Why stratified randomization

Ideally, the groups of patients being treated with the old and new treatments will be as similar as possible to each other, so that only the treatment is different between the groups. The special rules of a phase III trial help to make the groups of patients as similar as possible.

The special rules, or steps, are called "randomization", "stratification", and "blinding". Randomization means that, for each patient who agrees to participate in the study, one of the study treatments is selected for them randomly, like flipping a coin, or like drawing a name from a hat.

This is done to avoid having different numbers of patients receive one of the treatments, and to avoid doctors and patients biasing the results by choosing which treatment they think is better. Usually randomization is done by a computer or person at a central location who does not know the patient, but knows that they are eligible for the study. The objective of stratified randomization is to ensure balance of the treatment groups with respect to the various combinations of the prognostic variables.

Simple randomization will not ensure that these groups are balanced within these strata so permuted blocks are used within each stratum are used to achieve balance. If there are too many strata in relation to the target sample size, then some of the strata will be empty or sparse. This can be taken to the extreme such that each stratum consists of only one patient each, which in effect would yield a similar result as simple randomization.

The greater the differences between the strata, the greater the gain in precision. Unfortunately, this method of research cannot be used in every study. The method's disadvantage is that several conditions must be met for it to be used properly. Researchers must identify every member of a population being studied and classify each of them into one, and only one, subpopulation.

As a result, stratified random sampling is disadvantageous when researchers can't confidently classify every member of the population into a subgroup. Also, finding an exhaustive and definitive list of an entire population can be challenging. Overlapping can be an issue if there are subjects that fall into multiple subgroups.

When simple random sampling is performed, those who are in multiple subgroups are more likely to be chosen. The result could be a misrepresentation or inaccurate reflection of the population. The above examples make it easy: undergraduate, graduate, male, and female are clearly defined groups. In other situations, however, it might be far more difficult. Imagine incorporating characteristics such as race, ethnicity, or religion. The sorting process becomes more difficult, rendering stratified random sampling an ineffective and less than ideal method.

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Develop and improve products. List of Partners vendors. Your Money. In order to identify research papers, we performed a Medline search for The search yielded 33 articles that included original research on stratification or included stratification as the major focus. Additional resources included textbooks.