The objective of this lecture is to know the role of random error (chance) in factor-outcome relation and the types of systematic errors (Bias)

P-Value The probability that an effect at least as extreme as that observed in a particular study could have occurred by chance alone given that there is truly no relationship between the exposure and disease, by convention, in medical researches, if the P- value is less than or equal to 0.05 meaning that there is no more than %5 probability of observing a result as extreme as that observed due solely to chance, then the association between the exposure and disease is considered statistically significant. Alternatively; if the P-value is greater than 0.05, by convention, we consider that chance can not be excluded as a likely explanation, and the findings are stated to be not statistically significant at that level.

Statistical significance should never be viewed as a clear cut (yes or no statement) but rather merely as a guide to action. A statistically significant result does not mean that chance can not have accounted for the findings, only that such an explanation is unlikely. Similarly; a result that is not statistically significant does not mean that chance is responsible for the result, only that it can not be excluded as a likely explanation.

Confidence Interval (C.I) The range within which the true magnitude of effects lies with a certain degree of assurance. The narrower the CI the less variability is present in the estimate of effect, reflecting a larger sample size. The wider the CI, the greater the variability in the estimate of effect, and the smaller the sample size. When interpreting data that are not statistically significant or new results, the width of the CI, can be particularly informative. Specifically, a narrow CI implies that there is most likely no real effect of exposure , whereas a wide CI suggests that the data are also compatible with a true harmful or beneficial effect and that the sample size was simply not adequate to have sufficient statistical power to conclude that chance was not a likely explanation of a finding.

Statistical Power of the study To increase the statistical power of a study we should consider: Sufficient sample size 2. Accumulation of adequate end points - Selection of a high risk population - Adequate duration of follow up 3. The effect of compliance

Bias Systematic error in an epidemiologic study results in an incorrect estimate of the association between exposure and risk of disease (i.e. over or under estimation). It is not a randomly occurring error, it is -rather- a systematic disruption in the study either in the design, conduction or analysis, which leads either to magnification of a weak or false association, or dilution or obscuring a real one. It is very usual (almost obligatory) in every study and we cannot remove it completely, we can only define it from the beginning and try to avoid its effect or treat (by dilution).

Types of Bias: Selection bias: can occur when ever the identification of individual subjects for inclusion into the study on the basis of either exposure ( cohort ) or disease ( case-control ) status depends in some way on the other axis of interest. it is unlikely to occur in a prospective cohort study because exposure is usually ascertained before the development of any outcome of interest. Selected groups Special groups: age, sex, hospital, occupation… Volunteers: well informed, experts, motivated… Rx.: Randomization and avoid special groups.

2.Observation (information) bias: (usually in cross-sectional studies) Recall bias Memory bias Interviewer bias In the recall bias the subject is either Unable to remember really Don’t want to remember (afraid or ashamed) Not interested to remember Remember but in a defective way (personality) Remember and change the facts (for many reasons) Ignorant

The interviewer may fall his ideas on the subjects for several reasons: His personality (talkative) To show his knowledge Use the answers of other (previous) respondants Tends to certain ideas Thinks that he is helping them Is in a hurry Rx. Use documents: reports, investigations, drugs Link with important famous events Training and educating interviewers

3. Attrition Bias: usually in prospective studies Non eligibility Non participation Non response: for any reason (from the beginning) Non compliance Drop out (later): die, move, dislike, change job, loss of interest or motivation,…. Example: Accessible sample of 1000, 100 did not respond, =900 responded, 200 not eligible, =700 eligible, 100 did not participate, =600 participated, 200 drop out=500 continue Rx. Pilot project Education Motivation

4. Misclassification bias: usually in interventional studies. Differential: the rate of misclassification differs in study groups (classified as being exposed) ex. Mothers of leukemia children and history of X-ray. Leads to the appearance of an association that is not real. Non differential: results from the degree of inaccuracy of differentiation in the classification of cases and non cases (problem In the data collection method) Leads to crossover between cases and non cases, and so to dilution of RR Rx: case definition, training, and increase accuracy.

6.Subjectivity bias: interventional studies 5. Measurement bias: Instruments: calibration Individual variation: standardize the method Observers: inter and intra observer bias, Rx. By education and training 6.Subjectivity bias: interventional studies RX.: Masking of the drug Blinding: single, double and triple