Type: Package
Title: Vaccines Pharmacometrics
Version: 0.0.6
Depends: R (≥ 4.0)
Date: 2024-11-20
Maintainer: Julie Dudasova (MSD) <julie.dudasova@merck.com>
Description: Estimate vaccine efficacy (VE) using immunogenicity data. The inclusion of immunogenicity data in regression models can increase precision in VE. The methods are described in the publications "Elucidating vaccine efficacy using a correlate of protection, demographics, and logistic regression" and "Improving precision of vaccine efficacy evaluation using immune correlate data in time-to-event models" by Julie Dudasova, Zdenek Valenta, and Jeffrey R. Sachs (2024).
License: GPL-3
Copyright: Copyright © 2024 Merck & Co., Inc., Rahway, NJ, USA and its affiliates. All rights reserved.
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
Imports: methods (≥ 3.5.2), stats, MASS (≥ 7.3-51.6), dplyr (≥ 1.0.0), survival (≥ 3.2-11)
Suggests: knitr, rmarkdown, testthat
NeedsCompilation: no
Packaged: 2024-11-20 11:08:24 UTC; vavrycuk
Author: Julie Dudasova (MSD) [aut, cre]
Repository: CRAN
Date/Publication: 2024-11-20 11:30:03 UTC

Efficacy summary (mean, median, confidence intervals)

Description

Function summarizes efficacy statistics (mean, median, confidence intervals) based on the set of estimated efficacy values and chosen condfidence interval.

Usage

EfficacyCI(efficacySet, ci = 0.95)

Arguments

efficacySet

numeric vector - vector of estimated efficacy values

ci

numeric - required confidence level

Details

Confidence intervals are calculated using quantiles of estimated efficacy values.

Value

named list - mean, median, CILow, CIHigh

Examples

# Load required packages
library(dplyr)

# Load an example dataset
data(data_temp)
Data.vaccinated <- filter(data_temp, vaccine == 1)
Data.control <- filter(data_temp, vaccine == 0)

# Fit logistic model relating neutralizing titer to disease status
logisticFit <- glm(disease_any ~ nAb1, data = data_temp, family = binomial())

# Estimate 95\% confidence interval of vaccine efficacy based on the fitted model
efficacySet <- glmParametricSampling(logisticFit, nboot = 500, Data.vaccinated, Data.control)
EfficacyCI(efficacySet)

Accounting for the uncertainty on the fitted "coxph" model and observed data

Description

coxphParametricSampling is used for vaccine efficacy confidence interval construction. It provides a vector of vaccine efficacy values, with length of nboot. 95% confidence interval, defined by 2.5th and 97.5th quantile of this vector, accounts for the uncertainty on the model fit (via parametric resampling of the posterior distribution of the model parameters) and observed data (via bootstrapping).

Usage

coxphParametricSampling(Fit, nboot = 2000, Data.vaccinated, Data.control)

Arguments

Fit

an object of class inheriting from "coxph" representing the fitted model

nboot

a numeric value for number of bootstrap samples for confidence interval construction

Data.vaccinated

a data frame for the vaccinated group, containing the variables in the fitted model

Data.control

a data frame for the control group, containing the variables in the fitted model

Value

a vector of vaccine efficacy values VE_set, with length of nboot

Examples

# Load required packages
library(dplyr)
library(survival)

# Load an example dataset
data(data_temp)
Data.vaccinated <- filter(data_temp, vaccine == 1)
Data.control <- filter(data_temp, vaccine == 0)

# Fit Cox proportional hazards model relating neutralizing titer 
# to time to disease or end of follow-up
coxFit <- coxph(Surv(time_event, disease_any) ~ nAb1, data = data_temp)

# Estimate 95\% confidence interval of vaccine efficacy based on the fitted model
efficacySet <- coxphParametricSampling(coxFit, nboot = 500, Data.vaccinated, Data.control)
CI <- lapply(EfficacyCI(efficacySet),"*", 100)

Example of a hypothetical vaccine clinical trial data set

Description

A dataset containing immunogenicity data, and clinical outcome data in the vaccinated and control groups. The dataset is provided in the form of a data frame.

Usage

data_temp

Format

Data frame:

ID

identification of subjects

nAb1

value of neutralizing titer for serotype 1

nAb2

value of neutralizing titer for serotype 2

group

binary indicator of a baseline demographic characteristics of interest

vaccine

binary indicator of treatment arm, with value 1 in vaccinated and 0 in control subjects

type_disease

serotype of disease

disease_any

binary indicator of disease caused by any serotype

time_event

time to disease or end of follow-up in days

Accounting for the uncertainty on the fitted "glm" model and observed data

Description

glmParametricSampling is used for vaccine efficacy confidence interval construction. It provides a vector of vaccine efficacy values, with length of nboot. 95% confidence interval, defined by 2.5th and 97.5th percentile of this vector, accounts for the uncertainty on the model fit (via parametric resampling of the posterior distribution of the model parameters) and observed data (via bootstrapping).

Usage

glmParametricSampling(Fit, nboot = 2000, Data.vaccinated, Data.control)

Arguments

Fit

an object of class inheriting from "glm" representing the fitted model

nboot

a numeric value for number of bootstrap samples for confidence interval construction

Data.vaccinated

a data frame for the vaccinated group, containing the variables in the fitted model; data must include a column called "vaccine" with binary indicator of vaccination status

Data.control

a data frame for the control group, containing the variables in the fitted model; data must include a column called "vaccine" with binary indicator of vaccination status

Value

a vector of vaccine efficacy values VE_set, with length of nboot

Examples

# Load required packages
library(dplyr)

# Load an example dataset
data(data_temp)
Data.vaccinated <- filter(data_temp, vaccine == 1)
Data.control <- filter(data_temp, vaccine == 0)

# Fit logistic model relating neutralizing titer to disease status
logisticFit <- glm(disease_any ~ nAb1, data = data_temp, family = binomial())

# Estimate 95\% confidence interval of vaccine efficacy based on the fitted model
efficacySet <- glmParametricSampling(logisticFit, nboot = 500, Data.vaccinated, Data.control)
CI <- lapply(EfficacyCI(efficacySet),"*", 100)

vaxpmx

Description

pharmacometric modeling in vaccines

Author(s)

Julie Dudasova

Vaccine efficacy estimation

Description

Calculates vaccine efficacy and confidence interval as described in Dudasova et al., 2024, BMC Med Res Methodol and Dudasova et al., 2024, NPJ Vaccines

Usage

ve(Fit, Data, nboot = 2000)

Arguments

Fit

an object of class inheriting from "glm" or "coxph" representing the fitted model

Data

a data frame containing the variables in the fitted model; data must include a column called "vaccine" with binary indicator of vaccination status

nboot

a numeric value for number of bootstrap samples for confidence interval construction

Value

a value of vaccine efficacy VE and lower and upper bound of confidence interval CI

Examples

#' # Load required packages
library(survival)

# Load an example dataset
data(data_temp)

# Fit logistic model relating neutralizing titer to disease status
logisticFit <- glm(disease_any ~ nAb1, data = data_temp, family = binomial())

# Fit Cox proportional hazards model relating neutralizing titer 
# to time to disease or end of follow-up
coxFit <- coxph(Surv(time_event, disease_any) ~ nAb1, data = data_temp)

# Estimate vaccine efficacy and 95\% confidence interval based on the fitted models
ve(logisticFit, data_temp, nboot = 500)
ve(coxFit, data_temp, nboot = 500)