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Related Concept Videos

Randomized Experiments01:13

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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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Sample Size Calculation01:19

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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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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Estimating Population Mean with Unknown Standard Deviation01:22

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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One-Way ANOVA: Unequal Sample Sizes01:15

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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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Power and sample size for random coefficient regression models in randomized experiments with monotone missing data.

Nan Hu1, Howard Mackey1, Ronald Thomas2

  • 1Department of Biostatistics, Genentech Inc., San Francisco, CA, USA.

Biometrical Journal. Biometrische Zeitschrift
|February 15, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces new formulas and software for calculating statistical power and sample size in random coefficient regression, addressing limitations in existing methods for complex data structures. These tools improve study design for correlated data, especially with missing values.

Keywords:
growth curvemixed effects modelmixed model with repeated measuresrandom coefficient regression modelstatistical power

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Random coefficient regression (RCR) models are valuable for analyzing correlated data within experimental units.
  • Current power and sample size software for RCR often rely on simplified models that may be inaccurate.
  • Complex RCR models with multiple variance components are increasingly warranted.

Purpose of the Study:

  • To develop accurate variance, power, and sample size formulae for RCR models.
  • To provide user-friendly software (R Shiny app) for applying these formulae.
  • To enhance study design and planning for research involving correlated data, including those with missing data and variable follow-up.

Main Methods:

  • Derivation of novel variance, power, and sample size formulae for RCR models.
  • Development of an interactive R Shiny application for practical implementation.
  • Illustration of methods using real-world data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Main Results:

  • The study presents validated formulae for power and sample size calculations in RCR.
  • The R Shiny app facilitates accessible application of these statistical tools.
  • Analysis of variability drivers informs more efficient study design.

Conclusions:

  • The developed methods and software offer a more accurate approach to power and sample size determination for RCR models.
  • These tools are crucial for robust study design in fields utilizing longitudinal and multilevel data.
  • Improved statistical planning can lead to more reliable research findings, particularly in complex datasets.