Why stratify in clinical trials

For example, patients could be divided up according to age, gender, ethnicity, social background, medical history, or any other factor that you consider relevant. Groups of subjects are then included in the clinical trial to match each of these groups within the patient population.

With the strata established, different approaches can be taken to identify suitable test subjects. Stratified random sampling, or stratified randomization, uses random selection within each strata in an attempt to ensure that no bias, deliberate or accidental, interferes with the representative nature of the patient sample. Potential test subjects for a strata are identified, and those to be used in the trial are picked at random from that group. Clinical trial services also make use of stratified proportionate sampling to ensure representative tests.

Stratified proportionate sampling, which can be combined with randomized stratification, is a way of ensuring that the test population represents the wider population without the need for further statistical manipulation. The percentage of subjects taken from each strata is proportionate to the percentage of the population in that strata. So if seventy percent of the likely patients are female then seventy percent of the test subjects would be female, and so on for other stratification factors.

Proportionate sampling is not necessary to ensure valid results, as the impact of different strata on the overall picture can be factored in mathematically. But it removes the need for that extra statistical step. The maximum desirable number of strata is unknown, but experts argue for keeping it small. Stratified randomization is important only for small trials in which treatment outcome may be affected by known clinical factors that have a large effect on prognosis, large trials when interim analyses are planned with small numbers of patients, and trials designed to show the equivalence of two therapies.

Once the decision to stratify is made, investigators need to chose factors carefully and account for them in the analysis. The results of a phase III study may change the standard approach to treatment in a group of patients. Because of this, there are often rules which are present to limit the chance that wrong conclusions will be drawn from the study. In phase III studies, the goal is to compare one treatment to another.

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".

Data from a real clinical trial will be used for illustration. The benefit attributed to stratification needs to be re-examined in light of the findings presented, particularly given its widespread use.