Description Usage Arguments Details Value Author(s) References Examples
Fit a generalized competing event model by using Fine Gray
regression model with crr
function in cmprsk
package.
1 
Time 
survival time for event(s) of interest. 
Ind 
the status indicators including the primary event(s) of interest, competing event(s) of interest, and all kind of event(s) of interest, normally 0 = alive, 1 = dead from the specific event(s) of interest. 
Cov 
a data frame containing all covariates. 
N 
the number of bootstrap replicates 
M 
the number of bins for ω or ω+ plots. 
t 
survival time point for ω or ω+ plots. 
The gcerisk package is designed to help investigators optimize riskstratification methods for competing risks data, such as described in Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J, Noticewala S, McHale MT, Yashar CM, Vaida F, Mell LK. Validated competing event model for the stage III endometrial cancer population. Int J Radiat Oncol Biol Phys. 2014;89:88898. Standard risk models typically estimate the effects of one or more covariates on either a single event of interest (such as overall mortality, or disease recurrence), or a composite set of events (e.g., diseasefree survival, which combines events of interest with death from any cause). This method is inefficient in stratifying patients who may be simultaneously at high risk for the event of interest but low risk for competing events, and who thus stand to gain the most from strategies to modulate the event of interest. Compared to standard risk models, GCE models better stratify patients at higher (lower) risk for an event of interest and lower (higher) risk of competing events. GCE models focus on differentiating subjects based on the ratio of the cumulative hazard (or cumulative hazard of the subdistribution) for the event of interest to the cumulative hazard (or cumulative hazard of the subdistribution) for all events (ω), and the ratio of the cumulative hazard (or cumulative hazard of the subdistribution) for the event of interest to the cumulative hazard (or cumulative hazard of the subdistribution) for competing events (ω+).
The gcefg
function produces model estimates and confidence intervals from a generalized competing event model based on the FineGray model for subdistribution hazards. In the subdistribution hazards model, the function H(t)= log(1F(t)) represents the cumulative hazard of the subdistribution for the cumulative distribution function F(t).
The model assumes proportional subdistribution hazards for the composite set of events.
The function returns ω and ω+ ratio estimates for the chosen covariates, with 95% confidence intervals, and plots ω and ω+ at time t within M ordered subsets of subjects as a function of increasing risk (based on the linear predictor, i.e. the inner product of a subject's data vector and the coefficient vector).
$coef1 
generalized competing event model coefficients (log (ω ratio)) 
$coef2 
generalized competing event model coefficients (log (ω+ ratio)) 
$result1 
result table for generalized competing event model containing exponential of coefficients (ω ratio) and 95% confidence intervals 
$result2 
result table for generalized competing event model containing exponential of coefficients (ω+ ratio) and 95% confidence intervals 
$omegaplot1 
ω plot for generalized competing evet model 
$omegaplot2 
ω+ plot for generalized competing evet model 
$omegaplot3 
plot of ω vs time 
Hanjie Shen, Ruben Carmona, Loren Mell
Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J, Noticewala S, McHale MT, Yashar CM, Vaida F, Mell LK. (2014) Validated competing event model for the stage III endometrial cancer population. Int J Radiat Oncol Biol Phys.89:88898.
Carmona R, Green GB, Zakeri K, Gulaya S, Xu B, Verma R, Williamson C, Rose BS, Murphy JD, Vaida F, Mell LK. (2015) Novel method to stratify elderly patients with head and neck cancer. J Clin Oncol 33 (suppl; abstr 9534).
Carmona R, Zakeri K, Green GB, Triplett DP, Murphy JD, Mell LK. (2015) Novel method to stratify elderly patients with prostate cancer. J Clin Oncol 33 (suppl; abstr 9532).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  # sample data to test
data(Sample)
test < Sample
d < trunc(dim(test)[1]*0.1)
set.seed(seed=2017)
s < sample(dim(test)[1],d,replace = FALSE)
test < test[s,]
rm(list=setdiff(ls(), "test"))
test < transform(test, LRF_OR_DF_FLAG = as.numeric(test$LRFFLAG  test$DFFLAG))
test < transform(test, LRF_OR_DF_MO = pmin(test$LRFMO, test$DFMO))
test < transform(test, CMFLAG = as.numeric(test$OSFLAG & !test$LRFFLAG & !test$DFFLAG))
test < transform(test, ACMFLAG = as.numeric(test$LRF_OR_DF_FLAG  test$CMFLAG))
test < transform(test, ACM_MO = pmin(test$LRF_OR_DF_MO, test$OSMO))
cod1 < test$ACMFLAG
cod1[test$LRF_OR_DF_FLAG == 1] < 1
cod1[test$CMFLAG == 1] < 2
cod2 < test$ACMFLAG
Ind < data.frame(cod1 = cod1, cod2 = cod2)
Time < test$OSMO/30
Cov < test[,c(3,4,6,15)]
N < 50
M < 5
t < 5
fit < gcefg(Time, Ind, Cov, N, M, t)

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