stop_if_undergrouped_for_cumulation

The cumulative post-processing functions (<code>add_cumu_hazard</code>,
<code>add_surv_prob</code>, <code>add_cif</code>, <code>add_trans_prob</code>) accumulate hazards over the
rows of each group of <code>newdata</code>. If several covariate profiles (or causes /
transitions) share a group -- typically a forgotten <code>group_by()</code> -- the
accumulation silently runs across them and returns wrong curves. A correctly
grouped prediction grid has a unique <code>time_var</code> value per group, so a
duplicate within a group signals this situation. (The converse does not
hold: profiles stacked with disjoint time grids have unique <code>time_var</code>
values and pass undetected -- such input is indistinguishable from a
legitimate single profile with time-varying covariates, so it cannot be
guarded against. Grids built with <code>make_newdata()</code> always share time values
across profiles and are therefore always caught.) This generalises
the <code><a href="/link/stopifnot()?package=pammtools&version=0.8.1" data-mini-rdoc="pammtools::stopifnot()">stopifnot()</a></code> guard used for the RMST helper to the whole <code>add_*</code>
family, turning a silent numerical error into an explicit one.

internal

The Piece-wise exponential (Additive Mixed) Model
(PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) is a
powerful model class for the analysis of survival (or time-to-event) data,
based on Generalized Additive (Mixed) Models (GA(M)Ms).
It offers intuitive specification and robust estimation of complex survival
models with stratified baseline hazards, random effects, time-varying effects,
time-dependent covariates and cumulative effects (Bender and others (2019)),
as well as support for left-truncated data as well as competing risks,
recurrent events and multi-state settings.
pammtools provides tidy workflow for survival analysis with PAMMs,
including data simulation, transformation and other functions for data
preprocessing and model post-processing as well as visualization.

Andreas Bender

pammtools

Piece-Wise Exponential Additive Mixed Modeling Tools for
Survival Analysis

Fabian Scheipl

Johannes Piller

Philipp Kopper

Lukas Burk

stop_if_undergrouped_for_cumulation function

The cumulative post-processing functions (<code>add_cumu_hazard</code>,
<code>add_surv_prob</code>, <code>add_cif</code>, <code>add_trans_prob</code>) accumulate hazards over the
rows of each group of <code>newdata</code>. If several covariate profiles (or causes /
transitions) share a group -- typically a forgotten <code>group_by()</code> -- the
accumulation silently runs across them and returns wrong curves. A correctly
grouped prediction grid has a unique <code>time_var</code> value per group, so a
duplicate within a group signals this situation. (The converse does not
hold: profiles stacked with disjoint time grids have unique <code>time_var</code>
values and pass undetected -- such input is indistinguishable from a
legitimate single profile with time-varying covariates, so it cannot be
guarded against. Grids built with <code>make_newdata()</code> always share time values
across profiles and are therefore always caught.) This generalises
the <code><a href='https://rdrr.io/r/base/stopifnot.html'>stopifnot()</a></code> guard used for the RMST helper to the whole <code>add_*</code>
family, turning a silent numerical error into an explicit one.

Guard against silently-wrong cumulative results from mis-grouped newdata — stop_if_undergrouped_for_cumulation

Guard against silently-wrong cumulative results from mis-grouped <code>newdata</code>

stop_if_undergrouped_for_cumulation: Guard against silently-wrong cumulative results from mis-grouped `newdata`

Description

Usage

Arguments