Week 4: Random sampling error, bias, confounding

strong evidence:

1) lowest possible random sampling error
2) based on a good design

- high validity: of the study and measures association, Not the validity of the measure of events or exposure

Internal Validity

- do the observed results accurately reflect the true association?

*if a study lacks internal validity, external validity is irrelevant
*we do not compromise internal validity in an effort to achieve external validity (generalizability)

External validity (generalizability)

- to whom can results be applied?
- requires internal validity

Will be achieved by a sample that represents the target population

observing an association

Validity is:

having fewer errors
error = measured value - true value

sources of error:
- chance (random sampling error)
- bias
- systematic error in selection of participants and/ or measurement

Random sampling error

- random
- sample variation, sample to sample differences
p value shows how likely the observed results might be due to chance
-best way to minimize random sampling error is to increase sample size

P value

- NOT an error
- calculated as a guide for rejection or acceptance of null hypothesis

Bias

refers to a systematic error in the design or conduct of a study
- when bias occurs in a study, the observed association between the exposure and outcome will be different from the true association

confidence interval

how confident we are, that our research does not include bias
large sample size - confidence interval smaller

Selection bias

refers to a systematic study error in the way participants are selected or retained in a study

* occurs when individuals have different probabilities of being included or retained in the study according to the exposure and/or outcome
* if you see the word __**recruit**__ -----> selection bias

\*PARTICIPATION DIFFERS ON EXPOSURE AND DISEASE

Types of selection bias:

1. Inappropriate control selection (control-selection bias)
2. Differential participation (case-control cohort)
3. Differential loss to follow up (cohort, experimental)

Volunteer bias

Volunteers are more health-conscious or from a different socio-economic group
- Differential exposure
-Effect of interventions for enhancing physical activity in older adults

Non-response bias

those suffering from a disease with a particular belief
- differential outcome

membership bias

Healthy worker effect
eg. Service in Vietnam reduced mortality rates

Loss to follow-up bias

in clinical trials or longitudinal studies, the sickest usually leave the study early

Reducing selection bias

little or nothing can be done to fix selection bias once it has occurred
* CANNOT be reduced by increasing sample size

information bias

Interviewer asks to many questions - answers become inaccurate
eg. measurement error

Confounding bias

- occurs when the exposed group and the unexposed group are not exchangeable

Defining a confounding variable:

1. Causally associated with the outcome (a true risk factor)
2. non causally or causally associated with the exposure
3. not an intermediate in the causal pathway between exposure and outcome

how to identify a confounding variable

- literature review of comparable studies
- consult experts
- statistical tests

How to deal with confounding, at design stage:

1. restriction
- limit study inclusion criteria
2. matching
- produce case and control groups that have similar characteristics
3. randomization

How to deal with confounding, at the analysis stage:

1. standardization
- age standardization is in fact 'adjustment' for age
2. stratified analysis
3. confounding factor in a multivariate regression model

So the big 3 threats to validity:

CBC
1) Chance
2) Bias - selection/information
3) confounding

low P-value vs High P-value

low = more likely results are valid and reliable (not up to chance)