Healthcare Patient Stratification in Clinical Trials

Healthcare patient stratification is the division of a potential patient test group into subgroups, also referred to as strata or blocks. Each strata represents a particular section of your patient population. For example, patients could be divided up according to age, gender, ethnicity, obesity, 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. So a study into the intersection of genetics & medicine that is immunotherapy might need to include groups from different ethnic backgrounds and genders, to take into account genetic variations. With the strata established, different approaches can be taken to identify suitable test subjects.

In Healthcare Patient Stratification Randomization on pre-trial data and covariate analysis on post-trial data may negate the usefulness of stratification in large samples. For small samples, creating too many strata could have the opposite desired effect; Instead of a perfect balance of factors, there may be no balance of factors because there will be too few patients in each strata. As an extreme, you could end up with 400 strata with 400 patients meaning one patient per strata and no diversity at all. To avoid this pitfall, researchers are advised to choose no more than 5 important factors with 2-4 levels each to stratify for. Stratified random sampling, or stratified randomization, use 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. This leads to the next point:

Stratified proportionate sampling vs Disproportionate stratification

Proportionate sampling is used 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. Disproportionate sampling is an approach to stratification in situations where a particular strata represents a very small proportion of the population, and so testing them proportionately might not provide valid results.

For example, if a test is set up using a thousand subjects, and one percent of the target population is over sixty, then a proportionate sample would include only ten test subjects over sixty. But while the test population as a whole might be large enough to draw reliable conclusions about the impact of the drug, the small sample in that age group would prevent reliable conclusions about its impact on them. Perhaps the researchers might be particularly concerned about the effect of their drug on those reaching retirement, or just want to make sure that each strata is properly tested. In that case they could take a disproportionate sample from the over sixty group – say one hundred subjects – and then manipulate their data so that those subjects’ results only had a one percent impact on the overall conclusions.

Potential test subjects for a strata are identified, and those to be used in the trial are picked at random from that group. Whatever search tool you have used to identify possible test subjects, you can use your data & software to randomise your choices.

Other fields that use this definition for stratification include the social sciences, where people are often sorted into groups by rank, caste, or another social status. For example, in England, you are born into a specific social ranking (e.g. Royal, upper class, middle class). Similarly, “Socioeconomic status” has a low-income level on the bottom of a hierarchy and upper-income level at the top. It is possible in some cases to move up or down the social ladder.