Semi-Parametric Association Surfaces for Joint Longitudinal-Survival Models.

jmSurface

Semi-Parametric Association Surfaces for Joint Longitudinal-Survival Models

jmSurface implements interpretable multi-biomarker fusion in joint longitudinal-survival models via semi-parametric association surfaces for multi-state disease processes.

Key Features

• Two-stage estimation: Stage 1 fits mixed-effects longitudinal models (nlme); Stage 2 fits transition-specific penalized Cox models with tensor-product spline surfaces (mgcv)
• Multi-state support: Transition-specific association surfaces with automatic REML smoothing
• EDF diagnostics: Data-driven assessment of nonlinearity (EDF ≈ 1 = linear; EDF > 3 = nonlinear/interaction)
• Dynamic prediction: Personalized transition probabilities from landmark time
• Three visualization tools: 3D surface plots, contour heatmaps, marginal effect slices
• Interactive Shiny dashboard: Upload data, explore, fit models, and compute personalized risks
• Built-in simulator: Generate realistic CKD/CVD/Diabetes multi-state cohort data

Installation

# Install from CRAN
install.packages("jmSurface")

Quick Start

library(jmSurface)

# ── Option A: Use bundled CKD/CVD/Diabetes cohort (2,000 patients) ──
dat <- load_example_data()

fit <- jmSurf(
  long_data = dat$long_data,
  surv_data = dat$surv_data,
  covariates = c("age_baseline", "sex")
)

# Summary with EDF diagnostics
summary(fit)

# EDF table
edf_diagnostics(fit)

# Dynamic prediction for patient 1
pred <- dynPred(fit, patient_id = 1, landmark = 2, horizon = 3)
head(pred)

# Visualize association surfaces
plot_surface(fit, transition = "CKD -> CVD")
contour_heatmap(fit, transition = "CKD -> CVD")
marginal_slices(fit, transition = "CKD -> CVD")

# ── Option B: Simulate fresh data ──
sim <- simulate_jmSurface(n_patients = 500)
fit2 <- jmSurf(sim$long_data, sim$surv_data)

# ── Option C: Launch interactive Shiny app ──
run_shiny_app()

Bundled Data

The package ships with a real-scale example dataset (2,000 patients):

• longitudinal_biomarkers.csv: 68,112 biomarker measurements (eGFR, BNP, HbA1c)
• survival_events.csv: 4,701 survival rows with 9 transition types

Access via:

dat <- load_example_data()           # convenience loader (returns list)
# OR directly:
path <- system.file("extdata", "longitudinal_biomarkers.csv", package = "jmSurface")
long <- read.csv(path)

Model Overview

For each transition (r → s) in a multi-state process:

λᵢʳˢ(t | ηᵢ(t)) = λ₀ʳˢ(t) · exp{γᵣₛ'wᵢ + fᵣₛ(ηᵢ(t))}

where:

• ηᵢ(t) = BLUP-based latent longitudinal summaries (biomarker-specific)
• fᵣₛ(·) = semi-parametric association surface (tensor-product splines)
• Smoothing via REML; complexity controlled by EDF

Package Functions

References

Bhattacharjee, A. (2025). Interpretable Multi-Biomarker Fusion in Joint Longitudinal-Survival Models via Semi-Parametric Association Surfaces. Under review.

Bhattacharjee, A. (2024). jmBIG: Scalable Joint Models for Big Data. CRAN.

Wood, S.N. (2017). Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC.

License

GPL (>= 3)