Pregnancies exposed to coumarin derivatives

Description

Outcomes of pregnancies exposed to coumarin derivatives. The aim is to investigate whether exposition to coumarin derivatives increases the probability of spontaneous abortions. Apart from spontaneous abortion, pregnancy may end in induced abortion or live birth. Moreover, data are left-truncated as women usually enter the study several weeks after conception.

Usage

data(abortion)

Format

A data frame with 1186 observations on the following 5 variables.

id

Identification number

entry

Entry times into the cohort

exit

Event times

group

Group. 0: control, 1: exposed to coumarin derivatives

cause

Cause of failure. 1: induced abortion, 2: life birth, 3: spontaneous abortion

Source

Meiester, R. and Schaefer, C (2008). Statistical methods for estimating the probability of spontaneous abortion in observational studies – Analyzing pregnancies exposed to coumarin derivatives. Reproductive Toxicology, 26, 31–35

Examples

data(abortion)

Lines method for 'mvna' objects

Description

Lines method for mvna objects.

Usage

## S3 method for class 'mvna'
lines(x, tr.choice, col = 1, lty, conf.int = FALSE,
      level = 0.95, var.type = c("aalen", "greenwood"),
      ci.fun = c("log", "linear", "arcsin"), ci.col = col,
      ci.lty = 3, ...)

Arguments

Value

No value returned.

Author(s)

Arthur Allignol, arthur.allignol@gmail.com

See Also

mvna, plot.mvna

Examples

data(sir.adm)

## data set transformation
data(sir.adm) 
id <- sir.adm$id
from <- sir.adm$pneu
to <- ifelse(sir.adm$status==0,"cens",sir.adm$status+1)
times <- sir.adm$time
dat.sir <- data.frame(id,from,to,time=times)

## Possible transitions
tra <- matrix(ncol=4,nrow=4,FALSE)
tra[1:2,3:4] <- TRUE

na.pneu <- mvna(dat.sir,c("0","1","2","3"),
                tra,"cens")

plot(na.pneu, tr.choice = c("0 2"), conf.int = TRUE,
     col = 1, lty = 1, legend = FALSE)

lines(na.pneu, tr.choice = c("1 2"), conf.int = TRUE,
      col = 2, lty = 1)

Nelson-Aalen estimator in multistate models

Description

This function computes the multivariate Nelson-Aalen estimator of the cumulative transition hazards in multistate models, that is, for each possible transition, it computes an estimate of the cumulative hazard.

Usage

mvna(data, state.names, tra, cens.name)

Arguments

Details

This functions computes the Nelson-Aalen estimator as described in Anderson et al. (1993), along with the two variance estimators described in eq. (4.1.6) and (4.1.7) of Andersen et al. (1993) at each transition time.

Value

Returns a list named after the possible transitions, e.g. if we define a multistate model with two possible transitions: from state 0 to state 1, and from state 0 to state 2, the returned list will have two parts named "0 1" and "0 2". Each part contains a data.frame with columns:

The list also contains:

Note

The variance estimator (4.1.6) may overestimate the true variance, and the one defined eq. (4.1.7) may underestimate the true variance (see Klein (1991) and Andersen et al. (example IV.1.1, 1993)), especially with small sample set. Klein (1991) recommends the use of the variance estimator of eq. (4.1.6, "aalen") because he found it to be less biased.

Author(s)

Arthur Allignol, arthur.allignol@gmail.com

References

Andersen, P.K., Borgan, O., Gill, R.D. and Keiding, N. (1993). Statistical models based on counting processes. Springer Series in Statistics. New York, NY: Springer.

Beyersmann J, Allignol A, Schumacher M: Competing Risks and Multistate Models with R (Use R!), Springer Verlag, 2012 (Use R!)

Klein, J.P. Small sample moments of some estimators of the variance of the Kaplan-Meier and Nelson-Aalen estimators. Scandinavian Journal of Statistics, 18:333–340, 1991.

See Also

sir.adm,sir.cont

Examples

data(sir.cont)

# Modification for patients entering and leaving a state
# at the same date
sir.cont <- sir.cont[order(sir.cont$id, sir.cont$time), ]
for (i in 2:nrow(sir.cont)) {
  if (sir.cont$id[i]==sir.cont$id[i-1]) {
    if (sir.cont$time[i]==sir.cont$time[i-1]) {
      sir.cont$time[i-1] <- sir.cont$time[i-1] - 0.5
    }
  }
}

# Matrix of logical giving the possible transitions
tra <- matrix(ncol=3,nrow=3,FALSE)
tra[1, 2:3] <- TRUE
tra[2, c(1, 3)] <- TRUE

# Computation of the Nelson-Aalen estimates
na <- mvna(sir.cont,c("0","1","2"),tra,"cens")

# plot
if (require(lattice))
xyplot(na)

### example with left-truncation
data(abortion)

# Data set modification in order to be used by mvna
names(abortion) <- c("id", "entry", "exit", "from", "to")
abortion$to <- abortion$to + 1

## computation of the matrix giving the possible transitions
tra <- matrix(FALSE, nrow = 5, ncol = 5)
tra[1:2, 3:5] <- TRUE

na.abortion <- mvna(abortion, as.character(0:4), tra, NULL)

plot(na.abortion, tr.choice = c("0 4", "1 4"),
     curvlab = c("Control", "Exposed"),
     bty = "n", legend.pos = "topleft")

Plot method for a mvna object

Description

Plot method for an object of class mvna. This function plots estimates of the cumulative transition hazards in one panel.

Usage

## S3 method for class 'mvna'
plot(x, tr.choice, xlab = "Time",
     ylab = "Cumulative Hazard", col = 1, lty, xlim, ylim,
     conf.int = FALSE, level = 0.95,
     var.type = c("aalen", "greenwood"),
     ci.fun = c("log", "linear", "arcsin"),
     ci.col = col, ci.lty = 3,
     legend = TRUE, legend.pos, curvlab, legend.bty = "n", ...)

Arguments

Details

This plot method permits to draw several cumulative transition hazards on the same panel.

Value

No value returned

Author(s)

Arthur Allignol arthur.allignol@gmail.com

See Also

mvna

Examples

data(sir.cont)

# Modification for patients entering and leaving a state
# at the same date
sir.cont <- sir.cont[order(sir.cont$id, sir.cont$time), ]
for (i in 2:nrow(sir.cont)) {
  if (sir.cont$id[i]==sir.cont$id[i-1]) {
    if (sir.cont$time[i]==sir.cont$time[i-1]) {
      sir.cont$time[i-1] <- sir.cont$time[i-1] - 0.5
    }
  }
}

tra <- matrix(ncol=3,nrow=3,FALSE)
tra[1, 2:3] <- TRUE
tra[2, c(1, 3)] <- TRUE

na.cont <- mvna(sir.cont,c("0","1","2"),tra,"cens")

plot(na.cont, tr.choice=c("0 2", "1 2"))

Calculates Nelson-Aalen estimates at specified time-points

Description

This function gives the Nelson-Aalen estimates at time-points specified by the user.

Usage

## S3 method for class 'mvna'
predict(object, times, tr.choice, level = 0.95,
        var.type = c("aalen", "greenwood"),
        ci.fun = c("log", "linear", "arcsin"), ...)

Arguments

Value

Returns a list named after the possible transitions, e.g. if we define a multistate model with two possible transitions: from state 0 to state 1, and from state 0 to state 2, the returned list will have two parts named "0 1" and "0 2". Each part contains a data.frame with columns:

Author(s)

Arthur Allignol, arthur.allignol@gmail.com

References

Andersen, P.K., Borgan, O., Gill, R.D. and Keiding, N. (1993). Statistical models based on counting processes. Springer Series in Statistics. New York, NY: Springer.

See Also

mvna, summary.mvna

Examples

data(sir.cont)

# Modification for patients entering and leaving a state
# at the same date
sir.cont <- sir.cont[order(sir.cont$id, sir.cont$time), ]
for (i in 2:nrow(sir.cont)) {
  if (sir.cont$id[i]==sir.cont$id[i-1]) {
    if (sir.cont$time[i]==sir.cont$time[i-1]) {
      sir.cont$time[i-1] <- sir.cont$time[i-1] - 0.5
    }
  }
}

# Matrix of logical giving the possible transitions
tra <- matrix(ncol=3,nrow=3,FALSE)
tra[1, 2:3] <- TRUE
tra[2, c(1, 3)] <- TRUE

# Computation of the Nelson-Aalen estimates
na <- mvna(sir.cont,c("0","1","2"),tra,"cens")

# Using predict
predict(na,times=c(1,5,10,15))

Print method for 'mvna' object

Description

Print method for an object of class mvna. It prints estimates of the cumulative hazard along with estimates of the variance described in eq. (4.1.6) and (4.1.7) of Andersen et al. (1993) at several time points obtained with the quantile function.

Usage

## S3 method for class 'mvna'
print(x, ...)

Arguments

Value

No value returned.

Author(s)

Arthur Allignol, arthur.allignol@fdm-freiburg.de

See Also

mvna

Pneumonia on admission in intenive care unit patients

Description

Pneumonia status on admission for intensive care unit (ICU) patients, a random sample from the SIR-3 study.

Usage

data(sir.adm)

Format

The data contains 747 rows and 4 variables:

id:

Randomly generated patient id

pneu:

Pneumonia indicator. 0: No pneumonia, 1: Pneumonia

status

Status indicator. 0: censored observation, 1: discharged, 2: dead

time:

Follow-up time in day

age:

Age at inclusion

sex:

Sex. F for female and M for male

Source

Beyersmann, J., Gastmeier, P., Grundmann, H., Baerwolff, S., Geffers, C., Behnke, M., Rueden, H., and Schumacher, M. Use of multistate models to assess prolongation of intensive care unit stay due to nosocomial infection. Infection Control and Hospital Epidemiology, 27:493-499, 2006.

Examples

# data set transformation
data(sir.adm) 
id <- sir.adm$id
from <- sir.adm$pneu
to <- ifelse(sir.adm$status==0,"cens",sir.adm$status+1)
times <- sir.adm$time
dat.sir <- data.frame(id,from,to,time=times)

# Possible transitions
tra <- matrix(ncol=4,nrow=4,FALSE)
tra[1:2,3:4] <- TRUE

na.pneu <- mvna(dat.sir,c("0","1","2","3"),
                tra,"cens")

if(require("lattice")) {
xyplot(na.pneu,tr.choice=c("0 2","1 2","0 3","1 3"),
       aspect=1,strip=strip.custom(bg="white",
       factor.levels=c("No pneumonia on admission -- Discharge",
                       "Pneumonia on admission -- Discharge",
                       "No pneumonia on admission -- Death",
                       "Pneumonia on admission -- Death"),
       par.strip.text=list(cex=0.9)),
       scales=list(alternating=1),xlab="Days",
       ylab="Nelson-Aalen esimates")
}

Ventilation status in intensive care unit patients

Description

Time-dependent ventilation status for intensive care unit (ICU) patients, a random sample from the SIR-3 study.

Usage

data(sir.cont)

Format

A data frame with 1141 rows and 6 columns:

id:

Randomly generated patient id

from:

State from which a transition occurs

to:

State to which a transition occurs

time:

Time when a transition occurs

age:

Age at inclusion

sex:

Sex. F for female and M for male

The possible states are:

0: No ventilation

1: Ventilation

2: End of stay.

And cens stands for censored observations.

Details

This data frame consists in a random sample of the SIR-3 cohort data. It focuses on the effect of ventilation on the length of stay (combined endpoint discharge/death). Ventilation status is considered as a transcient state in an illness-death model.

The data frame is directly formated to be used with the mvna function, i.e., it is transition-oriented with one row per transition.

Source

Beyersmann, J., Gastmeier, P., Grundmann, H., Baerwolff, S., Geffers, C., Behnke, M., Rueden, H., and Schumacher, M. Use of multistate models to assess prolongation of intensive care unit stay due to nosocomial infection. Infection Control and Hospital Epidemiology, 27:493-499, 2006.

Examples

data(sir.cont)

# Matrix of possible transitions
tra <- matrix(ncol=3,nrow=3,FALSE)
tra[1, 2:3] <- TRUE
tra[2, c(1, 3)] <- TRUE

# Modification for patients entering and leaving a state
# at the same date
sir.cont <- sir.cont[order(sir.cont$id, sir.cont$time), ]
for (i in 2:nrow(sir.cont)) {
  if (sir.cont$id[i]==sir.cont$id[i-1]) {
    if (sir.cont$time[i]==sir.cont$time[i-1]) {
      sir.cont$time[i-1] <- sir.cont$time[i-1] - 0.5
    }
  }
}

# Computation of the Nelson-Aalen estimates
  na.cont <- mvna(sir.cont,c("0","1","2"),tra,"cens")

if (require("lattice")) {
  xyplot(na.cont,tr.choice=c("0 2","1 2"),aspect=1,
       strip=strip.custom(bg="white",
         factor.levels=c("No ventilation -- Discharge/Death",
           "Ventilation -- Discharge/Death"),
         par.strip.text=list(cex=0.9)),
       scales=list(alternating=1),xlab="Days",
       ylab="Nelson-Aalen estimates")
}

Summary method for objects of class 'mvna'

Description

Summary method for mvna objects. The function returns a list containing the cumulative transition hazards, variance and other informations.

Usage

## S3 method for class 'mvna'
summary(object, level = 0.95,
        var.type = c("aalen", "greenwood"),
        ci.fun = c("log", "linear", "arcsin"), ...)

## S3 method for class 'mvna'
print.summary(x, ...)

Arguments

Value

Returns an object of class mvna which is a list of data frames named after the possible transitions. Each data frame contains the following columns:

Author(s)

Arthur Allignol, arthur.allignol@gmail.com

See Also

mvna

Examples

data(sir.adm)

## data set transformation
data(sir.adm) 
id <- sir.adm$id
from <- sir.adm$pneu
to <- ifelse(sir.adm$status==0,"cens",sir.adm$status+1)
times <- sir.adm$time
dat.sir <- data.frame(id,from,to,time=times)

## Possible transitions
tra <- matrix(ncol=4,nrow=4,FALSE)
tra[1:2,3:4] <- TRUE

na.pneu <- mvna(dat.sir,c("0","1","2","3"),
                tra,"cens")

summ.na.pneu <- summary(na.pneu)

## cumulative hazard for 0 -> 2 transition:
summ.na.pneu$"0 2"$na

Panel plots for object of class 'mvna'

Description

xyplot function for objects of class mvna. Estimates of the cumulative hazards are plotted as a function of time for all the transitions specified by the user. The function can also plot several types of pointwise confidence interval (see Andersen et al. (1993) p.208).

Usage

## S3 method for class 'mvna'
xyplot(x, data = NULL, xlab = "Time",
       ylab = "Cumulative Hazard", tr.choice = "all",
       conf.int = TRUE, var.type = c("aalen", "greenwood"),
       ci.fun = c("log", "linear", "arcsin"), level = 0.95,
       col = c(1, 1, 1), lty = c(1, 3, 3),
       ci.type = c(1, 2), ...)

Arguments

Value

An object of class trellis.

Note

These plots are highly customizable, see Lattice and xyplot. For example, if one want to change strip background color and the title of each strip, it can be added 'strip=strip.custom(bg="a color",factor.levels="a title","another title")'. One can use 'aspect="1"' to get the size of the panels isometric.

Author(s)

Arthur Allignol, arthur.allignol@fdm-freiburg.de

References

Andersen, P.K., Borgan, O., Gill, R.D. and Keiding, N. (1993). Statistical models based on counting processes. Springer Series in Statistics. New York, NY: Springer.

Deepayan Sarkar (2006). lattice: Lattice Graphics. R package version 0.13-8.

See Also

xyplot, mvna, sir.adm,sir.cont