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Design and Analysis of Clinical Study 2. Bias and Confounders

Design and Analysis of Clinical Study 2. Bias and Confounders. Dr. Tuan V. Nguyen Garvan Institute of Medical Research Sydney, Australia. Biases & Confounding. Bias means “difference from the truth” There are 3 types of bias: Selection bias Information bias Confounding.

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Design and Analysis of Clinical Study 2. Bias and Confounders

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  1. Design and Analysis of Clinical Study 2. Bias and Confounders Dr. Tuan V. Nguyen Garvan Institute of Medical Research Sydney, Australia

  2. Biases & Confounding • Bias means “difference from the truth” • There are 3 types of bias: • Selection bias • Information bias • Confounding

  3. Selection Bias • Non-representativeness • Patients referred for specialist care are different from those in the community • Used hospitalized smokers as the exposed and healthy volunteer non-smokers as the unexposed • 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

  4. Selection bias - Berkson (a) General population – odds ratio = 1.06 (b) Hospitalized population – Odds ratio = 4.06 Ref: Roberts RS, et al. J Chron Dis 31:119-28

  5. “Bias by Indication” • Whenever we compare a group of patients who use a drug to those who don’t in a non experimental observational study (cohort, not randomized). • The 2 groups differ in many respects: “Bias by indication”. • Comparison of hypertensive patients who are on minoxidil or hydralazine and those on other agents: • That patients on those agents have higher BP • Is it because they don’t work as well ? • No, the opposite. They are reserved for those with severe resistant hypertension. • That is the indication for those agents.

  6. “Survivor Treatment Bias” • Patients who received statin during admission for MI had much lower in-hospital mortality. • Statin? • The ones who died are different. • Some died very soon after admission (no statin). • Some were so sick that they were treated with multiple drugs, modalities, ICU etc. • No statin

  7. Information Bias • 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)

  8. 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 Socioeconomic Class

  9. Example of Confounder - Sex Males Females

  10. Strategies for Reducing Biases • 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.

  11. How to Deal with Confouders 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.

  12. How to Deal with Confouders 2 • Design stage • Strict inclusion criteria • Matching • Randomization • Analysis stage • Do analysis by adjusting for several strata of the confounding variable • Multiple regression analysis

  13. How to Check for Confouders • 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.

  14. Validity • Are the conclusions true? • Common threats to validity • Selection bias • Measurement bias • Differential loss of subjects • Confounders • Unexpected events • Hawthorne effect

  15. How to Ensure 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.

  16. Cause-and-Effect Relationship Strength of Research Design is most important 1. Well - conducted randomized controlled trials (adequate sample size; blinding; standardized methods of measurement and analysis) 2. Cohort studies - next best (minimize selection & measurement bias; check for confounders)

  17. Temporal sequence (cause must precede effect) Strength of association (Relative risk or odds ratio) Dose-Response relationship Evidence for cause-and-effect • Reversible association (removal of cause decreases risk) • Consistency (several studies come up with same findings) • Biological plausibility • Specificity • Analogy

  18. Flow chart for cause-and-effect inference Association (O.R. R.R. Pearson’s r) Yes No Bias Not likely Likely Chance Excluded Possible No Possible Error CAUSE

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