Fit an integrative Cox model proposed by Wang et al. (2020) for right-censored survival data with uncertain event times due to imperfect data integration.
iCoxph( formula, data, subset, na.action, contrasts = NULL, start = iCoxph.start(), control = iCoxph.control(), ... )
| formula |
|
|---|---|
| data | An optional data frame, list, or environment that contains the
covariates and response variables included in the model. If not found in
data, the variables are taken from |
| subset | An optional logical vector specifying a subset of observations to be used in the fitting process. |
| na.action | An optional function that indicates what should the
procedure do if the data contains |
| contrasts | An optional list, whose entries are values (numeric
matrices or character strings naming functions) to be used as
replacement values for the contrasts replacement function and whose
names are the names of columns of data containing factors. See
|
| start | A list returned by function |
| control | A list returned by function |
| ... | Other arguments for future usage. A warning will be thrown if any invalid argument is specified. |
An iCoxph-class object, whose slots include
call: Function call.
formula: Formula used in the model fitting.
data: A processed data frame used for model fitting.
nObs: Number of observation.
estimates:
beta: Coefficient estimates.
pi: Estimated parameters in prior multinomial
distribution indicating the probabilities of corresponding
record being true.
baseline: A data frame that contains estimated
baseline hazard function with columns named time and
h0.
start: The initial guesses beta_mat and
pi_mat specified for the parameters to be estimated,
and the set of initial guess beta_start and pi_start
that resulted in the largest objective function, i.e.,
the observed-data likelihood function.
control: The control list specified for model fitting.
na.action: The procedure specified to deal with
missing values in the covariate.
xlevels: A list that records the levels in
each factor variable.
contrasts: Contrasts specified and used for each
factor variable.
convergeCode: code returned by function
nlm, which is an integer indicating why the
optimization process terminated. help(nlm) for details.
logL: A numeric vector containing the observed-data
log-likelihood over iterations.
Wang, W., Aseltine, R. H., Chen, K., & Yan, J. (2020). Integrative Survival Analysis with Uncertain Event Times in Application to A Suicide Risk Study. Annals of Applied Statistics, 14(1), 51--73.
iCoxph.start and iCoxph.control, respectively,
for starting and controlling iCoxph fitting;
summary,iCoxph-method for summary of fitted model;
coef,iCoxph-method for estimated covariate coefficients;
bootSe for SE estimates from bootstrap methods.
library(intsurv) ## generate simulated survival data with uncertain records set.seed(123) simuDat <- simData4iCoxph(nSubject = 200) ## fit the integertive Cox model fit <- iCoxph(Survi(ID, time, event) ~ x1 + x2 + x3 + x4, data = simuDat, start = iCoxph.start(methods = "nearest"), control = iCoxph.control(tol_beta = 1e-5)) ## estimated covariate coefficients coef(fit)#> x1 x2 x3 x4 #> 0.7359470 0.9545273 0.7262900 0.8679733## get SE estimates by bootstrap fit <- bootSe(fit, B = 30) ## summary of the fitted model summary(fit)#> Call: #> iCoxph(formula = Survi(ID, time, event) ~ x1 + x2 + x3 + x4, #> data = simuDat, start = iCoxph.start(methods = "nearest"), #> control = iCoxph.control(tol_beta = 1e-05)) #> #> Coefficients of covariates: #> coef exp(coef) se(coef) z Pr(>|z|) #> x1 0.73595 2.08746 0.24094 3.0545 0.002255 ** #> x2 0.95453 2.59744 0.29606 3.2241 0.001263 ** #> x3 0.72629 2.06740 0.16526 4.3949 1.108e-05 *** #> x4 0.86797 2.38208 0.59304 1.4636 0.143304 #> --- #> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 #> #> Log-likelihood under observed data: -1150.269