and look for outcome in future |
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Experiments
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-intervention occurs
-randomization and manipulation |
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Quasi experimental design
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-there is manipulation but not randomization
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Cohort studies
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-Cohorts are cohorts because of some common exposure
-Follow forward in time and look for an outcome -Gets incidence rates -Can add new exposures/outcomes -Temporal sequence know |
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Relative Risk
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Shows strength of association of exposure and disease
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Relative Risk
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-if RR > 1.0 then the factor is said to be causative
-if RR= 1.0 the factor is unrelated to the disease, "the factor is just as likely to occur among those with the factor as those without it" -if RR< 1.0 the factor is said to be "protective” |
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Attributable Risk
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-measure of how much disease could be eliminated if the exposure was not present
-estimates the proportion of disease among the exposed that is attributable to the exposure or the proportion of the disease that could be eliminated if people did not |
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Population attributable risk
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-indicates the proportion of all cases in the total defined population that can be ascribed to a factor
-the % of disease among the entire population that can be attributed to the exposure |
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Bias in case control studies
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*selective memory- those with the disease remember better than those without
*Selective survival-the result of differences between those who survive and those who live |
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Odds ratio
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-an estimate of the relative risk, will be similar unless the incidence is high in the exposed group
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internal validity
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can we ascribe the observed effect to the exposure / variable under investigation
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External validity
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do the results apply to others or other situations? Can we generalize the results beyond the observed findings to some universal statement?
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7 main threats to validity
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1. Testing: people improve over time
2. Instrumentation: same instruments should be used 3. Maturation: Over time people mature and maturation will change them whether or not they are in your study 4. Regression toward mean 5. Selection bias 6. History: Outside interference to study 7. Attrition: drops out of study |
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bias
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misclassify variables in a consistent way that tips the scale in a certain direction
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Reliability
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-measure of consistency & repeatability
- Does NOT mean accuracy |
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Inter rater reliability
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results might differ when 2 or more people rate something
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Intra rater reliability
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-same person rates observations
- same person might rate things differently |
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Calculate Inter rater reliability & Intra rater reliability
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# agreements / total # of possible combinations
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Validity Components
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sensitivity & specificity
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sensitivity
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if you have the disease/outcome how accurate is your measurement instrument in identifying that
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specificity
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if one truly doesn't have the disease, how well does the measurement tool accurately detect no disease.
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Calculate sensitivity
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(true positives) / (true positives + false negatives)
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Calculate specificity
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true negatives / all that are truly negative
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Positive predictive value:
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Proportion of true positives among those who test positive
a / (a + b) |
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Negative predictive value
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proportion of true negatives out of the total who test negative
d / (d + c) |
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Confounding
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occurs when the crude odds ratio or RR differs from the adjusted ones
occurs we say that an interaction has occurred |
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interaction
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occurred when a variable affects an outcome differently at different levels of another variable
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