In healthcare, Stratifying Patients involves the randomization of pre-trial data and covariate analysis on post-trial data. Stratifying Patients is usually more effective if there is a large sample of patients as creating too many strata for small samples could achieve the opposite effect. In extreme cases of stratifying small sample sizes, 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 stratum in an attempt to ensure that no bias, deliberate or accidental, interferes with the representative nature of the patient sample.


Benefits of Stratifying Patients

Some of the benefits of patient stratification include:


  • It expands the ability to identify the most critical candidates for Care Management programs

  • It provides the ability to tailor criteria to meet unique program requests

  • It reduces resource demands to identify individuals for Care Management programs

  • It leads to increased timeliness of identifying individuals for Care Management programs

  • It identifies closed-loop capabilities to determine the most impactful algorithms for outcomes improvement


Note that early patient stratification is critical because it enables effective and personalized drug discovery and development in clinical trials and pharmaceutical research endeavors.

Stratifying Patients Disease relevance

It is important to acknowledge the potential value of animal models of disease to identify a promising target for future exploration in humans. That is, animal models of disease can lead to new ideas that can be explored in humans. It is a much more demanding application, however, to use the animal model to make decisions about the predicted therapeutic efficacy of an investigational drug. Moreover, in order to confidently employ even a simple, positive vs negative response, scientists must have a reasonable idea of how that preclinical change predicts the clinical response. For many reasons, that is very hard to do. The second consideration for the lack of demonstrable Phase II efficacy is that the biological target or mechanism is not relevant to the disease under study. If the target is not relevant in humans, then how was it selected? In general, the rationale for selecting that target for prosecution is multifaceted, including input from human genetics, observational clinical studies like a measure related to the target is abnormal, and animal experiments. In most therapeutic areas, however, concrete decisions regarding the pursuit of a specific programme often hinge on animal models of disease. Examples of animal models of the disease include Apo E knockout mouse for atherosclerosis; the rat conditioned avoidance behavior model of schizophrenia; or the mouse tumor transplant model. From these models, partial correction of the abnormality may be achieved by knocking out or overexpressing a specific gene (biological target) or through the use of a pharmacological tool. A positive outcome, signaling potential human efficacy with a pharmacological agent is a ‘go forward’ result, ie, the project team is armed with evidence that the exerted pharmacology will yield a salutary outcome in people. Yet the problem with animal models of disease is that they very often do not reflect the human circumstance. If they did, then one would expect that success rates in Phase II would be significantly higher than 20%. Intuitively, this is not surprising, as the physiology and associated pharmacology of the test species is likely to be very different from that of the human despite similar and apparent clinical manifestations. Thus, a positive animal model result might be due to a target that is relevant to the animals per se, and much less so to humans.

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