clusteredMSM: Nonparametric Analysis of Clustered Multistate Processes
Nonparametric estimation of population-averaged transition
probabilities, with cluster-bootstrap pointwise confidence intervals,
simultaneous confidence bands, and two-sample Kolmogorov-Smirnov-type
tests for clustered or independent multistate process data.
Estimation follows 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>. Both methods use the
working-independence Aalen-Johansen estimator. The package supports
both progressive (acyclic) and non-monotone (e.g., illness-death
with recovery) multistate processes, right censoring, left
truncation, and informative cluster size. The user supplies data
in interval format (one row per mutually-exclusive time interval
per subject) and interacts with the package through a single
formula-based function, patp().
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