Package: SEQTaRget 1.4.5

Ryan ODea

SEQTaRget: Sequential Trial Emulation

Implementation of sequential trial emulation for the analysis of observational databases. The 'SEQTaRget' software accommodates time-varying treatments and confounders, as well as binary and failure time outcomes. 'SEQTaRget' allows to compare both static and dynamic strategies, can be used to estimate observational analogs of intention-to-treat and per-protocol effects, and can adjust for potential selection bias induced by losses-to-follow-up. (Paper to come).

Authors:Ryan O'Dea [aut, cre], Tom Palmer [aut], Paul Madley-Dowd [aut], Alejandro Szmulewicz [aut], Miguel A. Hernán [aut], The President and Fellows of Harvard College [cph]

SEQTaRget_1.4.5.tar.gz
SEQTaRget_1.4.5.zip(r-4.7-any)SEQTaRget_1.4.5.zip(r-4.6-any)SEQTaRget_1.4.5.zip(r-4.5-any)
SEQTaRget_1.4.5.tgz(r-4.6-any)SEQTaRget_1.4.5.tgz(r-4.5-any)
SEQTaRget_1.4.5.tar.gz(r-4.7-any)SEQTaRget_1.4.5.tar.gz(r-4.6-any)
SEQTaRget_1.4.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SEQTaRget/json (API)

# Install 'SEQTaRget' in R:
install.packages('SEQTaRget', repos = c('https://causalinference.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/causalinference/seqtarget/issues

Pkgdown/docs site:https://causalinference.github.io

Datasets:
  • SEQdata - Simulated observational example data for SEQuential
  • SEQdata.LTFU - Simulated lost-to-followup example data for 'SEQuential()'
  • SEQdata.multitreatment - Simulated multitreatment example data for 'SEQuential()' multinomial models

On CRAN:

Conda:

as-treatedbiostatisticscausal-inferenceepidemiologyinitiatorsintention-to-treatper-protocol

7.29 score 9 stars 26 scripts 340 downloads 16 exports 46 dependencies

Last updated from:e56a63385e. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK277
source / vignettesOK250
linux-releaseOK291
macos-releaseOK365
macos-oldrelOK327
windows-develOK267
windows-releaseOK215
windows-oldrelOK333
wasm-releaseOK117

Exports:compeventcovariatesdenominatordiagnosticsend_of_fuphazard_ratiokm_curvekm_datanumeratoroutcomerisk_comparisonrisk_dataSEQ_dataSEQoptsSEQuentialshow

Dependencies:BHbigmemorybigmemory.sriclicodetoolscpp11data.tabledigestevaluatefarverfastglmFormulafuturefuture.applyggplot2globalsgluegtablehighrisobandknitrlabelinglatticelifecyclelistenvmagrittrMatrixparallellyparglmR6RColorBrewerRcppRcppArmadilloRcppEigenrlangS7scalesstringistringrsurvivaluuidvctrsviridisLitewithrxfunyaml

Defining your SEQopts()
Behind SEQopts() | The options | General Options | Weighting Options | Plot Options (km.curves = TRUE) | Special Cases | Internal Options
Rendered fromseqopts.Rmdusingknitr::rmarkdown

Last update: 2026-10-12
Started: 2025-09-05

End-of-Follow-up Outcomes
What this is for | A minimal example | Missing measurements and the time window | Checking what contributed | Continuous outcomes | Per-protocol effects
Rendered fromendoffollowup.Rmdusingknitr::rmarkdown

Last update: 2026-10-12
Started: 2026-08-28

Per-Protocol: Censoring Analysis
Per-protocol, censoring, weights in pre-expanded data and no truncation, no excused conditions (i.e. static interventions) | Per-protocol, censoring, weights in post-expanded data and no truncation, no excused conditions (i.e. static interventions) | Per-protocol, censoring, weights in pre-expanded data and no truncation, excused conditions for initiators and non-initiators (i.e. dynamic interventions) | Per-protocol, censoring, a flexible baseline hazard in the weight models | Per-protocol, censoring, weights in post-expanded data and no truncation, excused conditions for initiators and non-initiators (i.e. dynamic interventions) | Per-protocol, censoring, weights in post-expanded data and no truncation, excused conditions for initiators and non-initiators (i.e. dynamic interventions) and a competing event | Per-protocol, censoring, weights in post-expanded data and no truncation, excused conditions for initiators and non-initiators (i.e. dynamic interventions) and hazard ratio
Rendered fromcensoring.Rmdusingknitr::rmarkdown

Last update: 2026-10-12
Started: 2025-09-05

Intention-To-Treat Analysis
ITT With 5 bootstrap samples | ITT with 5 bootstrap samples and losses-to-followup | ITT with 5 bootstrap samples and competing events | ITT hazard ratio with 5 bootstrap samples and competing events | ITT with 5 bootstrap samples and competing events in subgroups defined by sex
Rendered fromITT.Rmdusingknitr::rmarkdown

Last update: 2026-08-28
Started: 2025-09-05

Introduction to SEQuential
Setting up your Analysis | Step 1 - Defining your options | Step 2 - Running the Primary Function | Step 3 - Recovering your results
Rendered fromSEQuential.Rmdusingknitr::rmarkdown

Last update: 2026-08-28
Started: 2025-09-05

Per-Protocol: Dose-Response Analysis
Dose-response With 5 bootstrap samples | Dose-response with 5 bootstrap samples and losses-to-followup | Dose-response with 5 bootstrap samples and competing events | Dose-response hazard ratio with 5 bootstrap samples and competing events | Dose-response with 5 bootstrap samples and competing events in subgroups defined by sex
Rendered fromdoseresponse.Rmdusingknitr::rmarkdown

Last update: 2026-08-28
Started: 2025-09-05