Package: PoDBAY 1.4.3

PoDBAY: Vaccine Efficacy Estimation Package

Set of functions that implement the PoDBAY method, described in the publication 'A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data' by Julie Dudasova, Regina Laube, Chandni Valiathan, Matthew C. Wiener, Ferdous Gheyas, Pavel Fiser, Justina Ivanauskaite, Frank Liu and Jeffrey R. Sachs (NPJ Vaccines, 2021), <doi:10.1038/s41541-021-00377-6>.

Authors:Pavel Fiser [aut], Tomas Bartonek [aut], Julie Dudasova [aut, cre], Regina Laube [aut]

PoDBAY_1.4.3.tar.gz
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PoDBAY.pdf |PoDBAY.html✨
PoDBAY/json (API)
NEWS

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

Peer review:

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • PoDParams - PoD curve parameters
  • control - Dataset containing the information for control subjects
  • diseased - Dataset containing the information for diseased subjects
  • efficacySet - Estimated PoDBAY efficacies
  • estimatedParameters - Estimated PoD curve parameters
  • nondiseased - Dataset containing the information for non-diseased subjects
  • vaccinated - Dataset containing the information for vaccinated subjects

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.30 score 10 scripts 112 downloads 33 exports 32 dependencies

Last updated 3 years agofrom:e3ac5049cb. Checks:OK: 1 NOTE: 8. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 06 2024
R-4.5-win-x86_64NOTENov 06 2024
R-4.5-linux-x86_64NOTENov 06 2024
R-4.4-win-x86_64NOTENov 06 2024
R-4.4-mac-x86_64NOTENov 06 2024
R-4.4-mac-aarch64NOTENov 06 2024
R-4.3-win-x86_64NOTENov 06 2024
R-4.3-mac-x86_64NOTENov 06 2024
R-4.3-mac-aarch64NOTENov 06 2024

Exports:BlindSamplingClinicalTrialClinicalTrialCoveragecppMLEcppPoDEfficacyCIEfficacyCICoverageefficacyComputationefficacySquaredErrorExpectedPoDExtractDiseasedExtractNondiseasedfitPoDGenerateNondiseasedgeneratePopulationImmunogenicitySubsetincorrectInputincorrectPopulationInputJitterMeanMLEnumToBoolPmaxEstimationPoDPoDBAYEfficacyPoDCIPoDCurvePlotPoDEfficacySquaredErrorPoDMLEPoDParamEstimationPoDParamPointEstimationPoDParamsCIPoDParamsCICoveragewaldCI

Dependencies:clicolorspacedplyrfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerRcpprlangscalestibbletidyselectutf8vctrsviridisLitewithr

PoD curve point estimation using vaccine efficacy and population summary statistics

Pavel Fiser (MSD), Julie Dudasova (MSD)

Rendered fromcurve_estimation.Rmdusingknitr::rmarkdownon Nov 06 2024.

Last update: 2021-09-21
Started: 2021-09-21

PoDBAY efficacy estimation

Pavel Fiser (MSD), Julie Dudasova (MSD)

Rendered fromefficacyestimation.Rmdusingknitr::rmarkdownon Nov 06 2024.

Last update: 2021-09-21
Started: 2021-09-21

PoDBAY population class

Pavel Fiser (MSD), Julie Dudasova (MSD)

Rendered frompopulation.Rmdusingknitr::rmarkdownon Nov 06 2024.

Last update: 2021-09-21
Started: 2021-09-21

PoDBAY simulation

Pavel Fiser (MSD), Julie Dudasova (MSD)

Rendered fromsimulation.Rmdusingknitr::rmarkdownon Nov 06 2024.

Last update: 2021-09-21
Started: 2021-09-21

Readme and manuals

Help Manual

Help pageTopics
Assign probability of disease (PoD)assignPoD
Immunogenicity subset: vaccinated, control, non-diseasedBlindSampling
Clinical trial: estimation of case-count efficacyClinicalTrial
Clinical trial function expanded for usage in simulations when the calculation of coverage probability is needed for three confidence intervals: 80%, 90%, and user-definedClinicalTrialCoverage
Dataset containing the information for control subjectscontrol
Maximum likelihood estimation: cppcppMLE
Probability of disease calculationcppPoD
Dataset containing the information for diseased subjectsdiseased
PoDBAY efficacy summary: mean, median, confidence intervalsEfficacyCI
PoDBAY efficacy summary at three confidence levelsEfficacyCICoverage
PoDBAY efficacy equationefficacyComputation
Estimated PoDBAY efficaciesefficacySet
Optimization objective function: efficacy squared errorefficacySquaredError
Estimated PoD curve parametersestimatedParameters
Expected probability of diseaseExpectedPoD
Diseased subjects extractionExtractDiseased
Non-diseased subjects extractionExtractNondiseased
PoD curve: fitting functionfitPoD
Generation of upsampled non-diseased subjects titersGenerateNondiseased
Population class object generationgeneratePopulation
Diseased countgetDiseasedCount
Diseased titersgetDiseasedTiters
Non-diseased countgetNondiseasedCount
Non-diseased titersgetNondiseasedTiters
Subject level titersgetTiters
Generate unknowngetUnknown
Immunogenicity subsetImmunogenicitySubset
Error messageincorrectInput
Population class error messageincorrectPopulationInput
Population mean jitteringJitterMean
Maximum Likelihood estimationMLE
Dataset containing the information for non-diseased subjectsnondiseased
Numeric to booleannumToBool
PoD curve paramater, pmax, estimationPmaxEstimation
Probability of disease calculationPoD
PoDBAYPoDBAY
PoDBAY efficacy estimationPoDBAYEfficacy
PoD curve confidence ribbonPoDCI
PoD curve: plotPoDCurvePlot
Optimization function: finds PoD curve paramaters (et50, slope)PoDEfficacySquaredError
Setup for the maximum likelihood estimation (MLE)PoDMLE
PoD curve parameters estimationPoDParamEstimation
PoD curve point estimatePoDParamPointEstimation
PoD curve parametersPoDParams
Confidence intervals of PoD curve parametersPoDParamsCI
Confidence intervals of PoD curve parameters at three confidence levelsPoDParamsCICoverage
Population functionpopFun
Population classpopulation Population-class
Add noise to population titerspopX
Dataset containing the information for vaccinated subjectsvaccinated
Wald confidence interval estimationwaldCI