Get started

Start by loading all usual libraries.

library(ClinReport)
library(officer)
library(flextable)
library(dplyr)
library(reshape2)
library(nlme)
library(emmeans)
library(car)

Load your data.

# We will use fake data
data(datafake)
print(head(datafake))
#>    y_numeric y_logistic y_poisson   baseline   VAR GROUP TIMEPOINT SUBJID
#> 1 -0.4203490          1         5 -0.4203490 Cat 1     A        D0 Subj 1
#> 2 -0.1570941          1         5 -0.1570941 Cat 2     A        D0 Subj 1
#> 3         NA          0         3 -2.0853720 Cat 2     A        D0 Subj 1
#> 4 -0.4728527          0         5 -0.4728527 Cat 1     A        D0 Subj 1
#> 5 -0.8651713          1         4 -0.8651713 Cat 1     A        D0 Subj 1
#> 6 -1.5476907          1         3 -1.5476907 Cat 1     A        D0 Subj 1

Create a statistical output for a quantitative response and two explicative variables. For example a treatment group and a time variable corresponding to the visits of a clinical trial.

For that we use the report.quanti() function:

tab1=report.quanti(data=datafake,y="y_numeric",
        x1="GROUP",x2="TIMEPOINT",at.row="TIMEPOINT",
        subjid="SUBJID")

tab1
#> 
#> ############################################
#> Quantitative descriptive statistics of: y_numeric
#> ############################################
#> 
#>    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
#> 
#> ############################################

The at.row argument is used to space the results between each visit and the subjid argument is used to add in the columns header the total number of subjects randomized by treatment group.

Generally we want also the corresponding graphics. So you can use the specific plot function to print the corresponding graphic of your table:

g1=plot(tab1,title="The title that you want to display")
print(g1)

You can modify the plot by using the following arguments of the plot.desc() function:

args(ClinReport:::plot.desc)
#> function (x, ..., title = "", ylim = NULL, xlim = NULL, xlab = "", 
#>     ylab = "", legend.label = "Group", add.sd = F, add.ci = F, 
#>     size.title = 10, add.line = T) 
#> NULL

Then we can use the report.doc() function which use the flextable package to format the output into a flextable object, ready to export to Microsoft Word with the officer package.

The table will look like this (we can have a preview in HTML, just to check):

report.doc(tab1,title="Quantitative statistics (2 explicative variables)",
        colspan.value="Treatment group", init.numbering =T )            

All output numbers will be increased automatically after each call of the function report.doc().

You can restart the numbering of the outputs by using init.numbering=T argument in report.doc() function.

Finally, we add those results to a rdocx object:

doc=read_docx()
doc=report.doc(tab1,title="Quantitative statistics (2 explicative variables)",
        colspan.value="Treatment group",doc=doc,init.numbering=T)
doc=body_add_gg(doc, value = g1, style = "centered" )

Write the doc to a docx file:

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

#Open it
#shell.exec(file)

The basic descriptive statistics

Qualitative descriptive tables

An example of qualitative statistics with one explicative variable

tab=report.quali(data=datafake,y="y_logistic",
        x1="VAR",total=T,subjid="SUBJID")

report.doc(tab,title="Qualitative table with two variables",
colspan.value="A variable") 

An example of qualitative statistics with two explicative variables

tab=report.quali(data=datafake,y="y_logistic",
        x1="GROUP",x2="TIMEPOINT",at.row="TIMEPOINT",
        total=T,subjid="SUBJID")

report.doc(tab,title="Qualitative table with two variables",
colspan.value="Treatment group")    

Quantitative descriptive tables

An example of quantitative statistics with one explicative variable

tab=report.quanti(data=datafake,y="y_numeric",
        x1="VAR",total=T,subjid="SUBJID")

report.doc(tab,title="Quantitative table with one explicative variable",
colspan.value="A variable") 

An example of quantitative statistics with two explicative variables

tab=report.quanti(data=datafake,y="y_numeric",
        x1="GROUP",x2="TIMEPOINT",at.row="TIMEPOINT",
        total=T,subjid="SUBJID")

report.doc(tab,title="Quantitative table with two explicative variables",
colspan.value="Treatment group")    

Mix descriptive statistics of quantitative and qualitative nature

You can mix qualitative and quantitative outputs.

But it's only possible for 1 explicative variable, and it should be the same variable for both response:

tab1=report.quanti(data=datafake,y="y_numeric",
        x1="GROUP",subjid="SUBJID",y.label="Y numeric")

tab2=report.quali(data=datafake,y="y_logistic",
        x1="GROUP",subjid="SUBJID",y.label="Y logistic")

tab3=regroup(tab1,tab2,rbind.label="The label of your choice")


report.doc(tab3,title="Mixed Qualitative and Quantitative outputs",
colspan.value="Treatment group")

Hierarchical descriptive statistics

Hierarchical descriptive statistics are reported when there are several events per statistical unit. It's often use for adverse events, medical history or concomitant treatments.

Typically, adverse event are classified according to System Organ Class (SOC) and then sub classified by Preferred Terms (PT). Several observations of a same adverse event can be observed several times on the same subject. It's then useful to know how many persons are concerned by at least one of those adverse events and report the frequencies for each classifications: SOC and PT.

To do that, you can use the report.quali.hlev function.

# We use a fake standard adverse event data set
# In this data sets there are several observations per subject
# and the factor PTNAME is a sub classification of the factor SOCNAME

data(adverse_event)

# In the report.quali.hlev we specify which factor has the more levels in the var_upper
# argument. The var_lower argument indicates the classification with less levels.
# The x1 argument is used to split the results according to the levels of another factor.

test=report.quali.hlev(data=adverse_event,subjid="SUBJID",var_upper="PTNAME",
var_lower="SOCNAME",lower.levels="System Organ Class",upper.levels="Prefered Terms",x1="randtrt")

# Frequencies and Percentages for each level are shown in the 
# formatted table in HTML, using the usual report.doc function

ft=report.doc(test,valign=TRUE)
ft