Survival Data

Time-to-event data, including both survival and censoring times, are created using functions defSurv and genSurv. The survival data definitions require a variable name as well as a specification of a scale value, which determines the mean survival time at a baseline level of covariates (i.e. all covariates set to 0). The Weibull distribution is used to generate these survival times. In addition, covariates (which have been defined previously) that influence survival time can be included in the formula field. Positive coefficients are associated with longer survival times (and lower hazard rates). Finally, the shape of the distribution can be specified. A shape value of 1 reflects the exponential distribution.

# Baseline data definitions

def <- defData(varname = "x1", formula = 0.5, dist = "binary")
def <- defData(def, varname = "x2", formula = 0.5, dist = "binary")
def <- defData(def, varname = "grp", formula = 0.5, dist = "binary")

# Survival data definitions

sdef <- defSurv(varname = "survTime", formula = "1.5*x1", scale = "grp*50 + (1-grp)*25", 
    shape = "grp*1 + (1-grp)*1.5")
sdef <- defSurv(sdef, varname = "censorTime", scale = 80, shape = 1)

sdef

##       varname formula               scale               shape
## 1:   survTime  1.5*x1 grp*50 + (1-grp)*25 grp*1 + (1-grp)*1.5
## 2: censorTime       0                  80                   1

The data are generated with calls to genData and genSurv:

# Baseline data definitions

dtSurv <- genData(300, def)
dtSurv <- genSurv(dtSurv, sdef)

head(dtSurv)

##    id x1 x2 grp survTime censorTime
## 1:  1  0  1   0    10.87     88.965
## 2:  2  1  1   1    60.25     49.287
## 3:  3  1  0   0     5.01     59.099
## 4:  4  0  0   0    34.04      0.532
## 5:  5  0  0   1    10.11    101.262
## 6:  6  1  1   0   113.54     85.093

# A comparison of survival by group and x1

dtSurv[, round(mean(survTime), 1), keyby = .(grp, x1)]

##    grp x1    V1
## 1:   0  0 162.7
## 2:   0  1  20.0
## 3:   1  0  71.6
## 4:   1  1  12.3

Observed survival times and censoring indicators can be generated by defining new fields:

cdef <- defDataAdd(varname = "obsTime", formula = "pmin(survTime, censorTime)", dist = "nonrandom")
cdef <- defDataAdd(cdef, varname = "status", formula = "I(survTime <= censorTime)", 
    dist = "nonrandom")

dtSurv <- addColumns(cdef, dtSurv)

head(dtSurv)

##    id x1 x2 grp survTime censorTime obsTime status
## 1:  1  0  1   0    10.87     88.965  10.865   TRUE
## 2:  2  1  1   1    60.25     49.287  49.287  FALSE
## 3:  3  1  0   0     5.01     59.099   5.009   TRUE
## 4:  4  0  0   0    34.04      0.532   0.532  FALSE
## 5:  5  0  0   1    10.11    101.262  10.109   TRUE
## 6:  6  1  1   0   113.54     85.093  85.093  FALSE

# estimate proportion of censoring by x1 and group

dtSurv[, round(1 - mean(status), 2), keyby = .(grp, x1)]

##    grp x1   V1
## 1:   0  0 0.55
## 2:   0  1 0.18
## 3:   1  0 0.55
## 4:   1  1 0.12

Here is a Kaplan-Meier plot of the data by the four groups: