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)

plot of chunk unnamed-chunk-4

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 )            

Table 1: Quantitative statistics (2 explicative variables)

Treatment group

TIMEPOINT

Statistics

A (N=30)

B (N=21)

C (N=17)

D0

N

30

20

16

Mean (SD)

-0.93(0.86)

-0.67(1.09)

-1.19(0.92)

Median

-0.82

-0.69

-1.26

[Q1;Q3]

[-1.59;-0.16]

[-1.39;-0.06]

[-1.62;-0.83]

[Min;Max]

[-2.34;0.36]

[-2.44;2.10]

[-2.99;0.66]

Missing

1

1

0

D1

N

30

20

16

Mean (SD)

1.83(1.04)

4.17(1.28)

4.98(0.69)

Median

1.78

4.19

5.08

[Q1;Q3]

[ 0.94; 2.54]

[ 3.23; 4.92]

[ 4.58; 5.46]

[Min;Max]

[ 0.11;3.88]

[ 1.48;6.19]

[ 3.80;6.23]

Missing

1

0

0

D2

N

30

20

16

Mean (SD)

1.97(1.17)

4.04(0.89)

4.90(1.36)

Median

1.66

4.19

5.06

[Q1;Q3]

[ 1.23; 2.86]

[ 3.62; 4.36]

[ 4.34; 5.20]

[Min;Max]

[-0.18;4.36]

[ 2.03;5.63]

[ 2.39;7.96]

Missing

1

1

0

D3

N

30

20

16

Mean (SD)

1.78(1.17)

3.81(0.94)

5.07(1.12)

Median

1.78

3.63

5.22

[Q1;Q3]

[ 0.93; 2.42]

[ 3.13; 4.44]

[ 4.11; 5.66]

[Min;Max]

[-0.16;3.90]

[ 2.46;6.01]

[ 3.16;7.37]

Missing

0

1

1

D4

N

30

20

16

Mean (SD)

1.83(0.85)

3.80(0.95)

5.17(1.03)

Median

1.67

3.83

4.88

[Q1;Q3]

[ 1.26; 2.32]

[ 3.12; 4.42]

[ 4.69; 5.50]

[Min;Max]

[ 0.38;3.97]

[ 2.31;5.41]

[ 3.24;6.96]

Missing

1

1

1

D5

N

30

20

16

Mean (SD)

2.27(1.20)

3.64(1.19)

4.43(0.98)

Median

2.50

3.86

4.57

[Q1;Q3]

[ 1.77; 3.21]

[ 2.59; 4.60]

[ 3.44; 4.97]

[Min;Max]

[-1.19;4.31]

[ 0.91;5.12]

[ 2.95;6.54]

Missing

0

0

0

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") 

Table 2: Qualitative table with two variables

A variable

Levels

Statistics

Cat 1 (N=65)

Cat 2 (N=63)

Total (N=128)

0

n (column %)

100(48.08%)

86(45.74%)

186(46.97%)

1

n (column %)

103(49.52%)

97(51.60%)

200(50.51%)

Missing n(%)

5(2.40%)

5(2.66%)

10(2.53%)

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")    

Table 3: Qualitative table with two variables

Treatment group

TIMEPOINT

Levels

Statistics

A (N=30)

B (N=21)

C (N=17)

Total (N=68)

D0

0

n (column %)

11(36.67%)

11(55.00%)

7(43.75%)

29(43.94%)

1

n (column %)

18(60.00%)

8(40.00%)

7(43.75%)

33(50.00%)

Missing n(%)

1(3.33%)

1(5.00%)

2(12.50%)

4(6.06%)

D1

0

n (column %)

7(23.33%)

13(65.00%)

8(50.00%)

28(42.42%)

1

n (column %)

21(70.00%)

7(35.00%)

7(43.75%)

35(53.03%)

Missing n(%)

2(6.67%)

0(0%)

1(6.25%)

3(4.55%)

D2

0

n (column %)

18(60.00%)

7(35.00%)

11(68.75%)

36(54.55%)

1

n (column %)

12(40.00%)

13(65.00%)

5(31.25%)

30(45.45%)

Missing n(%)

0(0%)

0(0%)

0(0%)

0(0%)

D3

0

n (column %)

11(36.67%)

10(50.00%)

7(43.75%)

28(42.42%)

1

n (column %)

19(63.33%)

10(50.00%)

9(56.25%)

38(57.58%)

Missing n(%)

0(0%)

0(0%)

0(0%)

0(0%)

D4

0

n (column %)

18(60.00%)

12(60.00%)

6(37.50%)

36(54.55%)

1

n (column %)

12(40.00%)

8(40.00%)

8(50.00%)

28(42.42%)

Missing n(%)

0(0%)

0(0%)

2(12.50%)

2(3.03%)

D5

0

n (column %)

14(46.67%)

7(35.00%)

8(50.00%)

29(43.94%)

1

n (column %)

15(50.00%)

13(65.00%)

8(50.00%)

36(54.55%)

Missing n(%)

1(3.33%)

0(0%)

0(0%)

1(1.52%)

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") 

Table 4: Quantitative table with one explicative variable

A variable

Statistics

Cat 1 (N=65)

Cat 2 (N=63)

Total (N=128)

N

208

188

396

Mean (SD)

2.55(2.18)

2.56(2.23)

2.56(2.20)

Median

2.64

2.79

2.71

[Q1;Q3]

[0.94;4.36]

[1.07;4.19]

[1.04;4.33]

[Min;Max]

[-2.39;6.43]

[-2.99;7.96]

[-2.99;7.96]

Missing

4

6

10

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")    

Table 5: Quantitative table with two explicative variables

Treatment group

TIMEPOINT

Statistics

A (N=30)

B (N=21)

C (N=17)

Total (N=68)

D0

N

30

20

16

66

Mean (SD)

-0.93(0.86)

-0.67(1.09)

-1.19(0.92)

-0.92(0.95)

Median

-0.82

-0.69

-1.26

-0.86

[Q1;Q3]

[-1.59;-0.16]

[-1.39;-0.06]

[-1.62;-0.83]

[-1.55;-0.16]

[Min;Max]

[-2.34;0.36]

[-2.44;2.10]

[-2.99;0.66]

[-2.99;2.10]

Missing

1

1

0

2

D1

N

30

20

16

66

Mean (SD)

1.83(1.04)

4.17(1.28)

4.98(0.69)

3.33(1.73)

Median

1.78

4.19

5.08

3.57

[Q1;Q3]

[ 0.94; 2.54]

[ 3.23; 4.92]

[ 4.58; 5.46]

[ 1.78; 4.91]

[Min;Max]

[ 0.11;3.88]

[ 1.48;6.19]

[ 3.80;6.23]

[ 0.11;6.23]

Missing

1

0

0

1

D2

N

30

20

16

66

Mean (SD)

1.97(1.17)

4.04(0.89)

4.90(1.36)

3.32(1.70)

Median

1.66

4.19

5.06

3.57

[Q1;Q3]

[ 1.23; 2.86]

[ 3.62; 4.36]

[ 4.34; 5.20]

[ 1.89; 4.44]

[Min;Max]

[-0.18;4.36]

[ 2.03;5.63]

[ 2.39;7.96]

[-0.18;7.96]

Missing

1

1

0

2

D3

N

30

20

16

66

Mean (SD)

1.78(1.17)

3.81(0.94)

5.07(1.12)

3.15(1.75)

Median

1.78

3.63

5.22

3.15

[Q1;Q3]

[ 0.93; 2.42]

[ 3.13; 4.44]

[ 4.11; 5.66]

[ 1.80; 4.39]

[Min;Max]

[-0.16;3.90]

[ 2.46;6.01]

[ 3.16;7.37]

[-0.16;7.37]

Missing

0

1

1

2

D4

N

30

20

16

66

Mean (SD)

1.83(0.85)

3.80(0.95)

5.17(1.03)

3.22(1.66)

Median

1.67

3.83

4.88

3.16

[Q1;Q3]

[ 1.26; 2.32]

[ 3.12; 4.42]

[ 4.69; 5.50]

[ 1.69; 4.48]

[Min;Max]

[ 0.38;3.97]

[ 2.31;5.41]

[ 3.24;6.96]

[ 0.38;6.96]

Missing

1

1

1

3

D5

N

30

20

16

66

Mean (SD)

2.27(1.20)

3.64(1.19)

4.43(0.98)

3.21(1.45)

Median

2.50

3.86

4.57

3.28

[Q1;Q3]

[ 1.77; 3.21]

[ 2.59; 4.60]

[ 3.44; 4.97]

[ 2.42; 4.44]

[Min;Max]

[-1.19;4.31]

[ 0.91;5.12]

[ 2.95;6.54]

[-1.19;6.54]

Missing

0

0

0

0

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")

Table 6: Mixed Qualitative and Quantitative outputs

Treatment group

The label of your choice

Levels

Statistics

A (N=30)

B (N=21)

C (N=17)

Y numeric

N

180

120

96

Mean (SD)

1.46(1.50)

3.15(2.00)

3.87(2.52)

Median

1.59

3.75

4.73

[Q1;Q3]

[0.45;2.50]

[2.46;4.44]

[3.44;5.30]

[Min;Max]

[-2.34;4.36]

[-2.44;6.19]

[-2.99;7.96]

Missing

4

4

2

Y logistic

0

n (column %)

79(43.89%)

60(50.00%)

47(48.96%)

1

n (column %)

97(53.89%)

59(49.17%)

44(45.83%)

Missing n(%)

4(2.22%)

1(0.83%)

5(5.21%)

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

Table 7: Hierarchichal

System Organ Class

n (%) SOCNAME

At least one SOCNAME

Prefered Terms

n (%) PTNAME

At least one PTNAME

randtrt

ALL

451(100%)

195/321 = 60.75%

ALL

451(100%)

195/321 = 60.75%

A+B

A+B

BEHAVIOURAL DISORDERS

3(0.67%)

1/321 = 0.31%

ANXIETY

3(0.67%)

1/321 = 0.31%

A+B

A+B

BLOOD AND LYMPHATIC SYSTEM DISORDERS

10(2.22%)

7/321 = 2.18%

ANAEMIA NOS

1(0.22%)

1/321 = 0.31%

A+B

LYMPHADENITIS

1(0.22%)

1/321 = 0.31%

A+B

RED BLOOD CELL DISORDER NOS

2(0.44%)

1/321 = 0.31%

A+B

HAEMOLYTIC ANAEMIA

1(0.22%)

1/321 = 0.31%

A+B

LEUCOCYTOSIS

5(1.11%)

5/321 = 1.56%

A+B

A+B

CARDIO-VASCULAR SYSTEM DISORDERS

21(4.66%)

18/321 = 5.61%

HEART DISORDER AGGRAVATED

2(0.44%)

2/321 = 0.62%

A+B

HEART BLOCK

2(0.44%)

2/321 = 0.62%

A+B

HYPOTENSION

5(1.11%)

5/321 = 1.56%

A+B

HYPERTENSION

3(0.67%)

2/321 = 0.62%

A+B

CARDIAC ARREST

2(0.44%)

2/321 = 0.62%

A+B

CARDIAC DISORDER NOS

1(0.22%)

1/321 = 0.31%

A+B

ARRHYTHMIA

5(1.11%)

4/321 = 1.25%

A+B

VALVULAR DISORDER

1(0.22%)

1/321 = 0.31%

A+B

A+B

DIGESTIVE TRACT DISORDERS

72(15.96%)

55/321 = 17.13%

COLITIS

1(0.22%)

1/321 = 0.31%

A+B

GINGIVAL DISORDER

2(0.44%)

1/321 = 0.31%

A+B

DIGESTIVE TRACT NEOPLASM NOS

1(0.22%)

1/321 = 0.31%

A+B

EMESIS

27(5.99%)

23/321 = 7.17%

A+B

ENTERITIS

1(0.22%)

1/321 = 0.31%

A+B

ANAL SAC DISORDER

1(0.22%)

1/321 = 0.31%

A+B

NAUSEA

1(0.22%)

1/321 = 0.31%

A+B

HAEMORRHAGIC GASTROENTERITIS

1(0.22%)

1/321 = 0.31%

A+B

GASTROENTERITIS

5(1.11%)

5/321 = 1.56%

A+B

INTESTINAL STASIS

4(0.89%)

3/321 = 0.93%

A+B

HAEMORRHAGIC DIARRHOEA

2(0.44%)

2/321 = 0.62%

A+B

DIARRHOEA

17(3.77%)

15/321 = 4.67%

A+B

DIGESTIVE TRACT DISORDER NOS

5(1.11%)

5/321 = 1.56%

A+B

TOOTH DISORDER

2(0.44%)

2/321 = 0.62%

A+B

STOMATITIS

2(0.44%)

2/321 = 0.62%

A+B

A+B

EAR AND LABYRINTH DISORDERS

3(0.67%)

3/321 = 0.93%

OTITIS NOS

2(0.44%)

2/321 = 0.62%

A+B

OTITIS EXTERNA

1(0.22%)

1/321 = 0.31%

A+B

A+B

EYE DISORDERS

10(2.22%)

10/321 = 3.12%

GLAUCOMA

1(0.22%)

1/321 = 0.31%

A+B

CORNEAL ULCER

1(0.22%)

1/321 = 0.31%

A+B

KERATITIS

1(0.22%)

1/321 = 0.31%

A+B

CONJUNCTIVITIS

7(1.55%)

7/321 = 2.18%

A+B

A+B

HEPATO-BILIARY DISORDERS

5(1.11%)

4/321 = 1.25%

HEPATOPATHY

5(1.11%)

4/321 = 1.25%

A+B

A+B

MAMMARY GLAND DISORDERS

1(0.22%)

1/321 = 0.31%

MAMMARY GLAND NEOPLASM NOS

1(0.22%)

1/321 = 0.31%

A+B

A+B

METABOLISM AND NUTRITION DISORDERS

7(1.55%)

6/321 = 1.87%

HYPERPHOSPHATAEMIA

2(0.44%)

2/321 = 0.62%

A+B

ELECTROLYTE DISORDER

5(1.11%)

4/321 = 1.25%

A+B

A+B

MUSCULOSKELETAL DISORDERS

15(3.33%)

12/321 = 3.74%

ARTHROSIS

2(0.44%)

2/321 = 0.62%

A+B

MUSCULOSKELETAL DISORDER NOS

4(0.89%)

3/321 = 0.93%

A+B

ARTHRITIS

1(0.22%)

1/321 = 0.31%

A+B

LAMENESS

4(0.89%)

4/321 = 1.25%

A+B

BONE AND JOINT DISORDER NOS

1(0.22%)

1/321 = 0.31%

A+B

MUSCLE WEAKNESS NOS

1(0.22%)

1/321 = 0.31%

A+B

OSTEOSARCOMA

2(0.44%)

1/321 = 0.31%

A+B

A+B

NEUROLOGICAL DISORDERS

10(2.22%)

10/321 = 3.12%

CONVULSION

2(0.44%)

2/321 = 0.62%

A+B

CRANIAL NERVE DISORDER

1(0.22%)

1/321 = 0.31%

A+B

MUSCLE TREMOR

1(0.22%)

1/321 = 0.31%

A+B

EPILEPTIC SEIZURE

1(0.22%)

1/321 = 0.31%

A+B

MYDRIASIS

1(0.22%)

1/321 = 0.31%

A+B

ATAXIA

4(0.89%)

4/321 = 1.25%

A+B

A+B

RENAL AND URINARY DISORDERS

165(36.59%)

105/321 = 32.71%

URINE ABNORMALITIES

3(0.67%)

2/321 = 0.62%

A+B

CYSTITIS

5(1.11%)

5/321 = 1.56%

A+B

RENAL INSUFFICIENCY

144(31.93%)

96/321 = 29.91%

A+B

URINARY TRACT DISORDER NOS

2(0.44%)

2/321 = 0.62%

A+B

POLYURIA

4(0.89%)

4/321 = 1.25%

A+B

URINARY INCONTINENCE

6(1.33%)

6/321 = 1.87%

A+B

UROLITHIASIS

1(0.22%)

1/321 = 0.31%

A+B

A+B

REPRODUCTIVE SYSTEM DISORDERS

8(1.77%)

7/321 = 2.18%

PSEUDOPREGNANCY

1(0.22%)

1/321 = 0.31%

A+B

METRITIS

2(0.44%)

2/321 = 0.62%

A+B

FEMALE REPRODUCTIVE TRACT NEOPLASM NOS

2(0.44%)

1/321 = 0.31%

A+B

PROSTATIC DISORDER NOS

2(0.44%)

2/321 = 0.62%

A+B

MALE REPRODUCTIVE TRACT NEOPLASM NOS

1(0.22%)

1/321 = 0.31%

A+B

A+B

RESPIRATORY TRACT DISORDERS

35(7.76%)

33/321 = 10.28%

TRACHEITIS

3(0.67%)

3/321 = 0.93%

A+B

TRACHEAL COLLAPSE

1(0.22%)