ciregic_lt | R Documentation |
The function ciregic_lt
performs semiparametric regression on cumulative incidence function with left-truncated and interval-censored competing risks data. It fits the proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models. The least-square method is implemented to estimate the standard error of the regression coefficients.
ciregic_lt(formula, data, alpha, k = 1, do.par, nboot, ...)
formula |
a formula object relating the survival object |
data |
a data frame that includes the variables named in the formula argument |
alpha |
α = (α1, α2) contains parameters that define the link functions from class of generalized odds-rate transformation models. The components α1 and α2 should both be ≥ 0. If α1 = 0, the user assumes the proportional subdistribution hazards model or the Fine-Gray model for the cause of failure 1. If α2 = 1, the user assumes the proportional odds model for the cause of failure 2. |
k |
a parameter that controls the number of knots in the B-spline with 0.5 ≤ |
do.par |
an option to use parallel computing for bootstrap. If |
nboot |
a number of bootstrap samples for estimating variances and covariances of the estimated regression coefficients. If |
... |
further arguments |
The function ciregic_lt
is capable of analyzing left-truncated and interval-censored competing risks data. A triplet of time points (w, v, u)
is required if an observation is left-truncated and interval-censored. A part of left-truncation is also allowed by defining w = 0
for interval-censored only observation. The formula for the model has the form of response ~ predictors
. The response in the formula is a Surv2(v, u, w, event)
object where w
is a left-truncation time, v
is the last observation time prior to the failure, u
is the first observation time after the failure, and event
is the event or censoring indicator. event
should include 0, 1 or 2, denoting right-censoring, failure from cause 1 and failure from cause 2, respectively. If event=0
(i.e. right-censored observation) then u
is not included in any calculation as it corresponds to ∞. The user can provide any value in u
for the right-censored cases, even NA
. The function ciregic_lt
fits models that belong to the class of generalized odds rate transformation models which includes the proportional subdistribution hazards or the Fine-Gray model and the proportional odds model. The parameter α=(α1, α2) defines the link function/model to be fitted for cause of failure 1 and 2, respectively. A value of 0
corresponds to the Fine-Gray model and a value of 1
corresponds to the proportional odds model. For example, if α=(0,1) then the function ciregic_lt
fits the Fine-Gray model for cause 1 and the proportional odds model for cause 2.
The function ciregic_lt
provides an object of class ciregic_lt
with components:
varnames |
a vector containing variable names |
coefficients |
a vector of the regression coefficient estimates |
gamma |
a vector of the estimated coefficients for the B-splines |
vcov |
a variance-covariance matrix of the estimated regression coefficients |
alpha |
a vector of the link function parameters |
loglikelihood |
a loglikelihood of the fitted model |
convergence |
an indicator of convegence |
tms |
a vector of the minimum and maximum observation times |
Bv |
a list containing the B-splines basis functions evaluated at |
numboot |
a number of converged bootstrap |
notconverged |
a list of number of bootstrap samples that did not converge |
call |
a matched call |
Jun Park, jun.park@alumni.iu.edu
Giorgos Bakoyannis, gbakogia@iu.edu
Bakoyannis, G., Yu, M., and Yiannoutsos C. T. (2017). Semiparametric regression on cumulative incidence function with interval-censored competing risks data. Statistics in Medicine, 36:3683-3707.
Fine, J. P. and Gray, R. J. (1999). A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association, 94:496-509.
summary.ciregic_lt
for the summarized results and predict.ciregic_lt
for value of the predicted cumulative incidence functions. coef
and vcov
are the generic functions. dataprep
for reshaping data from a long format to a suitable format to be used in the function ciregic_lt
.
## Not run: ## Set seed in order to have reproducibility of the bootstrap standard error estimate set.seed(1234) ## Reshaping data from a long format to a suitable format newdata <- dataprep_lt(data = longdata_lt, ID = id, time = t, W = w, event = c, Z = c(z1, z2)) ## Estimation of regression parameters only. No bootstrap variance estimation. ## with 'newdata' fit_lt <- ciregic_lt(formula = Surv2(v = v, u = u, w = w, event = c) ~ z1 + z2, data = newdata, alpha = c(1, 1), nboot = 0, do.par = FALSE) fit_lt ## Bootstrap variance estimation based on 50 replications fit_lt <- ciregic_lt(formula = Surv2(v = v, u = u, w = w, event = c) ~ z1 + z2, data = newdata, alpha = c(1, 1), nboot = 50, do.par = FALSE) ## End(Not run) ## Note that the user can use parallel computing to decrease ## the computation time of the bootstrap variance-covariance ## estimation (e.g. nboot = 50) ## Summarize semiparametric regression model summary(fit_lt) ## Predict and draw plot the cumulative incidence function evaluated at z1 = 1 and z2 = 0.5 mint <- fit_lt$tms[1] maxt <- fit_lt$tms[2] pred <- predict(object = fit_lt, covp = c(1, 0.5), times = seq(mint, maxt, by = (maxt - mint) / 99)) pred plot(pred$t, pred$cif1, type = "l", ylim = c(0, 1)) points(pred$t, pred$cif2, type = "l", col = 2)
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