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Presentation at a Course on Principles of Epidemiology Health Research Faculty of Medicine, King Fahad Medical City October 11-12, 2017 by Professor Omar Hasan Kasule Sr. MB ChB (MUK). MPH (Harvard), DrPH (Harvard) Chairman of the Institutional Review Board / Research Ethics Committee at King Fahad Medical City, Riyadh.
LECTURE 12-A: STUDY ANALYSIS AND INTERPRETATION: MEASURES OF ASSOCIATION and EFFECT:
GENERAL CONCEPTS:
• Data analysis involves construction of hypotheses and testing them.
• Simple manual inspection of the data is needed can help identify outliers, assess the normality of data, identify commonsense relationships, and alert the investigator to errors in computer analysis.
• Two procedures are employed in analytic epidemiology: test for association and measures of effect. The test for association is done first. The assessment of the effect measures is done after finding an association. Measures of effect are applied to discrete data.
• Measures of trend can discover relationships that are too small to be picked up by association and effect measures.
TESTS OF ASSOCIATION FOR CONTINUOUS DATA:
• The t-test is used for two sample means.
• Analysis of variance, ANOVA (F test) is used for more than 2 sample means.
• 1-way ANOVA involves one factor (explanatory variable).
• 2-way ANOVA involves 2 factors.
• Multiple analysis of variance, MANOVA, is used to test for more than 2 factors.
• Linear regression is used in conjunction with the t test for data that requires modeling.
TESTS OF ASSOCIATION FOR DISCRETE DATA:
• The Spearman chi-square test is used to test association of 2 or more proportions in contingency tables.
• The exact test is used to test proportions for small sample sizes.
• The Mantel-Haenszel chi-square statistic is used to test for association in stratified 2 x 2 tables.
• The chi-square works best for approximately Gaussian distributions. J
MEASURES OF EFFECT:
• The Mantel-Haenszel Odds Ratio is used for 2 proportions in single or stratified 2x2 contingency table.
• Logistic regression can be used as an alternative to the MH procedure.
• For paired proportions, a special form of the Manetl-Haenszel OR and a special form of logistic regression called conditional logistic regression, are used.
MEASURES OF EFFECT, Con’t.:
• Excessive disease risk is measured by Attributable Risk, Attributable Risk Proportion, and Population Attributable Risk.
• Variation of an effect measure by levels of a third variable is called effect modification by epidemiologists and interaction by statisticians.
VALIDITY and PRECISION:
• Validity is a measure of accuracy generally using measures of central tendency
• Precision measures variation in the estimate using variance or confidence interval
• Reliability is reproducibility.
• Internal validity is concerned with the results of each individual study.
• External validity is generalizability of results from a single large sample study or several small sample studies.
META ANALYSIS:
• Meta-analysis refers to methods used to combine data from more than one study to produce a quantitative summary statistic.
• Meta-analysis enables computation of an effect estimate for a larger number of study subjects thus enabling picking up statistical significance that would be missed if analysis were based on small individual studies.
• The summary effect measure, OR or b, is computed from the effect measures of individual studies using weighted logistic regression or computing a MH weighted average in which the weight of each measure is the inverse of its precision i.e. 1/(se)2.
LECTURE 12-B: STUDY ANALYSIS AND INTERPRETATION: SOURCES AND TREATMENT OF BIAS:
MISCLASSIFICATION BIAS:
• Misclassification is inaccurate assignment of exposure or disease status. It may be random or non-random
• Misclassification bias is classified as information bias, detection bias, and proto-pathic bias.
• Information bias is systematic incorrect measurement on response due to questionnaire defects, observer errors, respondent errors, instrument errors, diagnostic errors, and exposure mis-specification.
MISCLASSIFICATION BIAS, Con’t.:
• Detection bias arises when disease or exposure are sought more vigorously in one comparison more than the other group.
• Protopathic bias arises when early signs of disease cause a change in behaviour with regard to the risk factor. Misclassification bias can be prevented by using double-blind techniques to decrease observer and respondent bias.
• Treatment of misclassification bias is by the probabilistic approach or measurement of inter-rater variation.
SELECTION BIAS:
• Selection bias arises when subjects included in the study differ in a systematic way from those not included.
• Selection bias due to disease ascertainment procedures includes publicity, exposure, diagnostic, detection, referral, self-selection, and Berkson biases.
• The Hawthorne self-selection bias is also called the healthy worker effect since sick people are not employed or are dismissed.
SELECTION BIAS, Con’t.:
• The Berkson fallacy arises due to differential admission of some cases to hospital in proportions such that studies based on the hospital give a wrong picture of disease-exposure relations in the community.
• Selection bias during data collection is represented by non- response bias and follow-up bias.
• Prevention of selection bias is by avoiding its causes that were mentioned above. There is no treatment for selection bias once it has occurred.
CONFOUNDING BIAS:
• Confounding is mixing up of effects. Confounding bias arises when the disease-exposure relationship is disturbed by an extraneous factor called the confounding variable, related to both disease and exposure but unequally distributed.
• Prevention of confounding at the design stage by eliminating the effect of the confounding factor can be achieved using 4 strategies: pair-matching, stratification, randomisation, and restriction.
CONFOUNDING BIAS, Con’t.:
• Confounding can be treated at the analysis stage by various adjustment methods (both non-multivariate and multi-variate).
• Non-multivariate treatment of confounding employs standardization and stratified Mantel-Haenszel analysis.
• Multivariate treatment of confounding employs multivariate adjustment procedures: multiple linear regression, linear discriminant function, and multiple logistic regression.
MIS-SPECIFICATION BIAS:
• This type of bias arises when a wrong statistical model is used.
• An example use of parametric methods for non-parametric data biases the findings.
SURVEY ERROR and SAMPLING BIAS:
• Total survey error is the sum of the sampling error and three non-sampling errors (measurement error, non-response error, and coverage error).
• Sampling error decreases with increasing sample size.
• Sampling bias, positive or negative, arises when results from the sample are consistently wrong (biased) away from the true population parameter.
• The sources of bias are: incomplete or inappropriate sampling frame, use of a wrong sampling unit, non-response bias, measurement bias, coverage bias, and sampling bias.