Colossus

The goal of Colossus is to provide an open-source means of performing survival analysis on big data with complex risk formulas. 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 formulas highlighted in the vignettes. Additional plotting capabilities are available.

From versions 1.3.1 to 1.4.1 the expected inputs changed. Regressions are now run with CoxRun and PoisRun and formula inputs. Please see the ‘Unified Equation Representation’ vignette for more details.

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. Colossus 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.

Example

This is a basic example which shows you how to solve a common problem:

library(data.table)
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)
)

model <- Cox(Starting_Age, Ending_Age, Cancer_Status) ~ loglinear(a, 0) + linear(b, c, 1) + plinear(d, 2) + multiplicative()

a_n <- c(0.1, 0.1, 0.1, 0.1)

keep_constant <- c(0, 0, 0, 0)

control <- list(
  "lr" = 0.75, "maxiter" = 100, "halfmax" = 5, "epsilon" = 1e-9,
  "deriv_epsilon" = 1e-9, "step_max" = 1.0,
  "verbose" = 2, "ties" = "breslow"
)

e <- CoxRun(model, df, a_n = a_n, control = control)
print(e)
#> |-------------------------------------------------------------------|
#> Final Results
#>    Covariate Subterm Term Number Central Estimate Standard Error 2-tail p-value
#>       <char>  <char>       <int>            <num>          <num>          <num>
#> 1:         a  loglin           0         42.10452            NaN            NaN
#> 2:         b     lin           1         98.72266    3781273.501      0.9999792
#> 3:         c     lin           1         96.82311    3698137.325      0.9999791
#> 4:         d    plin           2        101.10000       2326.871      0.9653437
#> 
#> Cox Model Used
#> -2*Log-Likelihood: 1.35,  AIC: 9.35
#> Iterations run: 100
#> maximum step size: 1.00e+00, maximum first derivative: 1.92e-04
#> Analysis did not converge, check convergence criteria or run further
#> Run finished in 0.07 seconds
#> |-------------------------------------------------------------------|