pensim (version 1.3.6)

create.data: simulate correlated predictors with time-to-event or binary outcome

Description

This function creates multiple groups of predictor variables which may be correlated within each group, and binary or survival time (without censoring) response according to specified weights of the predictors.

Usage

create.data(nvars = c(100, 100, 100, 100, 600),
            cors = c(0.8, 0, 0.8, 0, 0),
            associations = c(0.5, 0.5, 0.3, 0.3, 0),
            firstonly = c(TRUE, FALSE, TRUE, FALSE, FALSE),
            nsamples = 100,
            censoring = "none",
            labelswapprob = 0,
            response = "timetoevent",
            basehaz = 0.2,
            logisticintercept = 0)

Value

Returns a list with items:

summary

a summary of the variable types produced

associations

weights of each variable in computing the outcome

covariance

covariance matrix used for generating potentially correlated random predictors

data

dataframe containing the predictors and response. Response is the last column for binary outcome ("outcome"), and the last two columns for timetoevent outcome ("time" and "cens")

Arguments

nvars

integer vector giving the number of variables of each variable type. The number of variable types is equal to the length of this vector.

cors

integer vector of the same length as nvars, giving the population pairwise Pearson correlation within each group.

associations

integer vector of the same length as nvars, giving the associations of each type with outcome

firstonly

logical vector of the same length as nvars, specifying whether only the first variable of each type is associated with outcome (TRUE) or all variables of that type (FALSE)

nsamples

an integer giving the number of observations

censoring

"none" for no censoring, or a vector of length two c(a,b) for uniform U(a,b) censoring.

labelswapprob

This provides an option to add uncertainty to binary outcomes by randomly switching labels with probability labelswapprob. The probability of a label being swapped is independent for each observation. The value is ignored if response is "timetoevent"

response

either "timetoevent" or "binary"

basehaz

baseline hazard, used for "timetoevent"

logisticintercept

intercept which is added to X%*%Beta for "binary"

Author

Levi Waldron et al.

Details

This function simulates "predictor" variables in one or more groups, which are standard normally distributed. The user can specify the population correlation within each variable group, the association of each variable group to outcome, and whether the first or all variables of that type should be associated with outcome. The simulated response variable can be time to event with an exponential distribution, or binary survival with a logistic distribution.

References

Waldron L., Pintilie M., Tsao M.-S., Shepherd F. A., Huttenhower C.*, and Jurisica I.* Optimized application of penalized regression methods to diverse genomic data. (2010). Under review. (*equal contribution)

Examples

##binary outcome example
set.seed(9)
x <-
  create.data(
    nvars = c(15, 5),
    cors = c(0, 0.8),
    associations = c(0, 2),
    firstonly = c(TRUE, TRUE),
    nsamples = 50,
    response = "binary",
    logisticintercept = 0.5
  )
summary(x)
x$summary
model <- glm(outcome ~ .,  data = x$data, family = binomial)
summary(model)
dat <- t(as.matrix(x$data[, -match("outcome", colnames(x$data))]))
heatmap(dat, ColSideColors = ifelse(x$data$outcome == 0, "black", "white"))

##censored survival outcome example:
set.seed(1)
x <- create.data(
  nvars = c(15, 5),
  cors = c(0, 0.8),
  associations = c(0, 2),
  firstonly = c(TRUE, TRUE),
  nsamples = 50,
  censoring = c(2, 10),
  response = "timetoevent"
)
sum(x$data$cens == 0) / nrow(x$data)  #34 percent censoring

library(survival)
surv.obj <- Surv(x$data$time, x$data$cens)
plot(survfit(surv.obj ~ 1), ylab = "Survival probability", xlab = "time")