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General issues of Vaccine


General issues of Tropical med.


General issues of Travel med.


Trematode (fluke, distoma)


Disease frequency measurement
Bradford Hill Criteria for assuming causality

In terms of the association between exposure and outcome, 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
Catalogue of Bias
Major classification

Specific name

Description

Affecting design

Selection bias

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 exemployees

Cohort, esp. historical

Detection bias

Diagnostic procedures are different between case and control

Casecontrol

Attrition bias (Loss to followup)

needless to explain

Cohort Clinical trial

Nonresponse 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

Metaanalysis Systematic review

Publication bias

Researches with negative results are more likely to be unpublished

Metaanalysis Systematic review

Information bias (Measurement bias)

Observation is not properly conducted

Nondifferential 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 inbetween
 under or overestimate effect size

Any design

 └Recall bias

 └(subcategory of differential misclassification)
Participants are more likely to recall their memory in skewed way according to their outcome status

Any design

 └Reporting bias

 └(subcategory of differential misclassification)
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 Clinical trial

Hawthorne effect

Observation itself can affect outcome

Clinical trial

Leadtime 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

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=Validity and Precision=Reliability
Definition
Accuracy 
Validity

 Closeness of observed values to true population values

Precision 
Reliability

 Closeness of observed values to each other among sample

Accuracy in
[math]\displaystyle{ x }[/math]axis, Precision in
[math]\displaystyle{ y }[/math]axis
*In this example, μ is sample mean, not the population mean
Precision in
[math]\displaystyle{ x }[/math]axis, Accuracy in
[math]\displaystyle{ y }[/math]axis
Tips in Casecontrol design
Sampling (selection bias)
Case sampling

Control sampling

 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

 Populationbased case
 from surveillance, registry, death certificates, etc.
 easy to define population

 Populationbased control
 random sampling from the same population
 randomdigit dialing, population registry, etc.
 For occupationbased case, beware of healthy worker effect and select control from similar occupation with the same exposure as the population

 Hospitalbased case
 from hospital
 difficult to define population

 Hospitalbased control
 sampling from the same hospital
 must be exposed at the same extent as the population

 Populationbased control
 Only when assumable that all patients from the population visit the hospital and patients from other than population never visit the hospital

 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)
Observer bias

 To mask case/control status to observers

Recall bias

 To use memory aids
 To mask research question/hypothesis to participants

Confounding and Effect modification
Sampling
Simple random sampling (SRS)
Standard Error with Finite Population Correction in SRS
The formula [math]\displaystyle{ SE = \frac{\sigma}{\sqrt{N}} }[/math] assume that samples are selected from infinite population with replacement (allowing repetitive sampling of the same individuals).
In reality, sampling is made from finite population without replacement (not allowing repetitive sampling of the same individuals).
When sampling from finite population without replacement, if fraction [math]\displaystyle{ \frac{n}{N} }[/math] > 5% (0.05), [math]\displaystyle{ SE }[/math] will be too large.
Repeating sampling from finite population of size of [math]\displaystyle{ N }[/math] by sample size of [math]\displaystyle{ n_i }[/math] decreases population size at every sampling:
population size at [math]\displaystyle{ m }[/math]^{th} sampling = [math]\displaystyle{ N  \sum_{i=1}^m {n_i} }[/math]
If the fraction [math]\displaystyle{ \frac{n}{N} }[/math] > 5% (0.05), [math]\displaystyle{ SE }[/math] should be corrected by Finite Population Correction.
Standard Error corrected by Finite Population Correction when [math]\displaystyle{ \frac{n}{N} }[/math] > 5% (0.05)

[math]\displaystyle{ Finite\ Population\ Correction\ (FPC) = \sqrt{\frac{Nn}{N1}} }[/math]

[math]\displaystyle{ Corrected\ SE = FPC \times SE = \sqrt{\frac{Nn}{N1}} \cdot \frac{\sigma}{\sqrt{N}} }[/math]

Twostage (multistage) sampling
In case of twostage sampling:
 Randomly sample primary sampling units (PSUs) (clusters) with probability proportional to size (PPS)
 larger size of PSUs (clusters) are more likely to be selected
 Randomly sample secondstage units (SSUs) (individuals) with the same number of individuals within PSUs (clusters)
 individuals in smaller size of PSUs are more likely to be selected
 final individual probability to be sampled is equivalent in entire population because of balancing probabilities between PSUs and SSUs
Stratified random sampling
To increase precision of estimates in heterogeneous groups in sample, separated (stratified) random sampling from each group in population can be chosen.
In stratified random sampling, sampling fractions in strata can be varied, e.g., sample size of minority groups can be larger than majority groups.