# Internal validity: chance, bias, confounding --- ## What is internal validity - Chance - Bias - Confounding --- ## 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 --- ## Null hypothesis - To rule out the play of chance - Use Null Hypothesis - Null Hypothesis is effect of NO DIFFERENCE --- ## 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 --- ## 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) | --- ## 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% --- ## 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" --- ## Interpretation of p-value - If H0 were true: - out of 100 iterations of the study, - We would find the findings p times --- ## How do reject the null - if p is very low - the probability is low - we rule out the chance factor --- ## 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 --- ## 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 --- ## Hands-on practice with sample size calculator - Visit - [http://www.openepi.com/SampleSize/SSCohort.htm](http://www.openepi.com/SampleSize/SSCohort.htm) --- ## Example --- ## Bias - Systematic error - The compared groups are unequal in different ways - These impact their outcomes --- ## 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 --- ## 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 --- ## Response Bias - When the information collected is - different for different groups that - distort the direction of association --- ## 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 --- ## 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 --- ## Confounding - Associated with Exposure - Associated with Outcome - Does not come in the causal pathway connecting the two --- ## Illustration of confounding  --- ## 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 --- ## Control for confounding - Randomisation (works for randomised controlled trial) - Matching for observational studies - Stratified analysis - Multivariate modelling and analysis --- ## 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