require(hreport)  # add to above: results='hide'
knitrSet(lang='markdown', fig.path='figure/')
options(prType='html')
mu   <- markupSpecs$html   # in Hmisc - HTML markups
frac <- mu$frac
mu$styles()              # define HTML styles, functions
## Generate test data
set.seed(1)
n <- 500
d <- data.frame(country=sample(c('US', 'Canada', 'Spain', 'France',
                  'Germany'), n, TRUE),
                site=sample(1:10, n, TRUE))
d$site   <- paste(substring(d$country, 1, 2), d$site, sep='')
d$region <- factor(ifelse(d$country %in% c('US', 'Canada'),
                          'North America', 'Europe'))

d <- upData(d, edate = as.Date('2005-01-01') +
            round(rgamma(n, 2, .01)) - 600 * (country == 'US'),
            rdate = edate + round(runif(n, 1, 30)), print=FALSE)
d$rdate[runif(nrow(d)) < 0.5] <- NA  # non-randomized subjects )

# with(d, table(region, country))

# For US manually compute # randomized per month
us   <- subset(d, country == 'US')
site <- us$site
ed   <- us$edate
rd   <- us$rdate
months <- difftime(as.Date('2007-12-31'), ed, units='days') /
  (365.25 / 12)
m <- max(months)
a <- sum(!is.na(rd)) / as.numeric(m)   # .8545774 (agrees with chart)
# Compute maximum months elapsed for each site then sum over sites
maxpersite <- tapply(months, site, max)
b <- sum(!is.na(rd)) / sum(maxpersite)
## 0.0864429 = 47 / 543.6715 chart: .08645 (rounded)

## Suppose there are more subjects enrolled and randomized than really
## made their way into the dataset
denom <- c(enrolled=nrow(d) * 1.1,
           randomized=sum(!is.na(d$rdate)) + 10)

sethreportOption(tx.var='treat', denom=denom)
## Initialize file to hold appendix information such as subject IDs
## so all later writing to this file can use append=TRUE

Introduction

Interactive Graphs

Most of the graphs produced here are semi-interactive. One can hover over elements of graphs with the mouse to have detailed information pop up.

Figure Captions

Needles represent the fraction of observations used in the current analysis. The first needle (red) shows the fraction of enrolled patients used. If randomization was taken into account, a second needle (green) represents the fraction of randomized subjects included in the analysis. When the analyses consider treatment assignment, two more needles may be added to the display, showing, respectively, the fraction of subjects randomized to treatment A used in the analysis and the fraction of subjects on treatment B who were analyzed. The colors of these last two needles are the colors used for the two treatments throughout the report. The following table shows some examples. dNeedle uses colors in sethreportOption(tx.col=, er.col=).

# Store using short variable names so Rmarkdown table column
# width will not be wider than actually needed
d1 <- dNeedle(1)
d2 <- dNeedle((3:4)/4)
d3 <- dNeedle((1:2)/4)
d4 <- dNeedle(c(1,2,3,1)/4)
Signpost Interpretation
image All enrolled subjects analyzed, randomization not considered
image Analysis uses 3⁄4 of enrolled subjects, and all randomized subjects
image Analysis uses 1⁄4 of enrolled subjects, and 1⁄2 of randomized subjects
image Same as previous example, and in addition the analysis utilized treatment assignment, analyzing 3⁄4 of those randomized to A and 1⁄4 of those randomized to B

Survival Curves

Graphs containing pairs of Kaplan-Meier survival curves show a shaded region centered at the midpoint of the two survival estimates and having a height equal to the half-width of the approximate 0.95 pointwise confidence interval for the difference of the two survival probabilities. Time points at which the two survival estimates do not touch the shaded region denote approximately significantly different survival estimates, without any multiplicity correction.

Accrual

accrualReport(enroll(edate) + randomize(rdate) ~
              region(region) + country(country) + site(site),
              data=d,
              dateRange=c('2005-01-01', '2007-12-31'),
              targetN=
                data.frame(edate=c(500, 1000), rdate=c(250, 500)),
              targetDate=c('2006-01-01', '2007-12-31'),
              closeDate='2007-12-31')
Study Numbers
Number Category
5 Countries
50 Sites
500 Participants enrolled
227 Participants randomized
4.5 Participants per site
50 Sites randomizing
4.5 Subjects randomized per randomizing site
54.6 Months from first subject randomized (2003-06-12) to 2007-12-31
1838.5 Site-months for sites randomizing
36.8 Average months since a site first randomized
0.12 Participants randomized per site per month
15.4 Mean days from enrollment to randomization
16 Median days from enrollment to randomization

∟ Participants enrolled over time

The blue line depicts the cumulative frequency. The thick grayscale line represent targets.
Category N Used
Enrolled 550 500
image

∟ Participants randomized over time

The blue line depicts the cumulative frequency. The thick grayscale line represent targets.
Category N Used
Enrolled 550 227
Randomized 237 227
image

∟ Days from enrollment to randomization

Quartiles and mean number of days by region and country
Category N Used
Enrolled 550 227
Randomized 237 227
image

∟ Number of sites × number of participants

Number of sites having the given number of participants
Category N Used
Enrolled 550 227
Randomized 237 227
image

∟ Participants enrolled by region and country

∟ Participants randomized by region and country

∟ Sites that enrolled by region and country

∟ Sites that randomized by region and country

∟ Fraction of enrolled participants randomized by region and country

∟ Participants randomized per month by region and country

∟ Partipants randomized per site per month by region and country

Exclusions

f <- 0.05
d <- upData(d,
            subjid = 1 : n,
            pend   = rbinom(n, 1, .1),
            e1     = rbinom(n, 1, f),
            e2     = rbinom(n, 1, f),
            e3     = rbinom(n, 1, f),
            e4     = ifelse(runif(n) < 0.25, NA, rbinom(n, 1, .10)),
            tested = rbinom(n, 1, .75),
            e5     = ifelse(tested, rbinom(n, 1, .04), NA),
            e6     = rbinom(n, 1, f),
            e7     = rbinom(n, 1, f),
            rndz   = rbinom(n, 1, .75),
            labels=c(e1='Prior MI', e2='History of Asthma',
              e3='History of Upper GI Bleeding',
              e4='No Significant CAD', e5='Inadequate Renal Function',
              e6='Pneumonia within 6 weeks', e7='Prior cardiac surgery'),
            print=FALSE)

erd <- data.frame(subjid = 1 : 50,
                  loc   = sample(c('gastric', 'lung', 'trachea'), 50, TRUE))

# To check warning messages, greportOption denom does not match pend, e1-e7
options(dumpfile='/tmp/z')    # for seeing frequencies of all combinations
exReport(~ pending(pend) + e1 + e2 + e3 + e4 + e5 + e6 + e7 +
         randomized(rndz) + id(subjid) + cond(e5, 'Tested', tested),
         erdata = erd,
         whenapp= c(e4='CCTA done'), data=d) #, hc=3.75, h=4)

∟ All combinations of exclusions of non-randomized participants

All combinations of exclusions occurring in non-randomized participants. 9 pending participants were excluded from numerators.

∟ Cumulative exclusions

Cumulative number of exclusions (\(y\)-axis) and number of additional exclusions after exclusions placed higher, for participants not actually randomized. Exclusions are sorted by descending number of incremental exclusions. 550 participants were enrolled, 9 non-excluded participants are pending randomization, and 39 participants were excluded. 367 participants were randomized. Note: Number of observations (500) does not equal number officially enrolled (550). Note: Number of enrolled (491) minus number excluded (39) does not match official number randomized (237).

◫ Exclusions

Incremental exclusions are those in addition to exclusions in earlier rows. Marginal exclusions are numbers of participants excluded for the indicated reason whether or not she was excluded for other reasons. The three Fractions are based on incremental exclusions.
Exclusions Incremental
Exclusions 		Marginal
Exclusions 		Fraction of
Enrolled 		Fraction of
Exclusions 		Fraction
Remaining
No Significant CAD (CCTA done, n=383) 11 11 0.022 0.282 0.978
Pneumonia within 6 Weeks 8 8 0.016 0.205 0.961
History of Upper GI Bleeding 7 8 0.014 0.179 0.947
Prior Cardiac Surgery 5 7 0.010 0.128 0.937
Prior MI 4 4 0.008 0.103 0.929
History of Asthma 4 4 0.008 0.103 0.921
Total 39 0.079 1.000 0.921

◫ Exclusions in randomized participants

Frequency of exclusions for participants marked as randomized
Exclusion Frequency
Prior MI 19
History of Asthma 25
History of Upper GI Bleeding 23
No Significant CAD 26
Inadequate Renal Function 10
Pneumonia within 6 Weeks 20
Prior Cardiac Surgery 11
Total Partcipants with Any Exclusion 114

Click to show participant IDs
Exclusion IDs
Prior MI 12, 27, 29, 33, 50, 86, 92, 111, 142, 160, 178, 213, 227, 294, 307, 385, 427, 482, 489
History of Asthma 1, 3, 29, 32, 73, 86, 92, 101, 130, 148, 167, 198, 204, 250, 272, 275, 282, 295, 332, 371, 414, 427, 432, 466, 483
History of Upper GI Bleeding 10, 14, 36, 65, 72, 86, 108, 110, 121, 176, 246, 266, 317, 328, 360, 363, 374, 385, 424, 444, 472, 484, 492
No Significant CAD 1, 8, 10, 32, 81, 85, 88, 178, 182, 184, 187, 211, 247, 258, 292, 305, 311, 313, 314, 328, 339, 353, 383, 412, 419, 426
Inadequate Renal Function 22, 47, 82, 131, 187, 195, 385, 432, 438, 444
Pneumonia within 6 Weeks 150, 156, 165, 222, 235, 270, 299, 301, 342, 352, 353, 387, 393, 439, 442, 445, 447, 468, 473, 477
Prior Cardiac Surgery 1, 21, 36, 97, 280, 294, 303, 385, 400, 411, 475
Click to see more information about those participants
subjid loc
1 lung
3 lung
8 lung
10 gastric
12 gastric
14 trachea
21 trachea
22 gastric
27 lung
29 lung
32 trachea
33 trachea
36 lung
47 trachea
50 trachea 

# Show exclusions in original variable order
if(FALSE) exReport(~ pending(pend) + e1 + e2 + e3 + e4 + e5 + e6 + e7 +
         randomized(rndz) + id(subjid) + cond(e5, 'Tested', tested),
         erdata=erd,
         whenapp=c(e4='CCTA done'), data=d, #hc=3.75, h=4,
         sort=FALSE, app=FALSE)

Baseline Variables

n <- 100
f <- function(na=FALSE) {
  x <- sample(c('N', 'Y'), n, TRUE)
  if(na) x[runif(100) < .1] <- NA
  x
}
set.seed(1)
outs <- c('home', 'hospitalized', 'organ support', 'dead')
d <- data.frame(x1=f(), x2=f(), x3=f(), x4=f(), x5=f(), x6=f(),
                x7=f(TRUE),
                age=rnorm(n, 50, 10),
                sbp=rnorm(n, 120, 7),
                dbp=rnorm(n,  80, 6),
                days=sample(1:n, n, TRUE),
                race=sample(c('Asian', 'Black/AA', 'White'), n, TRUE),
                sex=sample(c('Female', 'Male'), n, TRUE),
                treat=sample(c('A', 'B'), n, TRUE),
                region=sample(c('North America','Europe'), n, TRUE),
                meda=sample(0:1, n, TRUE),
                medb=sample(0:1, n, TRUE),
                                outcome=factor(sample(1:4, n, TRUE), 1:4, outs),
                subjid=1:n)
d$days[1] <- NA
d$race[1:10] <- NA
d <- upData(d, labels=c(x1='MI', x2='Stroke', x3='AKI', x4='Migraines',
                 x5='Pregnant', x6='Other event', x7='MD withdrawal',
                 race='Race', sex='Sex', treat='treatment',
                 sbp='Systolic BP', days='Time Since Randomization',
                 meda='Medication A', medb='Medication B',
                                 outcome='Ordinal Outcome'),
            units=c(sbp='mmHg', dbp='mmHg', age='years', days='days'),
            print=FALSE)
dasna <- subset(d, region=='North America')
# with(dasna, table(race, treat))
den <- c(enrolled=n + 50, randomized=n, table(d$treat))
sethreportOption(denom=den, tx.var='treat')
           
dReport(race + sex +
        ynbind(x1, x2, x3, x4, x5, x6, x7, label='Exclusions') ~ 1,
        head='Overall frequencies of categorical demographic variables and exclusions',
        data=d, w=4, h=4.5)

∟ Overall frequencies of categorical demographic variables and exclusions

Overall frequencies of categorical demographic variables and exclusions. N=90 to 100
Category N Used
Enrolled 150 100
Randomized 100 100
Variable N
Race 90
Sex 100
Exclusions 100
image 
dReport(race + sex ~ treat,
        groups='treat',
        head='Categorical demographic variables',
        data=d, w=4, h=4.5)

∟ Categorical demographic variables stratified by treatment

Categorical demographic variables stratified by treatment. N=90 to 100
Category N Used
Enrolled 150 100
Randomized 100 100
A 43 43
B 57 57
Variable A B
Race 38 52
Sex 43 57
image 
dReport(race + sex ~ region, data=addMarginal(d, region),
        w=4.75, h=3.75,
        head='Demographics')

∟ Demographics stratified by region

Demographics stratified by region. N=90 to 100
Category N Used
Enrolled 150 100
Randomized 100 100
Variable N
Race 90
Sex 100
image 
dReport(race + sex ~ region + treat, data=addMarginal(d, region),
        groups='treat',
        w=4.75, h=3.75,
        head='Demographics')

∟ Demographics stratified by region and treatment

Demographics stratified by region and treatment. N=90 to 100
Category N Used
Enrolled 150 100
Randomized 100 100
A 43 43
B 57 57
Variable A B
Race 38 52
Sex 43 57
image 
## Add a new block of variables that apply only to males
dReport(race + sex +
        pBlock(race, subset=sex=='Male', label='Race: Males') ~ region,
        data=d, groups='region',
        head='Demographics with race for males')

∟ Demographics with race for males stratified by region

Demographics with race for males stratified by region. N=39 to 100
Category N Used
Enrolled 150 100
Randomized 100 100
Variable N
Race 90
Sex 100
Race: Males 39
image 
# Show raw data and smoothed relationship between age and sbp, stratified.
dReport(sbp ~ age + treat, groups='treat', data=d,
        popts=list(fitter=loess, showpts=TRUE), what='xy')

∟ Systolic BP vs. age stratified by treatment

Systolic BP vs. age stratified by treatment. N=100
Category N Used
Enrolled 150 100
Randomized 100 100
A 43 43
B 57 57
Variable A B
Systolic BP 43 57
image 
# Show ECDFs of sbp and age stratified by treatment+reference lines at quartiles
dReport(sbp + age ~ treat, groups='treat', what='ecdf', data=d,
        sopts=list(q=(1:3)/4, ncols=2, width=750))

∟ Empirical cumultive distribution functions for systolic BP and age stratified by treatment

Empirical cumultive distribution functions for systolic BP and age stratified by treatment. N=100
Category N Used
Enrolled 150 100
Randomized 100 100
A 43 43
B 57 57
Variable A B
Systolic BP 43 57
age 43 57
image 
# Show means and confidence intervals by treatment as dot chart
f <- function(x) {
  x <- x[! is.na(x)]
  c(smean.cl.normal(x, na.rm=FALSE), n=length(x))
}

dReport(sbp ~ treat, data=d,
        fun = f, head='Mean and confidence limits')

∟ Mean and confidence limits stratified by treatment

Mean and confidence limits stratified by treatment. N=100
Category N Used
Enrolled 150 100
Randomized 100 100
A 43 43
B 57 57
Variable A B
Systolic BP 43 57
image 
#        popts = list(textplot='Mean', digits=1),

Ordinal Outcome Summary

dReport(outcome ~ treat + sex, groups='treat', what='stacked', data=d, w=800, h=400)

∟ Stacked bar chart of proportions for ordinal outcome stratified by treatment and sex

Stacked bar chart of proportions for ordinal outcome stratified by treatment and sex. N=100
Category N Used
Enrolled 150 100
Randomized 100 100
A 43 43
B 57 57
Variable A B
Ordinal Outcome 43 57
image

Medication Usage Over Time

dReport(meda + medb ~ days + treat, what='xy',
        groups='treat', data=d,
        popts=list(fitter=loess, xlim=c(0, 130), ylim=c(0, 1), width=750),
        head='Medication usage',
        tail='Tick marks indicate mean measurement times within intervals.')

∟ Medication usage stratified by treatment

Medication usage stratified by treatment. N=100. Tick marks indicate mean measurement times within intervals.
Category N Used
Enrolled 150 100
Randomized 100 100
A 43 43
B 57 57
Variable A B
Medication A 43 57
Medication B 43 57
image 
# Show number being followed as days since randomization gets larger
# make sure nriskReport doesn't get fooled by duplicate data
d2 <- rbind(d, d)
require(data.table)

nriskReport(days ~ region + id(subjid),
            data=addMarginal(d2, region),
            head='Number of subjects followed for medication usage')

∟ Number of subjects followed for medication usage stratified by region

Number of subjects followed for medication usage stratified by region
Category N Used
Enrolled 150 99
Randomized 100 99
Variable N
Time Since Randomization 99
image 
# Make up some new visits to have more than 1/subj.
# Make up a new definition of time zero
d2$days[(n + 1) : (2 * n)] <- sample(1 : n, n, TRUE)
nriskReport(days ~ treat + id(subjid), data=d2, time0='PCI')

∟ Number of participants followed at least x days from PCI stratified by treatment

Number of participants followed at least x days from PCI stratified by treatment
Category N Used
Enrolled 150 100
Randomized 100 100
A 43 43
B 57 57
Variable A B
Time Since Randomization 43 57
image 
nriskReport(days ~ treat + region, data=d)

∟ Number of participants followed at least x days from randomization stratified by treatment and region

Number of participants followed at least x days from randomization stratified by treatment and region
Category N Used
Enrolled 150 99
Randomized 100 99
A 43 43
B 57 56
Variable A B
Time Since Randomization 43 56
image 
# Make a dataset with many records per subject
d <- data.frame(days    = sample(30:365, 1000, replace=TRUE),
                subjid  = sample(1:100,  1000, replace=TRUE))
# Get a richer set of output when there is not stratification
nriskReport(days ~ id(subjid), data=d)

∟ Number of participants followed at least x days from randomization

Number of participants followed at least x days from randomization
Category N Used
Enrolled 150 100
Randomized 100 100
Variable N
days 100
image

∟ Distributions of follow-up contacts, with times in days

Distributions of follow-up contacts, with times in days. Top left panel is a histogram showing the distribution of the number of contacts per participant. Top right panel is a histogram showing the distribution of time from randomization to all 1000 contacts. Bottom left panel is a histogram showing the distribution of the longest time gap between contacts per participant. Bottom right panel shows the relationship between the time lapse between randomization and last contact per participant and the average number of contacts for the participant.
Category N Used
Enrolled 150 100
Randomized 100 100
Variable N
days 100
image

Time to Hospitalization and Surgery

set.seed(1)
n <- 400
dat <- data.frame(t1=runif(n, 2, 5), t2=runif(n, 2, 5),
                  e1=rbinom(n, 1, .5), e2=rbinom(n, 1, .5),
                  cr1=factor(sample(c('cancer','heart','censor'), n, TRUE),
                             c('censor', 'cancer', 'heart')),
                  cr2=factor(sample(c('gastric','diabetic','trauma', 'censor'),
                                    n, TRUE),
                             c('censor', 'diabetic', 'gastric', 'trauma')),
                  treat=sample(c('a','b'), n, TRUE))
dat$t1[1:7] <- NA
dat <- upData(dat,
              labels=c(t1='Time to operation',
                       t2='Time to rehospitalization',
                       e1='Operation', e2='Hospitalization',
                       treat='Treatment'),
              units=c(t1='Year', t2='Year'), print=FALSE)
denom <- c(enrolled=n + 40, randomized=400, a=sum(dat$treat=='a'),
           b=sum(dat$treat=='b'))
sethreportOption(denom=denom, tx.var='treat')
survReport(Surv(t1, e1) + Surv(t2, e2) ~ treat, data=dat)

∟ Kaplan-Meier cumulative incidence estimates for operation stratified by treatment

Kaplan-Meier cumulative incidence estimates for operation stratified by treatment, along with half-height of 0.95 confidence limits for differences centered at estimate midpoints. \(N\)=393.
Category N Used
Enrolled 440 393
Randomized 400 393
A 201 201
B 199 199
Variable a b
Operation 198 195
Hospitalization 201 199
image

∟ Kaplan-Meier cumulative incidence estimates for hospitalization stratified by treatment

Kaplan-Meier cumulative incidence estimates for hospitalization stratified by treatment, along with half-height of 0.95 confidence limits for differences centered at estimate midpoints. \(N\)=400.
Category N Used
Enrolled 440 400
Randomized 400 400
A 201 201
B 199 199
Variable a b
Operation 198 195
Hospitalization 201 199
image 
# Show estimates combining treatments
survReport(Surv(t1, e1) + Surv(t2, e2) ~ 1, data=dat,
           what='S', times=3, ylim=c(.1, 1))

∟ Kaplan-Meier estimates for operation

Kaplan-Meier estimates for operation, along with pointwise 0.95 confidence bands. \(N\)=393.
Category N Used
Enrolled 440 393
Randomized 400 393
Variable N
Operation 393
Hospitalization 400
image

∟ Kaplan-Meier estimates for hospitalization

Kaplan-Meier estimates for hospitalization, along with pointwise 0.95 confidence bands. \(N\)=400.
Category N Used
Enrolled 440 400
Randomized 400 400
Variable N
Operation 393
Hospitalization 400
image 
# Cumulative incidence under completing risks
survReport(Surv(t1, cr1) ~ treat, data=dat, cause=c('cancer', 'heart'))

∟ Cumulative incidence of cancer with competing event heart stratified by treatment

Cumulative incidence of cancer with competing event heart stratified by treatment, along with half-height of 0.95 confidence limits for differences centered at estimate midpoints. \(N\)=393.
Category N Used
Enrolled 440 393
Randomized 400 393
A 201 198
B 199 195
Variable a b
Time to operation 198 195
image

∟ Cumulative incidence of heart with competing event cancer stratified by treatment

Cumulative incidence of heart with competing event cancer stratified by treatment, along with half-height of 0.95 confidence limits for differences centered at estimate midpoints. \(N\)=393.
Category N Used
Enrolled 440 393
Randomized 400 393
A 201 198
B 199 195
Variable a b
Time to operation 198 195
image

Adverse Events

For this example, the denominators for the two treatments in the pop-up needles will be incorrect because the dataset did not have subject IDs.

# Original source of aeanonym: HH package
# aeanonym <- read.table(hh("datasets/aedotplot.dat"), header=TRUE, sep=",")
# Modified to remove denominators from data and to generate raw data
# (one record per event per subject)

ae <-
structure(list(RAND = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("a", 
"b"), class = "factor"), PREF = structure(c(12L, 12L, 
18L, 18L, 26L, 26L, 33L, 33L, 5L, 5L, 27L, 27L, 6L, 6L, 15L, 
15L, 22L, 22L, 23L, 23L, 31L, 31L, 17L, 17L, 2L, 2L, 3L, 3L, 
13L, 13L, 25L, 25L, 28L, 28L, 14L, 14L, 4L, 4L, 8L, 8L, 19L, 
19L, 21L, 21L, 29L, 29L, 10L, 10L, 20L, 20L, 16L, 16L, 32L, 32L, 
11L, 11L, 1L, 1L, 30L, 30L, 24L, 24L, 9L, 9L, 7L, 7L),
  .Label = tolower(c("ABDOMINAL PAIN", 
"ANOREXIA", "ARTHRALGIA", "BACK PAIN", "BRONCHITIS", "CHEST PAIN", 
"CHRONIC OBSTRUCTIVE AIRWAY", "COUGHING", "DIARRHEA", "DIZZINESS", 
"DYSPEPSIA", "DYSPNEA", "FATIGUE", "FLATULENCE", "GASTROESOPHAGEAL REFLUX", 
"HEADACHE", "HEMATURIA", "HYPERKALEMIA", "INFECTION VIRAL", "INJURY", 
"INSOMNIA", "MELENA", "MYALGIA", "NAUSEA", "PAIN", "RASH", "RESPIRATORY DISORDER", 
"RHINITIS", "SINUSITIS", "UPPER RESP TRACT INFECTION", "URINARY TRACT INFECTION", 
"VOMITING", "WEIGHT DECREASE")), class = "factor"), SAE = c(15L, 
9L, 4L, 9L, 4L, 9L, 2L, 9L, 8L, 11L, 4L, 11L, 9L, 12L, 5L, 12L, 
7L, 12L, 6L, 12L, 6L, 12L, 2L, 14L, 2L, 15L, 1L, 15L, 4L, 16L, 
4L, 17L, 11L, 17L, 6L, 20L, 10L, 23L, 13L, 26L, 12L, 26L, 4L, 
26L, 13L, 28L, 9L, 29L, 12L, 30L, 14L, 36L, 6L, 37L, 8L, 42L, 
20L, 61L, 33L, 68L, 10L, 82L, 23L, 90L, 76L, 95L)), .Names = c("RAND", 
"PREF", "SAE"), class = "data.frame", row.names = c(NA, 
-66L))

subs <- rep(1 : nrow(ae), ae$SAE)
ae <- ae[subs, c('RAND', 'PREF')]
names(ae) <- c('treat', 'event')
label(ae$treat) <- 'Treatment'

denom <- c(enrolled=1000,
           randomized=400,
                     a=212, b=188)

sethreportOption(tx.var='treat', denom=denom)

eReport(event ~ treat, data=ae, minincidence=.05)

∟ Proportion of adverse events by Treatment

Proportion of adverse events by Treatment sorted by descending risk difference. 3 events with less than 0.05 incidence in all groups are not shown ( :Hyperkalemia, :Rash, :Weight Decrease).
Category N Used
Enrolled 1000 400
Randomized 400 400
A 212 212
B 188 188
image

To show hazard rate estimates from exponential distributions (events per person time of exposure), make up total exposure time for patients on each treatment, in person-years.

exposure <- c(a=10000, b=9000)
eReport(event ~ treat, exposure=exposure, expunit='year', data=ae)

∟ Rate of adverse events by Treatment

Rate of adverse events by Treatment sorted by descending hazard rate ratio
Category N Used
Enrolled 1000 400
Randomized 400 400
A 212 212
B 188 188
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