The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Poisson SuperLearner

The package provides an implementation of piece-wise constant hazard models for time-to-event analysis of survival and competing risks data. The piecewise constant hazard models can be combined in an ensemble, the Poisson Superlearner, via cross-validated risk minimization for flexible hazard estimation. It enables estimation of survival functions and risk predictions.

The package provides:

• Flexible specification of base learners (e.g. penalized and unpenalized Poisson regression).
• Automatic time-splitting into piecewise-constant hazard intervals.
• Cross-validated ensemble learning.
• Absolute risk prediction at arbitrary time horizons.

Installation

# install.packages("devtools")
# devtools::install_github("gpitt71/poissonsuperlearner")

library(poissonsuperlearner)

Example: Piecewise-Constant Hazard Model

Fit a single PCH model and obtain absolute risk predictions.

library(poissonsuperlearner)

set.seed(42)

# Simulate synthetic survival data
d <- simulateStenoT1(
  n = 50,
  scenario = "alpha"
)

# Define an unpenalized Poisson hazard learner
l_glm <- Learner_glmnet(
  covariates = c("sex", "diabetes_duration"),
  cross_validation = FALSE,
  lambda = 0,
  intercept = TRUE,
  penalise_nodes = FALSE
)

# Fit piecewise-constant hazard model
fit_glm <- fit_learner(
  data = d,
  id = "id",
  status = "event",
  event_time = "time",
  learner = l_glm,
  number_of_nodes = 5
)

# Absolute risk prediction at time horizon t = 5
predictRisk(fit_glm, newdata = d[1, ], times = 5)

What happens internally?

• The follow-up time is split into number_of_nodes intervals.
• A Poisson regression is fitted to the expanded dataset.
• The estimated hazards are integrated to produce survival and absolute risk.

Example: Superlearner for Piecewise Hazards

Combine multiple hazard learners into an ensemble.

library(poissonsuperlearner)

set.seed(42)

d <- simulateStenoT1(
  n = 50,
  scenario = "alpha"
)

# Base learner 1: unpenalized Poisson regression
l_glm <- Learner_glmnet(
  covariates = c("sex", "diabetes_duration"),
  cross_validation = FALSE,
  lambda = 0,
  intercept = TRUE,
  penalise_nodes = FALSE
)

# Base learner 2: Lasso-penalized Poisson regression
l_lasso <- Learner_glmnet(
  covariates = c("value_Smoking", "value_LDL"),
  cross_validation = TRUE,
  alpha = 1,
  intercept = TRUE,
  penalise_nodes = FALSE
)

learners_list <- list(
  glm   = l_glm,
  lasso = l_lasso
)

# Fit the superlearner
sl_fit <- Superlearner(
  data = d,
  id = "id",
  status = "event",
  event_time = "time",
  learners = learners_list,
  number_of_nodes = 5
)

# Absolute risk prediction from the ensemble
predictRisk(sl_fit, newdata = d[1, ], times = 5)

Superlearner workflow

1. Each base learner is fitted on cross-validation folds.
2. Out-of-sample Poisson deviance is computed.
3. A meta-learner combines the base hazard predictions.
4. Final hazards are aggregated and converted to absolute risk.

Main Functions

• Learner_glmnet() – define a Poisson hazard learner.
• fit_learner() – fit a single piecewise-constant hazard model.
• Superlearner() – fit an ensemble of hazard learners.
• predictRisk() – obtain absolute risk predictions.

Typical Use Case

poissonsuperlearner is designed for:

• Survival analysis via Poisson representations.
• Flexible hazard modeling with regularization.
• Ensemble-based absolute risk prediction.
• Methodological research in time-to-event modeling.

The package focuses on modular learners, transparent cross-validation, and direct control of the piecewise hazard structure.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.