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Related Concept Videos

Sample Size Calculation01:19

Sample Size Calculation

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Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
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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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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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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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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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Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

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Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
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Updated: May 23, 2025

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
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Sample-size determination for decentralized clinical trials.

Feng Tian1, Ruitao Lin1, Suyu Liu1

  • 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

International Journal of Epidemiology
|May 20, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new sample-size calculation method for decentralized clinical trials (DCTs), addressing a gap in current research. The method accurately determines sample sizes for various DCT designs, improving study power and precision.

Keywords:
decentralized clinical trialsdigital health technologyoffsite patientssample size calculationstudy power

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Area of Science:

  • Clinical Trials Methodology
  • Epidemiology
  • Drug Development

Background:

  • Decentralized clinical trials (DCTs) are gaining prominence in epidemiology and drug development.
  • Determining appropriate sample size is crucial for DCT design but lacks comprehensive guidance.
  • This paper addresses this gap by proposing a novel sample-size calculation method for DCTs.

Purpose of the Study:

  • To propose a robust and accurate sample-size calculation method specifically for decentralized clinical trials.
  • To provide a tool that accounts for data heterogeneity between onsite and offsite sources in DCTs.

Main Methods:

  • Developed a sample-size calculation method using a weighted z-test to handle variance and mean differences in onsite and offsite data.
  • Derived closed-form sample-size formulas for cross-sectional, longitudinal, and cluster study designs.
  • Validated the method using examples from cardiovascular disease and pain-management trials.

Main Results:

  • The proposed method provides accurate and robust sample-size determination for DCTs under various conditions (effect sizes, ICCs, variances, patient ratios).
  • Demonstrated superior precision and power preservation compared to conventional sample-size formulas for traditional trials.
  • Numerical studies confirmed the method's validity across diverse scenarios.

Conclusions:

  • The developed method offers an accurate, easy-to-use tool for sample-size calculation in DCTs.
  • The approach is applicable to both cross-sectional and longitudinal/cluster trial designs.
  • User-friendly software supports the implementation of this sample-size determination method.