lect 4 c - study designs- errors bias confounding

Epidemiologic Study Designs

Errors (Bias) & Confounding

8 March 2011 1Faculty of Medicine

Errors

There are basically 2 types of error in research. 1. One is random error due to random variation in

subjects responses or measurement.Inferential statistics (the p value and 95% confidence interval) measure the amount of random error and thus allow us to draw conclusion based on our research data.

2. However, there is another type of error, Bias or systematic error.

8 March 2011 Faculty of Medicine 2

Random error

Random error is the divergence, due tochance alone, of an observation on a samplefrom the true population value, leading tolack of precision in the measurement ofassociation.

There are three major sources of randomerror:

1. Individual biological variation2. Sampling error3. Measurement error


Random error

Random error can never be completely eliminatedsince we can study only a sample of the population,individual variation always occurs and nomeasurement is perfectly accurate.

Random error can be reduced by the carefulmeasurement of exposure and outcome thus makingindividual measurements as precise as possible

Sampling error occurs as part of the process ofselecting study participants who are always a sampleof a larger population. Best way to reduce it isincrease size of the study


Sample Size Calculations

The sample size must be large enough for the study to have sufficient statistical power to detect the differences deemed important.

In reality, sample size is often determined by logistic and financial considerations

The desirable size of study can be assessed using standardformulae. Information on the following required: Required level of statistical significance of the expected result Acceptable chance of missing a real effect Magnitude of effect under investigation Amount of disease in population Relative sizes of the groups being compared


Bias (Systematic error)

A systematic deviation of results or inferences from the truth or processes leading to such systematic deviation;

Any systematic tendency in the collection, analysis, interpretation, publication, or review of data that can lead to conclusions that are systematically different from the truth.

Over 30 specific types of bias have been identified. The principal biases are: selection bias measurement (or classification) bias.


Bias (Systematic error)

A study with a small systematic error is said to have high accuracy

Accuracy not effected by sample size Some variables of interest are particularly

difficult to measure (e.g. personality type, alcohol consumption) and this can lead to systematic error


Selection bias

Occurs when there is a systematic differencebetween the characteristics of the peopleselected for the study and those who are not

Obvious source is when the participants selectthemselves, either because they are unwell orworried about exposure


Measurement Bias

Measurement / classification inaccurate i.e.they do not measure correctly what they aresupposed to measure.

A form of bias of particular importance inretrospective studies is known as recall bias. Itcan either exaggerate the degree of effectassociated with exposure or underestimate it.


Recall bias Recall bias (or reporting bias) is a type of systematic bias

which occurs when the way a survey respondent answers a question is affected not just by the correct answer, but also by the respondent's memory. This can affect the results of the survey

As a hypothetical example, suppose that a survey in 2005 asked respondents whether they believed that O.J. Simpson had killed his wife, 10 years after the criminal trial. Respondents who believed him innocent might be more likely to have forgotten about the case, and therefore to state no opinion, than respondents who thought him guilty.

If this is the case, then the survey would find a higher-than-accurate proportion of people who believed that Simpson did kill his wife.


Confounding

Can occur when another exposure exists in the study population and is associated both with the disease and the exposure being measured.

Problem arises if this extraneous factor( that is a determinant or risk factor for the health outcome) is unequally distributed between the exposure subgroups

Confounding occurs when the effects of two exposures have not been separated and it is therefore incorrectly concluded that the effect is due to one rather than the other exposure.


Confounding


Confounding Age and social class are often confoundersControl of ConfoundingDesign: Randomisation Restriction MatchingAnalysis: Stratification Statistical Modelling


ConfoundingRandomisation Applicable only to experimental studies.

Ensures that confounding variable equally distributed among groups. Sample size needs to be large

Restriction Limit the study to people who have

particular characteristics - study of heart disease: limit to non smokers


Matching Matching is used to control confounding by

selecting study participants so as to ensure that potential confounding variables are evenly distributed in the two groups being compared.

Confounding


Confounding

Stratification : In large studies it is usually preferable to control for confounding in the analytical phase rather than in the design phase.

Confounding can then be controlled by stratification, which involves the measurement of the strength of associations in well defined and homogeneous categories of the confounding variable. If age is a confounder, the association may be measured in,

say, 10-year age groups; if sex or ethnicity is a confounder, the association is measured separately in men and women or in the different ethnic groups.


lect 4 c - study designs- errors bias confounding

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