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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.
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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.
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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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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 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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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.
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Sample size calculation based on precision for pilot sequential multiple assignment randomized trial (SMART).

Xiaoxi Yan1, Palash Ghosh1,2, Bibhas Chakraborty1,3,4

  • 1Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore.

Biometrical Journal. Biometrische Zeitschrift
|June 13, 2020
PubMed
Summary
This summary is machine-generated.

Pilot sequential multiple assignment randomized trials (SMART) need precise sample size calculations. This study introduces a precision-based approach to ensure pilot SMART studies yield informative results for future research.

Keywords:
adaptive interventionsdynamic treatment regimesprecisionsample sizesequential multiple assignment randomized trials

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Health Services Research

Background:

  • Dynamic treatment regimes (DTRs) are crucial for personalized medicine but are less researched.
  • Pilot sequential multiple assignment randomized trials (SMART) are essential for DTR development.
  • Determining an adequately small yet informative sample size for pilot SMART is a key challenge.

Purpose of the Study:

  • To develop a precision-based sample size calculation method for pilot SMART studies.
  • To ensure pilot studies yield meaningful data for future full-scale SMART trials.
  • To provide sample size guidance for two-stage SMART designs and diverse outcome types.

Main Methods:

  • Developed a precision-based approach for sample size calculation.
  • Focused on confining the marginal mean outcome of a DTR within a prespecified margin of error.
  • Derived sample size formulas for two-stage SMART designs.

Main Results:

  • The proposed method allows for precise sample size determination in pilot SMART studies.
  • Calculations are adaptable to various common outcome types.
  • The approach ensures pilot studies are meaningfully informative despite smaller sample sizes.

Conclusions:

  • A precision-based approach offers a robust method for sample size calculation in pilot SMART studies.
  • This methodology supports efficient DTR development by ensuring informative pilot trials.
  • The findings are applicable to two-stage SMART designs and various outcome measures.