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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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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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Observational Studies01:11

Observational Studies

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Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
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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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Group Design02:01

Group Design

10.0K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Related Experiment Video

Updated: Dec 14, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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Weighted nearest neighbours-based control group selection method for observational studies.

Szabolcs Szekér1,2, Ágnes Vathy-Fogarassy1,2

  • 1Department of Computer Science and Systems Technology, University of Pannonia, Veszprém, Hungary.

Plos One
|July 24, 2020
PubMed
Summary

A new Weighted Nearest Neighbours method offers improved control group selection in observational studies, outperforming traditional propensity score matching by balancing individuals in their original covariate space for better results.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Observational Studies

Background:

  • Propensity score matching is a common method for balancing groups in observational studies.
  • However, it faces criticism for compressing high-dimensional covariate data into a single dimension.
  • This limitation can lead to suboptimal control group selection and potential biases.

Purpose of the Study:

  • To introduce a novel multivariate weighted k-nearest neighbours (k-NN) approach for control group selection.
  • To address the limitations of propensity score matching by operating in the original covariate space.
  • To enhance the balance of control groups in observational research.

Main Methods:

  • A weighted k-NN based control group selection method was developed.
  • Dissimilarities between individuals were calculated using weighted distances in the original covariate space.
  • Weight factors were derived from a logistic regression model predicting treatment assignment.

Main Results:

  • Monte Carlo simulations demonstrated the effectiveness of the proposed method.
  • The Weighted Nearest Neighbours Control Group Selection with Error Minimization method achieved superior balance compared to greedy propensity score matching.
  • The improvement was particularly notable for individuals with fewer descriptive features.

Conclusions:

  • The proposed weighted k-NN method provides a more effective alternative to propensity score matching for control group selection.
  • This approach mitigates the drawbacks of dimensionality reduction inherent in propensity scores.
  • It offers enhanced control group balance, crucial for robust causal inference in observational studies.