Dev

Project.toml

@@ -1,7 +1,7 @@
 authors = ["Vladimir Arnautov (mail@pharmcat.net)"]
 name = "ClinicalTrialUtilities"
 uuid = "535c2557-d7d0-564d-8ff9-4ae146c18cfe"
-version = "0.2.5"
+version = "0.2.6"
 
 [deps]
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
@@ -24,13 +24,13 @@ Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 test = ["CSV", "Test", "Plots"]
 
 [compat]
-julia = "1.0, 1.1, 1.2, 1.3"
+julia = "1.0, 1.1, 1.2, 1.3, 1.4"
 Distributions = "0.16, 0.17, 0.18, 0.19, 0.20, 0.21, 0.22, 0.23"
 StatsBase = "0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.30, 0.31, 0.32, 0.33"
 DataFrames = "0.19, 0.20"
 QuadGK = "2.0, 2.1, 2.2, 2.3"
 SpecialFunctions = "0.8, 0.9, 0.10"
 Roots = "0.7, 0.8, 1.0"
-CSV = "0.5"
-RecipesBase = "0.7, 0.8"
+CSV = "0.5, 0.6"
+RecipesBase = "0.7, 0.8, 1.0"
 Reexport = "0.1, 0.2"

README.md

@@ -1,6 +1,6 @@
 # ClinicalTrialUtilities
 
- Clinical trial related calculation: descriptive statistics, power and sample size calculation, power simulations, confidence interval, pharmacokinetics/pharmacodynamics parameters calculation.
+ Clinical trial related calculation: descriptive statistics, power and sample size calculation, power simulations, confidence interval, pharmacokinetics/pharmacodynamics parameters calculation. 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.
 
 [![Build Status](https://travis-ci.com/PharmCat/ClinicalTrialUtilities.jl.svg?branch=master)](https://travis-ci.com/PharmCat/ClinicalTrialUtilities.jl)
 [![Build status](https://ci.appveyor.com/api/projects/status/35f8b5vq259sbssg?svg=true)](https://ci.appveyor.com/project/PharmCat/clinicaltrialutilities-jl)
@@ -17,17 +17,26 @@ The package is designed to perform calculations related to the planning and anal
 using Pkg; Pkg.add("ClinicalTrialUtilities");
 ```
 
-### <a name="Usage">Usage</a>
+### <a name="Features">Main features</a>
+
+- Clinical trial sample size calculation
+- Power calculation
+- Confidence Interval calculation
+- NCA Pharmacokinetics parameters calculation
+- Descriptive statistics and frequencies
+- Randomization
+
+
+### <a name="Examples">Examples</a>
+
+#### SampleSize
 
 **NB! Hypothesis types:**
 
 - :ea - Equality: two-sided;
 - :ei - Equivalencens: two one-sided hypothesis;
 - :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;
 
-### <a name="Examples">Examples</a>
-
-#### SampleSize
 ```
 #Sample size for one proportion equality
 ctsamplen(param=:prop, type=:ea, group=:one, a=0.3, b=0.5)
@@ -93,6 +102,37 @@ pooledcv([0.12, 0.2, 0.25], [14, 22, 32], [:d2x2, :d2x2, :d2x2])
 
 ```
 
+#### Confidence Intervals
+```
+using  ClinicalTrialUtilities
+ci = propci(38, 100, alpha=0.05, method=:cp)
+
+ci = orpropci(30, 100, 40, 90; alpha=0.05, method=:mn)
+
+ci = diffpropci(30, 100, 40, 90; alpha=0.05, method=:wald)
+
+ci = meanci(30, 10, 30, alpha = 0.05, method=:norm)
+```
+
+#### NCA
+```
+using CSV, DataFrames, ClinicalTrialUtilities
+pkdata2 = CSV.File("pkdata.csv") |> DataFrame
+pkds    = pkimport(pkdata2, [:Subject, :Formulation]; time = :Time, conc = :Concentration)
+pk      = nca!(pkds)
+ncadf   = DataFrame(pk; unst = true)
+ds      = ClinicalTrialUtilities.descriptive(ncadf, stats = [:n, :mean, :sd], sort = [:Formulation])
+dsdf    = ClinicalTrialUtilities.DataFrame(ds; unst = true)
+
+```
+
+#### Randomization
+```
+using DataFrames, ClinicalTrialUtilities
+rt = ClinicalTrialUtilities.randomtable(;blocksize = 4, subject = 32, group = 2, ratio = [1,1], grseq = ["TR", "RT"], seed = 36434654652452)
+```
+
+
 ### <a name="Copyrights">Copyrights</a>
 
 Clinical Trial Utilities

cange.log

@@ -1,3 +1,17 @@
+v0.2.6
+
+  - findfirst
+  - Documents update
+  - export applyncarule!
+  - code cleaning
+  - improve coverage
+  - plots kwargs
+  - Proportion meta-analysis
+  - Tests restruct
+  - Hypothesis test for simulation
+  - Deps update
+  - Docs
+
 v0.2.5
 
   - SpecialFunctions 0.10

docs/make.jl

@@ -14,7 +14,8 @@ makedocs(
         "Descriptive statistics" => "ds.md",
         "NCA" => ["NCA" => "nca.md",
         "Pharmakokinetics" => "pk.md",
-        "Pharmacodynamics" => "pd.md"],
+        "Pharmacodynamics" => "pd.md",
+        "PK Plots" => "pkplot.md"],
         "Randomization" => "random.md",
         "Simulations" => "sim.md",
         "Utilities" => "utils.md",

docs/src/ds.md

@@ -6,3 +6,18 @@ Calculation descriptive statistics by groups.
 ```@docs
 ClinicalTrialUtilities.descriptive
 ```
+
+### freque
+```@docs
+ClinicalTrialUtilities.freque
+```
+
+### contab
+```@docs
+ClinicalTrialUtilities.contab
+```
+
+### metaprop
+```@docs
+ClinicalTrialUtilities.metaprop
+```

docs/src/pkplot.md

@@ -0,0 +1,6 @@
+### PK plots
+
+#### pkplot
+```@docs
+ClinicalTrialUtilities.pkplot
+```

src/ClinicalTrialUtilities.jl

@@ -19,15 +19,9 @@ using Distributions, Random, Roots, QuadGK, RecipesBase, Reexport
 @reexport using StatsBase
 
 import SpecialFunctions
-import Base.show
-import Base.showerror
-import Base.getindex
-import Base.length
-import Base.in
-import Base.iterate
-import Base.eltype
+import Base: show, findfirst, getproperty, showerror, getindex, length, in, iterate, eltype, deleteat!, findall
 import StatsBase.confint
-import DataFrames: DataFrame, DataFrames, names!, unstack, deleterows!
+import DataFrames: DataFrame, DataFrames, names!, unstack, deleterows!, rename!
 
 function lgamma(x)
     return SpecialFunctions.logabsgamma(x)[1]
@@ -42,10 +36,15 @@ const PI2INV = 0.5 / π
 include("abstracttypes.jl")
 
 include("design.jl")
-include("hypothesis.jl")
 
 include("proportion.jl")
 
+include("means.jl")
+
+#Confidence interval calculation
+include("ci.jl")
+
+include("hypothesis.jl")
 
 #Owen function calc: owensQ, owensQo, ifun1, owensTint2, owensT, tfn
 include("owensq.jl")
@@ -55,8 +54,6 @@ include("powertost.jl")
 include("powersamplesize.jl")
 #Main sample size and power functions: sampleSize, ctpower, besamplen
 include("samplesize.jl")
-#Confidence interval calculation
-include("ci.jl")
 #Simulations
 include("sim.jl")
 #info function
@@ -101,6 +98,7 @@ pooledcv,
 descriptive,
 freque,
 contab,
+metaprop,
 htmlexport,
 #CI
 confint,
@@ -124,11 +122,13 @@ DataFrame,
 ElimRange,
 DoseTime,
 LimitRule,
+applyncarule!,
 keldata,
 # Simulation
 ctsim,
 #Plots
-plot
+pkplot,
+pkplot!
 
 
 

src/ci.jl

@@ -880,7 +880,7 @@ function diffmcnmwaldci(a::Int, b::Int, c::Int, d::Int; alpha = 0.05)
     p1       = (a + b)/n
     p2       = (a + c)/n
     estimate = p1 - p2
-    se       = ((a+d)*(b+c)+4*b*c)/n^3
+    se       = sqrt(((a+d)*(b+c)+4*b*c)/n^3)
     z        = quantile(ZDIST, 1 - alpha / 2)
     ConfInt(estimate  - z * se, estimate  + z * se, estimate , alpha)
 end
@@ -889,8 +889,19 @@ function diffmcnmwaldccci(a::Int, b::Int, c::Int, d::Int; alpha = 0.05)
     p1       = (a + b)/n
     p2       = (a + c)/n
     estimate = p1 - p2
-    se       = ((a+d)*(b+c)+4*b*c)/n^3
+    se       = sqrt(((a+d)*(b+c)+4*b*c)/n^3)
     z        = quantile(ZDIST, 1 - alpha / 2)
     ConfInt(estimate  - z * se - 1 / n, estimate  + z * se + 1 / n, estimate , alpha)
+end
 
+#=
+function diffmcnmci(a::Int, b::Int, c::Int, d::Int; alpha = 0.05)
+    n        = a + b + c + d
+    p1       = (a + b)/n
+    p2       = (a + c)/n
+    estimate = p1 - p2
+    se       = sqrt((p1 * (1 - p1)  + p2 * (1 - p2)  - 2 * (a * d - b * c) / n^2) / n)
+    z        = quantile(ZDIST, 1 - alpha / 2)
+    ConfInt(estimate  - z * se, estimate  + z * se, estimate , alpha)
 end
+=#

src/dataset.jl

@@ -1,12 +1,81 @@
 struct DataSet{T <: AbstractData}
     data::Vector{T}
 end
+
+#!!!
+function in(a::Dict, b::Dict)
+    k = collect(keys(a))
+    if any(x -> x  ∉  collect(keys(b)), k) return false end
+    for i = 1:length(a)
+        if a[k[i]] != b[k[i]] return false end
+    end
+    return true
+end
+function in(a::Pair, b::Dict)
+    if a[1]  ∉  collect(keys(b)) return false end
+    if a[2] != b[a[1]] return false end
+    return true
+end
+
+#length
+function Base.length(a::DataSet)::Int
+    return length(a.data)
+end
+#Iterate
 function Base.iterate(iter::DataSet)
     return Base.iterate(iter.data)
 end
 function Base.iterate(iter::DataSet, i::Int)
     return Base.iterate(iter.data, i)
 end
+#Eltype
 function Base.eltype(obj::DataSet)
     return eltype(obj.data)
 end
+#Getindex
+function Base.getindex(a::DataSet{T}, i::Int)::T where T
+    return a.data[i]
+end
+function Base.getindex(a::DataSet{T}, d::Pair)::T where T
+    for i = 1:length(a)
+        if d ∈ a[i].sort return a[i] end
+    end
+    throw(ArgumentError("Element not found!"))
+end
+function Base.getindex(a::DataSet{T}, d::Dict)::T where T
+    for i = 1:length(a)
+        if d ∈ a[i].sort return a[i] end
+    end
+    throw(ArgumentError("Element not found!"))
+end
+function Base.getindex(a::DataSet{T}, d::Tuple{Vararg{Pair}})::T where T
+    return a[Dict(d)]
+end
+
+
+function Base.getindex(a::DataSet{T}, i::Int, s::Symbol)::Real where T
+    return a.data[i].result[s]
+end
+#
+function Base.findall(a::DataSet{T}, sort::Dict) where T
+    inds = Vector{Int}(undef, 0)
+    for i = 1:length(a)
+        if sort ∈ a.data[i].sort push!(inds, i) end
+    end
+    inds
+end
+#
+function Base.deleteat!(a::DataSet{T}, i::Int) where T
+    deleteat!(a.data, i)
+    return a
+end
+
+function Base.deleteat!(a::DataSet{T}, inds::AbstractArray) where T
+    deleteat!(a.data, inds)
+    return a
+end
+
+function Base.deleteat!(a::DataSet{T}, inds::Dict) where T
+    deleteat!(a.data, findall(a, inds))
+    return a
+end

src/descriptives.jl

@@ -39,30 +39,6 @@ function Base.show(io::IO, obj::DataSet{Descriptive})
         println(io, "____________________")
     end
 end
-function Base.getindex(a::DataSet{T}, i::Int)::T where T
-    return a.data[i]
-end
-function Base.getindex(a::DataSet{T}, i::Int, s::Symbol)::Real where T
-    return a.data[i].result[s]
-end
-function Base.getindex(a::DataSet{T}, d::Pair)::T where T
-    for i = 1:length(a)
-        if d ∈ a[i].sort return a[i] end
-    end
-    throw(ArgumentError("Element not found!"))
-end
-function Base.getindex(a::DataSet{T}, d::Dict)::T where T
-    for i = 1:length(a)
-        if d ∈ a[i].sort return a[i] end
-    end
-    throw(ArgumentError("Element not found!"))
-end
-function Base.getindex(a::DataSet{T}, d::Tuple{Vararg{Pair}})::T where T
-    return a[Dict(d)]
-end
-function Base.length(a::DataSet{T})::Int where T <: AbstractData
-    return length(a.data)
-end
 """
     descriptive(data::DataFrame;
         sort::Union{Symbol, Array{T,1}} = Array{Symbol,1}(undef,0),