Merge pull request #48 from PharmCat/dev

v0.3.1

  - deleteat! fix
  - simulation crush fix
  - Equivalence Hypothesis alpha level fix!

Project.toml

@@ -1,7 +1,7 @@
 authors = ["Vladimir Arnautov ([email protected])"]
 name = "ClinicalTrialUtilities"
 uuid = "535c2557-d7d0-564d-8ff9-4ae146c18cfe"
-version = "0.3.0"
+version = "0.3.1"
 
 [deps]
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"

README.md

@@ -8,9 +8,9 @@
 [![Coverage Status](https://coveralls.io/repos/github/PharmCat/ClinicalTrialUtilities.jl/badge.svg?branch=master)](https://coveralls.io/github/PharmCat/ClinicalTrialUtilities.jl?branch=master)
 [![Latest docs](https://img.shields.io/badge/docs-latest-blue.svg)](https://pharmcat.github.io/ClinicalTrialUtilities.jl/dev/)
 
-## Difference since v0.2.7
+## Difference since v0.3.0
 
-  Changes in Equivalence Hypothesis alpha level! See [here](https://github.com/PharmCat/ClinicalTrialUtilities.jl/issues/46)!
+  Wrong Equivalence Hypothesis alpha level! Use 0.2.7 or 0.3.1 version.
 
 ## Description
 
@@ -38,7 +38,7 @@ using Pkg; Pkg.add("ClinicalTrialUtilities");
 **NB! Hypothesis types:**
 
 - :ea - Equality: two-sided;
-- :ei - Equivalencens: two one-sided hypothesis;
+- :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;
 
 ```

cange.log

@@ -1,3 +1,9 @@
+v0.3.1
+
+  - deleteat! fix
+  - simulation crush fix
+  - Equivalence Hypothesis alpha level fix!
+
 v0.3.0
   - #44 , #45 , #46
   - update test

src/ClinicalTrialUtilities.jl

@@ -30,13 +30,14 @@ catch
     global lgamma(x) = SpecialFunctions.lgamma(x)
 end
 
+#=
 try
     if collect(methods(DataFrames.delete!, (AbstractDataFrame, Any)))[1].file == Symbol("deprecated.jl")
         DataFrames.delete!(df::AbstractDataFrame, inds) = deleterows!(df, inds)
     end
 catch
 end
-
+=#
 
 const ZDIST  = Normal()
 const LOG2   = log(2)
@@ -53,7 +54,10 @@ include("proportion.jl")
 include("means.jl")
 
 include("hypothesis.jl")
-
+#DataSET
+include("dataset.jl")
+#Frequency
+include("freque.jl")
 #Confidence interval calculation
 include("ci.jl")
 
@@ -69,14 +73,11 @@ include("samplesize.jl")
 include("sim.jl")
 #info function
 include("info.jl")
-#DataSET
-include("dataset.jl")
+
 #Descriptive statistics
 include("descriptives.jl")
 #PK
 include("pk.jl")
-#Frequency
-include("freque.jl")
 #Export
 include("export.jl")
 #Randomization

src/ci.jl

@@ -69,9 +69,12 @@ end
 function StatsBase.confint(param::Mean; level = 0.95, method = :default)::ConfInt
     meanci(param.m, param.sd^2, param.n; alpha = 1 - level, method = method)
 end
-function StatsBase.confint(param::DiffMean{true}; level = 0.95, method = :default)::ConfInt where T <: Mean
+function StatsBase.confint(param::DiffMean{true}; level = 0.95, method = :default)::ConfInt
     diffmeanci(param.a.m, param.a.sd^2, param.a.n, param.b.m, param.b.sd^2, param.b.n; alpha = 1 - level, method = method)
 end
+function StatsBase.confint(param::MetaProp; level = 0.95, method = :default)::ConfInt
+    error()
+end
 
 """
 
@@ -109,6 +112,7 @@ function basepval(param::T, hyp::Equality; method = :default, atol = 1E-6) where
     end
     p
 end
+#-------------------------------------------------------------------------------
 """
     propci(x::Int, n::Int; alpha=0.05, method = :default)::ConfInt
 
@@ -190,6 +194,11 @@ function diffpropci(x1::Int, n1::Int, x2::Int, n2::Int; alpha::Real = 0.05, meth
         throw(ArgumentError("Method unknown!"))
     end
 end
+#TEST
+function diffpropci(tab::ConTab{2,2}; alpha::Real = 0.05, method::Symbol = :default)::ConfInt
+    diffpropci(tab.a, tab.a + tab.b, tab.c, tab.c + tab.d; alpha = alpha, method = method)
+end
+
 """
     rrpropci(x1::Int, n1::Int, x2::Int, n2::Int; alpha::Real = 0.05,
         method::Symbol = :default)::ConfInt
@@ -293,7 +302,11 @@ function diffmeanci(m1::Real, s1::Real, n1::Real, m2::Real, s2::Real, n2::Real;
         end
 end
 
-    #-----------------------------PROPORTIONS-----------------------------------
+
+################################################################################
+#                           ATOMIC FUNCTIONS
+################################################################################
+#-----------------------------PROPORTIONS---------------------------------------
 
     #Wilson’s confidence interval for a single proportion, wilson score
     #Wilson, E.B. (1927) Probable inference, the law of succession, and statistical inferenceJ. Amer.Stat. Assoc22, 209–212

src/freque.jl

@@ -119,7 +119,7 @@ function contab(data::DataFrame, sort; row::Symbol, col::Symbol)
     tempdf = DataFrame(row = Vector{eltype(data[!, row])}(undef, 0), col = Vector{eltype(data[!, col])}(undef, 0))
     rename!(tempdf, rcv)
     for si = 1:size(slist, 1)
-        if size(tempdf, 1) > 0 delete!(tempdf, 1:size(tempdf, 1)) end
+        if size(tempdf, 1) > 0 deleterows!(tempdf, 1:size(tempdf, 1)) end
         for i = 1:size(data, 1)
             if data[i, sort] == slist[si, :]
                 push!(tempdf, data[i, rcv])
@@ -160,7 +160,7 @@ function mcnmcontab(data::DataFrame, sort; row::Symbol, col::Symbol)
     tempdf = DataFrame(row = Vector{eltype(data[!, row])}(undef, 0), col = Vector{eltype(data[!, col])}(undef, 0))
     rename!(tempdf, rcv)
     for si = 1:size(slist, 1)
-        if size(tempdf, 1) > 0 delete!(tempdf, 1:size(tempdf, 1)) end
+        if size(tempdf, 1) > 0 deleterows!(tempdf, 1:size(tempdf, 1)) end
         for i = 1:size(data, 1)
             if data[i, sort] == slist[si, :]
                 push!(tempdf, data[i, rcv])
@@ -351,6 +351,8 @@ Meta-analysis for 2x2 tables.
 
 tab: vectro of ConTab{2,2} or McnmConTab;
 
+Inverce Variance method used to get variance estimate for fixed effect.
+
 type - type of measure:
 - :rr
 - :or/
@@ -438,7 +440,7 @@ function Base.show(io::IO, obj::MetaProp)
     println(io, "  Trial number (k): $(obj.k)")
     println(io, "  Model: $(obj.model)")
     println(io, "  Type: $(obj.type)")
-    if obj.type == :fixed
+    if obj.type == :diff
         println(io, "  Estimate: $(obj.est)")
         println(io, "  Variance: $(obj.var)")
     else

src/pk.jl

@@ -885,7 +885,7 @@ function getdatai(data, sort, cols, func; sortby = nothing)
         datai[!, i] = Vector{eltype(data[!, i])}(undef, 0)
     end
     for i = 1:size(sortlist, 1)
-        if size(datai, 1) > 0 delete!(datai, 1:size(datai, 1)) end
+        if size(datai, 1) > 0 deleterows!(datai, 1:size(datai, 1)) end
         for c = 1:size(data, 1) #For each line in data
             if data[c, sort] == sortlist[i,:]
                 push!(datai, data[c, cols])

src/powersamplesize.jl

@@ -25,7 +25,7 @@ function one_mean_equality(μ₀::Real, μ₁::Real, σ::Real, α::Real, β::Rea
     return ((quantile(ZDIST, 1 - α / 2) + quantile(ZDIST, 1 - β)) * σ / (μ₀ - μ₁))^2
 end
 function one_mean_equivalence(μ₀::Real, μ₁::Real, σ::Real, δ::Real, α::Real, β::Real)::Float64
-    return (σ*(quantile(ZDIST, 1 - 2 * α) + quantile(ZDIST, 1 - β / 2))/(δ - abs(μ₀ - μ₁)))^2
+    return (σ*(quantile(ZDIST, 1 - α) + quantile(ZDIST, 1 - β / 2))/(δ - abs(μ₀ - μ₁)))^2
 end
 function one_mean_superiority(μ₀::Real, μ₁::Real, σ::Real, δ::Real, α::Real, β::Real)::Float64 #Non-inferiority / Superiority
     return (σ*(quantile(ZDIST, 1 - α) + quantile(ZDIST, 1 - β))/(μ₀ - μ₁ - δ))^2
@@ -35,7 +35,7 @@ function two_mean_equality(μ₀::Real, μ₁::Real, σ::Real, α::Real, β::Rea
     return (1 + 1 / k)*(σ*(quantile(ZDIST, 1 - α / 2) + quantile(ZDIST, 1 - β))/(μ₀ - μ₁))^2
 end
 function two_mean_equivalence(μ₀::Real, μ₁::Real, σ::Real, δ::Real, α::Real, β::Real, k::Real)::Float64
-    return (1 + 1 / k)*(σ*(quantile(ZDIST, 1 - 2 * α) + quantile(ZDIST, 1 - β / 2))/(δ - abs(μ₀ - μ₁)))^2
+    return (1 + 1 / k)*(σ*(quantile(ZDIST, 1 - α) + quantile(ZDIST, 1 - β / 2))/(δ - abs(μ₀ - μ₁)))^2
 end
 function two_mean_superiority(μ₀::Real, μ₁::Real, σ::Real, δ::Real, α::Real, β::Real, k::Real)::Float64 #Non-inferiority / Superiority
     return (1 + 1 / k)*(σ*(quantile(ZDIST, 1 - α) + quantile(ZDIST, 1 - β))/(μ₀ - μ₁ - δ))^2
@@ -46,7 +46,7 @@ function one_proportion_equality(p₀::Real, p₁::Real, α::Real, β::Real)::Fl
     return p₀*(1 - p₀)*((quantile(ZDIST, 1 - α / 2)+quantile(ZDIST, 1 - β))/(p₀ - p₁))^2
 end
 function one_proportion_equivalence(p₀::Real, p₁::Real, δ::Real, α::Real, β::Real)::Float64
-    return p₀*(1 - p₀)*((quantile(ZDIST, 1 - 2 * α) + quantile(ZDIST, 1 - β / 2))/(abs(p₀ - p₁) - δ))^2
+    return p₀*(1 - p₀)*((quantile(ZDIST, 1 - α) + quantile(ZDIST, 1 - β / 2))/(abs(p₀ - p₁) - δ))^2
 end
 function one_proportion_superiority(p₀::Real, p₁::Real, δ::Real, α::Real, β::Real)::Float64
     return p₀*(1 - p₀)*((quantile(ZDIST, 1 - α)+quantile(ZDIST, 1 - β))/(p₀ - p₁ - δ))^2
@@ -56,7 +56,7 @@ function two_proportion_equality(p₀::Real, p₁::Real, α::Real, β::Real, k::
     return (p₀ * (1 - p₀) / k + p₁ * (1 - p₁)) * ((quantile(ZDIST, 1 - α / 2) + quantile(ZDIST, 1 - β))/(p₀-p₁))^2
 end
 function two_proportion_equivalence(p₀::Real, p₁::Real, δ::Real, α::Real, β::Real, k::Real)::Float64
-    return (p₀ * (1 - p₀)/k + p₁ * (1 - p₁)) * ((quantile(ZDIST, 1 - 2 * α)+quantile(ZDIST, 1 - β / 2))/(δ - abs(p₀-p₁)))^2
+    return (p₀ * (1 - p₀)/k + p₁ * (1 - p₁)) * ((quantile(ZDIST, 1 - α)+quantile(ZDIST, 1 - β / 2))/(δ - abs(p₀-p₁)))^2
 end
 function two_proportion_superiority(p₀::Real, p₁::Real, δ::Real, α::Real, β::Real, k::Real)::Float64
     return (p₀ *(1 - p₀) / k + p₁ *(1 - p₁)) * ((quantile(ZDIST, 1 - α)+quantile(ZDIST, 1 - β))/(p₀-p₁-δ))^2
@@ -66,7 +66,7 @@ function co_proportion_equality(seq, σ::Real, ϵ::Real, α::Real, β::Real)
     (quantile(ZDIST, 1 - α / 2) + quantile(ZDIST, 1 - β) * σ) ^ 2 / (seq * ϵ ^ 2)
 end
 function co_proportion_equivalence(seq, σ::Real, ϵ::Real, δ::Real, α::Real, β::Real)
-    (quantile(ZDIST, 1 - 2 * α) + quantile(ZDIST, 1 - β / 2) * σ) ^ 2 / (seq * (δ - abs(ϵ)) ^ 2)
+    (quantile(ZDIST, 1 - α) + quantile(ZDIST, 1 - β / 2) * σ) ^ 2 / (seq * (δ - abs(ϵ)) ^ 2)
 end
 function co_proportion_superiority(seq, σ::Real,  ϵ::Real, δ::Real, α::Real, β::Real)
     (quantile(ZDIST, 1 - α) + quantile(ZDIST, 1 - β) * σ) ^ 2 / (seq * (ϵ - δ) ^ 2)
@@ -79,7 +79,7 @@ function or_equality(p₀::Real, p₁::Real, α::Real, β::Real, k::Real)::Float
 end
 function or_equivalence(p₀::Real, p₁::Real, δ::Real, α::Real, β::Real, k::Real)::Float64
     OR = p₀ * (1 - p₁) / p₁ / (1 - p₀)
-    return (1 / k / p₀ / (1 - p₀) + 1 / p₁ / (1 - p₁)) * ((quantile(ZDIST, 1 - 2 * α)+quantile(ZDIST, 1 - β / 2))/(log(OR) - δ))^2
+    return (1 / k / p₀ / (1 - p₀) + 1 / p₁ / (1 - p₁)) * ((quantile(ZDIST, 1 - α)+quantile(ZDIST, 1 - β / 2))/(log(OR) - δ))^2
 end
 function or_superiority(p₀::Real, p₁::Real, δ::Real, α::Real, β::Real, k::Real)::Float64
     OR = p₀ *(1 - p₁) / p₁ / (1 - p₀)
@@ -106,7 +106,7 @@ function cox_equality(θ, θ₀, p, α::Float64, β::Real, k::Real)
     1.0/p/(1.0/(1+k)*(1.0-1.0/(1+k)))*((quantile(ZDIST, 1 - α / 2) + quantile(ZDIST, 1 - β))/(log(θ)-log(θ₀)))^2
 end
 function cox_equivalence(θ, θ₀, p, α::Float64, β::Real, k::Real)
-    1.0/p/(1.0/(1+k)*(1.0-1.0/(1+k)))*((quantile(ZDIST, 1 - 2 * α) + quantile(ZDIST, 1 - β / 2))/(log(θ₀) - abs(log(θ))))^2
+    1.0/p/(1.0/(1+k)*(1.0-1.0/(1+k)))*((quantile(ZDIST, 1 - α) + quantile(ZDIST, 1 - β / 2))/(log(θ₀) - abs(log(θ))))^2
 end
 function cox_superiority(θ, θ₀, p, α::Float64, β::Real, k::Real)
     1.0/p/(1.0/(1+k)*(1.0-1.0/(1+k)))*((quantile(ZDIST, 1 - α) + quantile(ZDIST, 1 - β))/(log(θ)-log(θ₀)))^2
@@ -121,7 +121,7 @@ function one_mean_equality_pow(μ₀::Real, μ₁::Real, σ::Real, α::Float64,
 end
 function one_mean_equivalence_pow(μ₀::Real, μ₁::Real, σ::Real, δ::Real, α::Float64, n::Int)::Float64
     z = (abs(μ₀ - μ₁) - δ)/σ * sqrt(n)
-    return max(0, 2 * (cdf(ZDIST,z - quantile(ZDIST, 1 - 2 * α)) + cdf(ZDIST, -z - quantile(ZDIST, 1 - 2 * α))) - 1)
+    return max(0, 2 * (cdf(ZDIST,z - quantile(ZDIST, 1 - α)) + cdf(ZDIST, -z - quantile(ZDIST, 1 -  α))) - 1)
 end
 function one_mean_superiority_pow(μ₀::Real, μ₁::Real, σ::Real, δ::Real, α::Float64, n::Int)::Float64 #Non-inferiority / Superiority
     z = (μ₀ - μ₁ - δ) / σ*sqrt(n)
@@ -134,7 +134,7 @@ function two_mean_equality_pow(μ₀::Real, μ₁::Real, σ::Real, α::Float64,
 end
 function two_mean_equivalence_pow(μ₀::Real, μ₁::Real, σ::Real, δ::Real, α::Float64, n::Int, k::Real)::Float64
     z=(abs(μ₀ - μ₁) - δ)/(σ * sqrt((1 + 1 / k) / n))
-    return max(0, 2 * (cdf(ZDIST, z - quantile(ZDIST, 1 - 2 * α)) + cdf(ZDIST, - z - quantile(ZDIST, 1 - 2 * α))) - 1)
+    return max(0, 2 * (cdf(ZDIST, z - quantile(ZDIST, 1 - α)) + cdf(ZDIST, - z - quantile(ZDIST, 1 -  α))) - 1)
 end
 function two_mean_superiority_pow(μ₀::Real, μ₁::Real, σ::Real, δ::Real, α::Float64, n::Int, k::Real)::Float64 #Non-inferiority / Superiority
     z = (μ₀ - μ₁ - δ)/(σ * sqrt((1 + 1 / k) / n))
@@ -148,7 +148,7 @@ function one_proportion_equality_pow(p₀::Real, p₁::Real, α::Float64, n::Int
 end
 function one_proportion_equivalence_pow(p₀::Real, p₁::Real, δ::Real, α::Float64, n::Int)::Float64
     z=(abs(p₀-p₁)-δ)/sqrt(p₀*(1-p₀)/n)
-    return max(0, 2 * (cdf(ZDIST, z - quantile(ZDIST, 1 - 2 * α)) + cdf(ZDIST,- z - quantile(ZDIST, 1 - 2 * α))) - 1)
+    return max(0, 2 * (cdf(ZDIST, z - quantile(ZDIST, 1 - α)) + cdf(ZDIST,- z - quantile(ZDIST, 1 -  α))) - 1)
 end
 function one_proportion_superiority_pow(p₀::Real, p₁::Real, δ::Real, α::Float64, n::Int)::Float64
     z=(p₀-p₁-δ)/sqrt(p₀*(1-p₀)/n)
@@ -162,7 +162,7 @@ function two_proportion_equality_pow(p₀::Real, p₁::Real, α::Float64, n::Int
 end
 function two_proportion_equivalence_pow(p₀::Real, p₁::Real, δ::Real, α::Float64, n::Int, k::Real)::Float64
     z = (δ - abs(p₀ - p₁)) / sqrt(p₀ * (1 - p₀) / n / k + p₁ * (1 - p₁ ) / n)
-    return max(0, 2 * (cdf(ZDIST, z - quantile(ZDIST, 1 - 2 * α)) + cdf(ZDIST, - z - quantile(ZDIST, 1 - 2 * α))) - 1)
+    return max(0, 2 * (cdf(ZDIST, z - quantile(ZDIST, 1 - α)) + cdf(ZDIST, - z - quantile(ZDIST, 1 - α))) - 1)
 end
 function two_proportion_superiority_pow(p₀::Real, p₁::Real, δ::Real, α::Float64, n::Int, k::Real)::Float64
     z = (p₀ - p₁ - δ)/sqrt(p₀ * (1 - p₀) / n / k + p₁ * (1 - p₁) / n)
@@ -178,7 +178,7 @@ function or_equivalence_pow(p₀::Real, p₁::Real, δ::Real, α::Float64, n::In
     #println("$p₀ $p₁ $δ $α $n $k")
     OR = p₀ * (1 - p₁)/p₁/(1 - p₀)
     z  = (abs(log(OR)) - δ) * sqrt(n)/sqrt(1/(k * p₀ * (1 - p₀)) + 1/(p₁ * (1 - p₁)))
-    return  max(0, 2 * (cdf(ZDIST, z - quantile(ZDIST, 1 - 2 * α)) + cdf(ZDIST,-z - quantile(ZDIST, 1 - 2 * α))) - 1)
+    return  max(0, 2 * (cdf(ZDIST, z - quantile(ZDIST, 1 - α)) + cdf(ZDIST,-z - quantile(ZDIST, 1 - α))) - 1)
 end
 function or_superiority_pow(p₀::Real, p₁::Real, δ::Real, α::Float64, n::Int, k::Real)::Float64
     OR = p₀ * (1 - p₁)/p₁/(1 - p₀)

src/samplesize.jl

@@ -188,9 +188,9 @@ Clinical trial sample size estimation.
 - :or - Odd Ratio;
 
 **type (Hypothesis type):**
-- :ea - Equality (two-sided hypothesis, 100\\*(1 - α)% CI);
-- :ei - Equivalence (two-sided hypothesis, 100\\*(1 - **2\\*α**)% CI);
-- :ns - Non-Inferiority / Superiority (one-sided hypothesis, 100\\*(1 - α)% CI);
+- :ea - Equality (two-sided hypothesis, 100\\*(1 - α)% two-sided CI);
+- :ei - Equivalence (two-sided hypothesis, 100\\*(1 - **2\\*α**)% two-sided CI);
+- :ns - Non-Inferiority / Superiority (one-sided hypothesis, one side of 100\\*(1 -**2\\*α**)% two-sided CI);
 - :mcnm - McNemar's Equality test;
 
 **group (group num):**
@@ -313,9 +313,9 @@ Clinical trial power estimation.
 - :or   - Odd Ratio;
 
 **type (Hypothesis type):**
-- :ea - Equality (two-sided hypothesis, 100\\*(1 - α)% CI);
-- :ei - Equivalence (two-sided hypothesis, 100\\*(1 - **2\\*α**)% CI);
-- :ns - Non-Inferiority / Superiority (one-sided hypothesis, 100\\*(1 - α)% CI);
+- :ea - Equality (two-sided hypothesis, 100\\*(1 - α)% two-sided CI);
+- :ei - Equivalence (two-sided hypothesis, 100\\*(1 - **2\\*α**)% two-sided CI);
+- :ns - Non-Inferiority / Superiority (one-sided hypothesis, one side of 100\\*(1 -**2\\*α**)% two-sided CI);
 - :mcnm - McNemar's Equality test;
 
 **group (group num):**

src/sim.jl

@@ -41,7 +41,7 @@ end
 Proportion difference power simulation.
 
 """
-function ctsim(t::CTask{DiffProportion{P, P}, D, H, Power}; nsim = 100, seed=0, method = :default, dropout = 0.0)  where P <: Proportion where D where H <: AbstractHypothesis
+function ctsim(t::CTask{DiffProportion{P1, P2}, D, H, Power}; nsim = 100, seed=0, method = :default, dropout = 0.0)  where P1 <: Proportion where P2 <: Proportion where D where H <: AbstractHypothesis
     if seed != 0  Random.seed!(seed) end
     n₁      = getval(t.objective)
     n₂      = Int(ceil(getval(t.objective) / t.k))