Transcript Bias - Oxford Journals

Mother and Child Health:
Research Methods
G.J.Ebrahim
Editor
Journal of Tropical Pediatrics, Oxford
University Press.
Bias
• Bias means “different”
• 3 types of bias:
– Selection Bias
– Information Bias
– Confounding
Selection Bias
Examples
• Patients referred for specialist care are different
from those in the community
• Migration bias. People with chronic lung disease
tend to move out of urban areas; those with
psychiatric problems seek the anonymity of cities
• High dropout rates. Those who drop out of a study
tend to be different from those continuing
Information Bias
Examples
• Response Bias occurs when subjects give
inaccurate responses.
• Measurement Bias occurs when instruments
are faulty
• Observer error
• A process tends to show improvement when
being observed. (Hawthorne Effect)
Strategies for Avoiding Bias
• Have clear and precise definitions (e.g. for cases;
controls;exposure;criteria for inclusion/exclusion)
• “Blinding” where appropriate
• Reduce measurement error by ‘quality control”
• careful check of study design; choice of subjects;
ascertainment of disease and exposure;planning of
questionnaires; methods of data collection.
Confounders
• Confounders act by being associated with
both a risk factor and outcome in a way that
makes the two seem related.
Poor
Maternal
Nutrition
Low Birth
Weight
Low
Socioecono
mic Class
Dealing with Confounders - 1
• Think about possible confounders at the
design stage, and gather data on all possible
confounders.
• A quick test about a possible confounder is
to check whether it is unevenly distributed
between study and comparison groups.
• Suspect confounding if the odds ratio gets
altered after adjusting for another factor.
Method of Checking for a
Possible Confounder
• First calculate Odds Ratio for the exposure
variable.
• Next calculate odds ratio for different strata
of the confounding variable
• If the odds ratios are not materially different
then there is no confounding.
Strategies for dealing with
Confounding
• Design Stage
– Strict inclusion criteria
– Matching
– Randomization
• Analysis Stage
– Do analysis by adjusting for several strata of the
confounding variable
– Multiple regression analysis
Validity
• Are the conclusions true?
• Common threats to validity
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Selection bias
Measurement bias
Differential loss of subjects
Confounders
Unexpected events
Hawthorne effect
Strategies for ensuring validity
• Have a control group. Helps against confounding, unexpected
events, Hawthorne effect.
• Random assignment of subjects to different groups.
• Before / After measurements.
• Carefully prepared research designs.
• Quality control of equipment
• Knowledge of environmental events especially if the study is of
long duration.
• Unobtrusive methods of observation.