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Behavioral > Epidemiology > Flashcards

Flashcards in Epidemiology Deck (28)
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1

Odds ratio < 1

Indicates study favors the tx

2

Fixed Effect Model

Assumes each study answers same question, has same effect size, so *results differ only by chance*

3

Length-time Bias

Slow developing conditions are more likely to be picked up in screening, and screening will MISS many of the fast progressions

4

Confounding bias/factors

When a factor is related to both the exposure and outcome, but not on the causal pathway - the factor distorts or confuses the effect of exposure on outcome
(E.g. - age, known risk factors, known prognosis factors)

5

Types of studies for systematic review

Randomized trials
Cohort
Case-control
Diagnostic tests

6

Meta-Analysis

Use of statistical methods to combine results of individual studies, usually from systematic reviews
- advantages: adequate sample size and power to evaluate small tx effects; good if analysis can be done w/ data from individual pt
- disadvantages: quality is dependent on studies; may be too heterogeneous to combine; pt's are variable

7

Random Effect Model

Assumes studies address different but related questions, takes heterogeneity into account, less likely to overestimate precision, wider CI, more realistic

8

Subgroup analyses (without specifying in advance)

Analyses and outcomes must be specified before study is conducted

9

Odds ratio = 1

Indicates no association

10

Validity (accuracy)

Extent that the measurement represents what it's supposed to
-compromised by systematic error

11

Odds ratio > 1

Indicates the study favors the placebo/control

12

Selection Bias

Error in assigning subjects to a study group resulting in an unrepresentative sample
*most commonly a sampling bias

13

Compliance Bias

Compliant pt tend to have better prognosis regardless of preventative activities

14

Reliability (consistency; precision)

Extent to which repeated measurements are similar
-compromised by random error

15

Multiple comparisons (to find something)

It can't be OK to keep testing one subgroup against another forever until one is significant

16

Measurement Bias

*AKA Information Bias
Information is gathered in a systematically distorted/inconsistent manner

17

Non-respondent bias (volunteer effect)

A sampling bias, where the research only includes those who say “yes” and those ppl are different from the ppl that say “no” so that doesnt tell you much about a population

18

Ascertainment bis

Sampling bias where ppl w/ more severe cases are more likely to be seen so we miss the more subtle cases

19

Late-look bias

Sampling bias where ppl w/ severe dz are less likely to be included in a study bc they’re hard to access or already dead (so bias is toward less sick cases)

19

Solution to sampling biases

Random sample (getting random ppl into the study), weigh data so sample matches the population

19

Selection bias (design bias)

Different ppl in the treatment and control groups (its like comparing apples and oranges)
Solution: random assignment (which part of the study for the participant to be in)

19

Hawthorn effect

The fact of measurement can change what is measured (act different when you think someone is watching)
Solution: control group

19

Recall bias

Ppl don’t remember what happened in the past so they make things up
Solution: confirmation

19

Observer bias

You see what you’re tuned in to see (makes assessment based on prior knowledge or experience)
Solution: multiple observers

20

Lead-time bias

False estimate of benefits of an intervention (early detection is confused w/ living longer)
Solution: use life expectancy

21

Expectancy bias

Researcher unintentionally acts to influence behavior of subjects and change results
Solution: double-blind design (if researchers dont have knowledge they can’t influence how they deal w/ subjects)

22

Proficiency bias

New intervention/tx are not applied w/ equal skill to all research subjects
Solution: tx providers selected @ random

23

Confounding bias

“Found within” aka things wrapped up together

*an additional variable, not the subject of research interest that produces the observed results (often the “hidden cause” or underlying issue)

Solution: thoughtful research design, multiple studies (meta-analysis)