Exam 2 Flashcards
1
Q
Ecologic Studies
A
- unit: group level
- can be used for making hypothesis
- level of exposure for each individual is unknown
- uses secondary data
2
Q
ecologic comparison study
A
assess the correlation between exposure rates and disease rates among different groups over the same time period
3
Q
ecologic trend study
A
- correlation of changes in exposure with changes in disease within the same community, population, etc
4
Q
Ecologic Fallacy
A
- occurs when incorrect inferences about the individual are made from group-level data.
- observations made at the group level may not represent the exposure-disease relationship at the individual level
5
Q
advantages and disadvantages of ecologic studies
A
- advantages:
- quick, simple, inexpensive, good for
generating hypotheses when a disease is
of unknown etiology
- quick, simple, inexpensive, good for
- disadvantages:
- ecological fallacy, imprecise measurement
of exposure and disease
- ecological fallacy, imprecise measurement
6
Q
cross-sectional study
A
- aka prevalence study
- exposure and disease measures done at the individual level
- Single period of observation
- Exposure and disease histories are collected simultaneously.
- Both probability and non-probability sampling are used
7
Q
limitations of cross-sectional studies
A
- Can’t really infer disease etiology
- Doesn’t have incidence data
- Can’t study low-prevalence diseases
- Can’t determine the time between exposure and disease
8
Q
cross-sectional survey strengths
A
- Studies several associations at once
- Takes a short period of time
- Produce prevalence data
- Biases due to observation (recall and interviewer bias) and loss to follow-up do not exist
- Can provide evidence of the need for analytic epidemiologic study
9
Q
types of data
A
- nominal
- ordinal
- discrete
- continous
10
Q
nominal data
A
- categorical: unordered categories
- two levels: dichotomous
- more than two levels: multichotomous
- examples: sex, disease (yes, no), race, marital status, education status
11
Q
ordinal data
A
- categorical: ordering informative
- examples: preference rating (agree, neutral, disagree)
12
Q
discrete data
A
- quantitative: integers
- example: number of cases
13
Q
continuous data
A
- quantitative: values on a continuum
- examples: dose of ionizing radiation
14
Q
crude vs age-adjusted rates
A
- The crude rate is calculated without restrictions, such as by age or sex, or who is counted in the numerator or denominator.
- These rates are limited if we try to compare them between subgroups of the population or over time because of confounding influences, such as differences in age distribution between groups.
15
Q
numerical methods
A
- measures of central tendency
- measures of dispersion
16
Q
measures of central tendency
A
- mean: average of a set of values
- median: middle number in a sorted list of numbers
- mode: value that appears the most frequently in a data set
17
Q
measures of dispersion
A
- range
- inter-quartile range
- variance
- standard deviation
- coefficient of variation
- empirical rule
- Chebychev’s inequality
18
Q
experimental studies
A
- aka intervention studies
- investigators influence the exposure of subjects
- 2 types: controlled trials, community trials
19
Q
Within-group design
A
- the outcome in a single-group is compared before and after the intervention
- strengths: individual characteristics that confound an association are controlled
- weakness: susceptible to confounding from time-related factors such as the media
20
Q
controlled trial
A
- unit of analysis is the individual
- randomized controlled trial in a clinical setting is called a clinical trial
21
Q
natural experiment
A
- researchers don’t assign subjects unlike controlled
- assignment into treatment and control is random like controlled experiments
- researchers don’t design/ administer treatment unlike controlled
- ex: John Snow cholera experiment