PyMSM contains a few example datasets which can easily be loaded:

AID-SI

A simple 2 state competing risks dataset

From Data Analysis with Competing Risks and Intermediate States, by Ronald B. Geskus:

The data contains information on two event types, “AIDS” and “SI”, which compete to be the first to occur. Time is given in years since HIV infection. There are two different representations of the information on the event type that occurred first.
In the cause column, the event type is described in words, whereas in the status column a numeric respresentation is used.
The ccr5 column contains the information on the presence of the deletion CCR5-∆32. Individuals that don’t have the deletion have the value WW (W stands for “wild type”). Individuals that have the deletion on one of the chromosomes have the value WM (M stands for “mutation”)

See also: https://www.rdocumentation.org/packages/mstate/versions/0.3.1/topics/aidssi

from pymsm.datasets import load_aidssi, prep_aidssi, plot_aidssi
data = load_aidssi()
data.head()

	patnr	time	status	cause	ccr5
1	1	9.106	1	AIDS	WW
2	2	11.039	0	event-free	WM
3	3	2.234	1	AIDS	WW
4	4	9.878	2	SI	WM
5	5	3.819	1	AIDS	WW

competing_risk_dataset, covariate_cols, state_labels = prep_aidssi(data)
competing_risk_dataset.head()

	sample_id	time_entry_to_origin	origin_state	target_state	time_transition_to_target	ccr5_WW
0	1	0	1	2	9.106	1
1	2	0	1	0	11.039	0
2	3	0	1	2	2.234	1
3	4	0	1	3	9.878	0
4	5	0	1	2	3.819	1

plot_aidssi(competing_risk_dataset, state_labels)

Rotterdam

A 3-state Illness-death dataset

The rotterdam data set includes 2982 primary breast cancers patients whose data records were included in the Rotterdam tumor bank. Patients were followed for a time ranging between 1 to 231 months (median 107 months), and outcomes were defined as disease recurrence or death from any cause.

This data includes 2982 patients, with 15 covariates, and was extracted from R survival package.
For more information see page 113 in https://cran.r-project.org/web/packages/survival/survival.pdf.

from pymsm.datasets import load_rotterdam, prep_rotterdam, plot_rotterdam
rotterdam = load_rotterdam()
rotterdam.head()

	pid	year	age	meno	size	grade	nodes	pgr	er	hormon	chemo	rtime	recur	dtime	death
0	1	1992	74	1	<=20	3	0	35	291	0	0	1798.999948	0	1798.999948	0
1	2	1984	79	1	20-50	3	0	36	611	0	0	2828.000021	0	2828.000021	0
2	3	1983	44	0	<=20	2	0	138	0	0	0	6011.999804	0	6011.999804	0
3	4	1985	70	1	20-50	3	0	0	12	0	0	2623.999895	0	2623.999895	0
4	5	1983	75	1	<=20	3	0	260	409	0	0	4914.999997	0	4914.999997	0

dataset, state_labels = prep_rotterdam()
plot_rotterdam(dataset, state_labels)

EBMT

A Multi-state dataset

Data from the European Society for Blood and Marrow Transplantation (EBMT)

A data frame of 2279 patients transplanted at the EBMT between 1985 and 1998.
(from R mstate package, for more information see: https://www.rdocumentation.org/packages/mstate/versions/0.3.1/topics/EBMT%20data)

from pymsm.datasets import load_ebmt, prep_ebmt_long, plot_ebmt

load_ebmt().head()

	id	from	to	trans	Tstart	Tstop	time	status	match	proph	year	agecl
1	1	1	2	1	0.0	22.0	22.0	1	no gender mismatch	no	1995-1998	20-40
2	1	1	3	2	0.0	22.0	22.0	0	no gender mismatch	no	1995-1998	20-40
3	1	1	5	3	0.0	22.0	22.0	0	no gender mismatch	no	1995-1998	20-40
4	1	1	6	4	0.0	22.0	22.0	0	no gender mismatch	no	1995-1998	20-40
5	1	2	4	5	22.0	995.0	973.0	0	no gender mismatch	no	1995-1998	20-40

competing_risk_dataset, covariate_cols, state_labels = prep_ebmt_long()
competing_risk_dataset.head()

	sample_id	origin_state	target_state	time_entry_to_origin	time_transition_to_target	match_no gender mismatch	proph_yes	year_1990-1994	year_1995-1998	agecl_<=20	agecl_>40
0	1	1	2	0.0	22.0	1	0	0	1	0	0
1	1	2	0	22.0	995.0	1	0	0	1	0	0
2	2	1	3	0.0	12.0	1	0	0	1	0	0
3	2	3	4	12.0	29.0	1	0	0	1	0	0
4	2	4	5	29.0	422.0	1	0	0	1	0	0

plot_ebmt(competing_risk_dataset, state_labels, covariate_cols, terminal_states=[5, 6])

Covid hospitalizations in Israel

Complex multis-state, recurring events and time-varying covariates dataset
Israel COVID-19 hospitalization public data, as described in Roimi et. al. 2021.

from pymsm.datasets import prep_covid_hosp_data, plot_covid_hosp
dataset, state_labels = prep_covid_hosp_data()
plot_covid_hosp(dataset, state_labels)

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