Description Usage Arguments Value References Examples
This function estimates the AUC, TPR(c), FPR(c), PPV(c), and NPV(c) for for a specific timepoint and marker cutoff value c with survival data from a casecohort subcohort design.
1 2 3 4 5 6 7  survMTP.cch(time, event, marker,
weights,
subcoh,
data,
estimation.method = 'NP',
predict.time,
marker.cutpoint = 'median')

time 
time to event 
event 
indicator for the status of event of interest. event = 0 for censored observations, and event = 1 for event of interest. 
marker 
marker variable of interest 
weights 
sample weights from a casecohort sample design. Should be equal to 1/Pr(sampled from cohort). 
subcoh 
indicator for subjects included in the subcohort sample (1=included, 0=not included) 
data 
data frame in which to look for input variables. This should be the full cohort data, including time, event, weights, subcoh, and marker values when subcoh = 1. 
estimation.method 
should nonparametric ('NP') or semiparametric ('SP') estimates be calculated. Semiparametric methods assume a proportional hazards model. 
predict.time 
numeric value of the timepoint of interest for which to estimate the risk measures 
marker.cutpoint 
numeric value indicating the value of the
cutpoint 'c' at which to estimate 'FPR', 'TPR', 'NPV' or
'PPV'. default is 
a list with components
estimates 
point estimates for risk measures 
model.fit 
*only returned if estimation.method = 'SP'* object of type
'coxph' from fitting the model

marker.cutoff, estimation.method, predict.time 
function inputs 
1. Liu D, Cai T, Zheng Y. Evaluating the predictive value of biomarkers with stratified casecohort design. *Biometrics* 2012, 4: 12191227.
2. Pepe MS, Zheng Y, Jin Y. Evaluating the ROC performance of markers for future events. *Lifetime Data Analysis.* 2008, 14: 86113.
3. Zheng Y, Cai T, Pepe MS, Levy, W. Timedependent predictive values of prognostic biomarkers with failure time outcome. *JASA* 2008, 103: 362368.
4. Cai T. and Zheng Y . Resampling Procedures for Making Inference under Nested Casecontrol Studies. *JASA* 2013 (in press).
5. Cai T and Zheng Y/*, Evaluating Prognostic Accuracy of Biomarkers under nested casecontrol studies. *Biostatistics* 2012, 13,1, 89100.
(* equal contributor and corresponding author).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55  #simulated data for illustration
data(SimData)
#generate a subcohort from SimData
set.seed(12321)
#create a sample index. 1 if sampled, 0 if not
N < nrow(SimData)
sampleInd < rep(0, N)
# sample all with observed failure time. (200 individuals)
sampleInd[SimData$status==1] < 1
#sample 150 more observations from the entire data set without replacement
sampleInd[sample(1:N, 150)] < 1
table(sampleInd) #total number of subcohort is 293
cohortData_cch < SimData
#first calculate the Pr(Sampled from cohort) for each observation
sampleProb < numeric(500)
#all noncensored observations were sampled, so their sample probability is 1
sampleProb[cohortData_cch$status==1] < 1
#all other individuals had a 150/N chance to be sampled
sampleProb[cohortData_cch$status==0] < 150/N
#the sample weights are 1/(probability of being sampled)
cohortData_cch$weights < 1/sampleProb
#indicator of inclusion in the subcohort
cohortData_cch$subcohort = sampleInd
#estimate accuracy measures using nonparametric estimates
#by setting estimation.method = "NP"
survMTP.cch(time =survTime,
event = status,
marker = Y,
weights = weights,
subcoh = subcohort,
data = cohortData_cch,
estimation.method = "NP",
predict.time = 2,
marker.cutpoint = 0)
#estimate accuracy measures using semiparametric estimates
survMTP.cch(time =survTime,
event = status,
marker = Y,
weights = weights,
subcoh = subcohort,
data = cohortData_cch,
estimation.method = "SP",
predict.time = 2,
marker.cutpoint = 0)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.