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clusteredMSM 0.1.0

Initial CRAN release

• Initial CRAN release of clusteredMSM.

Highlights

• Nonparametric estimation of population-averaged transition probabilities for clustered or independent multistate process data, with cluster-bootstrap pointwise confidence intervals and simultaneous confidence bands.
• Two-sample Kolmogorov-Smirnov-type test, dispatched automatically when a grouping variable appears on the formula’s right-hand side. Three regimes via a design argument: "shared" (multicenter, every cluster carries both groups), "cluster_random" (cluster-randomized trial, stratified cluster bootstrap), and "indep_random" (independent observational comparison, unstratified cluster bootstrap). "auto" infers the regime from the data.
• Native support for non-monotone (recovery) multistate processes via a generic product-integral estimator (prodint_AJ()).
• Self-contained: depends only on survival (which ships with R). Removes the dependency on mstate that limited earlier implementations to progressive models.
• Estimation methodology from Bakoyannis (2021) doi:10.1111/biom.13327; two-sample inference for the cluster-randomized and independent-samples designs follows Bakoyannis and Bandyopadhyay (2022) doi:10.1007/s10463-021-00819-x.

Public API

The package exposes a single user-facing function, patp(), with a formula-based interface modelled after survival::Surv():

# One-sample
patp(msm(Tstart, Tstop, Sstart, Sstop) ~ 1,
     data = mydata, tmat = tmat,
     id = "subj_id", cluster = "site",
     h = 1, j = 2, B = 1000, cband = TRUE)

# Two-sample (estimate + test)
patp(msm(Tstart, Tstop, Sstart, Sstop) ~ treatment,
     data = mydata, tmat = tmat,
     id = "subj_id", cluster = "site",
     h = 1, j = 2, B = 1000)

patp() returns an S3 object of class patp with print() and summary() methods.

Input format

Data are supplied in interval format: one row per mutually-exclusive time interval per subject, with columns for interval start time, end time, starting state, and ending state. Censoring is encoded as Sstart == Sstop on the final row. Within each subject, intervals must be temporally and spatially contiguous; this is enforced by strict validation (validate_intervals()).

Known limitations

• The two-sample Kolmogorov-Smirnov-type test uses unit weight at every time point. Bakoyannis (2021) Section 2.5 recommends the harmonic-mean weight W(t) = prod_p Y_p(t) / sum_p Y_p(t), which downweights regions where one group’s at-risk set is small and tames tail instability of the difference estimator. A weighted variant following Bakoyannis (2021) Section 2.5 and Bakoyannis & Bandyopadhyay (2022) is planned for v0.2, alongside the linear and L2 tests.
• Confidence bands use an equal-precision-type construction on the cloglog scale; the Hall-Wellner-type construction from Bakoyannis
  2021. Section 2.3 will be added in v0.2.

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.
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