---
title: Part IV chance bias confounding
author: Arindam Basu
tags: epidemiology, hlth301, hlth214, hlth403
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# Internal validity: chance, bias, confounding

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## What is internal validity
- Chance
- Bias
- Confounding

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## Chance
- is the observed association explained due to chance alone?
- Study finds those with high concentrations of arsenic in water have skin diseases
- Is it possible that this finding could arise by chance?
- Chance is POSSIBLE, so
- Rule out play of chance

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## Null hypothesis
- To rule out the play of chance
- Use Null Hypothesis
- Null Hypothesis is effect of NO DIFFERENCE

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## An example of Null Hypothesis
- Suppose we know that exposure to inorganic arsenic in drinking water causes skin disease
- **Risk of Skin disease equal between those with and without high Arsenic exposure**
- Null Hypothesis can be TRUE of FALSE
- Null Hypothesis should be rejected or failed to be rejected

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## Alpha and beta errors

| Study | H0 TRUE | H0 FALSE |
| ----------------- | -------------------- | -------------------- |
| Reject H0 | Type I error (alpha) | :heavy_check_mark: |
| Fail to Reject H0 | :heavy_check_mark: | Type II error (beta) |

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## Before planning the study
- Set a value for the Type I error (alpha error)
- Usually type 1 error set at 5%
- Set a value for Type II erro (beta error)
- Usually set at 20%

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## After completion of study
- What is the probability of the findings, if
- Null Hypothesis (H0) were true?
- If that probability is LOW,
- Reject the null hypothesis
- That probability is "p-value"

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## Interpretation of p-value
- If H0 were true:
- out of 100 iterations of the study,
- We would find the findings p times

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## How do reject the null
- if p is very low
- the probability is low
- we rule out the chance factor

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## Alternative approach
- Construct a 95% confidence interval
- If the study were to be conducted 100 times
- 95 out of 100 times, the findings
- Would be between the lower and upper value

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## You rule out the play of chance
- Before the study you set the values for Type I and Type II error
- Decide on the effect size you want to see as "significant"
- Estimate sample size

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## Hands-on practice with sample size calculator
- Visit
- [http://www.openepi.com/SampleSize/SSCohort.htm](http://www.openepi.com/SampleSize/SSCohort.htm)

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## Example

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## Bias
- Systematic error
- The compared groups are unequal in different ways
- These impact their outcomes

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## Selection Bias
- You want to study effect of X on Y
- You will select different values of X in a way that
- That will favour your conclusions

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## Example of selecion bias
- Suppose you want to study association between indoor smoking and respiratory illnesses
- You know that indoor smoking is common in poor households
- You also know that many elderly people in poor households suffer from respiratory illnesses
- For your case control study you select
- Cases from poor neighbourhoods
- This will stack the results in your favour

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## Response Bias
- When the information collected is
- different for different groups that
- distort the direction of association

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## Example of Response Bias
- You want to study association association between indoor smoking and respiratory illnesses
- In your case control study,
- Cases if they know the purpose of the study could provide
- More accurate information about smoking than controls
- This can DISTORT relationships between smoking and lung disease

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## Steps to eliminate bias
- Objective measurement of exposure and outcome
- If using subjective tools such as interviews,
- Train interviewers and use checks and balances
- Blinding and concealment of information from all parties
- Do everything at the design stage of the study

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## Confounding
- Associated with Exposure
- Associated with Outcome
- Does not come in the causal pathway connecting the two

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## Illustration of confounding

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## Example of confounding
- You want to study association between indoor smoking exposure and heart disease
- Male spouse of smokers are both at increased risk of exposure
- Males are also more likely to suffer from heart disease
- Yet maleness DOES NOT come in the causal pathway
- Hence "gender of the spouse" is a confounder

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## Control for confounding
- Randomisation (works for randomised controlled trial)
- Matching for observational studies
- Stratified analysis
- Multivariate modelling and analysis

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## Summary
- Chance, bias and confounding are three important factors
- Chance can be ruled out with adequate sample size estimation
- Bias can be eliminated with design
- Confounding can controlled with several strategies
- Next up: Causal inference