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Related Concept Videos

Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Sample Size Calculation01:19

Sample Size Calculation

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...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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.
William S. Gosset (1876–1937) of the Guinness...
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

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:
What are Estimates?01:06

What are Estimates?

It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such as the mean,...

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Estimating required information size by quantifying diversity in random-effects model meta-analyses.

Jørn Wetterslev1, Kristian Thorlund, Jesper Brok

  • 1Copenhagen Trial Unit, Centre for Clinical Intervention Research, Department 3344, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej 9, DK-2100 Copenhagen Ø, Denmark. Wetterslev@ctu.rh.dk

BMC Medical Research Methodology
|January 1, 2010
PubMed
Summary
This summary is machine-generated.

Calculating the necessary information size for meta-analyses is crucial. A new measure, diversity (D2), better accounts for model variation than inconsistency (I2) in random-effects meta-analyses.

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Area of Science:

  • Biostatistics
  • Medical Research Methodology

Background:

  • Meta-analyses require adequate information size to reliably detect intervention effects.
  • Current methods for calculating information size may not fully account for model variance.

Purpose of the Study:

  • To derive an adjusting factor for required information size in random-effects meta-analyses.
  • To introduce and evaluate a new measure of diversity (D2) for meta-analyses.

Main Methods:

  • Derivation of an adjusting factor for information size calculations in random-effects models.
  • Development of a diversity measure (D2) representing relative variance reduction.
  • Comparison of D2 with inconsistency (I2) using simulations and clinical examples.

Main Results:

  • A new measure, diversity (D2), is introduced, quantifying relative variance reduction.
  • D2 accounts for total model variance, unlike inconsistency (I2), which may underestimate required information size.
  • Simulations and examples demonstrate D2 is equal to or greater than I2 (D2 >= I2) in all meta-analyses.

Conclusions:

  • Diversity (D2) is a superior alternative to inconsistency (I2) for assessing model variation in random-effects meta-analyses.
  • D2 can effectively adjust the required information size for random-effects meta-analyses.