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https://github.com/PharmCat/ClinicalTrialUtilities.jl
Clinical Trial related calculation: descriptive statistics, power and sample size calculation, randomization.
https://github.com/PharmCat/ClinicalTrialUtilities.jl
clinical-trials julia sample-size-calculation
Last synced: about 2 months ago
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Clinical Trial related calculation: descriptive statistics, power and sample size calculation, randomization.
- Host: GitHub
- URL: https://github.com/PharmCat/ClinicalTrialUtilities.jl
- Owner: PharmCat
- License: mit
- Created: 2019-01-18T17:20:45.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-04-24T16:12:15.000Z (over 1 year ago)
- Last Synced: 2024-04-26T03:03:00.468Z (5 months ago)
- Topics: clinical-trials, julia, sample-size-calculation
- Language: Julia
- Homepage: https://pharmcat.github.io/ClinicalTrialUtilities.jl/
- Size: 2.08 MB
- Stars: 18
- Watchers: 4
- Forks: 5
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Changelog: change.log
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-julia - Clinical Trial related calculation: descriptive statistics, power and sample size calculation, power simulations, confidence interval, pharmacokinetics / pharmacodynamics parameters calculation
README
# ClinicalTrialUtilities
Clinical trial related calculation: power and sample size calculation, randomization. This program comes with absolutely no warranty. No liability is accepted for any loss and risk to public health resulting from use of this software.
[](https://codecov.io/gh/PharmCat/ClinicalTrialUtilities.jl)
[](https://pharmcat.github.io/ClinicalTrialUtilities.jl/dev/)
### Installation
```
using Pkg; Pkg.add("ClinicalTrialUtilities");
```
### Main features
- Clinical trial sample size calculation
- Power calculation
- Randomization
### Examples
#### SampleSize
**NB! Hypothesis types:**
- :ea - Equality: two-sided;
- :ei - Equivalencens: two one-sided hypothesis (TOST);
- :ns - Non-Inferiority / Superiority: one-sided hypothesis, for some cases you should use two-sided hypothesis for Non-Inferiority/Superiority, you can use alpha/2 for this;
```
#Sample size for one proportion equality
ctsamplen(param=:prop, type=:ea, group=:one, a=0.3, b=0.5)
#Equivalence for two means
ctsamplen(param=:mean, type=:ei, group=:two, diff=0.3, sd=1, a=0.3, b=0.5)
#Odd ratio non-inferiority
ctsamplen(param=:or, type=:ns, diff=-0.1, a=0.3, b=0.5, k=2)
#Odd ratio equality
ctsamplen(param=:or, type=:ea, a=0.3, b=0.5, k=2)
```
#### Bioequivalence sample size
```
besamplen(alpha=0.05, theta1=0.8, theta2=1.25, theta0=0.95, cv=0.15, method=:owenq)
besamplen(cv=0.20, method=:nct)
besamplen(cv=0.347, design=:parallel)
besamplen(cv=0.40)
besamplen(cv=0.347, design=:d2x2x4, method=:nct)
```
#### Power
```
ctpower(param=:mean, type=:ea, group=:one, a=1.5, b=2, sd=1,n=32, alpha=0.05)
```
#### Bioequivalence power
```
#2x2 design, default method - OwensQ
bepower(alpha=0.05, logscale=true, theta1=0.8, theta2=1.25, theta0=0.95, cv=0.2, n=20, design=:d2x2, method=:owenq)
#Same
bepower(alpha=0.05, cv=0.2, n=20, design=:d2x2)
#Bioequivalence power for cv 14%, 21 subjects, default OwensQ method, logscale false
bepower(alpha=0.1, logscale=false, theta1=-0.1, theta2=0.1, theta0=0, cv=0.14, n=21)
#Bioequivalence power for cv 14%, 21 subjects, shifted method, logscale false
bepower(alpha=0.1, logscale=false, theta1=-0.1, theta2=0.1, theta0=0, cv=0.14, n=21, method=:shifted)
#Simple notations
bepower(cv=0.4, n=35, design=:d2x4x4)
bepower(cv=0.14, n=21)
```
#### Bioequivalence CV from CI
```
cvfromci(;alpha = 0.05, theta1 = 0.9, theta2 = 1.25, n=30, design=:d2x2x4)
```
#### Polled CV
```
data = DataFrame(cv = Float64[], df = Int[])
push!(data, (0.12, 12))
push!(data, (0.2, 20))
push!(data, (0.25, 30))
pooledcv(data; cv=:cv, df=:df, alpha=0.05, returncv=true)
pooledcv([0.12, 0.2, 0.25], [14, 22, 32], [:d2x2, :d2x2, :d2x2])
```
#### Randomization
```
using DataFrames, ClinicalTrialUtilities
rt = ClinicalTrialUtilities.randomtable(;blocksize = 4, subject = 32, group = 2, ratio = [1,1], grseq = ["TR", "RT"], seed = 36434654652452)
```
#### Confidence Intervals
Proportion CI moved to [MetidaFreq.jl](https://github.com/PharmCat/MetidaFreq.jl)
#### NCA
NCA moved to [MetidaNCA.jl](https://github.com/PharmCat/MetidaNCA.jl)
### Copyrights
Clinical Trial Utilities
Copyright © 2019 Vladimir Arnautov aka PharmCat ([email protected])
If you want to check and get R code for power/sample size estimation, you can find examples here: http://powerandsamplesize.com/Calculators/