The companion package of the book "Dynamic Prediction in Survival Analysis"

Description

The companion package of the book "Dynamic Prediction in Survival Analysis".

Details

An overview of how to use the package, including the most important functions.

Author(s)

Hein Putter Maintainer: Hein Putter <H.Putter@lumc.nl>

References

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.

Calculate AUC(t) curve

Description

Calculate model-free curve of Area Under the Curve values over time, based on the dynamic/incident AUC of Heagerty and Zheng.

Usage

AUC(formula, data, plot = TRUE)

Arguments

Value

A list with elements

Author(s)

Hein Putter H.Putter@lumc.nl

References

Harrell FE, Lee KL & Mark DB (1996), Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors, Statistics in Medicine 15, 361-387.

Heagerty PJ & Zheng Y (2005), Survival model predictive accuracy and ROC curves, Biometrics 61, 92-105.

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.

Examples

data(ova)
AUC(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, data = ova)

Calculate dynamic AUC(t) curve

Description

Calculate dynamic model-free curve of Area Under the Curve values over time, based on the dynamic/incident AUC of Heagerty and Zheng.

Usage

AUCw(formula, data, width)

Arguments

Value

A data frame with columns

and with attribute "width" given as input.

Author(s)

Hein Putter H.Putter@lumc.nl

References

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.

Examples

data(ova)
AUCw(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, data = ova,
  width = 2)

Calculate cross-validated log-partial likelihood (with shrinkage)

Description

This function calculates the cross-validated log partial likelihood, with shrinkage if requested.

Usage

CVPL(formula, data, progress = TRUE, overall = FALSE, shrink = 1)

Arguments

Value

Numeric; the cross-validated log partial likelihood

Author(s)

Hein Putter H.Putter@lumc.nl

References

Verweij PJM & van Houwelingen HC (1994), Penalized likelihood in Cox regression, Statistics in Medicine 13, 2427-2436.

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.

Examples

data(ova)
CVPL(Surv(tyears, d) ~ 1, data = ova)
CVPL(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam,
  data = ova)
CVPL(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam,
  data = ova, overall=TRUE)

Calculate cross-validated c-index

Description

This function calculates cross-validated versions of Harrell's c-index.

Usage

CVcindex(formula, data, type = "single", matrix = FALSE)

Arguments

Value

A list with elements

and with attribute "type" as given as input.

Author(s)

Hein Putter H.Putter@lumc.nl

References

Harrell FE, Lee KL & Mark DB (1996), Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors, Statistics in Medicine 15, 361-387.

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.

Examples

data(ova)
# Real thing takes a long time, so on a smaller data set
ova2 <- ova[1:100,]
# Actual c-index
cindex(Surv(tyears,d) ~ Karn + Broders + FIGO + Ascites + Diam, data = ova2)
# Cross-validated c-indices
CVcindex(Surv(tyears,d) ~ Karn + Broders + FIGO + Ascites + Diam, data = ova2)
CVcindex(Surv(tyears,d) ~ Karn + Broders + FIGO + Ascites + Diam, data = ova2,
         type="pair")

CVcindex(Surv(tyears,d) ~ Karn + Broders + FIGO + Ascites + Diam, data = ova2,
         type="fullpairs")

Data from the European Society for Blood and Marrow Transplantation (EBMT)

Description

Data from the European Society for Blood and Marrow Transplantation (EBMT)

Format

A data frame of 2279 patients transplanted at the EBMT between 1985 and 1998. These data were used in Fiocco, Putter & van Houwelingen (2008) and van Houwelingen & Putter (2008). The included variables are

id

Patient identification number

rec

Time in days from transplantation to recovery or last follow-up

rec.s

Recovery status; 1 = recovery, 0 = censored

ae

Time in days from transplantation to adverse event (AE) or last follow-up

ae.s

Adverse event status; 1 = adverse event, 0 = censored

recae

Time in days from transplantation to both recovery and AE or last follow-up

plag.s

Recovery and AE status; 1 = both recovery and AE, 0 = no recovery or no AE or censored

rel

Time in days from transplantation to relapse or last follow-up

rel.s

Relapse status; 1 = relapse, 0 = censored

srv

Time in days from transplantation to death or last follow-up

srv.s

Relapse status; 1 = dead, 0 = censored

year

Year of transplantation; factor with levels "1985-1989", "1990-1994", "1995-1998"

agecl

Patient age at transplant; factor with levels "<=20", "20-40", ">40"

proph

Prophylaxis; factor with levels "no", "yes"

match

Donor-recipient gender match; factor with levels "no gender mismatch", "gender mismatch"

Source

We gratefully acknowledge the European Society for Blood and Marrow Transplantation (EBMT) for making available these data. Disclaimer: these data were simplified for the purpose of illustration of the analysis of competing risks and multi-state models and do not reflect any real life situation. No clinical conclusions should be drawn from these data.

References

Fiocco M, Putter H, van Houwelingen HC (2008). Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models. Statistics in Medicine 27, 4340–4358.

van Houwelingen HC, Putter H (2008). Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data. Lifetime Data Anal 14, 447–463.

Calculate dynamic "death within window" curve

Description

Calculate dynamic "death within window" curve, in other words, one minus fixed width conditional survival curves, defined as P(T<=t+w|T>t), for a fixed window width w.

Usage

Fwindow(object, width, variance = TRUE, conf.level = 0.95)

Arguments

Details

"Die within window function" with window w, Fw(t) = P(T<=t+w|T>t), evaluated at all time points t where the estimate changes value, and associated pointwise confidence intervals (if variance=TRUE).

Both estimate and pointwise lower and upper confidence intervals are based on the negative exponential of the Nelson-Aalen estimate of the cumulative hazard, so P(T<=t+w|T>t) is estimated as exp(- int_t^t+w hatH_NA(s) ds), with hatH_NA the non-parametric Nelson-Aalen estimate.

Note: in object, no event time points at or below zero allowed

Value

A data frame with columns

and with attribute "width" as given as input.

Author(s)

Hein Putter H.Putter@lumc.nl

References

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.

Examples

data(wbc1)
c0 <- coxph(Surv(tyears, d) ~ 1, data = wbc1, method="breslow")
sf0 <- survfit(c0)
Fw <- Fwindow(sf0,4)

Clinical and follow-up data of breast cancer patients as collected in the Dutch Cancer Institute (NKI) in Amsterdam

Description

A data frame of 295 patients with breast cancer. The included variables are

patnr

Patient identification number

d

Survival status; 1 = death; 0 = censored

tyears

Time in years until death or last follow-up

diameter

Diameter of the primary tumor

posnod

Number of positive lymph nodes

age

Age of the patient

mlratio

Estrogen level?

chemotherapy

Chemotherapy used (yes/no)

hormonaltherapy

Hormonal therapy used (yes/no)

typesurgery

Type of surgery (excision or mastectomy)

histolgrade

Histological grade (Intermediate, poorly, or well differentiated)

vasc.invasion

Vascular invasion (-, +, or +/-)

crossval.clin.class

??

PICV

Estrogen level?

Format

A data frame, see data.frame.

References

van't Veer LJ, Dai HY, van de Vijver MJ, He YDD, Hart AAM, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R \& Friend SH (2002). Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536.

van de Vijver MJ, He YD, van 't Veer LJ, Dai H, Hart AAM, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH \& Bernards R (2002). A gene-expression signature as a predictor of survival in breast cancer. New England Journal of Medicine 347, 1999–2009.

van Houwelingen HC, Bruinsma T, Hart AAM, van't Veer LJ \& Wessels LFA (2006). Cross-validated Cox regression on microarray gene expression data. Statistics in Medicine 25, 3201–3216.

Data originate from two clinical trials on the use of different combination chemotherapies, carried out in The Netherlands around 1980

Description

A data frame of 358 patients with ovarian cancer. The included variables are

tyears

Time in years until death or last follow-up

d

Survival status; 1 = death; 0 = censored

Karn

Karnofsky score

Broders

Broders score: factor with levels "unknown", "1", "2", "3", "4"

FIGO

FIGO stage; factor with levels "III", "IV"

Ascites

Presence of ascires; factor with levels "unknown", "absent", "present"

Diam

Diameter of the tumor; factor with levels "micr.", "<1cm", "1-2cm", "2-5cm", ">5cm"

Format

A data frame, see data.frame.

References

Neijt, J. P., ten Bokkel Huinink, W. W., van der Burg, M. E., van Oosterom, A. T., Vriesendorp, R., Kooyman, C. D., van Lindert, A. C., Hamerlynck, J. V., van Lent, M. & van Houwelingen, J. C. (1984), 'Randomised trial comparing two combination chemotherapy regimens (Hexa-CAF vs CHAP- 5) in advanced ovarian carcinoma', Lancet 2, 594–600.

Neijt, J. P., ten Bokkel Huinink, W. W., van der Burg, M. E., van Oosterom, A. T., Willemse, P. H., Heintz, A. P., van Lent, M., Trimbos, J. B., Bouma, J. & Vermorken, J. B. (1987), 'Randomized trial comparing two combination chemotherapy regimens (CHAP-5 vs CP) in advanced ovarian carcinoma', Journal of Clinical Oncology 5, 1157–1168.

van Houwelingen, J. C., ten Bokkel Huinink, W. W., van der Burg, M. E., van Oosterom, A. T. & Neijt, J. P. (1989), 'Predictability of the survival of patients with advanced ovarian cancer.', Journal of Clinical Oncology 7, 769–773.

Data from the Benelux CML study

Description

A data frame of 210 patients with Chronic Myeloid Leukemia from the Benelux CML study (Kluin-Nelemans et al. 1998). Data have been used in two methodological papers, de Bruijne et al. (2001) and van Houwelingen (2007), and in the book van Houwelingen \& Putter (2011), especially Chapter 8. More background is given in Appendix A.2 of van Houwelingen \& Putter (2011). Interest is in the time-dependent covariate White Blood Cell count (WBC). Data set wbc1 contains the follow-up data and time-fixed covariates, while wbc2 contains the WBC measurements. The included variables in wbc1 are

patnr

Patient identification number

tyears

Time in years from randomization to death or last follow-up

d

Survival status; 1 = dead, 0 = censored

sokal

Clinical index based on spleen size, percentage of circulating blasts, platelet and age at diagnosis

age

Age at diagnosis

Format

A data frame, see data.frame.

References

Kluin-Nelemans JC, Delannoy A, Louwagie A, le Cessie S, Hermans J, van der Burgh JF, Hagemeijer AM, van den Berghe H \& Benelux CML Study Group (1998). Randomized study on hydroxyurea alone versus hydroxyurea combined with low-dose interferon-alpha 2b for chronic myeloid leukemia. Blood 91, 2713–2721.

de Bruijne MHJ, le Cessie S, Kluin-Nelemans HC \& van Houwelingen HC (2001). On the use of Cox regression in the presence of an irregularly observed time-dependent covariate. Statistics in Medicine 20, 3817–3829.

van Houwelingen HC (2007). Dynamic prediction by landmarking in event history analysis. Scandinavian Journal of Statistics 34, 70–85.

van Houwelingen HC, Putter H (2012). Dynamic Predicting in Clinical Survival Analysis. Chapman \& Hall.

Data from the Benelux CML study

Description

A data frame of 210 patients with Chronic Myeloid Leukemia from the Benelux CML study (Kluin-Nelemans et al. 1998). Data have been used in two methodological papers, de Bruijne et al. (2001) and van Houwelingen (2007), and in the book van Houwelingen \& Putter (2011), especially Chapter 8. More background is given in Appendix A.2 of van Houwelingen \& Putter (2011). Interest is in the time-dependent covariate White Blood Cell count (WBC). Data set wbc1 contains the follow-up data and time-fixed covariates, while wbc2 contains the WBC measurements. The included variables in wbc2 are

patnr

Patient identification number

tyears

Time of WBC measurement in years from randomization

lwbc

Log-transformed and standardized WBC measurement, more precisely, defined as lwbc=log10(wbc)-0.95

Format

A data frame, see data.frame.

References

Kluin-Nelemans JC, Delannoy A, Louwagie A, le Cessie S, Hermans J, van der Burgh JF, Hagemeijer AM, van den Berghe H \& Benelux CML Study Group (1998). Randomized study on hydroxyurea alone versus hydroxyurea combined with low-dose interferon-alpha 2b for chronic myeloid leukemia. Blood 91, 2713–2721.

de Bruijne MHJ, le Cessie S, Kluin-Nelemans HC \& van Houwelingen HC (2001). On the use of Cox regression in the presence of an irregularly observed time-dependent covariate. Statistics in Medicine 20, 3817–3829.

van Houwelingen HC (2007). Dynamic prediction by landmarking in event history analysis. Scandinavian Journal of Statistics 34, 70–85.

van Houwelingen HC, Putter H (2012). Dynamic Predicting in Clinical Survival Analysis. Chapman \& Hall.

Calculate Harrell's c-index

Description

This function calculates Harrell's c-index.

Usage

cindex(formula, data)

Arguments

Value

A list with elements

Author(s)

Hein Putter H.Putter@lumc.nl

References

Harrell FE, Lee KL & Mark DB (1996), Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors, Statistics in Medicine 15, 361-387.

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.

Examples

data(ova)
cindex(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, data = ova)

Create landmark data set

Description

Create landmark data set from original data, which can be either in wide or long format, see details.

Usage

cutLM(data, outcome, LM, horizon, covs, format = c("wide", "long"), id, rtime,
  right = TRUE)

Arguments

Details

For a given landmark time point LM, patients who have reached the event of interest (outcome) or are censored before or at LM are removed. Administrative censoring is applied at the time horizon. Time-varying covariates are evaluated at the landmark time point LM. Time-varying covariates can be specified in the varying item of the covs argument, in two ways. In the first way (data in long format) different values of time-dependent covariate(s) are stored different rows of the data, with id identifying which values belong to the same subject; the column specified through rtime then contains the time points at which the value of the covariate changes value; with right=TRUE (default), it is assumed that the covariate changes value at the time point specified in rtime (and hence is not used for prediction of an event at rtime), while with right=FALSE, it is assumed that the covariate changes value just before the time point specified in rtime. The second way (data in wide format) can only be used for a specific type of time-varying covariates, often used to model whether some other event has occurred or not, namely those that change value from 0 (event not yet occurred) to 1 (event has occurred).

Value

A landmark data set, containing the outcome and the values of time-fixed and time-varying covariates taken at the landmark time points. The value of the landmark time point is stored in column LM.

Author(s)

Hein Putter H.Putter@lumc.nl

References

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.

Examples

test0 <- data.frame(id=c(1,1,1,2,2,2),survyrs=c(2.3,2.3,2.3,2.7,2.7,2.7),
  survstat=c(1,1,1,0,0,0),age=c(76,76,76,68,68,68),gender=c(1,1,1,2,2,2),
  bp=c(80,84,88,92,90,89),bptime=c(1,2,2.2,0,1,2))
cutLM(test0, outcome=list(time="survyrs", status="survstat"),
  LM=1, horizon=2.5, covs=list(fixed=c("age","gender"),varying="bp"),
  format="long", id="id", rtime="bptime")
# Note how the previous example does not use the value of the time-varying
# covariate AT time=LM, only just before (if available). This is in line
# with the time-varying covariates being predictable.
# If you want the value of the time-varying covariate at time=LM if it
# changes value at LM, then use right=FALSE
cutLM(test0, outcome=list(time="survyrs", status="survstat"),
  LM=1, horizon=2.5, covs=list(fixed=c("age","gender"),varying="bp"),
  format="long", id="id", rtime="bptime", right=FALSE)

# An example of a time-varying covariate in wide format; recyrs and recstat
# are time and status of a (cancer) recurrence. Here it is assumed that the
# value of the time-varying covariate is 0 and changes value to 1 at recyrs.
# The status variable is not used!
test1 <- data.frame(id=1:4,survyrs=c(7.6,8.4,5.3,2.6),survstat=c(0,1,1,0),
  age=c(48,52,76,18),gender=c(1,2,2,1),recyrs=c(7.6,5.2,0.8,2.6),
  recstat=c(0,1,1,0))
cutLM(test1, outcome=list(time="survyrs", status="survstat"),
  LM=3, horizon=8, covs=list(fixed=c("id","age","gender"),varying="recyrs"))

# The same example in long format, similar to (but not the same as) the way
# one would use a time-varying covariate in long format.
test2 <- data.frame(id=c(1,2,2,3,3,4),survyrs=c(7.6,8.4,8.4,5.3,5.3,2.6),
  survstat=c(0,1,1,1,1,0),age=c(48,52,52,76,76,18),gender=c(1,2,2,2,2,1),
  rec=c(0,0,1,0,1,0),rectime=c(0,0,5.2,0,0.8,0))
cutLM(test2, outcome=list(time="survyrs", status="survstat"),
  LM=3, horizon=8, covs=list(fixed=c("age","gender"),varying="rec"),
  format="long", id="id", rtime="rectime")

Debugging function

Description

A simple but useful debugging function. It first announces the object to printed and then prints it.

Usage

deb(x, method = c("print", "cat"))

Arguments

Author(s)

Hein Putter H.Putter@lumc.nl

Examples

tm <- c(0.2,0.5,1,1.2,1.8,4)
ta <- 2*tm
dfr <- data.frame(time=tm, stepf=ta)
deb(dfr, method="print")
deb(tm, method="cat")

Evaluate step function at a set of new time points

Description

Given one or more right-continuous step functions of time, given by vector time and vector of matrix stepf, this function evaluates the step function(s) at a vector of new time points given by newtime. Typical application is when the step function is given by a non- or semi-parametric estimated of cumulative hazard or survival function, and the value of this function is required at a set of time points.

Usage

evalstep(time, stepf, newtime, subst = -Inf, to.data.frame = FALSE)

Arguments

Details

The argument time should be ordered, and not contain duplicated or +/- Inf, and should be of the same length as stepf. There are no restrictions on ordering or duplicates of newtime. For elements of newtime that are smaller than the minimum of time, the value of subst is substituted.

Value

Either a vector/matrix containing the step function(s) evaluated at the new time points (if to.data.frame=FALSE (default)), or a data frame with column vectors newtime containing the new time points and res containing the step function evaluated at the new time points (if to.data.frame=TRUE)

Author(s)

Hein Putter H.Putter@lumc.nl

Examples

tm <- c(0.2,0.5,1,1.2,1.8,4)
ta <- 2*tm
data.frame(time=tm, stepf=ta)
evalstep(time=tm, stepf=ta, newtime=c(0,0.2,0.3,0.6,1,1.5,3,4,5,0.1), subst=0)
evalstep(time=tm, stepf=data.frame(ta=ta,ta2=1/ta),
	newtime=c(0,0.2,0.3,0.6,1,1.5,3,4,5,0.1), subst=0)

Calculate prediction error curve

Description

Calculate prediction error curve.

Usage

pe(time, status, tsurv, survmat, tcens, censmat, FUN = c("KL", "Brier"), tout)

pecox(formula, censformula, data, censdata, FUN = c("KL", "Brier"), tout,
  CV = FALSE, progress = FALSE)

Arguments

Details

The censformula is used to calculate inverse probability of censoring weights (IPCW).

Value

A data frame with columns

Author(s)

Hein Putter H.Putter@lumc.nl

References

Graf E, Schmoor C, Sauerbrei W & Schumacher M (1999), Assessment and comparison of prognostic classification schemes for survival data, Statistics in Medicine 18, 2529-2545.

Gerds & Schumacher (2006), Consistent estimation of the expected Brier score in general survival models with right-censored event times, Biometrical Journal 48, 1029-1040.

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.

Examples

data(ova)
# Example on a subset, because the effect of CV is clearer
ova2 <- ova[1:100,]
pecox(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, Surv(tyears, 1-d) ~ 1,
  data = ova2, FUN="Brier", tout=seq(0,6,by=0.5))
pecox(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, Surv(tyears, 1-d) ~ 1,
  data = ova2, FUN="Brier", tout=seq(0,6,by=0.5), CV=TRUE, progress=TRUE)


pecox(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, Surv(tyears, 1-d) ~ 1,
  data = ova, FUN="Brier", tout=seq(0,6,by=0.5))
pecox(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, Surv(tyears, 1-d) ~ 1,
  data = ova, FUN="Brier", tout=seq(0,6,by=0.5), CV=TRUE, progress=TRUE)

Calculate dynamic prediction error curve

Description

Calculate dynamic fixed width prediction error curve.

Usage

pew(time, status, tsurv, survmat, tcens, censmat, width, FUN = c("KL",
  "Brier"), tout)

pewcox(formula, censformula, width, data, censdata, FUN = c("KL", "Brier"),
  tout, CV = FALSE, progress = FALSE)

Arguments

Details

Corresponds to Equation (3.6) in van Houwelingen and Putter (2011). The censformula is used to calculate inverse probability of censoring weights (IPCW).

Value

A data frame with columns

and with attribute "width" given as input.

Author(s)

Hein Putter H.Putter@lumc.nl

References

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.

Examples

data(ova)
# Example on a subset, because the effect of CV is clearer
ova2 <- ova[1:100,]
pewcox(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, Surv(tyears, 1-d) ~ 1,
  width=2, data = ova2, FUN="Brier", tout=seq(0,6,by=0.5))
pewcox(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, Surv(tyears, 1-d) ~ 1,
  width=2, data = ova2, FUN="Brier", tout=seq(0,6,by=0.5), CV=TRUE, progress=TRUE)


pewcox(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, Surv(tyears, 1-d) ~ 1,
  width=2, data = ova, FUN="Brier", tout=seq(0,6,by=0.5))
pewcox(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, Surv(tyears, 1-d) ~ 1,
  width=2, data = ova, FUN="Brier", tout=seq(0,6,by=0.5), CV=TRUE, progress=TRUE)

Create scatter plot with imputed survival times

Description

Create scatter plot with imputed survival times.

Usage

scatterplot(formula, data, horizon, plot = TRUE, xlab)

Arguments

Details

Imputation is used for censored survival times.

Value

A data frame with columns

and with attribute "horizon" (copied from input or default).

Author(s)

Hein Putter H.Putter@lumc.nl

References

Royston P (2001), The lognormal distribution as a model for survival time in cancer, with an emphasis on prognostic factors, Statistica Neerlandica 55, 89-104.

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.

Examples

data(ova)
scatterplot(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, data = ova)

Create a tolerance plot

Description

Create a tolerance plot according to the methods of Henderson, Jones & Stare (2001)

Usage

toleranceplot(formula, data, coverage = 0.8, horizon, plot = TRUE, xlab)

Arguments

Details

Warnings will be issued each time the survival curve corresponding to a value of x never goes below (1-coverage)/2; these warnings may be ignored.

Value

A data frame with columns

and with attributes "coverage" and "horizon" (copied from input or default).

Author(s)

Hein Putter H.Putter@lumc.nl

References

Henderson R, Jones M & Stare J (2001), Accuracy of point predictions in survival analysis, Statistics in Medicine 20, 3083-3096.

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analysis. Chapman & Hall.

Examples

data(ova)
toleranceplot(Surv(tyears, d) ~ Karn + Broders + FIGO + Ascites + Diam, data = ova)