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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

Function	Description
`jmSurf()`	Main fitting function (two-stage)
`fit_longitudinal()`	Stage 1: LME models per biomarker
`fit_gam_cox()`	Stage 2: GAM-Cox with spline surface
`edf_diagnostics()`	EDF table with complexity labels
`dynPred()`	Dynamic prediction of transition probabilities
`plot_surface()`	3D perspective surface plot
`contour_heatmap()`	Filled contour heatmap
`marginal_slices()`	Marginal effect slices at quantiles
`simulate_jmSurface()`	Simulate CKD/CVD/Diabetes data
`run_shiny_app()`	Launch interactive Shiny dashboard

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)

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.