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ページの作成:「==Self-assessment quiz== *[https://www.cdc.gov/csels/dsepd/ss1978/index.html CDC Principles of Epidemiology] *[https://www.med.soton.ac.uk/stats_eLearning/quizzes/index.h…」
==Self-assessment quiz==
*[https://www.cdc.gov/csels/dsepd/ss1978/index.html CDC Principles of Epidemiology]
*[https://www.med.soton.ac.uk/stats_eLearning/quizzes/index.html Statistics - University of Southampton]
==Disease frequency measurement==
{{#mermaid:
flowchart TB
a[Disease frequency]
p[Prevalence]
i[Incidence]
a -- existing cases at one time ---p
a -- new cases over time ---i
i1[Cumulative incidence<br>= Risk] -.- t1(accompanied by<br>observed length of time)
i2[Incidence rate] -.- t2(given by<br>per person-time)
i --- i1 & i2
p1[Point prevalence]
p2[Period prevalence]
p --- p1 & p2
}}
{{#mermaid:
flowchart TB
t3[/Risk ratio = Incident rate ratio = Relative risk\]
}}
==Bradford Hill Criteria for assuming causality==
{|class="wikitable"
|-
!
!In terms of the association between exposure and outcome,<br>exposure is more likely to be causal if ....
|-
!1. Strength of association
|'''the stronger the association''' is.
|-
!2. Consistency
|'''in the more varieties settings''' the association is observed.
|-
!3. Specificity
|the more specific the exposure is, i.e., the exposure is '''associated with only single outcome'''.
|-
!4. Temporality
|'''the exposure comes first''' and the outcome appears after the exposure
|-
!5. Biological gradient
|the more (or less) exposure is associated with more frequent outcome ('''exposure dose dependent''').
|-
!6. Plausibility
|the mechanism between the exposure and the outcome can be '''explained according to biological body of knowledge'''.
|-
!7. Coherence
|the mechanism between the exposure and the outcome can be '''proven by certain medical testings'''.
|-
!8. Experiment
|some '''intervention to the exposure can change frequency and/or extent''' of the outcome.
|-
!9. Analogy
|'''another similar exposure''' is associated with '''another similar outcome'''.
|}
==Classification of Bias==
[https://catalogofbias.org/ Catalogue of Bias]
{|class="wikitable"
|-
!colspan="2"|Major classification
!style="width:75px"|Specific name
!style="width:400px"|Description
!style="width:180px"|Affecting design
|-
!style="width:50px" rowspan="7"|Selection bias
|style="width:100px" rowspan="7"|Sample is not properly representative of population
!Ascertainment bias
|Inappropriate definition of population
|Any observational design
|-
!Healthy worker effect
|Current employees are more likely to be healthy than general population and ex-employees
|Cohort, esp. historical
|-
!Detection bias
|Diagnostic procedures are different between case and control
|Case-control
|-
!Attrition bias<br>(Loss to follow-up)
|needless to explain
|Cohort<br>Clinical trial
|-
!Non-response bias
|a.k.a. Healthy volunteer effect; Voluntary participants are not representative of population
|Any observational design
|-
!Language bias
|Medical articles written in languages unfamiliar to researchers are more likely to be ignored
|Meta-analysis<br>Systematic review
|-
!Publication bias
|Researches with negative results are more likely to be unpublished
|Meta-analysis<br>Systematic review
|-
!rowspan="9"|Information bias<br>(Measurement bias)
|rowspan="9"|Observation is not properly conducted
!Non-differential misclassification bias
|Failure to properly measure exposure/outcome and inappropriate allocations of groups in the same weight for both
*underestimate effect size (decrease power)
|Any design
|-
!Differential misclassification bias
|Failure to properly measure exposure/outcome and inappropriate allocations of groups in different weights in-between
*under- or overestimate effect size
|Any design
|-
!
:└Recall bias
|
:└(subcategory of differential misclassification)<br>Participants are more likely to recall their memory in skewed way according to their outcome status
|Any design
|-
!
:└Reporting bias
|
:└(subcategory of differential misclassification)<br>Participants are more (or less) likely to report their exposure according to their outcome status
|Any design
|-
!Observer/interviewer bias
|Observers/interviewers are more likely to observe/draw interviews toward exposure according to case outcome status
|Any design
|-
!Ecological fallacy
|
|Ecological design
|-
!Regression to the mean
|Extreme observations in initial investigations will be toward true population value in later investigations
|Cohort<br>Clinical trial
|-
!Hawthorne effect
|Observation itself can affect outcome
|Clinical trial
|-
!Lead-time bias
|Screening trial can diagnose outcome even in its earlier latent period which fallaciously show longer survival
|Screening trial
|-
!Confounding
|Third factors affect both of exposure and outcome
|colspan="3"|
Confounding may occur even in RCT if randomization process is inappropriate (as systematic error) or randomly allocated groups are heterogenous by chance (as random error)
|}
==Difference between Population and Sample==
===Accuracy and Precision===
{|class="wikitable"
|+Definition
|-
!Accuracy
|
*Closeness of observed values '''to true population values'''
**AKA 'Trueness'
|-
!Precision
|
*Closeness of observed values '''to each other among sample'''
|}
[[File:Accuracy and Precision 2.gif|none|400px]]
Accuracy in <math>x</math>-axis, Precision in <math>y</math>-axis
<nowiki>*</nowiki>In this example, μ is sample mean, not the population mean
[[File:Accuracy and Precision 1.webp|none|400px]]
Precision in <math>x</math>-axis, Accuracy in <math>y</math>-axis
==Tips in Case-control design==
===Sampling (selection bias)===
{|class="wikitable" style="max-width:600px; vertical-align:top"
|-
!style="width:50%"|Case sampling
!style="width:50%"|Control sampling
|- style="vertical-align:top"
|
*'''Incident case'''
**for outcome with short duration or poor survival
*'''Prevalent case'''
**for rare or chronic outcome
|
*'''The same inclusion criteria as case'''
**except for outcome itself
|- style="vertical-align:top"
|
*'''Population-based case'''
**from surveillance, registry, death certificates, etc.
**easy to define population
|
*'''Population-based control'''
**random sampling from the same population
**random-digit dialing, population registry, etc.
*For occupation-based case, beware of '''healthy worker effect''' and select control '''from similar occupation''' with '''the same exposure as the population'''
|- style="vertical-align:top"
|rowspan="2"|
*'''Hospital-based case'''
**from hospital
**difficult to define population
|
*'''Hospital-based control'''
**sampling from the same hospital
**must be exposed at the same extent as the population
|- style="vertical-align:top"
|
*'''Population-based control'''
**Only when assumable that all patients from the population visit the hospital and patients from other than population never visit the hospital
|-
|colspan="2"|
*Matching '''increases precision of observations''', i.e. '''increases power'''
**Matching '''does not decrease bias'''
*Matching masks the effect of matching variables themselves
*'''Overmatching''' makes extents of exposure of both groups too similar, which leads to '''decrease power'''
**Matching with '''age''' and '''sex''' only is a better way
*1:n matching (n>1) increases power
**1:4 is maximum; more matching brings no more benefit
|}
===Measurement (Information bias)===
{|class="wikitable" style="max-width:600px"
|-
!Observer bias
|
*To mask case/control status to observers
|-
!Recall bias
|
*To use memory aids
*To mask research question/hypothesis to participants
|}
*[https://www.cdc.gov/csels/dsepd/ss1978/index.html CDC Principles of Epidemiology]
*[https://www.med.soton.ac.uk/stats_eLearning/quizzes/index.html Statistics - University of Southampton]
==Disease frequency measurement==
{{#mermaid:
flowchart TB
a[Disease frequency]
p[Prevalence]
i[Incidence]
a -- existing cases at one time ---p
a -- new cases over time ---i
i1[Cumulative incidence<br>= Risk] -.- t1(accompanied by<br>observed length of time)
i2[Incidence rate] -.- t2(given by<br>per person-time)
i --- i1 & i2
p1[Point prevalence]
p2[Period prevalence]
p --- p1 & p2
}}
{{#mermaid:
flowchart TB
t3[/Risk ratio = Incident rate ratio = Relative risk\]
}}
==Bradford Hill Criteria for assuming causality==
{|class="wikitable"
|-
!
!In terms of the association between exposure and outcome,<br>exposure is more likely to be causal if ....
|-
!1. Strength of association
|'''the stronger the association''' is.
|-
!2. Consistency
|'''in the more varieties settings''' the association is observed.
|-
!3. Specificity
|the more specific the exposure is, i.e., the exposure is '''associated with only single outcome'''.
|-
!4. Temporality
|'''the exposure comes first''' and the outcome appears after the exposure
|-
!5. Biological gradient
|the more (or less) exposure is associated with more frequent outcome ('''exposure dose dependent''').
|-
!6. Plausibility
|the mechanism between the exposure and the outcome can be '''explained according to biological body of knowledge'''.
|-
!7. Coherence
|the mechanism between the exposure and the outcome can be '''proven by certain medical testings'''.
|-
!8. Experiment
|some '''intervention to the exposure can change frequency and/or extent''' of the outcome.
|-
!9. Analogy
|'''another similar exposure''' is associated with '''another similar outcome'''.
|}
==Classification of Bias==
[https://catalogofbias.org/ Catalogue of Bias]
{|class="wikitable"
|-
!colspan="2"|Major classification
!style="width:75px"|Specific name
!style="width:400px"|Description
!style="width:180px"|Affecting design
|-
!style="width:50px" rowspan="7"|Selection bias
|style="width:100px" rowspan="7"|Sample is not properly representative of population
!Ascertainment bias
|Inappropriate definition of population
|Any observational design
|-
!Healthy worker effect
|Current employees are more likely to be healthy than general population and ex-employees
|Cohort, esp. historical
|-
!Detection bias
|Diagnostic procedures are different between case and control
|Case-control
|-
!Attrition bias<br>(Loss to follow-up)
|needless to explain
|Cohort<br>Clinical trial
|-
!Non-response bias
|a.k.a. Healthy volunteer effect; Voluntary participants are not representative of population
|Any observational design
|-
!Language bias
|Medical articles written in languages unfamiliar to researchers are more likely to be ignored
|Meta-analysis<br>Systematic review
|-
!Publication bias
|Researches with negative results are more likely to be unpublished
|Meta-analysis<br>Systematic review
|-
!rowspan="9"|Information bias<br>(Measurement bias)
|rowspan="9"|Observation is not properly conducted
!Non-differential misclassification bias
|Failure to properly measure exposure/outcome and inappropriate allocations of groups in the same weight for both
*underestimate effect size (decrease power)
|Any design
|-
!Differential misclassification bias
|Failure to properly measure exposure/outcome and inappropriate allocations of groups in different weights in-between
*under- or overestimate effect size
|Any design
|-
!
:└Recall bias
|
:└(subcategory of differential misclassification)<br>Participants are more likely to recall their memory in skewed way according to their outcome status
|Any design
|-
!
:└Reporting bias
|
:└(subcategory of differential misclassification)<br>Participants are more (or less) likely to report their exposure according to their outcome status
|Any design
|-
!Observer/interviewer bias
|Observers/interviewers are more likely to observe/draw interviews toward exposure according to case outcome status
|Any design
|-
!Ecological fallacy
|
|Ecological design
|-
!Regression to the mean
|Extreme observations in initial investigations will be toward true population value in later investigations
|Cohort<br>Clinical trial
|-
!Hawthorne effect
|Observation itself can affect outcome
|Clinical trial
|-
!Lead-time bias
|Screening trial can diagnose outcome even in its earlier latent period which fallaciously show longer survival
|Screening trial
|-
!Confounding
|Third factors affect both of exposure and outcome
|colspan="3"|
Confounding may occur even in RCT if randomization process is inappropriate (as systematic error) or randomly allocated groups are heterogenous by chance (as random error)
|}
==Difference between Population and Sample==
===Accuracy and Precision===
{|class="wikitable"
|+Definition
|-
!Accuracy
|
*Closeness of observed values '''to true population values'''
**AKA 'Trueness'
|-
!Precision
|
*Closeness of observed values '''to each other among sample'''
|}
[[File:Accuracy and Precision 2.gif|none|400px]]
Accuracy in <math>x</math>-axis, Precision in <math>y</math>-axis
<nowiki>*</nowiki>In this example, μ is sample mean, not the population mean
[[File:Accuracy and Precision 1.webp|none|400px]]
Precision in <math>x</math>-axis, Accuracy in <math>y</math>-axis
==Tips in Case-control design==
===Sampling (selection bias)===
{|class="wikitable" style="max-width:600px; vertical-align:top"
|-
!style="width:50%"|Case sampling
!style="width:50%"|Control sampling
|- style="vertical-align:top"
|
*'''Incident case'''
**for outcome with short duration or poor survival
*'''Prevalent case'''
**for rare or chronic outcome
|
*'''The same inclusion criteria as case'''
**except for outcome itself
|- style="vertical-align:top"
|
*'''Population-based case'''
**from surveillance, registry, death certificates, etc.
**easy to define population
|
*'''Population-based control'''
**random sampling from the same population
**random-digit dialing, population registry, etc.
*For occupation-based case, beware of '''healthy worker effect''' and select control '''from similar occupation''' with '''the same exposure as the population'''
|- style="vertical-align:top"
|rowspan="2"|
*'''Hospital-based case'''
**from hospital
**difficult to define population
|
*'''Hospital-based control'''
**sampling from the same hospital
**must be exposed at the same extent as the population
|- style="vertical-align:top"
|
*'''Population-based control'''
**Only when assumable that all patients from the population visit the hospital and patients from other than population never visit the hospital
|-
|colspan="2"|
*Matching '''increases precision of observations''', i.e. '''increases power'''
**Matching '''does not decrease bias'''
*Matching masks the effect of matching variables themselves
*'''Overmatching''' makes extents of exposure of both groups too similar, which leads to '''decrease power'''
**Matching with '''age''' and '''sex''' only is a better way
*1:n matching (n>1) increases power
**1:4 is maximum; more matching brings no more benefit
|}
===Measurement (Information bias)===
{|class="wikitable" style="max-width:600px"
|-
!Observer bias
|
*To mask case/control status to observers
|-
!Recall bias
|
*To use memory aids
*To mask research question/hypothesis to participants
|}
