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Related Concept Videos

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
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Random Sampling Method01:09

Random 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. Data are the result of sampling from a 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. Among the various sampling methods used by...
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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

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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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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.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Systematic Sampling Method01:17

Systematic 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. Data are the result of sampling from a 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.
Systematic sampling is one of the simplest methods...
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Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations.

Oleksandr Sverdlov1, Yevgen Ryeznik2, Volodymyr Anisimov3

  • 1Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA. alex.sverdlov@novartis.com.

BMC Medical Research Methodology
|February 28, 2024
PubMed
Summary

Dynamic balancing randomization (DBR) offers a superior balance-randomness tradeoff for multi-center randomized controlled trials (RCTs) with competitive patient recruitment. Carefully chosen thresholds enhance DBR

Keywords:
Allocation randomnessMaximum tolerated imbalanceMulti-center clinical trialPoisson-gamma modelRecruitment time

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Health Services Research

Background:

  • Multi-center randomized controlled trials (RCTs) require careful design considerations, including sample size, center selection, and participant recruitment strategies.
  • Sequential randomization methods are crucial for multi-center RCTs, with or without stratification factors.
  • This study focuses on assessing randomization methods under a competitive patient recruitment policy.

Purpose of the Study:

  • To systematically evaluate various randomization methods for multi-center 1:1 RCTs.
  • To assess these methods within the context of a competitive patient recruitment process.
  • To compare the performance of different stratification and balancing techniques.

Main Methods:

  • A Poisson-gamma model was used to simulate the patient recruitment process.
  • Sixteen distinct randomization methods were investigated, including unstratified, region-stratified, center-stratified, and dynamic balancing randomization (DBR).
  • Monte Carlo simulations assessed statistical properties like recruitment time, treatment imbalance, and allocation efficiency.

Main Results:

  • Maximum tolerated imbalance (MTI) methods (e.g., big stick, Ehrenfest urn) outperform conventional permuted block designs (PBD) in balance-randomness tradeoff.
  • DBR effectively controls imbalance across trial, region, and center levels while preserving randomization.
  • Increasing study centers accelerates recruitment but can increase center-level imbalances; larger block sizes or MTI thresholds improve the randomness-balance tradeoff.

Conclusions:

  • The selection of an appropriate randomization method is critical for multi-center RCT design.
  • Dynamic balancing randomization (DBR) emerges as a highly effective strategy for trials with competitive patient recruitment.
  • Optimizing MTI thresholds within DBR is recommended for robust trial design.