| Title: | Censored and Truncated Quantile Regression |
|---|---|
| Description: | Estimation of quantile regression models for survival data. |
| Authors: | Paolo Frumento |
| Maintainer: | Paolo Frumento <[email protected]> |
| License: | GPL-2 |
| Version: | 2.1 |
| Built: | 2024-12-09 06:39:17 UTC |
| Source: | CRAN |
Fit quantile regression models to survival data, allowing for right censoring, left truncation, and interval censoring.
| Package: | ctqr |
| Type: | Package |
| Version: | 2.1 |
| Date: | 2024-02-12 |
| License: | GPL-2 |
The main function ctqr is used for model fitting. Other documented functions are predict.ctqr, to obtain prediction from a ctqr object, plot.ctqr, to plot quantile regression coefficients, and ctqr.control, that can be used to set the operational parameters for the estimation algorithm.
Paolo Frumento
Maintainer: Paolo Frumento <[email protected]>
Frumento, P., and Bottai, M. (2017). An estimating equation for censored and truncated quantile regression. Computational Statistics and Data Analysis, Vol.113, pp.53-63. ISSN: 0167-9473.
Frumento, P. (2022). A quantile regression estimator for interval-censored data. The International Journal of Biostatistics, 19 (1), pp. 81-96.
pchreg, that is used to compute a preliminary estimate of the conditional outcome distribution.
Fits a quantile regression model to possibly censored and truncated data, e.g., survival data.
ctqr(formula, data, weights, p = 0.5, CDF, control = ctqr.control(), ...)ctqr(formula, data, weights, p = 0.5, CDF, control = ctqr.control(), ...)
formula |
an object of class “ |
data |
an optional data frame containing the variables in the model. |
weights |
an optional vector of weights to be used in the fitting process. The weights will always be normalized to sum to the sample size. This implies that, for example, using double weights will not halve the standard errors. |
p |
numerical vector indicating the order of the quantile(s) to be fitted. |
CDF |
an object of class “ |
control |
a list of operational parameters for the optimization algorithm, usually passed via |
... |
for future arguments. |
This function implements the method described in Frumento and Bottai (2017) for censored, truncated quantile regression, and Frumento (2022) for interval-censored quantile regression.
The left side of formula must be of the form Surv(time, event) if the data are right-censored, Surv(time0, time, event) if the data are right-censored and left-truncated (time0 < time, time0 can be -Inf), and Surv(time1, time2, type = "interval2") if the data are interval-censored (use time1 = time2 for exact observations, time1 = -Inf or NA for left-censored, and time2 = Inf or NA for right-censored). Using Surv(time) is also allowed and indicates that the data are neither censored nor truncated.
The conditional distribution function (CDF) of the response variable represents a nuisance parameter
and is estimated preliminarly via pchreg. If missing, CDF = pchreg(formula) is used as default. See the “Note” and the documentation of pchreg.
Estimation is carried out using an algorithm for gradient-based optimization. To estimate the asymptotic covariance matrix, standard two-step procedures are used (e.g., Ackerberg et al., 2012).
An object of class “ctqr”, which is a list with the following items:
p |
the quantile(s) being estimated. |
coefficients |
a named vector or matrix of quantile regression coefficients. |
call |
the matched call. |
n.it |
the number of iterations. |
converged |
logical. The convergence status. |
fitted |
the fitted values. |
terms |
the |
mf |
the model frame used. |
covar |
the estimated asymptotic covariance matrix. |
CDF |
the used |
Note that the dimension of all items, except call, terms, mf, and CDF,
is the same as the dimension of p. For example, if p = c(0.25,0.5,0.75), coefficients
and fitted will be 3-columns matrices; n.it and converged will be vectors of 3 elements;
and covar will be a list of three covariance matrices.
The generic accessor functions summary, plot, predict, coef, terms, nobs,
can be used to extract information from the model. The functions
waldtest (from the package lmtest), and linearHypothesis (from the package car) can be
used to perform Wald test, and to test linear restrictions. These functions, however,
will only work if p is scalar.
NOTE 1. The first-step estimator (the CDF argument) is computed using the pchreg function of the
pch package. To be correctly embedded in ctqr, a pch object must be constructed using
the same observations, in the same order.
If the first-step estimator is biased, and there is censoring or truncation, the estimates of the quantile regression coefficients and their standard errors will also be biased.
If the data are neither censored nor truncated, the CDF does not enter the estimating equation of the model. However, since the first-step estimator is used to compute the starting points,
the final estimates may be sensitive to the supplied CDF.
NOTE 2. Right-censoring is a special case of interval censoring, in which exact events are identified by time2 = time1, while censored observations have time2 = Inf. Note, however, that ctqr(Surv(time1, time2, type = "interval2") ~ x) will not be identical to ctqr(Surv(time = time1, event = (time2 < Inf)) ~ x). The estimating equation used for interval-censored data is that described in Frumento (2022), while that used for right-censored data is that of Frumento and Bottai (2017). The two estimating equations are only asymptotically equivalent.
Paolo Frumento <[email protected]>
Ackerberg, D., Chen, X., and Hahn, J. (2012). A practical asymptotic variance estimator for two-step semiparametric estimators. The Review of Economics and Statistics, 94 (2), 481-498.
Frumento, P., and Bottai, M. (2017). An estimating equation for censored and truncated quantile regression. Computational Statistics and Data Analysis, Vol.113, pp.53-63. ISSN: 0167-9473.
Frumento, P. (2022). A quantile regression estimator for interval-censored data. The International Journal of Biostatistics, 19 (1), pp. 81-96.
plot.ctqr, predict.ctqr, pchreg
# Using simulated data # Example 1 - censored data #################################################### n <- 1000 x1 <- runif(n); x2 <- runif(n) # covariates t <- runif(n, 0, 1 + x1 + x2) # time variable (e.g., time to death) c <- runif(n,0,5) # censoring variable (e.g., end of follow-up) y <- pmin(t,c) # observed variable = min(t,c) d <- (t <= c) # 1 = event (e.g., death), 0 = censored CDF1 <- pchreg(Surv(y,d) ~ x1 + x2) model1 <- ctqr(Surv(y,d) ~ x1 + x2, p = 0.5, CDF = CDF1) model2 <- ctqr(Surv(y,d) ~ x1, p = 0.5, CDF = CDF1) # model1 is identical to ctqr(Surv(y,d) ~ x1 + x2, p = 0.5) # model2 is NOT identical to ctqr(Surv(y,d) ~ x1, p = 0.5), # which would have default CDF = pchreg(Surv(y,d) ~ x1) # Example 2 - censored and truncated data ###################################### n <- 1000 x1 <- runif(n); x2 <- runif(n) # covariates t <- runif(n, 0, 1 + x1 + x2) # time variable c <- runif(n,0,5) # censoring variable y <- pmin(t,c) # observed variable = min(t,c) d <- (t <= c) # 1 = event, 0 = censored z <- rnorm(n) # truncation variable (e.g., time at enrollment) w <- which(y > z) # data are only observed when y > z z <- z[w]; y <- y[w]; d <- d[w]; x1 <- x1[w]; x2 <- x2[w] # implement various CDFs and choose the model with smallest AIC CDFs <- list( pchreg(Surv(z,y,d) ~ x1 + x2, breaks = 5), pchreg(Surv(z,y,d) ~ x1 + x2, breaks = 10), pchreg(Surv(z,y,d) ~ x1 + x2 + x1:x2, breaks = 5), pchreg(Surv(z,y,d) ~ x1 + x2 + x1^2 + x2^2, breaks = 10) ) CDF <- CDFs[[which.min(sapply(CDFs, function(obj) AIC(obj)))]] summary(ctqr(Surv(z,y,d) ~ x1 + x2, p = 0.5, CDF = CDF)) # Example 3 - interval-censored data ########################################### # t is only known to be in the interval (t1,t2) ################################ n <- 1000 x1 <- runif(n); x2 <- runif(n) # covariates t <- runif(n, 0, 10*(1 + x1 + x2)) # time variable t1 <- floor(t) # lower extreme of the interval t2 <- ceiling(t) # upper extreme of the interval model <- ctqr(Surv(t1,t2, type = "interval2") ~ x1 + x2, p = 0.5)# Using simulated data # Example 1 - censored data #################################################### n <- 1000 x1 <- runif(n); x2 <- runif(n) # covariates t <- runif(n, 0, 1 + x1 + x2) # time variable (e.g., time to death) c <- runif(n,0,5) # censoring variable (e.g., end of follow-up) y <- pmin(t,c) # observed variable = min(t,c) d <- (t <= c) # 1 = event (e.g., death), 0 = censored CDF1 <- pchreg(Surv(y,d) ~ x1 + x2) model1 <- ctqr(Surv(y,d) ~ x1 + x2, p = 0.5, CDF = CDF1) model2 <- ctqr(Surv(y,d) ~ x1, p = 0.5, CDF = CDF1) # model1 is identical to ctqr(Surv(y,d) ~ x1 + x2, p = 0.5) # model2 is NOT identical to ctqr(Surv(y,d) ~ x1, p = 0.5), # which would have default CDF = pchreg(Surv(y,d) ~ x1) # Example 2 - censored and truncated data ###################################### n <- 1000 x1 <- runif(n); x2 <- runif(n) # covariates t <- runif(n, 0, 1 + x1 + x2) # time variable c <- runif(n,0,5) # censoring variable y <- pmin(t,c) # observed variable = min(t,c) d <- (t <= c) # 1 = event, 0 = censored z <- rnorm(n) # truncation variable (e.g., time at enrollment) w <- which(y > z) # data are only observed when y > z z <- z[w]; y <- y[w]; d <- d[w]; x1 <- x1[w]; x2 <- x2[w] # implement various CDFs and choose the model with smallest AIC CDFs <- list( pchreg(Surv(z,y,d) ~ x1 + x2, breaks = 5), pchreg(Surv(z,y,d) ~ x1 + x2, breaks = 10), pchreg(Surv(z,y,d) ~ x1 + x2 + x1:x2, breaks = 5), pchreg(Surv(z,y,d) ~ x1 + x2 + x1^2 + x2^2, breaks = 10) ) CDF <- CDFs[[which.min(sapply(CDFs, function(obj) AIC(obj)))]] summary(ctqr(Surv(z,y,d) ~ x1 + x2, p = 0.5, CDF = CDF)) # Example 3 - interval-censored data ########################################### # t is only known to be in the interval (t1,t2) ################################ n <- 1000 x1 <- runif(n); x2 <- runif(n) # covariates t <- runif(n, 0, 10*(1 + x1 + x2)) # time variable t1 <- floor(t) # lower extreme of the interval t2 <- ceiling(t) # upper extreme of the interval model <- ctqr(Surv(t1,t2, type = "interval2") ~ x1 + x2, p = 0.5)
This functions can be used within a call to ctqr,
to control the operational parameters of the root search algorithm.
ctqr.control(tol = 1e-06, maxit = 1000, a = 0.5, b = 1.25)ctqr.control(tol = 1e-06, maxit = 1000, a = 0.5, b = 1.25)
tol |
positive convergence tolerance: the algorithm stops when the maximum absolute change between two consecutive estimates is smaller than tol. |
maxit |
maximum number of iterations. |
a, b
|
numeric scalar with 0 < a < 1 and b > 1. See ‘Details’. |
For a current estimate beta, a new estimate is computed as beta_new = beta + delta*s(beta), where s(beta) is the current value of the estimating equation and delta is a positive multiplier. If sum(s(beta_new)^2) < sum(s(beta)^2), the iteration is accepted and delta is multiplied by b. Otherwise, beta_new is rejected and delta is multiplied by a. By default, a = 0.5 and b = 1.25. Choosing a,b closer to 1 may result in a more accurate estimate, but will require a larger number of iterations.
The function returns its arguments. If some was not correctly specified, it is set to its default and a warning message is returned.
Plots quantile regression coefficients
as a function of ,
based on a fitted model of class “ctqr”.
## S3 method for class 'ctqr' plot(x, which = NULL, ask = TRUE, ...)## S3 method for class 'ctqr' plot(x, which = NULL, ask = TRUE, ...)
x |
an object of class “ |
which |
an optional numerical vector indicating which coefficient(s) to plot. If which = NULL, all coefficients are plotted. |
ask |
logical. If which = NULL and ask = TRUE (the default), you will be asked interactively which coefficients to plot. |
... |
additional graphical parameters, that can include xlim, ylim, xlab, ylab, col, lwd.
See |
With this command, a plot of versus is created, provided that at least
two quantiles have been estimated. Dashed lines represent 95% confidence intervals, while the horizontal dotted line indicates the zero.
Paolo Frumento [email protected]
# using simulated data n <- 1000 x <- runif(n) t <- 1 + x + rexp(n) c <- runif(n, 1,10) y <- pmin(c,t) d <- (t <= c) par(mfrow = c(1,2)) plot(ctqr(Surv(y,d) ~ x, p = seq(0.05,0.95,0.05)), ask = FALSE)# using simulated data n <- 1000 x <- runif(n) t <- 1 + x + rexp(n) c <- runif(n, 1,10) y <- pmin(c,t) d <- (t <= c) par(mfrow = c(1,2)) plot(ctqr(Surv(y,d) ~ x, p = seq(0.05,0.95,0.05)), ask = FALSE)
This function returns predictions for an object of class “ctqr”.
## S3 method for class 'ctqr' predict(object, newdata, se.fit = FALSE, ...)## S3 method for class 'ctqr' predict(object, newdata, se.fit = FALSE, ...)
object |
a |
newdata |
optional data frame in which to look for variables with which to predict. It must include all the covariates that enter the quantile regression model. If omitted, the fitted values are used. |
se.fit |
logical. If TRUE, standard errors of the predictions are also computed. |
... |
for future methods. |
This function produces predicted values obtained by evaluating the regression function at newdata (which defaults to model.frame(object)).
If se = FALSE, a matrix of fitted values, with rows corresponding to different observations, and one column for each value of object$p. If se = TRUE, a list with two items:
fit |
a matrix of fitted values, as described above. |
se.fit |
a matrix of estimated standard errors. |
Paolo Frumento [email protected]
# Using simulated data n <- 1000 x1 <- runif(n) x2 <- runif(n) t <- 1 + x1 + x2 + runif(n, -1,1) c <- rnorm(n,3,1) y <- pmin(t,c) d <- (t <= c) model <- ctqr(Surv(y,d) ~ x1 + x2, p = c(0.25,0.5)) pred <- predict(model) # the same as fitted(model) predict(model, newdata = data.frame(x1 = c(0.2,0.6), x2 = c(0.1,0.9)), se.fit = TRUE)# Using simulated data n <- 1000 x1 <- runif(n) x2 <- runif(n) t <- 1 + x1 + x2 + runif(n, -1,1) c <- rnorm(n,3,1) y <- pmin(t,c) d <- (t <= c) model <- ctqr(Surv(y,d) ~ x1 + x2, p = c(0.25,0.5)) pred <- predict(model) # the same as fitted(model) predict(model, newdata = data.frame(x1 = c(0.2,0.6), x2 = c(0.1,0.9)), se.fit = TRUE)