KerusCloud® Parameter Converter
User Guide
Section titled “User Guide”This guide documents the KerusCloud® Parameter Converter (KPC) and explains how KPC fits into the KerusCloud® clinical trial simulation workflow.
Purpose and Audience
Section titled “Purpose and Audience”KPC converts distribution parameterizations commonly used in clinical trial simulations (e.g., Beta, Log-Normal, Weibull, Poisson).
This guide is intended for clinical statisticians and clinical trial modelers using KerusCloud®.
KPC matters because reliable parameter conversion reduces the risk of incorrect simulation assumptions and helps clinical teams translate statistical inputs into consistent KerusCloud® model parameters.
Quick Start — Using KPC
Section titled “Quick Start — Using KPC”1. Select the Distribution Type
Section titled “1. Select the Distribution Type”Choose from the Variable Type dropdown.
The interface currently supports:
- Beta
- Exponential
- Log-Normal
- Uniform
- Weibull
- Binomial
- Negative Binomial
- Poisson
2. Select the Parameterization Method
Section titled “2. Select the Parameterization Method”Pick the form that matches your available inputs (e.g., Mean & SD, Alpha & Beta).
3. Enter Numeric Values
Section titled “3. Enter Numeric Values”Fill out the input fields.
- Inline validation will show an error if a field is empty or non-numeric.
- Changing any input automatically clears previously computed results.
4. Click Convert Parameters
Section titled “4. Click Convert Parameters”Results appear in the results panel.
Use the Copy button next to each result to copy values for use in KerusCloud®.
Supported Distributions and Typical Clinical Uses
Section titled “Supported Distributions and Typical Clinical Uses”The converter supports the following distribution types and common conversion patterns:
Convert between:
- Alpha/Beta
- Mean/Sum
- Mean/Standard Deviation
parameterizations.
Exponential
Section titled “Exponential”Convert a rate parameter to a mean.
Log-Normal
Section titled “Log-Normal”Convert between:
- Mean/Standard Deviation
- Median/Multiplicative Standard Deviation
parameterizations.
Uniform
Section titled “Uniform”Convert between:
- Minimum/Maximum
- Mean/Standard Deviation
parameterizations.
Weibull
Section titled “Weibull”Convert between:
- Shape/Median
- Mean/Standard Deviation
parameterizations.
Commonly used for:
- Time-to-event simulations
- Survival analyses
- Dropout simulations
Binomial
Section titled “Binomial”Convert a mean to a probability.
Negative Binomial
Section titled “Negative Binomial”Convert between:
- Mean/Heterogeneity
- Mean/Standard Deviation
- Mean/Variance
parameterizations.
Poisson
Section titled “Poisson”Convert a rate to a mean.
Important Technical Notes
Section titled “Important Technical Notes”Weibull Mean/SD Conversion
Section titled “Weibull Mean/SD Conversion”Implemented using an iterative bisection search to determine the shape parameter:
- Computes the target coefficient of variation as
(standard deviation / mean)². - Uses gamma functions to compute distribution moments.
- Expands search bounds dynamically when needed.
- Runs a bounded iteration loop to test convergence.
- Returns an error if the conversion cannot converge.
Input and Result Behavior
Section titled “Input and Result Behavior”- Changing any parameter clears previous results (prevents stale output).
- Real-time validation flags non-numeric entries.
Copy Behavior
Section titled “Copy Behavior”- Results are copied with full floating-point precision.
Troubleshooting & Known Limitations
Section titled “Troubleshooting & Known Limitations”Weibull Mean/SD May Fail
Section titled “Weibull Mean/SD May Fail”Cause: Coefficient of variation is unrealistic.
Recommendation: Use the Shape & Median parameterization instead.
Log-Normal Constraints
Section titled “Log-Normal Constraints”- Mean and median must be positive.
- Zero or negative values will produce invalid results.