Method library WORKFLOW / 040

Research workflow/4 steps

Confounder-adjusted survival curves for a treatment (adjustedCurves)

@adjustedcurves ✓ P D · Jun 29, 2026 · 908 views

@misc{adjustedcurves,
  title        = {adjustedCurves},
  author       = {Robin Denz},
  howpublished = {\url{https://robindenz1.github.io/adjustedCurves/}},
  note         = {Software / documentation}
}

In the margins · Peer notes@censoring_carl · Reporting the RMST difference alongside the curves is the part clinicians actually act on. Good to see it as its o… 2

The method workspace

Follow the analysis.

Select a node to explore the method

Method mapInput → 4 steps → output

START WITHInput data

ARRIVE ATResults

Read step 01

Inside the methodReasoning, equations & implementation

Data prep

Time-to-event, treatment, confounders

Step notesField note & discussion1

Data preparation — shapes the raw inputs into what the estimator expects.

What happens here

Set up the censored outcome, the treatment, and the confounders of treatment assignment.

Formula

T=\min(T^{*},C),\qquad \delta=\mathbb 1\{T^{*}\le C\}

Reads fromNot specified Feeds intoNot specified

Key code

# time (T), status (event 1/0), group (treatment), x1,x2 (confounders)

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Estimation

Adjust for confounding (IPTW / g-formula)

Step notesField note & discussion1

The core estimate — where the causal quantity itself is computed.

What happens here

Weight by the inverse propensity of treatment (or standardise via an outcome model) and form a weighted survival estimate.

Formula

\hat S_a(t)=\dfrac{\sum_i \frac{\mathbb 1\{A_i=a\}}{\hat\pi_i(A_i)}\,\mathbb 1\{T_i>t\}}{\sum_i \frac{\mathbb 1\{A_i=a\}}{\hat\pi_i(A_i)}}

Reads fromNot specified Feeds intoNot specified

Key code

adjustedsurv(data=df, variable='group', ev_time='time',
             event='status', method='iptw_km', treatment_model=ps)

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Inference

Curves with confidence bands

Step notesField note & discussion1

Uncertainty quantification — standard errors, intervals, and aggregation.

What happens here

Bootstrap or asymptotic variance gives pointwise (and simultaneous) bands around each adjusted curve.

Formula

\hat S_1(t)-\hat S_0(t)\ \pm\ z_{1-\alpha/2}\,\widehat{\mathrm{se}}(t)

Reads fromNot specified Feeds intoNot specified

Key code

adjustedsurv(..., conf_int=TRUE, bootstrap=TRUE)

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Reporting

Summaries: RMST difference, survival at t

Step notesField note & discussion1

Reporting — turn the numbers into a figure or table a reader can act on.

What happens here

Collapse the curves to interpretable numbers: restricted mean survival time, or survival probability at a chosen horizon.

Formula

\mathrm{RMST}_a(\tau)=\int_0^{\tau}\hat S_a(t)\,dt,\qquad \Delta=\mathrm{RMST}_1-\mathrm{RMST}_0

Reads fromNot specified Feeds intoNot specified

Key code

adjusted_rmst(adj, to=5)

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Data contract

Inputs & data

Right-censored time-to-event data: an event time, an event/censoring indicator, a treatment, and the confounders.

Show data format & exampleHide example

Format — one row per unit: time, status (1=event, 0=censored), group (treatment), confounders x*.

library(adjustedCurves)
adj <- adjustedsurv(data=df, variable='group', ev_time='time',
                    event='status', method='iptw_km',
                    treatment_model=glm(group ~ x1 + x2, family='binomial'))
plot(adj)

Read the evidence

Results & interpretation

2 figures Workflow discussion 2

01FigureConfounder-adjusted survival curves for treated (1) vs control (0), with 95% confidence bands — the adjusted gap is the causal effect on survival, not a raw Kaplan-Meier.

02FigureA scenario where, once confounders are accounted for, the two groups' adjusted curves nearly coincide — no survival benefit after de-confounding.

Figures reproduced from adjustedCurves — Robin Denz — unofficial community showcase; all credit to the original authors.

Context & interpretation

⚠️ Unofficial community showcase of adjustedcurves. Not affiliated with the authors; all credit to them.

Compare survival between treatment groups after removing confounding — via IPTW, the g-formula or AIPW — instead of a raw Kaplan-Meier that quietly bakes in selection.

Peer notes

Discuss the workflow (2)

2

@yui_tanaka · Jun 29, 2026

Raw Kaplan-Meier between treatment groups in an observational study is the mistake I see most. IPTW-adjusted curves should be the default.
0

@censoring_carl · Jun 29, 2026

Reporting the RMST difference alongside the curves is the part clinicians actually act on. Good to see it as its own step.

1

@yui_tanaka · Jun 29, 2026

Yes — survival-at-t plus RMST difference communicates far better than a hazard ratio nobody can interpret.