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Case Control Studies Flashcards

1
Q

Definition of case control studies

A

People with disease (cases) are compared to people without the disease (controls) and past exposures are measured

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2
Q

Definition of odds ratio

A

Diseased and exposed : disease and unexposed

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3
Q

Definition of power

A

Probability of detecting true effect and not finding a FN/Type II error

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4
Q

Definition of non differential information bias

A

Errors are distributed evenly between cases and controls

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5
Q

Definition of differential information bias

A

Difference in follow up completeness between groups

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6
Q

Definition of selection bias

A

Population used as control must be representative pf the general population

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7
Q

Definition of admission bias

A

Exposed cases have a different chance of admission than controls. Exposed cases in the study are not representative of all exposed cases

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8
Q

Definition of diagnostic bias

A

Diagnostic approach related to knowing exposure status

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9
Q

Definition of survival bias

A

Only survivors of a study are analysed

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10
Q

Definition of non response bias

A

Controls don’t respond => large difference between those who responded and those who didn’t

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11
Q

Definition of recall bias

A

Cases remember exposure differently to controls

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12
Q

Definition of interviewer bias

A

Different questions/questioning styles used by interviewers

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13
Q

Definition of confounding

A

Alternative explanations for observed exposure outcome association due to another exposure

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14
Q

Definition of population stratification

A

Presence of systematic differences in allele frequencies between subpopulations in a population

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15
Q

Definition of statistical interaction/effect modification

A

Association between exposures and outcomes differ according to a 3rd factor

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16
Q

When are case control studies most often used

A

GWAS

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17
Q

Name the 8 Bradford Hill criteria for causation

What are they?

A

Strength of association
-Stronger the relationship between IV and DV => increased credibility and less likely to be due to confounding

Consistency (reproducibility)
-Consistency of results in different studies

Specificity
-Causation likely if there is no other explanation

Temporality
-Does cause always precede consequence?

Dose response
-Does increased IV => increased DV

Biological plausibility
-Does it make sense with existing biological knowledge

Coherence
-Compatibility with existing knowledge

Experimental evidence
-If IV altered => does it lead to the corresponding disease outcome

Analogy
-Results due to chance/bias/confounders

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18
Q

Why are reports of associations between genotype and outcome so often inconsistent
5 reasons

A

Variation of underlying association between genotype and outcome between populations

Heterogenous phenotypes

Confounding by population stratification

Failure to exclude chance as an explanation

Publication bias

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19
Q

What are case control studies

A

People with disease (cases)
People without disease (controls)
Measure past exposure for both via genes and compare prevalence of exposure in both groups

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20
Q

What are the 3 advantages of case control studies

A

Inexpensive and quick
Good for rare outcomes and multiple risk factors
Can look at risk factors in detail

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21
Q

What are the 4 disadvantages of case control studies

A

Not good for rare exposures
Selection bias
Recall bias
No estimate for diseases incidence

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22
Q

In what way must the cases and controls be similar

Describe the ratios between controls and cases

A

The sample population of controls must be similar to the cases

By increasing the ratio of controls

  • increased power
  • decreased p
  • increased 95% CI
  • no chance in OR

By doing so, can compare if controls and cases cary on different risk factors

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23
Q

What are the 6 key features of a case control study

A

Start with disease/outcome
Retrospective, info obtained from past/is lifelong (genotype)
Can be prospective but takes longer to complete
Observational
No follow ups needed
Suitable for rare diseases, all accessible cases can be located, controls can be found afterwards

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24
Q

How are cases selected

What are the 4 possible types of cases

A

Strict diagnostic criteria

  • specificity of disease
  • consider diagnostic bias and validity of diagnosis

Population based cases
-Include all patients/random sample of all subjects with disease at 1 point/during timeframe

Hospital based cases
-All patients in hospital dept at 1 point in time

Incident cases

Prevalent cases

25
Describe the importance of controls | 2 factors
Study base -characterise distribution of exposure that is representative and random Comparable accuracy -equal reliability in all info obtained => no systemic misclassification
26
What are the 6 sources of control Which sources of control should you be careful with
Hospital patients Population of defined geographical area Probability sample of total population ``` Neighbors Friends (watch out for similar exposure characteristics to cases) Relatives (watch out for similar genetics to cases) ```
27
How would you calculate the odds ratio | How would you interpret the odds ratio
AD / BC = disease and exposed:disease and unexposed OR = not 1, p<0.05 - greater than 1 => increased risk - less than 1 => protective factor 95% CI contains 1 => can't reject null No difference between exposed and non exposed population
28
What is the power
Probability of detecting true effect and not finding a FN/Type II error
29
Describe the use of p values in GWAS
Includes 1000s of comparisons | Generally p = 5x10-8
30
Descibe analysis of a rare disease | -what would you calculate
Risk ratio, rate ratio, odds ratio are numerically similar | Can be used interchangeably
31
Describe 2 methods of data collection for the event and exposure
``` Event External data sources -Disease registries -Death certificates -Hospital records ``` Internal data sources - Questionnaires - Physical exams - Blood and diagnostic tests Exposure External data sources -Hospital records -Employers Internal data sources - Questionnaires - Physical exams - Blood and diagnostic tests
32
What are the 3 main types of bias
Selection bias Information bias Confounders
33
What are 3 sources of misclassfication bias | What are the 2 types of misclassification bias
Sources - disease status - determining exposure status - confounders Types - Non differential - Differential
34
What is selection bias and the effect on the odds ratio
The population used as the control must be representative of the general population If not => underestimation of OR
35
What is admission bias and the effect on the odds ratio
Exposed cases have a different chance of admission than controls Not representative of all cases => overestimation of OR
36
What is diagnostic bias and the effect on the odds ratio
Diagnostic approach related to knowing exposure status | More likely to be diagnosed => not representative of all cases => overestimation of OR
37
What is survival bias and the effects on the odds ratio
Only survivors of a study are analysed Contact with risk factor => rapid death Leads to underestimation of OR
38
What is non response bias and the effects on the odds ratio
Controls don't respond => large difference between those who responded and those who didn't Leads to underestimation of OR
39
What is recall bias and the effects on the odds ratio
Cases remember exposures differently to controls | Leads to overestimation of OR
40
What is interviewer bias and the effects on the odds ratio
Different questions and questioning styles used by interviewers May ask more leading questions to cases Leads to overestimation of OR
41
What are the 4 characteristics of both non differential and differential misclassification What are examples of both types of misclassification
Non differential - Random error - Unrelated to exposure or outcome - Not a bias - Weakens measure of association Use of technology (calibration) Poor quality controls in DNA processing Differential - Systematic error - Related to exposure/outcome - Results in bias - Measure of association distorted in any direction Collecting diff types of DNA between cases and controls Diff technologies used to sequence case and control DNA
42
How would increasing the study size affect the size of random and systematic error
Random -As study size increases => decrease in error Systematic -As study size increases => no change in error
43
What are 4 ways to reduce bias present in a case control study
Carefully consider your - choice of study population - methods of data - sources of exposure and disease info - assess extent and direction of bias
44
What are confounders | What is the effect of confounding
Alternative explanations for observed exposure outcome association due to another exposure Causes bias in estimate
45
What are the 2 methods of addressing confounders
Design Collect sufficient data and either -restrict study to specific populations -matching controls and cases on confounders However, its not always possible to match/restrict, there will always be some residual confounding Analysis Population stratification could be present in genetic studies -adjust for confounders with modeling of logistic regression
46
What are the 3 criteria for confounding
Must be causally/non causally associated with exposure in source population in study Must be a causal risk facto for disease in unexposed Must not be on causal pathway between exposure and pathway
47
What are the 3 common confounders
Age Sex Socioeconomic status
48
What is population stratification
Presence of systematic differences in allele frequencies between subpopulations in a population Is not always obvious, but can lead to FP and false associations Dealt with by analytics programs
49
What is statistical interaction/effect modification
Association between exposures and outcomes differ according to a third factor Variation in groups/strata not due to chance Normally need to report stratum specific rate ratios
50
How would you interpret statistical interaction | Is this common in statistical studies
Rate ratio cary per stratum If rate ratio varies according to a factor, there is an interaction Can test for this True interaction rare in genetic studies
51
How can you deal with bias in a study that focuses on genetic risk factors
Bias in exposure (genotype) eliminated in correctly designed studies Selection bias not likely to be an issue unless population stratification present Confounders - population stratification - linkage disequilibrium
52
How can you measure risk factors in a study that focuses on genetic risk factors
Can measure all risk factors by comparing all loci in a genome
53
How can you use data gained from studies that focus on genetic risk factors
Data collected can be banked and shared indefinitely Records must be anonymized so consent only needs to be gained once
54
How do you measure exposure in studies that focus on genetic risk factors
Genotype can be measured retrospectively via case control
55
How can you deal with bias in a study that focuses in environmental risk factors
Bias in different exposures can cause problems Selection bias is an issue mainly in case controls Confounders can't be adequately controlled
56
How can you measure risk factors in a study that focuses on environmental risk factors
Unlimited risk factors
57
How can you use data gained from studies that focus on environmental risk factors
Studies limited to baseline exposures, sharing data => active collab Not always necessary to break link between records and ID after data collected
58
How do you measure exposure in studies that focus on environmental risk factors
Exposure can be affected by disease onset so prospective studies needed

Epidemiology and Biostatistics (16 decks)

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