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Introduction

The aim of ClinReport is to create statistical outputs (tables and graphics) in ‘Microsoft Word’ according to ‘clinical standards’ of reporting.

The formatted outputs are also suitable for any publications, so it can also be used outside the scope of clinical trials.

Installation

You can install the released version of ClinReport from CRAN with:

install.packages("ClinReport")

Basic example


library(ClinReport)
library(officer)
library(flextable)

# Load data

data(data)

# Descriptive statistics (Numerical response and 2 explicative variables):

tab1=report.quanti(data=data,y="y_numeric",
        x1="GROUP",x2="TIMEPOINT",at.row="TIMEPOINT",subjid="SUBJID")
tab1
#>    TIMEPOINT Statistics      A (N=30)      B (N=21)      C (N=17)
#> 1         D0          N            30            20            16
#> 2         D0  Mean (SD)   -0.93(0.86)   -0.67(1.09)   -1.19(0.92)
#> 3         D0     Median         -0.82         -0.69         -1.26
#> 4         D0    [Q1;Q3] [-1.59;-0.16] [-1.39;-0.06] [-1.62;-0.83]
#> 5         D0  [Min;Max]  [-2.34;0.36]  [-2.44;2.10]  [-2.99;0.66]
#> 6         D0    Missing             1             1             0
#> 7                                                                
#> 8         D1          N            30            20            16
#> 9         D1  Mean (SD)    1.83(1.04)    4.17(1.28)    4.98(0.69)
#> 10        D1     Median          1.78          4.19          5.08
#> 11        D1    [Q1;Q3] [ 0.94; 2.54] [ 3.23; 4.92] [ 4.58; 5.46]
#> 12        D1  [Min;Max]  [ 0.11;3.88]  [ 1.48;6.19]  [ 3.80;6.23]
#> 13        D1    Missing             1             0             0
#> 14                                                               
#> 15        D2          N            30            20            16
#> 16        D2  Mean (SD)    1.97(1.17)    4.04(0.89)    4.90(1.36)
#> 17        D2     Median          1.66          4.19          5.06
#> 18        D2    [Q1;Q3] [ 1.23; 2.86] [ 3.62; 4.36] [ 4.34; 5.20]
#> 19        D2  [Min;Max]  [-0.18;4.36]  [ 2.03;5.63]  [ 2.39;7.96]
#> 20        D2    Missing             1             1             0
#> 21                                                               
#> 22        D3          N            30            20            16
#> 23        D3  Mean (SD)    1.78(1.17)    3.81(0.94)    5.07(1.12)
#> 24        D3     Median          1.78          3.63          5.22
#> 25        D3    [Q1;Q3] [ 0.93; 2.42] [ 3.13; 4.44] [ 4.11; 5.66]
#> 26        D3  [Min;Max]  [-0.16;3.90]  [ 2.46;6.01]  [ 3.16;7.37]
#> 27        D3    Missing             0             1             1
#> 28                                                               
#> 29        D4          N            30            20            16
#> 30        D4  Mean (SD)    1.83(0.85)    3.80(0.95)    5.17(1.03)
#> 31        D4     Median          1.67          3.83          4.88
#> 32        D4    [Q1;Q3] [ 1.26; 2.32] [ 3.12; 4.42] [ 4.69; 5.50]
#> 33        D4  [Min;Max]  [ 0.38;3.97]  [ 2.31;5.41]  [ 3.24;6.96]
#> 34        D4    Missing             1             1             1
#> 35                                                               
#> 36        D5          N            30            20            16
#> 37        D5  Mean (SD)    2.27(1.20)    3.64(1.19)    4.43(0.98)
#> 38        D5     Median          2.50          3.86          4.57
#> 39        D5    [Q1;Q3] [ 1.77; 3.21] [ 2.59; 4.60] [ 3.44; 4.97]
#> 40        D5  [Min;Max]  [-1.19;4.31]  [ 0.91;5.12]  [ 2.95;6.54]
#> 41        D5    Missing             0             0             0

# You can have the corresponding graphic:

gg=plot(tab1,title="Mean response evolution as a function of time by treatment group",
 legend.label="Treatment groups",ylab="Y mean")
gg


doc=read_docx()
doc=report.doc(tab1,title="Example of a quantitative descriptive table",doc=doc) 

# See the results

file=paste(tempfile(),".docx",sep="")
print(doc, target =file)

#shell.exec(file)