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Related Concept Videos

Stratified Sampling Method01:16

Stratified Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Related Experiment Video

Updated: Mar 11, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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An Intelligible Risk Stratification Model Based on Pairwise and Size Constrained Kmeans.

Longfei Han, Senlin Luo, Huaiqing Wang

    IEEE Journal of Biomedical and Health Informatics
    |December 4, 2016
    PubMed
    Summary

    A new Pairwise and Size Constrained K-means (PSCKmeans) method effectively stratifies individuals into risk tiers for disease prevention. This approach improves diabetes risk assessment by considering similarity and balancing cluster sizes for better clinical intervention.

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    Area of Science:

    • Computational biology
    • Data science
    • Public health

    Background:

    • Clinical disease prevention relies on accurate risk stratification.
    • Current risk scores may not reflect individual health conditions or intervention needs.
    • Determining the optimal number of risk tiers is often unclear.

    Purpose of the Study:

    • To develop a novel method for risk stratification that integrates supervised information and size constraints.
    • To improve the identification of high-risk populations based on similarity measurements.
    • To achieve a balanced and feasible stratification solution.

    Main Methods:

    • The Pairwise and Size Constrained K-means (PSCKmeans) algorithm was proposed.
    • It integrates limited supervised information and size constraints for clustering.
    • The method was evaluated on the China Health and Nutrition Survey dataset.

    Main Results:

    • PSCKmeans naturally stratified diabetes risk into four tiers.
    • Achieved 73.8% sensitivity, 85.1% specificity, and a 0.95 ratio of minimum to expected on testing data.
    • Outperformed eight previous semi-supervised clustering methods.

    Conclusions:

    • Semi-supervised clustering with multiple constraints enhances domain-relevant partitioning.
    • The PSCKmeans model provides a valuable tool for clinical disease risk stratification.
    • This method facilitates targeted interventions for individuals with similar health conditions.