The ictest function performs several different tests for
interval censored data, and the wlr_trafo function takes interval censored data and returns one of several rank-based
scores as determined by the scores option. The default for ictest is to perform a permutation test, either asymptotic or exact
depending on the size of the data. Other types of tests (the scores test form or multiple imputation form) are supported.
The 5 different score options allow different tests including
generalizations to interval censored data of either the
Wilcoxon-Mann-Whitney test
(scores="wmw") or the logrank test (scores="logrank1" or
scores="logrank2") (see details).
The function calls the icfit function, if an icfit object is not provided.
# S3 method for default
ictest(L, R, group,
scores = c("logrank1","logrank2","wmw","normal","general"),
rho=NULL,
alternative= c("two.sided", "less", "greater"),
icFIT=NULL,
initfit=NULL,
icontrol=icfitControl(),
exact=NULL,
method=NULL,
methodRule=methodRuleIC1,
mcontrol=mControl(),
Lin=NULL,
Rin=NULL,
dqfunc=NULL, ...)# S3 method for formula
ictest(formula, data, subset, na.action, ...)
# S3 method for default
wlr_trafo(x, R=NULL,
scores = c("logrank1", "logrank2", "wmw","normal","general"),
icFIT = NULL, initfit =NULL, control=icfitControl(),
Lin=NULL,Rin=NULL,dqfunc=NULL,...)
# S3 method for Surv
wlr_trafo(x,...)
# S3 method for data.frame
wlr_trafo(x,...)
The function wlr_trafo returns only the numeric vector of scores, while
ictest returns an object of class `ictest', which is a list with the following values.
This is a vector the same length as L and R, containing the rank scores (i.e., the ci values
in Fay, 1999 equation 2). These scores are calculated by wlr_trafo.
The efficient score vector. When group is a factor or character vector then each element of U has the interpretation as the weighted sum of "observed" minus "expected" deaths for the group element defined by the label of U. Thus negative values indicate better than average survival (see Fay, 1999).
number of observations in each group
full description of the test
description of data variables
character vector giving algorithm used in calculation,
value of method or
of result of methodRule. One of
`pclt', 'exact.network', etc.
either the chi-square or Z statistic, or NULL for exact methods
degrees of freedom for chi-square statistic
alternative hypothesis
phrase used to describe the alternative hypothesis
p value associated with alternative
vector of p-values under different alternatives
confidence interval on p.value, for method='exact.mc' only
number of Monte Carlo replications, for method='exact.mc' only
number of within subject resamplings, for WSR methods only
covariance matrix for U, output for method='scoretest' only
second derivative of log likelihood with respect to beta,output for method='scoretest' only
second derivative of log likelihood with respect to gamma, output for method='scoretest' only
derivative of log likelihood with respect to beta and gamma, output for method='scoretest' only
output of test statistic from permutation method, difference in means in scores, output only for permutation methods
0, null value of test statistics from permutation method, output only with permutation methods
number of permutation replications within each WSR, for method='wsr.mc' only
object of class 'icfit' giving results of NPMLE of all responses combined (ignoring group variable)
the matched call
numeric vector of left endpoints of censoring interval (equivalent to first element of Surv when type='interval2'), if R is NULL then represents exact failure time
numeric vector of right endpoints of censoring interval (equivalent to second element of Surv when type='interval2', see details)
response, either a Surv object or a numeric vector representing the left endpoint. If the latter and R is NULL then x is treated as exact
a vector denoting the group for which the test is desired. If group is a factor or character then a k-sample test is performed, where k is the number of unique values of group. If group is numeric then a "correlation" type test is performed. If there are only two groups, both methods give the same results.
character vector defining the scores: "logrank1" (default), "logrank2", "wmw" or others (see details)
either 0 (gives scores="logrank1"), or 1 (gives scores="wmw"), ignored if NULL (see Note)
character giving alternative for two-sample and trend tests, K-sample should be two.sided (see details)
a precalculated icfit object for increased computation speed. This should be the icfit from the pooled data. Normally initfit should be used instead (see Warning)
an object of class icfit or icsurv or a character vector giving a function name, used for the initial estimate (see Warning).
Ignored if icFIT is not null
list of arguments for controling NPMLE algorithm in call to icfit (default icfitControl)
a formula with response a numeric vector (which assumes no censoring) or Surv object, the right side of the formula is the group variable. No strata() is allowed
data frame for variables in formula
an optional vector specifying a subset of observations to be used
a function which indicates what should happen when the data contain NAs. Defaults to getOption("na.action")
a Surv object, see Surv
a logical value, TRUE denotes exact test, ignored if method is not NULL
a character value, one of 'pclt','exact.network','exact.ce','exact.mc', 'scoretest', 'wsr.HLY', 'wsr.pclt', 'wsr.mc'. If NULL method is chosen by methodRule which may use the value of exact.
a function used to choose the method, default methodRuleIC1. (see details in perm)
list of arguments for controling algorithms of different methods (see mControl)
logical vector, should L be included in the interval? (see details)
logical vector, should R be included in the interval? (see details)
function used with general scores (see details)
list of arguments for controling NPMLE algorithm in call to icfit (default icfitControl)
values passed to other functions
Michael P. Fay
Because the input of icFIT is only for saving computational time,
no checks are made to determine if the icFIT is in fact the correct one. Thus you may get
wrong answers with no warnings if you input the wrong icFIT object. The safer way to save
computational time is to input into initfit either a precalculated icfit object or
an icsurv object from a function in the Icens package such as EMICM.
When this is done, you will get either the correct answer or a warning even when you input a bad guess for the
initfit. Additionally, you may specify a function name for initfit. The default is NULL which
uses a simple initial fit function (it is a weighted average of the A matrix, see icfit.default code).
A fast but somewhat unstable function uses initcomputeMLE
which uses the computeMLE function from the 'MLEcens'
package.
See help for icfit for details on the initfit option.
The censoring in the default case (when Lin=Rin=NULL) assumes there are n (n=length(L)) failure times, and the ith one is in the interval between L[i] and R[i]. The default is not to include L[i] in the interval unless L[i]=R[i], and to include R[i] in the interval unless R[i]=Inf. When Lin and Rin are not NULL they describe whether to include L and R in the associated interval. If either Lin or Rin is length 1 then it is repeated n times, otherwise they should be logicals of length n.
Three different types of scores are compared in depth in Fay (1999): When scores='logrank1' this gives the most commonly used logrank scores for right censored data, and reduces to the scores of Sun (1996) for interval censored data. When scores='logrank2' this gives the scores associated with the grouped proportional hazards model of Finkelstein (1986). When scores='wmw' this gives the generalized Wilcoxon-Mann-Whitney scores.
The other options for scores only allow the permutation methods and
follow cases where the error under the grouped continuous model is either normally distributed ( scores='normal') or
distributed by some other distribution (scores='general') (see Fay, 1996). For scores='general' the
user must supply the function (dqfunc) which represents the density function of the inverse distribution function
of the error. For example, scores='general' with dqfunc equal to function(x){ dnorm(qnorm(x))} gives
the same results as scores='normal' or with dqfunc equal to function(x){ dlogis(qlogis(x))} gives
the same results (theoretically, but perhaps not exactly when calculated) as scores='wmw'.
For censored data two common likelihoods are the marginal likelihood of the ranks and the likelihood with nuisance parameters for the baseline survival. Here we use the latter likelihood (as in Finkelstein, 1986, Fay, 1996, and Sun, 1996).
Because of theoretical difficulties (discussed below), the default method (method=NULL with methodRule=methodRuleIC1) is to
perform a permutation test on the scores. There are several ways to perform the permutation
test, and the function methodRuleIC1 chooses which of these ways will be used. The choice
is basically between using a permutational central limit theorem (method="pclt") or using an exact method.
There are several algorithms for the exact method (see perm ). Note that there are two
exact two-sided methods and the default is to essentially double the smaller of the one-sided p-values (tsmethod='central'), while the
default in the coin package is different (see mControl and the tsmethod option).
Another method is to perform a standard score test (method="scoretest"). It is difficult to prove the asymptotic validity of the standard score tests for this likelihood because the number of nuisance parameters typically grows with the sample size and often many of the parameters are equal at the nonparametric MLE, i.e., they are on the boundary of the parameter space (Fay, 1996). Specifically, when the score test is performed then an adjustment is made so that the nuisance parameters are defined based on the data and do not approach the boundary of the parameter space (see Fay, 1996). Theoretically, the score test should perform well when there are many individuals but few observation times, and its advantage in this situation is that it retains validity even when the censoring mechanism may depend on the treatment.
Another method is to use multiple imputation, or within subject resampling (method="wsr.HLY") (Huang, Lee, and Yu, 2008). This method samples interval censored observations from the nonparametric distribution, then performs the usual martingale-based variance. A different possibility is to use a permutational central limit theorem variance for each wsr (method="wsr.pclt") or use Monte Carlo replications to get an possibly exact method from each within subject resampling (method="wsr.mc").
Note that when icfit and ictest are used on right censored data, because of the method of estimating variance is different, even Sun's method does not produce exactly the standard logrank test results.
Fay and Shaw (2010) gives the mathematical expressions for the different tests.
Note that the default method performs reasonably well even when the assessment times depend on the treatment (see Fay and Shih, 2012, Fay and Hunsberger, 2013). If your primary concern is with retaining type I error and you do not mind conservativeness, then the wsr.pclt or wsr.mc methods can be used.
Fay, MP (1996). "Rank invariant tests for interval censored data under the grouped continuous model". Biometrics, 52: 811-822.
Fay, MP (1999). "Comparing Several Score Tests for Interval Censored Data." Statistics in Medicine, 18: 273-285 (Correction: 1999, 18: 2681).
Fay, MP and Shaw, PA (2010). Exact and Asymptotic Weighted Logrank Tests for Interval Censored Data: The interval R package. Journal of Statistical Software. tools:::Rd_expr_doi("10.18637/jss.v036.i02"). 36 (2):1-34.
Fay, MP and Shih JH. (2012). Weighted Logrank Tests for Interval Censored Data when Assessment Times Depend on Treatment. Statistics in Medicine 31, 3760-3772.
Fay, MP and Hunsberger, SA. (2013). Practical Issues on Using Weighted Logrank Tests with Interval Censored Events in Clinical Trials. Chapter 13 in Interval-Censored Time-to-Event Data: Methods and Applications, Chen, D-G, Sun, J, and Peace, KE (editors) Chapman and Hall/CRC.
Finkelstein, DM (1986). "A proportional hazards model for interval censored failure time data" Biometrics, 42: 845-854.
Huang, J, Lee, C, Yu, Q (2008). "A generalized log-rank test for interval-censored failure time data via multiple imputation" Statistics in Medicine, 27: 3217-3226.
Sun, J (1996). "A non-parametric test for interval censored failure time data with applications to AIDS studies". Statistics in Medicine, 15: 1387-1395.
icfit, EMICM, computeMLE
## perform a logrank-type test using the permutation form of the test
data(bcos)
testresult<-ictest(Surv(left,right,type="interval2")~treatment, scores="logrank1",data=bcos)
testresult
## perform a Wilcoxon rank sum-type test
## using asymptotic permutation variance
left<-bcos$left
right<-bcos$right
trt<-bcos$treatment
## save time by using previous fit
ictest(left,right,trt, initfit=testresult$fit, method="pclt",scores="wmw")
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