Package: outstandR 2.0.0

Nathan Green

outstandR: Model-Based Standardisation for Indirect Treatment Comparison with Limited Subject-Level Data

For the problem of indirect treatment comparison with limited subject-level data, this package provides tools for model-based standardisation with several different computation approaches. See Remiro‐Azócar A, Heath A, Baio G (2022) ``Parametric G‐computation for compatible indirect treatment comparisons with limited individual patient data'', Res. Synth. Methods, 1–31. ISSN 1759-2879, <doi:10.1002/jrsm.1565>.

Authors:Nathan Green [aut, cre, cph], Chengyang Gao [aut], Antonio Remiro-Azocar [aut]

outstandR_2.0.0.tar.gz
outstandR_2.0.0.tar.gz(r-4.7-any)outstandR_2.0.0.tar.gz(r-4.6-any)
outstandR_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
outstandR/json (API)
NEWS

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

Bug tracker:https://github.com/statisticshealtheconomics/outstandr/issues

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

Datasets:

On CRAN:

Conda:

2.49 score 31 scripts 168 downloads 27 exports 126 dependencies

Last updated from:baf84f832a. Checks:4 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK344
source / vignettesOK248
linux-release-x86_64OK350
wasm-releaseOK177

Exports:calc_ALD_statscalc_gcomp_bayescalc_gcomp_mlcalc_IPD_statscalculate_atecalculate_trial_meancalculate_trial_mean_binarycalculate_trial_mean_continuouscalculate_trial_mean_countcalculate_trial_variancecalculate_trial_variance_binarycalculate_trial_variance_continuouscalculate_trial_variance_countcheck_balance_formulacheck_formulaestimate_var_sandwichget_allowed_var_methodsget_treatment_effectmarginal_treatment_effectmarginal_variancenew_strategyoutstandRstrategy_gcomp_bayesstrategy_gcomp_mlstrategy_maicstrategy_mimstrategy_stc

Dependencies:abindADGofTestbackportsbase64encbayesplotBHbootbslibcachemcallrcheckmatecliclustercolorspacecolourpickercommonmarkcopulacpp11crayoncrosstalkdescdigestdistributionaldplyrDTdygraphsevaluatefarverfastmapfontawesomefsgenericsggplot2ggridgesgluegridExtragslgtablegtoolshighrhtmltoolshtmlwidgetshttpuvigraphinlineisobandjquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelitedownlme4loomagrittrmarkdownMASSMatrixmatrixStatsmemoisemimeminiUIminqamvtnormnlmenloptrnumDerivotelpcaPPpillarpkgbuildpkgconfigplyrposteriorprocessxpromisespspsplinepurrrQuickJSRR6rappdirsrbibutilsRColorBrewerRcppRcppEigenRcppParallelRdpackreformulasreshape2rlangrmarkdownrstanrstanarmrstantoolsS7sassscalesshinyshinyjsshinystanshinythemessourcetoolsstabledistStanHeadersstringistringrsurvivaltensorAthreejstibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunxtablextsyamlzoo

Readme and manuals

Help Manual

Help pageTopics
Individual-level patient data for binary outcome, continuous covariatesAC_IPD_binY_contX
Individual-level patient data for continuous outcome, mixed covariatesAC_IPD_contY_mixedX
Individual-level patient data for count outcome, continuous covariatesAC_IPD_countY_contX
Aggregate level patient data for binary outcome, continuous covariatesBC_ALD_binY_contX
Aggregate level patient data for continuous outcome, mixed covariatesBC_ALD_contY_mixedX
Aggregate level patient data for count outcome, continuous covariatesBC_ALD_countY_contX
Aggregate-level data mean and variance statisticscalc_ALD_stats
Bayesian G-computation using Stancalc_gcomp_bayes
G-computation Maximum Likelihood Bootstrapcalc_gcomp_ml
Calculate individual-level patient data statisticscalc_IPD_stats calc_IPD_stats.default calc_IPD_stats.gcomp_bayes calc_IPD_stats.gcomp_ml calc_IPD_stats.maic calc_IPD_stats.mim calc_IPD_stats.stc
Calculate Average Treatment Effectcalculate_ate
Calculate Trial Mean Wrappercalculate_trial_mean
Calculate Trial Mean Binary Datacalculate_trial_mean_binary
Calculate Trial Mean Continuous Datacalculate_trial_mean_continuous
Calculate Trial Mean Count Datacalculate_trial_mean_count
Calculate trial variancecalculate_trial_variance
Calculate trial variance binarycalculate_trial_variance_binary
Calculate trial variance continuouscalculate_trial_variance_continuous
Calculate trial variance countcalculate_trial_variance_count
Estimate Variance Sandwich Estimatorestimate_var_sandwich
Get treatment effect scale corresponding to a link functionget_treatment_effect
Marginal treatment effect from reported event countsmarginal_treatment_effect
Marginal effect variance using the delta methodmarginal_variance
Calculate the difference between treatments using all evidenceoutstandR
outstandR classoutstandR-class
Default Plot Method for outstandR Objectsplot.outstandR
Print a Summary of a outstandR Objectprint.outstandR
Convert aggregate data from wide to long formatreshape_ald_to_long
Convert aggregate data from long to wide formatreshape_ald_to_wide
New strategy objectsnew_strategy strategy strategy_gcomp_bayes strategy_gcomp_ml strategy_maic strategy_mim strategy_stc
Strategy class and subclassesstrategy-class
Summary method for outstandRprint.summary.outstandR summary.outstandR