Desired Validity Coefficient
This Calculator using Fleiss Formula is designed to help users determine the number of repeated measurements needed to achieve a desired validity coefficient for a given intraclass correlation coefficient (ICC). This tool is useful for researchers and practitioners who need to ensure their measurements are reliable and valid.
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Sample Size for MOE
This calculator helps researchers determine the necessary sample size to achieve their desired precision, which is critical for planning studies and ensuring statistically valid results. Adequate sample size calculation is also essential for justifying the study design to peer reviewers and ensuring the reliability and validity of the findings.
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Minimum Detectable Effect (MDE) Calculators for Linear Regression: Optimizing number of subjects (Calculator LIN-N)
The Simple Linear Regression (SLR) Repeated Measures of Exposures Calculator (LIN-N) with Measurement Error is designed to help users interactively optimize for number of subjects (the “sample size”) required to achieve a desired minimum detectable effect (MDE) size in a linear regression model given a defined number of samples (M) per subject in a study.
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Minimum Detectable Effect (MDE) Calculators for Linear Regression: Optimizing number of samples per individual (Calculator LIN-M)
The Simple Linear Regression (SLR) Repeated Measures of Exposures Calculator (LIN-M) with Measurement Error is designed to help users interactively optimize for number of repeat measures of exposure (m) required to achieve a desired minimum detectable effect (MDE) size in a linear regression model and given a defined number of subjects in a study.
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Power and Bias Calculator for Logistic Regression (LOGIT-PB)
The Power and Bias Calculator for Logistic Regression (LOGIT-PB) calculator is designed to help users interactively examine the sensitivity of power and bias of simple logistic regression model to errors in exposure estimates (covariate). It uses simulated data to provide insights into how different factors influence the outcomes of logistic regression with classical additive measurement errors in the exposure estimates.
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LINEAR-CM: Linear Model with Multiplicative Measurement Error
The LIN-CM Calculator is a Shiny-based R application designed to simulate and analyze measurement error effects in a linear regression model with classical multiplicative measurement error. This tool allows users to explore how measurement error impacts estimated regression coefficients, bias, and statistical power.
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LOGIT-CM: Logistic Model with Multiplicative Measurement Error
The LOGIT-CM Calculator is an R Shiny application designed to simulate and analyze measurement error effects in a logistic regression model with classical multiplicative measurement error. This tool helps users understand how measurement error impacts estimated odds ratios, bias, and statistical power in logistic regression models.
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