The goal of Colossus is to provide an open-source means of performing survival analysis on big data with complex risk formula. Colossus is designed to perform Cox Proportional Hazard regressions and Poisson regressions on datasets loaded as data.tables or data.frames. The risk models allowed are sums or products of linear, log-linear, or several other radiation dose response formula highlighted in the vignettes. Additional plotting capabilities are available.
By default a fully portable version of the code is compiled, which does not support OpenMP on every system. Note that Colossus requires OpenMP support to perform parallel calculations. The environment variable “R_COLOSSUS_NOT_CRAN” is checked to determine if OpenMP should be disabled for linux compiling with clang. The number of cores is set to 1 if the environment variable is empty, the operating system is detected as linux, and the default compiler or R compiler is clang. Colossues testing checks for the “NOT_CRAN” variable to determine if additional tests should be run. Setting “NOT_CRAN” to “false” will disable the longer tests. Currently OpenMP support is not configured for linux compiling with clang and MacOS systems.
This is a basic example which shows you how to solve a common problem:
library(data.table)
#> Warning: package 'data.table' was built under R version 4.3.3
library(parallel)
library(Colossus)
## basic example code reproduced from the starting-description vignette
df <- data.table(
"UserID" = c(112, 114, 213, 214, 115, 116, 117),
"Starting_Age" = c(18, 20, 18, 19, 21, 20, 18),
"Ending_Age" = c(30, 45, 57, 47, 36, 60, 55),
"Cancer_Status" = c(0, 0, 1, 0, 1, 0, 0),
"a" = c(0, 1, 1, 0, 1, 0, 1),
"b" = c(1, 1.1, 2.1, 2, 0.1, 1, 0.2),
"c" = c(10, 11, 10, 11, 12, 9, 11),
"d" = c(0, 0, 0, 1, 1, 1, 1)
)
# For the interval case
time1 <- "Starting_Age"
time2 <- "Ending_Age"
event <- "Cancer_Status"
names <- c("a", "b", "c", "d")
term_n <- c(0, 1, 1, 2)
tform <- c("loglin", "lin", "lin", "plin")
modelform <- "M"
fir <- 0
a_n <- c(0.1, 0.1, 0.1, 0.1)
keep_constant <- c(0, 0, 0, 0)
der_iden <- 0
control <- list(
"lr" = 0.75, "maxiter" = 100, "halfmax" = 5, "epsilon" = 1e-9,
"dbeta_max" = 0.5, "deriv_epsilon" = 1e-9, "abs_max" = 1.0,
"change_all" = TRUE, "dose_abs_max" = 100.0, "verbose" = FALSE,
"ties" = "breslow", "double_step" = 1
)
e <- RunCoxRegression(df, time1, time2, event, names, term_n, tform, keep_constant, a_n, modelform, fir, der_iden, control)
print(e)
#> $LogLik
#> [1] -0.6753644
#>
#> $First_Der
#> [1] 0.000000e+00 -7.187040e-05 7.361232e-05 1.919948e-04
#>
#> $Second_Der
#> [,1] [,2] [,3] [,4]
#> [1,] 0.000000e+00 0.000000e+00 0.000000e+00 4.965508e-19
#> [2,] 0.000000e+00 1.742209e-08 7.238366e-07 2.311365e-07
#> [3,] 0.000000e+00 7.238366e-07 -1.501037e-06 -2.356033e-07
#> [4,] 4.965508e-19 2.311365e-07 -2.356033e-07 -3.687577e-06
#>
#> $beta_0
#> [1] 41.26157 98.72266 96.82311 101.10000
#>
#> $Standard_Deviation
#> [1] NaN NaN 177.9643 0.0000
#>
#> $AIC
#> [1] 9.350729
#>
#> $BIC
#> [1] 8.517767
#>
#> $Parameter_Lists
#> $Parameter_Lists$term_n
#> [1] 0 1 1 2
#>
#> $Parameter_Lists$tforms
#> [1] "loglin" "lin" "lin" "plin"
#>
#> $Parameter_Lists$names
#> [1] "a" "b" "c" "d"
#>
#>
#> $Control_List
#> $Control_List$Iteration
#> [1] 100
#>
#> $Control_List$`Maximum Step`
#> [1] 1
#>
#> $Control_List$`Derivative Limiting`
#> [1] 0.0001919948
#>
#>
#> $Converged
#> [1] FALSE
#>
#> $Status
#> [1] "PASSED"