1. Bias in Clinical Trials

4. Bias
Having a preference to one particular person / group / point of view - “one-sided inclination”
Prejudice – negative bias
In statistics, if a bias exists it means that the processes involved are not uniformly random and one outcome is favoured over others.

5. Factors Influencing CT Results
Factors other than the intervention under study can influence results on the study
Random Error Natural variation
Systematic Error Bias
All efforts are made to reduce both types of error

6. Random Error
Error that occurs due to natural / biological / random variation in the process
May be on either side of true value

7. How to deal with Random Error
Sample size large enough to detect clinically meaningful difference
Repeated sampling

8. Bias or Systematic Error
Difference between the true value and observed value due to all causes other than random variability
A flaw in either the study design or data analysis
Leads to an erroneous result
Intentional or unintentional

10. Bias in Clinical Trials
The control and intervention groups must be similar enough so that any differences detected in patient outcomes can reasonably be attributed only to the intervention under study.
If systematic differences exist between the control and intervention groups, then it is possible that the results of the study are biased.

11. 1. Sampling Bias

12. Sampling Bias
Systematic error due to study of a non-random sample of a population
Sample is not a random sample - some individuals are more likely than others to be chosen
For example, if you are asking college students how much they study, going to the library and randomly selecting people there to ask would introduce obvious bias: People who spend more time in the library are more likely to be chosen, and presumably report spending more time studying. Going to the Campus canteen at mealtime is subtler.

13. Sampling Bias
A special kind of sampling bias of particular significance is non-response bias.
This occurs when individuals have a choice of whether or not to respond. If significant numbers of individuals choose not to respond, you very likely have response bias, because it is likely that those who refuse have different answers than those that agree.
This is a serious problem with modern polls, because large percentages of people refuse to cooperate with pollsters. These end up being a sampling of the most passionate people, who's views are generally dramatically different from the broad middle.

14. Sources of Sampling Bias
Failure to adhere to the random sampling procedures.
Omission of specific subgroups of the population from the sampling frame and therefore from the sample.
Faulty measuring devices (this may be in terms of the specific questions used in a questionnaire, and may also arise in a survey that involves taking physical measurements, when the measuring device is incorrect, e.g., using a defective BP machine, so that all measurements are low / high).
Non-response to a survey by specific subgroups of the population that are relevant to the measures of concern in the survey.

15. Preventing Sampling Bias
Random sampling
Sampling all subgroups (representative sampling)
Accurate measurements
Taking into account non-responders in a survey

16. 2. Comparator Bias

18. Comparator Bias
Not using control treatment known to be beneficial / standard
For example, even though the effectiveness of erythropoietin in preventing anemia in cancer patients had been convincingly demonstrated by a number of controlled trials, some researchers continued to compare their drug with placebos.
Comparator biases will be introduced when patients are denied effective treatments, and the active treatments studied in the trial will be given an unfair advantage.

19. Comparator Bias Giving an inappropriately low dose of a treatment
This has occurred in comparisons of new non-steroidal anti-inflammatory agents used for arthritis with older drugs in the same class (Rochon et al. 1994).
Inappropriately low doses can also result from giving a treatment by an inappropriate route, for example, by comparing intravenous administration of a drug with oral administration of a drug that is poorly absorbed from the gastro-intestinal tract (Johanson and Gøtzsche 1999).

20. Comparator Bias Giving an inappropriately high dose of a treatment
Some of the newer drugs for treating schizophrenia, for example, have been shown to be preferable to established drugs for this reason. However, this apparent advantage may be because the newer agents have been compared with inappropriately high doses of the older, comparator drug (Waraich et al. 2004).
The net usefulness of treatments often requires trade-offs between wanted and unwanted effects. Treatments may be of real value if, although their beneficial effects are no better than alternatives, they have fewer adverse effects.

21. How to Reduce Comparator Bias
Appropriate choice of comparator group by systematic review of existing evidence
Use of placebo only when essential
Using appropriate dose & route of administration of comparator drug
Evaluation of net effects of treatments (benefits vs risks)

22. 3. Selection Bias

24. Selection Bias
Selecting & allocating participants to treatment groups depending upon investigator’s beliefs about efficacy / safety of treatments or other subjective reasons
Results in ‘dissimilar’ groups

25. Ways to minimize Selection Bias
Randomization – single most effective way to reduce selection / allocation bias
Every subject has equal chance of receiving test / comparator treatment
Results in similar ‘intervention’ & ‘control’ groups
Provides basis for statistical inference

26. Randomization Alternate allocation to groups Tossing a coin
Randomization Tables
Computarized randomization (all patients, blocked, stratified)
Methods for concealed randomization

27. 4. Expectation Bias

28. Expectation Bias
Both the patient’s and therapist’s expectations can influence the results of a clinical trial (even after randomization)
Not let one / both know which treatment is being given - Blinding
Blinding reduces expectation bias

29. Blinding & Techniques Look alike trial medications / placebo
Single-blind
Double-blind
Assessor-blind
Look alike trial medications / placebo
Sham techniques

30. Effect of blinding on outcome of trials of acupuncture for chronic back pain

32. 5. Analysis Bias

33. Bias in Analysis
Analyzing only select group of subjects for showing positive outcome
Not including drop-outs, withdrawn subjects in analysis
Multiple subgroup analysis (not pre-planned) to find some favourable outcomes
Confounding variables - factors other than intervention (e.g. age, degree of severity of disease, previous treatment) that may influence outcome

34. Ways to minimize bias in analysis
Have a statistical analysis plan before the study & include in protocol
Stratified design for significant variables
Intention-to-treat analysis
Separate subgroup analysis for significant variables
Stratified or multivariate analysis for confounding variables

35. 6. Reporting Bias

36. Reporting / Publishing Bias
Only reporting studies with good outcome
Not reporting / publishing studies with unfavourable outcome
Hiding evidence of negative outcomes

38. Ways to Minimize Reporting Bias
Clinical Trial Registry – registering all clinical trials on new drugs
Compulsion to submit results of all studies to regulatory authority
Publishing results of all clinical trials on websites
Publishing significant negative / no-difference studies on new treatments in well recognized journals