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clusteredMSM

Nonparametric analysis of clustered multistate process data.

clusteredMSM provides population-averaged transition probability estimates, pointwise confidence intervals, simultaneous confidence bands, and two-sample Kolmogorov-Smirnov-type tests for multistate process data with cluster-correlated observations. Estimation follows Bakoyannis (2021); two-sample inference for the cluster-randomized and independent-samples designs follows Bakoyannis & Bandyopadhyay (2022). Both rest on the working-independence Aalen-Johansen estimator with a cluster-bootstrap variance.

Unlike its predecessor (the clustered-multistate repository, which relied on the mstate package), clusteredMSM is self-contained (depending only on survival) and supports non-monotone multistate processes, including illness-death with recovery and other models with cyclic transitions.

Installation

# install.packages("devtools")
devtools::install_github("gbakoyannis/clusteredMSM")

After CRAN release:

install.packages("clusteredMSM")

A single function with a formula interface

clusteredMSM exposes one main function, patp(), modelled after survival::Surv():

library(clusteredMSM)

# Synthetic clustered illness-death-with-recovery data (40 subjects,
# 8 clusters); see ?example_msm.
data(example_msm)

# Define the transition structure (illness-death with recovery)
tmat <- trans_mat(list(c(2, 3), c(1, 3), integer(0)),
                  names = c("Healthy", "Ill", "Dead"))

# One-sample analysis: P(Ill at t | Healthy at 0)
fit <- patp(msm(Tstart, Tstop, Sstart, Sstop) ~ 1,
            data = example_msm, tmat = tmat,
            id = "id", cluster = "cluster",
            h = 1, j = 2, s = 0,
            B = 1000, cband = TRUE)
fit

If the formula’s right-hand side has a grouping variable, patp() automatically estimates both group-specific curves AND tests their equality:

# Two-sample analysis (estimate + test in one call)
tt <- patp(msm(Tstart, Tstop, Sstart, Sstop) ~ treatment,
           data = example_msm, tmat = tmat,
           id = "id", cluster = "cluster",
           h = 1, j = 2, B = 1000)
tt

Loading your own data

The same example is shipped as a CSV under inst/extdata/, so you can mimic the typical workflow of reading a user-supplied file:

f <- system.file("extdata", "example_data.csv", package = "clusteredMSM")
mydata <- read.csv(f)
head(mydata)

Input data format

Each row of your data represents one mutually-exclusive time interval for one subject, with columns:

The column names are arbitrary — msm(...) and the id/cluster arguments tell the package which is which.

Censoring is encoded as Sstart == Sstop on the final row of a subject’s record. Subjects in absorbing states have no row after them.

Within each subject, intervals must be: - Temporally contiguous: Tstop[k] == Tstart[k+1] - State contiguous: Sstop[k] == Sstart[k+1]

Validation is strict and informative — any violation triggers an error with a clear message.

Examples of valid input

Progressive illness-death (subject who got ill, then died):

Subject censored healthy:

Recovery (Healthy → Ill → Healthy → censored):

Core functions

References

Bakoyannis, G. (2021). Nonparametric analysis of nonhomogeneous multistate processes with clustered observations. Biometrics, 77(2), 533-546. doi:10.1111/biom.13327

Bakoyannis, G., & Bandyopadhyay, D. (2022). Nonparametric tests for multistate processes with clustered data. Annals of the Institute of Statistical Mathematics, 74(5), 837-867. doi:10.1007/s10463-021-00819-x

You can retrieve the BibTeX entries within R via toBibtex(citation("clusteredMSM")).

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.