4.2 - Clinical Biases4.2 - Clinical Biases
If a bias is small relative to the random error, then we do not expect it to be a large component of the total error. A strong bias can yield a point estimate that is very distant from the true value. Remember the 'bulls eye' graphic? Investigators seldom know the direction and magnitude of bias, so adjustments to the estimators are not possible.
There are many sources of bias in clinical studies:
- Selection bias
- Procedure selection bias
- Post-entry exclusion bias
- Bias due to selective loss of data
- Assessment bias
1. Selection Bias
Selection bias refers to selecting a sample that is not representative of the population because of the method used to select the sample. Selection bias in the study cohort can diminish the external validity of the study findings. A study with external validity yields results that are useful in the general population. Suppose an investigator decides to recruit only hospital employees in a study to compare asthma medications. This sample might be convenient, but such a cohort is not likely to be representative of the general population. The hospital employees may be more health-conscious and conscientious in taking medications than others. Perhaps they are better at managing their environment to prevent attacks. The convenient sample easily produces bias. How would you estimate the magnitude of this bias? It is unlikely to find an undisputed estimate and the study will be criticized because of the potential bias.
If the trial is randomized with a control group, however, something may be salvaged. Randomized controls increase the internal validity of a study. Randomization can also provide external validity for treatment group differences. Selection bias should affect all randomized groups equally, so in taking differences between treatment groups, the bias is removed via subtraction. Randomization in the presence of selection bias cannot provide external validity for absolute treatment effects. The graph below illustrates these concepts).
The estimates of the response from the sample are clearly biased below the population values. However, the observed difference between treatment and control is of the same magnitude as that in the population. In other words, it could be the observed treatment difference accurately reflects the population difference, even though the observations within the control and treatment groups are biased.
2. Procedure Selection Bias
Procedure selection bias, a likely result when patients or investigators decide on treatment assignment, can lead to extremely large biases. The investigator may consciously or subconsciously assign particular treatments to specific types of patients. Randomization is the primary design feature that removes this bias.
3. Post-entry exclusion bias
Post-entry exclusion bias can occur when the exclusion criteria for subjects are modified after examination of some or all of the data. Some enrolled subjects may be recategorized as ineligible and removed from the study. In the past, this may have been done for the purposes of manufacturing statistically significant results but would be regarded as an unethical practice now.
4. Bias due to selective loss of data
Bias due to selective loss of data is related to post-entry exclusion bias. In this case, data from selected subjects are eliminated from the statistical analyses. Protocol violations (including adding on other medications, changing medications or withdrawal from therapy) and other situations may cause an investigator to request an analysis using only the data from those who adhered to the protocol or who completed the study on their assigned therapy.
The latter two types of biases can be extreme. Therefore, statisticians prefer that intention-to-treat analyses be performed as the main statistical analysis.
In an intention-to-treat analysis, all randomized subjects are included in the data analysis, regardless of protocol violations or lack of compliance. Though it may seem unreasonable to include data from a patient who simply refused to take the study medication or violated the protocol in a serious manner, the intention-to-treat analysis usually prevents more bias than it introduces. Once all the patients are randomized to therapy, use all of the data collected. Other analyses may supplement the intention-to-treat analysis, perhaps substantiating that protocol violations did not affect the overall inferences, but the analysis including all subjects randomized should be primary.
5. Assessment bias
As discussed earlier, clinical studies that rely on patient self-assessment or physician assessment of patient status are susceptible to assessment bias. In some circumstances, such as in measuring pain or symptoms, there are no alternatives, so attempts should be made to be as objective as possible and invoke randomization and blinding. What is a mild cough for one person might be characterized as a moderate cough by another patient. Not knowing whether or not they received the treatment (blinding) when making these subjective evaluations will help to minimize this self-assessment or assessment bias..
Well-designed and well-conducted clinical trials can eliminate or minimize biases.
Key design features that achieve this goal include:
- Randomization (minimizes procedure selection bias)
- Masking (minimizes assessment bias)
- Concurrent controls (minimizes treatment-time confounding and/or adjusts for disease remission/progression, as the graph below illustrates. Both treatment and control had an increase in response, but the treatment group experienced a greater increase.)
- Objective assessments (minimizes assessment bias)
- Active follow-up and endpoint ascertainment (minimizes assessment bias)
- No post hoc exclusions (minimizes post-entry exclusion bias)