Package: multipleOutcomes 0.8

multipleOutcomes: Asymptotic Covariance Matrix of Regression Models for Multiple Outcomes

Regression models can be fitted for multiple outcomes simultaneously. This package computes estimates of parameters across fitted models and returns the matrix of asymptotic covariance. Various applications of this package, including PATED (Prognostic Variables Assisted Treatment Effect Detection), multiple comparison adjustment, are illustrated.

Authors:Han Zhang [aut, cre]

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multipleOutcomes.pdf |multipleOutcomes.html✨
multipleOutcomes/json (API)

# Install 'multipleOutcomes' in R:
install.packages('multipleOutcomes', repos = c('https://zhangh12.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/zhangh12/multipleoutcomes/issues

Uses libs:
  • openblas– Optimized BLAS
Datasets:
  • actg - ACTG 320 Clinical Trial Dataset

On CRAN:

Conda-Forge:

fortranopenblas

3.60 score 1 scripts 236 downloads 9 exports 75 dependencies

Last updated 4 months agofrom:61f1cf74b3. Checks:1 OK, 10 WARNING. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 18 2025
R-4.5-win-x86_64WARNINGFeb 18 2025
R-4.5-mac-x86_64WARNINGFeb 18 2025
R-4.5-mac-aarch64WARNINGFeb 18 2025
R-4.5-linux-x86_64WARNINGFeb 18 2025
R-4.4-win-x86_64WARNINGFeb 18 2025
R-4.4-mac-x86_64WARNINGFeb 18 2025
R-4.4-mac-aarch64WARNINGFeb 18 2025
R-4.3-win-x86_64WARNINGFeb 18 2025
R-4.3-mac-x86_64WARNINGFeb 18 2025
R-4.3-mac-aarch64WARNINGFeb 18 2025

Exports:coxphMOgeeMOglmMOkmMOlogrankMOmultipleOutcomespatedquantileMOsimulateMoData

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecorrplotcowplotcpp11DerivdoBydplyrfansifarverFormulagenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmomentfitmunsellmvtnormnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynompurrrquantregR6rbibutilsRColorBrewerRcppRcppEigenRdpackreformulasrlangrstatixsandwichscalesSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithrzoo

test

Rendered fromtest.Rmdusingknitr::rmarkdownon Feb 18 2025.

Last update: 2024-06-12
Started: 2024-05-27

Readme and manuals

Help Manual

Help pageTopics
ACTG 320 Clinical Trial Datasetactg
Compute asymptotic variance-covariance matrix of parameters in given models It is used for models where bootstrap is not neededasymptoticMultipleOutcomes
Compute bootstrapped variance-covariance matrix of parameters in given models It is used when at least one of the specified model needs bootstrap, for example, Kaplan-Merier estimate for probability of survival, or quantiles are used for prognostic variables.bootstrapMultipleOutcomes
Process inputs of 'multipleOutcomes' when bootstrap will be used to estimate variance-covariance matrixcheckBootstrapInput
Process inputs of 'multipleOutcomes' when asymptotic properties are used to estimate variance-covariance matrixcheckDefaultInput
Extract Model Coefficientscoef.multipleOutcomes
Generate two curves of survival probability with pointwise 95% confidence interval: 1. PATED adjusted KM curve 2. Conventional KM curvecomparePointwiseConfidenceIntervalWidth
return estimate of log HR from coxph model to be used when bootstrap is needed.coxphMO
Create curve of survival probability based on conventional KM method, or PATED adjusted KM estimates. Refer to km_res or pated_res in 'pated' about the format of 'input'. Transformations are supported when computing confidence intervals. Refer to https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.4/statug/statug_lifetest_details08.htm or the LIFETEST proc in SAS for more details of transformation.createKaplanMeierCurve
Figure out the time points with at least one event. This is used to generate KM-like curves.extractKaplanMeierTimes
return estimates from GEE model to be used when bootstrap is needed.geeMO
g(S(t)), a transformed estimate of survival probability. See https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.4/statug/statug_lifetest_details08.htmgFunction
Inversed function of g(S(t)). See https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.4/statug/statug_lifetest_details08.htmgInverseFunction
return estimates from GLM model to be used when bootstrap is needed.glmMO
df is of length that equals to the number of specified models dfi is the number of parameters in model i This function returns index of parameters of model i in the vector of parameters of all modelsIDMapping
Fitting Regression Models for Multiple Outcomes and Returning the Matrix of CovariancemultipleOutcomes
Prognostic Variables Assisted Treatment Effect Detectionpated
Title Summarize an Analysis of Multiple Outcomes.print.summary.multipleOutcomes
Return difference in quantile between arms formula can be endpoint ~ trtquantileMO
Generate bootstrap dataset. When missing data presents, dataset is split into groups. Patients in the same group have missing on the same covariates. Bootstrap is carried out in each of the groupssampleWithReplacement
Generating Data for Simulation and TestingsimulateMoData
Object Summariessummary.multipleOutcomes
Calculate Variance-Covariance Matrix for a Fitted Model Objectvcov.multipleOutcomes