Biostatistics

Biostatistics

·        Choosing study subjects:

o  Populations may be people, institutions, records or events

o  Sampling frame is a complete list of individuals in the accessible population

o  A sampling procedure is used to select a representative intended sample from the sample frame

·        Avoiding systematic errors:

o  Trying to use sample mean to estimate the population mean

o  Statistic = parameter + bias + confounding bias + chance

o  Statistic: summary measure in a sample

o  Parameter: underlying value in the target population 

o  Bias or Confounding ® ¯internal validity 

o  External validity is whether the study can be applied to the population I‟m interested in – is it similar enough to the study population?

·        Bias: 

o  Bias = systematic deviation between the statistic and the parameter, due to defect in the design, conduct or interpretation

o  Occurs predominantly in design and data collection

o   Selection bias: systematic error due to those who were selected and those who were not, so sample not representative of the defined population 

o   Information bias: a flaw in measurement exposures or outcomes that results in a differential quality of information between sub-groups/individuals. 

§  Misclassification bias: Subjects erroneously categorised. If a random bias then ¯ association in results and odds ratio moves towards 1 

§  Interviewer bias: systematic difference in soliciting, recording and interpreting of responses (¯ by training the interviewers – always check this has been done) 

§  Recall bias: should be < 2 weeks for health events. Diet recall ~ 24 hours. If not random (eg case-control studies) then biased (eg if cases taken from records then there is variability in what was asked and recorded, verses uniform questionnaire for controls) 

o   Response bias: systematic error due to differences between those who volunteer and those who do not (eg bias from drop-outs and non-responders)

o   See Topic: EBM Glossary, for further examples of bias

o   Confounding bias: 

§  A measure of the effect of an exposure on the risk of an outcome is distorted by an association of the exposure with other factors that influence the outcome

§  Standard ones: age, gender, ethnicity, socio-economic status, obesity, smoking, alcohol

§  As long as you collect data about the confounding factor, you can do something about it

§  Can control for confounding using matching, logistic regression or stratifying data

·        Chance effect: