Stratification is a process of arranging or classifying something into disparate groups. Sampling is a process of taking samples from a larger population or events for analysis. It can be random or systematic sampling. In statistics, it is used to create a subgroup from a larger group based on a pre-decided number of observations or characteristics to proceed with the analysis being performed.
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Simplified Versus Stratified Random Sampling
A simple random sampling involves a sample of subjects from the population. In this, the subjects are chosen randomly from the existing population and are selected for the sample. This process of selecting random subjects for research or analysis is a fair representation of the population. However, when it comes to the samples of a widely varied population, stratified randomization sampling is preferable.
When the population can’t be categorised into subcategories because of too many differences, or lack of information about the population, then researchers or investigators prefer simplified random sampling over stratified randomization sampling.
Stratified randomization is a subcategory of stratified sampling. It is a process of sampling the complete population being studied into subgroups, considering the same traits, or peculiarities, or attributes, like economic status or level of education, known as strata. This leads to simple random sampling. In simple random sampling, researchers unbiasedly select each attribute within the strata or subgroup during the process of sampling. This selection can be done at any stage of the process randomly.
Need for Patient Stratification in Drug Development
The drug discovery paradigm is not only expensive but time taking. After the Covid-19 pandemic, people realise the need for change in the world of drug testing and development. Lack of efficiency in the procedure is one of the major contributors to the overall failure rate of drug testing and developments. There are two primary reasons for the efficacy failure.
The first one is that the researcher did not achieve the required pharmacology. The second one is the mechanism targeted by the researching agent did not essentially contribute to the disease in the tested subject population.
The second issue occurs mostly because of an inadequate study of power stemming from subject heterogeneity. If the subcategory of diseased subjects driven by the procedure that is likely to respond to the drug can be recognised and gets selected before initiating the enrollment, then it will lead to improvement in response rate. It will lead to a minimisation of the number of subjects at risk of side effects, as well as improve the drug approval percentages.
Stratified Randomization in Clinical Trials
When shared attributes or characteristics vary widely, or exist partially between subgroups of the analysed population, and the variation is enough to require exclusive considerations or clear distinction, then stratified randomization is recommended by the experts.
In clinical investigations, the sampling method needs to be distinguished from cluster sampling. In this, researchers choose a simple random sample of multiple clusters to represent the population, or systematic sampling is done after the stratification process, also known as stratified systematic sampling. It is also known as quota random sampling.
Complications in Clinical Trial
No one has the resources to test a vaccine or drug or treatment on the entire human population, and therefore clinical tests and trials are performed on a limited set of the population that reflects the potential population for the drug in question.
One of the most important statistical and logistical challenges of carrying out clinical trials is ensuring that the data accurately represents the drugs’ potential population. For example, if the drug is being tested for a geriatric population, then the sample must represent that particular population.
Or, if the drug is being tested for kids, the sample must represent that particular population, and so on. Whatever the group of potential patients is, an investigator or analyst wants to ensure that the aim of these clinical trials is to figure out the impact of drugs with utmost accuracy. Patient stratification is the solution for such scenarios.
The importance of patient stratification is clearly valued in clinical trials. It is the practice of categorising people and results by a parameter other than the provided treatment. It is used to confirm the unbiased allocation of subgroups of humans to trial or investigation. It can be age, gender, ethnicity, medical history, or any other demographic parameter.
Patient stratification is the process of segmenting potential patient groups into subgroups, also known as strata, or blocks. Each stratum represents a section of the potential patient population.
Post strata establishment, analysts consider multiple approaches for identifying suitable test subjects.
Stratified Proportionate Sampling
A combination of stratified proportionate sampling and randomized stratification is a method of ensuring that the test population reflects the wider population, without the requirement of further statistical manipulation.
In stratified proportionate sampling, the percentage of the people selected from each stratum is proportionate to the percentage of the population in that strata. For example, if thirty per cent of the likely patients are male, then thirty per cent of tested people would be male. It doesn’t necessarily confirm the validity of results, because there are other different factors. However, it eliminates the requirement for an extra statistical process.
Disproportionate Stratification Sampling
Sometimes, particular strata might represent a smaller proportion of the population. In such scenarios, stratified proportionate sampling may not provide valid results. For instance, a hundred subjects are going for the trial test, and one per cent of the potential population is over fifty, then a proportionate sample would include only one person over fifty.
In such cases, even though as a whole might be a large enough number to figure out a reliable conclusion, a small sample in that group might make the results unreliable. In certain scenarios, the researchers prefer disproportionate stratification sampling, because it helps in determining valid results by not applying proportionate stratification sampling.
Quota vs Convenience
Finding and enrolling the right pool of test subjects is one of the most important factors in the world of drug testing and development. Using the right search tool for accurate genetic data helps to refine the data further. It is a common problem faced by researchers when performing patient stratification.
An easier and inexpensive method to enrol is to take a test sample that is easily available. However, it can primarily undermine the results of the trial. For relevant and accurate research, it is essential to apply stratification, and use the right sophisticated tools to analyse the results.
It is important to choose accuracy over convenience anytime when it comes to human health and welfare.
Stratified Randomization in Data Science
Stratified Randomization is an essential branch of data science. Industries and businesses are finding the application of data science increasingly useful. Therefore, a lot of beginners, as well as professionals are seeking certification, diploma, degree, or even a doctorate in data science.
If you are looking for an internship or your first job in an analytics company or a corporation’s analytics department, then a course in data science might be helpful. The best part is that just like MBA, data science is not limited to any industry. Be it Edtech, Fintech, or Health-tech, data scientists are needed and valued everywhere. It can be a great add-on for many professions.
Stratified random sampling is a sophisticated method used by investigating agents or testers to find a sample population that best represents the entire population being studied. Stratified random sampling is a process of dividing the entire population into homogeneous groups called strata. Simple random sampling differs from stratified randomization sampling. It involves the random selection of data from an entire population, so each possible sample is equally likely to occur.
Also Read: Data Science Life Cycle
Stratified Randomization is a branch of data science that is utilised to make various decisions. upGrad offers multiple courses in data science and business analytics. Many of these courses cover stratified randomization sampling, along with several other important topics. These courses are available for multiple levels. Depending on the convenience and requirement, one can easily opt to do a short course of merely six months, or a detailed course of almost two years.
If you have doubts, then upGrad provides counsellor help and customer support to resolve all your queries. To make it easier, upGrad also offers a no-cost EMI option with most of the courses, as well as job assistance. Join today for a better tomorrow!
- For established characteristics that influence prognosis or treatment responsiveness, stratified randomization prevents imbalance between treatment groups. As a result, stratification may reduce type I error and increase power in short trials (under 400 patients), but only if the stratification factors have a significant impact on prognosis. - Stratification can be used to ensure that subgroups of participants are randomly assigned to each experimental condition. Gender, age, and other demographic data may be used to accomplish this. Stratification can be used to eliminate confounding variables (variables not studied by the researcher), making it easier to find and evaluate correlations between variables in the study. - Strata should be kept to a bare minimum. It is typically advised that between one and five factors (i.e. randomization variables) be used, with each component having between two and four levels. Typically, one or two stratification criteria are recommended. There is no requirement that the number of patients in each tier be equal.
When do you stratify randomization?
- For active control equivalence trials, stratification has a significant impact on sample size, but not for superiority trials. Theoretical advantages include making subgroup analysis and interim analysis easier. Although the ideal number of strata is uncertain, experts say that it should be kept low.
- Modest studies in which treatment results may be altered by known clinical characteristics with a major effect on prognosis, large trials with interim analyses planned with small numbers of patients, and trials aimed to prove the equivalence of two medicines require stratified randomization.
What is the purpose of stratification in clinical trials?<br />
- For example, in a fitness research wherein age and gender were believed to impact the outcomes, the confounding variable may be used to divide participants into groups. One drawback of this strategy is that it necessitates knowledge of the factors that must be controlled.
How many strata are in stratified randomization?
- Choose the most significant clinical factors rather than attempting to balance all of them. There may be too few patients in each stratum if there are too many strata. In the worst-case scenario, each layer could have only one — or even no — patient.
- For established characteristics that influence prognosis or treatment responsiveness, stratified randomization prevents imbalance between treatment groups. As a result, stratification may reduce type I error and increase power in short trials (under 400 patients), but only if the stratification factors have a significant impact on prognosis.
- Stratification can be used to ensure that subgroups of participants are randomly assigned to each experimental condition. Gender, age, and other demographic data may be used to accomplish this. Stratification can be used to eliminate confounding variables (variables not studied by the researcher), making it easier to find and evaluate correlations between variables in the study.
- Strata should be kept to a bare minimum. It is typically advised that between one and five factors (i.e. randomization variables) be used, with each component having between two and four levels. Typically, one or two stratification criteria are recommended. There is no requirement that the number of patients in each tier be equal.