In some early clinical trials, randomization was performed by constructing two balanced groups of patients and then randomly assigning the two groups to the two treatment groups. This is not always practical as most trials do not have all the patients recruited on day one of the studies. Most clinical trials today invoke a procedure in which individual patients, upon entering the study, are randomized to treatment.
Randomization is effective in reducing bias because it guarantees that treatment assignment will not be based on the patient's prognostic factors. Thus, investigators cannot favor one treatment group over another by assigning patients with better prognoses to it, either knowingly or unknowingly. Procedure selection bias has been documented to have a very strong effect on outcome variables.
Another benefit of randomization which might not be as obvious is that it typically prevents confounding of the treatment effects with other prognostic variables. Some of these factors may or may not be known. The investigator usually does not have a complete picture of all the potential prognostic variables, but randomization tends to balance the treatment groups with respect to the prognostic variables.
Some researchers argue against randomization because it is possible to conduct statistical analysis, e.g., analysis of covariance (ANCOVA), that adjusts for the prognostic variables. It always is best, however, to prevent a problem rather than adjust for it later. In addition, ANCOVA does not necessarily resolve the problem satisfactorily because the investigator may be unaware of certain prognostic variables and because it assumes a specific statistical model that may not be correct.
Although randomization provides great benefit in clinical trials, there are certain methodological problems and biases that it cannot prevent. One example where randomization has little, if any, the impact is external validity in a trial that has imposed very restrictive eligibility criteria. Another example occurs with respect to assessment bias, which treatment masking and other design features can minimize. For instance, when a patient is asked "how do you feel?" or "how bad is your pain?" to describe their condition the measurement bias is introduced.
Simple Randomization Section
The most popular form of randomization is simple randomization. In this situation, a patient is assigned a treatment without any regard for previous assignments. This is similar to flipping a coin - the same chance regardless of what happened in the previous coin flip.
One problem with simple randomization is the small probability of assigning the same number of subjects to each treatment group. Severe imbalance in the numbers assigned to each treatment is a critical issue with small sample sizes.
Another disadvantage of simple randomization is that it can lead to an imbalance among the treatment groups with respect to prognostic variables that affect the outcome variables.
For example, suppose disease severity in a trial is designated as mild, moderate, and severe. Suppose that simple randomization to treatment groups A and B is applied. The following table illustrates what possibly could occur.
|Severity||Group A||Group B|
The moderate is fairly well balanced, the mild and severe groups are much more imbalanced. This results in Group A getting more of the severe cases and Group B more of the mild cases.