Package 'wally'

divat data

Description

Extracted data from a french population based cohort (DIVAT cohort). The dataset includes followup information on kidney graft failure outcome and predicted 5-year risks based on based on the subject specific information which includes age, gender, cardiovascular and diabetes histories, monitoring of the evolution of the kidney function measured via serum creatinine and relevant characteristics of his or her kidney donor. Graft failure is defined as either death with functioning kidney graft or return to dialysis. The prediction model from which the predictions have been computed has been previously fitted using an independent training sample from the DIVAT data. Details about data and modeling can be found in Fournier et al. (2016).

Format

A subsample consisting of 1300 observations on the following 3 variables.

pi

5-year risk prediction of kidney graft failure.

status

0=censored, 1=kidney graft failure

time

time to event (i.e., time to kidney graft failure or loss of follow-up)

References

Fournier, M. C., Foucher, Y., Blanche, P., Buron, F., Giral, M., & Dantan, E. (2016). A joint model for longitudinal and time-to-event data to better assess the specific role of donor and recipient factors on long-term kidney transplantation outcomes. European journal of epidemiology, 31(5), 469-479.

Examples

data(divat)

---

threecity data

Description

Extracted data from a french population based cohort (Three-City cohort). The dataset includes followup information on dementia outcome and predicted 5-year risks based on based on the subject specific information which includes age, gender, education level and cognitive decline measured by a psychometric test (Mini Mental State Examination). The prediction model from which the predictions have been computed has been fitted on independent training data from the Paquid cohort, another french population based cohort with similar design (see Reference Blanche et al. 2015 for details) .

Format

A subsample consisting of 2000 observations on the following 3 variables.

pi

5-year absolute risk predictions of dementia.

status

0=censored, 1=dementia, 2=death dementia free

time

time to event (i.e., time to either dementia, death dementia free or loss of follow-up)

Source

Web-appendix of Blanche et al. (2015).

References

Blanche, P., Proust-Lima, C., Loubere, L., Berr, C., Dartigues, J. F., Jacqmin-Gadda, H. (2015). Quantifying and comparing dynamic predictive accuracy of joint models for longitudinal marker and time-to-event in presence of censoring and competing risks. Biometrics, 71(1), 102-113.

Examples

data(threecity)

---

Wally plots to assess calibration of a risk or survival prediction

Description

##' Wally plots to assess calibration of a risk or survival prediction

Usage

wallyPlot(object, time, formula, data, cause = 1, q = 10, ylim,
  hanging = FALSE, seed = NULL, mar = c(4.1, 4.1, 2, 2),
  colbox = "red", verbose = TRUE, col = c("grey90", "grey30"),
  xlab = "Risk groups", labels = "quantiles.labels", ...)

Arguments

object	Probabilistic survival predictions or probabilistic event risk predictions evaluated at time for the subjects in data. Either given in form of a numeric vector of probabilistic predictions or as an object which has a predictRisk method
time	Time interest for evaluating calibration of the predictions.
formula	A survival or event history formula. The left hand side is used to compute the expected event status. If formula is missing, try to extract a formula from the first element in object.
data	A data frame in which to validate the prediction models and to fit the censoring model. If data is missing, try to extract a data set from the first element in object.
cause	For competing risks settings the cause of interest.
q	The number of quantiles. Defaults to 10.
ylim	Limits of y-axis. If missing the function tries to find appropriate limits based on the simulated and real data.
hanging	If TRUE, hang bars corresponding to observed frequencies at the value of the corresponding prediction.
seed	A seed value to make results reproducible.
mar	Plot margins passed to par.
colbox	Color of the box which identifies the real data calibration plot.
verbose	If TRUE warn about missing formula and data.
col	Colour of the bars.
xlab	Label for x-axis
labels	Label below the bars. Either "quantiles" or "quantiles.label"
...	Further arguments passed to the subroutine wallyCalPlot and if hanging is TRUE also to subroutine lines.

Value

List of simulated and real data.

Author(s)

Paul F. Blanche <[email protected]> and Thomas A. Gerds <[email protected]>

References

Blanche P, Gerds T A, Ekstrom C T (2017). The Wally plot approach to assess the calibration of clinical prediction models, submitted.

Examples

# Survival setting
library(prodlim)
library(data.table)
library(survival)
set.seed(180)
d = SimSurv(180)
f = coxph(Surv(time,status)~X1+X2,data=d,x=TRUE)
## Not run: 
wallyPlot(f,
          time=4,
          q=10,
          data=d,
          formula=Surv(time,status)~1)
 wallyPlot(f,
          time=4,
          q=10,
          hanging=TRUE,
          data=d,
          formula=Surv(time,status)~1)

## End(Not run)

# Competing risks setting
library(prodlim)
library(survival)
library(riskRegression)
set.seed(180)
d2 = SimCompRisk(180)
f2 = CSC(Hist(time,event)~X1+X2,data=d2)
## Not run: 
wallyPlot(f2,
          time=5,
          q=3,
          hanging=TRUE,
          data=d2,
          formula=Hist(time,event)~1)
          

## End(Not run)

# Reproduce Wally plots presented in Blanche et al. (2017)
## Not run: 
data(threecity)
wallyPlot(threecity$pi,
time=5,
hanging=TRUE,
formula=Hist(time,status)~1,
data=threecity,
ylim=c(-.1,.25),
seed= 511,
hline.lwd=3,
mar=c(1.01, 4.1, 1.15, 2))

## End(Not run)

## Not run: 
data(divat)
wallyPlot(divat$pi,
time=5,
hanging=TRUE,
formula=Hist(time,status)~1,
data=divat,
ylim=c(-.1,.60),
seed= 123459,
hline.lwd=3,
mar=c(1.01, 4.1, 1.15, 2))

## End(Not run)

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