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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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Risk Factors & Birth Outcomes Explorer

This tool is designed to allow the user to explore the strength of evidence between thousands of combinations of Risk Factors and Birth Outcomes. These strength of evidence scores have been generated by experts in the field of studying birth outcomes in epidemiology studies. This resource will be a valuable tool to help researchers identify risk factors that should be the focus of additional research and which ones should be considered as important confounders in their study design.

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