It’s A Math, Math World (Clinical Trial Bias)

 Some of the information in this article (i.e. some definitions and examples) is attributed to a lecture at Rutgers University by Adele Gilpin during the spring 2004 semester.

In any clinical trial, we want to ensure that our response is not biased. By definition, bias is systematic error introduced into sampling or testing by selecting or encouraging one outcome over another. If the same error occurs in the treated and untreated groups, then the study is still internally valid, but systematic error can cause an entire collection of measurements too lose their meaning.

There are 2 types of bias we will look at: (1) Treatment-related, and (2) Non-treatment related.

Treatment related bias: Systematic error that is treatment related can really harm a trial.  This is a bias related to treatment assignment that affects the observed treatment differences in the trial. It is important to ensure, to the best of one’s ability, that no such bias exists before the trial begins.

Treatment related bias can occur in 3 ways:

  • During treatment assignment:  This occurs through an assignment process that allows groups to be different at baseline.
  • During treatment process:  If you have comparable groups, you may treat them differently other than the assigned treatment administration. For example, one group might receive systematically better care than another.
  • During Measurement or Data Collection process: In terms of measurement, one may listen more carefully to heart sounds on mercury column when taking BP because they think that group will produce a person with higher BP. In terms of data collection, an investigator might document adverse events more carefully because he/she thinks they are more serious because one treatment is more dangerous than another.

Non-treatment related bias:  This is study error not related to treatment assignment. This can cause a “conservative bias” that can make it more difficult to detect a treatment effect. Conservative bias is not good for developer of treatment or patients with condition of interest because it is harder to determine whether treatment is effective against primary outcome.

Requirements to reduce bias:

  • Establish comparable study groups that are free of selection bias.
  • Use a data collection schedule in which the probability of observing an event is the same for all patients.
  • Use data collection procedures that are reproducible and standardized over all treatment groups.

Methods of Bias control:

  • Masking (blinding):

 This is used to conceal the intervention assignment from either the patient, investigator or both.

  • Randomization:

This is used to create comparable treatment and control groups.

  • Standardization:

Written treatment protocol

Tested forms and other documentation, including manuals

Written definitions of what response is

Standard equipment that is tested and calibrated

Training and certification of study personnel

  • Surveillance:

Each trial must be carefully and independently monitored to ensure protocol and regulatory compliance.

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3 Responses to “It’s A Math, Math World (Clinical Trial Bias)”

  • Xi Chen says:

    In the current clinical trial practice, the bias is much more serious than whatever you have summarized. Frankly, many commonly acceptable clinical trial set ups are in fact biased. For example, the definition of noninferiority intentionally ignores the difference between superiority and equivalence. In most of trial set ups, it is common to believe that the null hypthesis is true until it is rejected, and the null hypothesis has to be one against the study medication. In fact, if the the null hypothesis is true and it is against the study medication, the trial should not start at all, since the pre-assumption is that the study medication will not work. The discussion regarding the bias in clinical trial practice will go much beyond the standard textbooks.

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