riskRegression (version 2020.12.08)

Score.list: Score risk predictions

Description

Methods to score the predictive performance of risk markers and risk prediction models

Usage

# S3 method for list
Score(
  object,
  formula,
  data,
  metrics = c("auc", "brier"),
  summary = NULL,
  plots = NULL,
  cause,
  times,
  landmarks,
  use.event.times = FALSE,
  null.model = TRUE,
  se.fit = TRUE,
  conservative = FALSE,
  multi.split.test = FALSE,
  conf.int = 0.95,
  contrasts = TRUE,
  probs = c(0, 0.25, 0.5, 0.75, 1),
  cens.method = "ipcw",
  cens.model = "cox",
  split.method,
  B,
  M,
  seed,
  trainseeds,
  parallel = c("no", "multicore", "snow", "as.registered"),
  ncpus = 1,
  cl = NULL,
  progress.bar = 3,
  keep,
  predictRisk.args,
  debug = 0L,
  useEventTimes,
  nullModel,
  censMethod,
  censModel,
  splitMethod,
  ...
)

Arguments

object

List of risk predictions (see details and examples).

formula

A formula which identifies the outcome (left hand side). E.g., Y ~ 1 for binary and Hist(time,status) ~ 1 for time-to-event outcome. In right censored data, the right hand side of the formula is used to estimate the inverse probability of censoring weights (IPCW) model.

data

data.frame or data.table in which the formula can be interpreted.

metrics

Character vector specifying which metrics to apply. Case does not matter. Choices are "AUC" and "Brier".

summary

Character vector specifying which summary statistics to apply to the predicted risks. Choices are "risks", "IPA", "riskQuantile" and "ibs". Can be all c("risks","IPA","riskQuantile","ibs") or a subset thereof.

• "risks" adds the predicted risks to the output.

• "ipa" computes the index of prediction accuracy (AKA R-squared) based on Brier scores for model vs null model

• "riskQuantile" calculates time-point specific boxplots for the predicted risks (or biomarker values) conditional on the outcome at the time-point.

• "ibs" calculates integrated Brier scores across the time points at which the Brier score is computed. This works only with time-to-event outcome and the results depend on the argument times.

Set to NULL to avoid estimation of summary statistics.

plots

Character vector specifying for which plots to put data into the result. Currently implemented are "ROC", "Calibration" and "boxplot". In addition, one can plot AUC and Brier score as function of time as soon as times has at least two different values.

cause

Event of interest. Used for binary outcome Y to specify that risks are risks of the event Y=event and for competing risks outcome to specify the cause of interest.

times

For survival and competing risks outcome: list of prediction horizons. All times which are greater than the maximal observed time in the data set are automatically removed. Note that the object returned by the function may become huge when the prediction performance is estimated at many prediction horizons.

landmarks

Not yet implemented.

use.event.times

If TRUE merge all unique event times with the vector given by argument times.

null.model

If TRUE fit a risk prediction model which ignores the covariates and predicts the same value for all subjects. The model is fitted using data and the left hand side of formula. For binary outcome this is just the empirical prevalence. For (right censored) time to event outcome, the null models are equal to the Kaplan-Meier estimator (no competing risks) and the Aalen-Johansen estimator (with competing risks).

se.fit

Logical or 0 or 1. If FALSE or 0 do not calculate standard errors.

conservative

Logical, only relevant in right censored data. If TRUE ignore variability of the estimate of the inverse probability of censoring weights when calculating standard errors for prediction performance parameters. This can potentially reduce computation time and memory usage at a usually very small expense of a slightly higher standard error.

multi.split.test

Logical or 0 or 1. If FALSE or 0 do not calculate multi-split tests. This argument is ignored when split.method is "none".

conf.int

Either logical or a numeric value between 0 and 1. In right censored data, confidence intervals are based on Blanche et al (see references). Setting FALSE prevents the computation confidence intervals. TRUE means compute 95 percent confidence intervals and corresponding p-values for AUC and Brier score. If set to 0.87, the level of significance is 13 percent. So, do not set it to 0.87.

contrasts

Either logical or a list of contrasts. A list of contrasts defines which risk prediction models (markers) should be contrasted with respect to their prediction performance. If TRUE do all possible comparisons. For example, when object is a list with two risk prediction models and null.model=TRUE setting TRUE is equivalent to list(c(0,1,2),c(1,2)) where c(0,1,2) codes for the two comparisons: 1 vs 0 and 2 vs 0 (positive integers refer to elements of object, 0 refers to the benchmark null model which ignores the covariates). This again is equivalent to explicitly setting list(c(0,1),c(0,2),c(1,2)). A more complex example: Suppose object has 7 elements and you want to do the following 3 comparisons: 6 vs 3, 2 vs 5 and 2 vs 3, you should set contrasts=c(6,3),c(2,5,3).

probs

Quantiles for retrospective summary statistics of the predicted risks. This affects the result of the function boxplot.Score.

cens.method

Method for dealing with right censored data. Either "ipcw" or "pseudo". Here IPCW refers to inverse probability of censoring weights and pseudo for jackknife pseudo values. Right now pseudo values are only used for calibration curves.

cens.model

Model for estimating inverse probability of censored weights. Implemented are the Kaplan-Meier method ("km") and Cox regression ("cox") both applied to the censored times. If the right hand side of formula does not specify covariates, the Kaplan-Meier method is used even if this argument is set to "cox".

split.method

Method for cross-validation. Right now the only choice is bootcv in which case bootstrap learning sets are drawn with our without replacement (argument M) from data. The data not included in the current bootstrap learning set are used as validation set to compute the prediction performance.

B

Number of bootstrap sets for cross-validation.

M

Size of subsamples for bootstrap cross-validation. If specified it has to be an integer smaller than the size of data.

seed

Super seed for setting training data seeds when randomly splitting (bootstrapping) the data during cross-validation.

trainseeds

Seeds for training models during cross-validation.

parallel

The type of parallel operation to be used (if any). If missing, the default is "no".

ncpus

integer: number of processes to be used in parallel operation.

cl

An optional parallel or snow cluster for use if parallel = "snow". If not supplied, a cluster on the local machine is created for the duration of the Score call.

progress.bar

Style for txtProgressBar. Can be 1,2,3 see help(txtProgressBar) or NULL to avoid the progress bar.

keep

list of characters (not case sensitive) which determines additional output. "residuals" provides Brier score residuals and "splitindex" provides sampling index used to split the data into training and validation sets. "vcov" provides the variance-covariance matrix of the estimated parameters.

predictRisk.args

A list of argument-lists to control how risks are predicted. The names of the lists should be the S3-classes of the object. The argument-lists are then passed on to the S3-class specific predictRisk method. For example, if your object contains one or several random forest model fitted with the function randomForestSRC::rfsrc then you can specify additional arguments for the function riskRegression::predictRisk.rfsrc which will pass these on to the function randomForestSRC::predict.rfsrc. A specific example in this case would be list(rfsrc=list(na.action="na.impute")).

A more flexible approach is to write a new predictRisk S3-method. See Details.

debug

Logical. If TRUE indicate landmark in progress of the program.

useEventTimes

obsolete.

nullModel

obsolete.

censMethod

obsolete.

censModel

obsolete.

splitMethod

obsolete.

...

Named list containging additional arguments that are passed on to the predictRisk methods corresponding to object. See examples.

Value

List with scores and assessments of contrasts, i.e., tests and confidence limits for performance and difference in performance (AUC and Brier), summaries and plots. Most elements are indata.table format.

Details

The function implements a toolbox for the risk prediction modeller: all tools work for the three outcomes: (1) binary (uncensored), (2) right censored time to event without competing risks, (3) right censored time to event with competing risks

Computed are the (time-dependent) Brier score and the (time-dependent) area under the ROC curve for a list of risk prediction models either in external validation data or in the learning data using bootstrap cross-validation. The function optionally provides results for plotting (time-point specific) ROC curves, for (time-point specific) calibration curves and for (time-point specific) retrospective boxplots.

For uncensored binary outcome the Delong-Delong test is used to contrast AUC of rival models. In right censored survival data (with and without competing risks) the p-values correspond to Wald tests based on standard errors obtained with an estimate of the influence function as described in detail in the appendix of Blanche et al. (2015).

This function works with one or multiple models that predict the risk of an event R(t|X) for a subject characterized by predictors X at time t. With binary endpoints (outcome 0/1 without time component) the risk is simply R(X). In case of a survival object without competing risks the function still works with predicted event probabilities, i.e., R(t|X)=1-S(t|X) where S(t|X) is the predicted survival chance for subject X at time t.

The already existing predictRisk methods (see methods(predictRisk)) may not cover all models and methods for predicting risks. But users can quickly extend the package as explained in detail in Mogensen et al. (2012) for the predecessors pec::predictSurvProb and pec::predictEventProb which have been unified as riskRegression::predictRisk.

Bootstrap Crossvalidation (see also Gerds & Schumacher 2007 and Mogensen et al. 2012)

B=10, M (not specified or M=NROW(data)) Training of each of the models in each of 10 bootstrap data sets (learning data sets). Learning data sets are obtained by sampling NROW(data) subjects of the data set with replacement. There are roughly .632*NROW(data) subjects in the learning data (inbag) and .368*NROW(data) subjects not in the validation data sets (out-of-bag).

These are used to estimate the scores: AUC, Brier, etc. Reported are averages across the 10 splits.

## Bootstrap with replacement set.seed(13) N=17 data = data.frame(id=1:N, y=rbinom(N,1,.3),x=rnorm(N)) boot.index = sample(1:N,size=N,replace=TRUE) boot.index inbag = 1:N outofbag = !inbag learn.data = data[inbag] val.data = data[outofbag] riskRegression:::getSplitMethod("bootcv",B=10,N=17)$index NOTE: the number .632 is the expected probability to draw one subject (for example subject 1) with replacement from the data, which does not depend on the sample size: B=10000 N=137 mean(sapply(1:B, function(b){match(1,sample(1:N,size=N,replace=TRUE),nomatch=0)})) N=30 mean(sapply(1:B, function(b){match(1,sample(1:N,size=N,replace=TRUE),nomatch=0)})) N=300 mean(sapply(1:B, function(b){match(1,sample(1:N,size=N,replace=TRUE),nomatch=0)}))

## Bootstrap without replacement (training size set to be 70 percent of data) B=10, M=.7

Training of each of the models in each of 10 bootstrap data sets (learning data sets). Learning data sets are obtained by sampling round(.8*NROW(data)) subjects of the data set without replacement. There are NROW(data)-round(.8*NROW(data)) subjects not in the learning data sets. These are used to estimate the scores: AUC, Brier, etc. Reported are averages across the 10 splits. set.seed(13) N=17 data = data.frame(id=1:N, y=rbinom(N,1,.3),x=rnorm(N)) boot.index = sample(1:N,size=M,replace=FALSE) boot.index inbag = 1:N outofbag = !inbag learn.data = data[inbag] val.data = data[outofbag] riskRegression:::getSplitMethod("bootcv",B=10,N=17,M=.7)$index

References

Ulla B. Mogensen, Hemant Ishwaran, Thomas A. Gerds (2012). Evaluating Random Forests for Survival Analysis Using Prediction Error Curves. Journal of Statistical Software, 50(11), 1-23. URL http://www.jstatsoft.org/v50/i11/.

Paul Blanche, Cecile Proust-Lima, Lucie Loubere, Claudine Berr, Jean- Francois Dartigues, and Helene Jacqmin-Gadda. 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, 2015.

P. Blanche, J-F Dartigues, and H. Jacqmin-Gadda. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Statistics in Medicine, 32(30):5381--5397, 2013.

E. Graf et al. (1999), Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine, vol 18, pp= 2529--2545.

Efron, Tibshirani (1997) Journal of the American Statistical Association 92, 548--560 Improvement On Cross-Validation: The .632+ Bootstrap Method.

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

Thomas A. Gerds, Martin Schumacher (2007) Efron-Type Measures of Prediction Error for Survival Analysis Biometrics, 63(4), 1283--1287 doi:10.1111/j.1541-0420.2007.00832.x

Martin Schumacher, Harald Binder, and Thomas Gerds. Assessment of survival prediction models based on microarray data. Bioinformatics, 23(14):1768-74, 2007.

Mark A. van de Wiel, Johannes Berkhof, and Wessel N. van Wieringen Testing the prediction error difference between 2 predictors Biostatistics (2009) 10(3): 550-560 doi:10.1093/biostatistics/kxp011

Michael W Kattan and Thomas A Gerds. The index of prediction accuracy: an intuitive measure useful for evaluating risk prediction models. Diagnostic and Prognostic Research, 2(1):7, 2018.

Examples

# NOT RUN {
# binary outcome
library(lava)
set.seed(18)
learndat <- sampleData(48,outcome="binary")
testdat <- sampleData(40,outcome="binary")

## score logistic regression models
lr1 = glm(Y~X1+X2+X7+X9,data=learndat,family=binomial)
lr2 = glm(Y~X3+X5,data=learndat,family=binomial)
Score(list("LR(X1+X2+X7+X9)"=lr1,"LR(X3+X5)"=lr2),formula=Y~1,data=testdat)

## ROC curve and calibration plot
xb=Score(list("LR(X1+X2+X7+X9)"=lr1,"LR(X3+X5+X6)"=lr2),formula=Y~1,
         data=testdat,plots=c("calibration","ROC"))
# }
# NOT RUN {
plotROC(xb)
plotCalibration(xb)
# }
# NOT RUN {
## compute AUC for a list of continuous markers
markers = as.list(testdat[,.(X6,X7,X8,X9,X10)])
Score(markers,formula=Y~1,data=testdat,metrics=c("auc"))

# cross-validation
# }
# NOT RUN {
    learndat=sampleData(400,outcome="binary")
    lr1a = glm(Y~X6,data=learndat,family=binomial)
    lr2a = glm(Y~X7+X8+X9,data=learndat,family=binomial)
    ## bootstrap cross-validation
    x1=Score(list("LR1"=lr1a,"LR2"=lr2a),formula=Y~1,data=learndat,split.method="bootcv",B=100)
    x1
    ## leave-one-out and leave-pair-out bootstrap
    x2=Score(list("LR1"=lr1a,"LR2"=lr2a),formula=Y~1,data=learndat,
             split.method="loob",
             B=100,plots="calibration")
    x2
# }
# NOT RUN {
# survival outcome

# Score Cox regression models
# }
# NOT RUN {
library(survival)
library(rms)
library(prodlim)
set.seed(18)
trainSurv <- sampleData(100,outcome="survival")
testSurv <- sampleData(40,outcome="survival")
cox1 = coxph(Surv(time,event)~X1+X2+X7+X9,data=trainSurv, y=TRUE, x = TRUE)
cox2 = coxph(Surv(time,event)~X3+X5+X6,data=trainSurv, y=TRUE, x = TRUE)
xs=Score(list("Cox(X1+X2+X7+X9)"=cox1,"Cox(X3+X5+X6)"=cox2),
         formula=Surv(time,event)~1,data=testSurv,conf.int=FALSE,times=c(5,8))
xs
# }
# NOT RUN {
# Integrated Brier score
# }
# NOT RUN {
xs=Score(list("Cox(X1+X2+X7+X9)"=cox1,"Cox(X3+X5+X6)"=cox2),
         formula=Surv(time,event)~1,data=testSurv,conf.int=FALSE,
         summary="ibs",
         times=sort(unique(testSurv$time)))
# }
# NOT RUN {
# time-dependent AUC for list of markers
# }
# NOT RUN {
survmarkers = as.list(testSurv[,.(X6,X7,X8,X9,X10)])
Score(survmarkers,
      formula=Surv(time,event)~1,metrics="auc",data=testSurv,
      conf.int=TRUE,times=c(5,8))

# compare models on test data
Score(list("Cox(X1+X2+X7+X9)"=cox1,"Cox(X3+X5+X6)"=cox2),
      formula=Surv(time,event)~1,data=testSurv,conf.int=TRUE,times=c(5,8))
# }
# NOT RUN {
# crossvalidation models in traindata
# }
# NOT RUN {
    library(survival)
    set.seed(18)
    trainSurv <- sampleData(400,outcome="survival")
    cox1 = coxph(Surv(time,event)~X1+X2+X7+X9,data=trainSurv, y=TRUE, x = TRUE)
    cox2 = coxph(Surv(time,event)~X3+X5+X6,data=trainSurv, y=TRUE, x = TRUE)
    x1 = Score(list("Cox(X1+X2+X7+X9)"=cox1,"Cox(X3+X5+X6)"=cox2),
               formula=Surv(time,event)~1,data=trainSurv,conf.int=TRUE,times=c(5,8),
               split.method="loob",B=100,plots="calibration")

    x2= Score(list("Cox(X1+X2+X7+X9)"=cox1,"Cox(X3+X5+X6)"=cox2),
              formula=Surv(time,event)~1,data=trainSurv,conf.int=TRUE,times=c(5,8),
              split.method="bootcv",B=100)
# }
# NOT RUN {
# restrict number of comparisons
# }
# NOT RUN {
    Score(list("Cox(X1+X2+X7+X9)"=cox1,"Cox(X3+X5+X6)"=cox2),
          formula=Surv(time,event)~1,data=trainSurv,contrasts=TRUE,
          null.model=FALSE,conf.int=TRUE,times=c(5,8),split.method="bootcv",B=3)

    # competing risks outcome
    set.seed(18)
    trainCR <- sampleData(40,outcome="competing.risks")
    testCR <- sampleData(40,outcome="competing.risks")
    library(riskRegression)
    library(cmprsk)
    # Cause-specific Cox regression
    csc1 = CSC(Hist(time,event)~X1+X2+X7+X9,data=trainCR)
    csc2 = CSC(Hist(time,event)~X3+X5+X6,data=trainCR)
    # Fine-Gray regression
    fgr1 = FGR(Hist(time,event)~X1+X2+X7+X9,data=trainCR,cause=1)
    fgr2 = FGR(Hist(time,event)~X3+X5+X6,data=trainCR,cause=1)
    Score(list("CSC(X1+X2+X7+X9)"=csc1,"CSC(X3+X5+X6)"=csc2,
               "FGR(X1+X2+X7+X9)"=fgr1,"FGR(X3+X5+X6)"=fgr2),
          formula=Hist(time,event)~1,data=testCR,se.fit=1L,times=c(5,8))
# }
# NOT RUN {


# }
# NOT RUN {
    # reproduce some results of Table IV of Blanche et al. Stat Med 2013
    data(Paquid)
    ResPaquid <- Score(list("DSST"=-Paquid$DSST,"MMSE"=-Paquid$MMSE),
                       formula=Hist(time,status)~1,
                       data=Paquid,
                       null.model = FALSE,
                       conf.int=TRUE,
                       metrics=c("auc"),
                       times=c(3,5,10),
                       plots="ROC")
    ResPaquid
    plotROC(ResPaquid,time=5)
# }
# NOT RUN {
# parallel options
# by erikvona: Here is a generic example of using future
# and doFuture, works great with the current version:
library(riskRegression)
library(future)
library(foreach)
library(doFuture)
library(survival)
# Register all available cores for parallel operation
plan(multiprocess, workers = availableCores())
registerDoFuture()
trainSurv <- sampleData(400,outcome="survival")
cox1 = coxph(Surv(time,event)~X1+X2+X7+X9,data=trainSurv,
             y=TRUE, x = TRUE)
# Bootstrapping on multiple cores
x1 = Score(list("Cox(X1+X2+X7+X9)"=cox1),
     formula=Surv(time,event)~1,data=trainSurv, times=c(5,8), 
     parallel = "as.registered", split.method="bootcv",B=100)
# }
# NOT RUN {


# }