dialysis

Longitudinal data on the renal function of 116 patients observed on up to
five different occasions.

datasets

Fits singular linear models to longitudinal data. Singular linear
models are useful when the number, or timing, of longitudinal observations
may be informative about the observations themselves. They are described
in Farewell (2010) <doi:10.1093/biomet/asp068>, and are extensions of the
linear increments model <doi:10.1111/j.1467-9876.2007.00590.x> to general
longitudinal data.

Daniel Farewell

slim

Singular Linear Models for Longitudinal Data

dialysis function

A data.table with 116 rows and 5 variables:
<table><tr><td>
id </td>
<td> patient identifier, a character string </td>
</tr><tr><td>
group </td>
<td> treatment group identifier, a character string </td>
</tr><tr><td>
vintage </td>
<td> days since starting dialysis, an integer </td>
</tr><tr><td>
month </td>
<td> month of observation, an integer </td>
</tr><tr><td>
renalfn </td>
<td> renal function of the patient at that month, numeric </td>
</tr></table>

dialysis: Renal Function in Three Groups of Peritoneal Dialysis Patients

Description

Usage

Arguments

Format

References

id	patient identifier, a character string
group	treatment group identifier, a character string
vintage	days since starting dialysis, an integer
month	month of observation, an integer
renalfn	renal function of the patient at that month, numeric