pl_data: Simulated AML data with binary outcome

In prioritylasso: Analyzing Multiple Omics Data with an Offset Approach

Simulated AML data with binary outcome

Description

A data set containing the binary outcome and 1028 predictor variables of 400 artificial AML patients.

Usage

pl_data

Format

A data frame with 400 rows and 1029 variables:

pl_out: (pl_data[,1029])

binary outcome representing refractory status.

b1: (pl_data[,1:4])

4 binary variables representing variables with a known influence on the outcome.

b2: (pl_data[,5:9])

5 continuous variables representing clinical variables.

b3: (pl_data[,10:28])

19 binary variables representing mutations.

b4: (pl_data[,29:1028])

1000 continuous variables representing gene expression data.

Details

We generated the data in the following way: We took the empirical correlation of 1028 variables related to 315 AML patients. This correlation served as a correlation matrix when generating 1028 multivariate normally distributed variables with the R function rmvnorm. Because we didn't have a positive definite matrix, we took the nearest positive definite matrix according to the function nearPD. The variables that should be binary were dichotomized, so that their marginal probabilities corresponded to the marginal probabilities they were based on. The coefficients were defined by

• beta_b1 <- c(0.8, 0.8, 0.6, 0.6)

• beta_b2 <- c(rep(0.5,3), rep(0,2))

• beta_b3 <- c(rep(0.4, 4), rep(0,15))

• beta_b4 <- c(rep(0.5, 5), rep(0.3, 5), rep(0,990)).

We included them in the vector beta <- c(beta_b1, beta_b2, beta_b3, beta_b4) and calculated the probability through

pi = exp(\beta*x) / (1 + exp(\beta*x))

where x denotes our data matrix with 1028 predictor variables. Finally we got the outcome through pl_out <- rbinom(400, size = 1, p = pi).

prioritylasso documentation built on April 11, 2023, 6:02 p.m.