knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) library(dplyr) library(ggplot2) library(PoDBAY) set.seed(1234)
This document describes how to estimate PoD curve parameters using PoDBAY package. This process can be applied when user doesn't have individual level data about vaccinated and control populations, but only summary statistics data and corresponding estimatedcase-count vaccine efficacy.
The goal of this document is to show how to estimate point estimate of PoD curve parameters in two steps
Required input:
reference efficacy - vaccine case-count efficacy estimate from large clinical trial (converging to the true value of efficacy)
$p_{max}$ estimation
Required input:
Function PoDEfficacySquaredError()
is used to estimate $et_{50}$ and $\gamma$.
As the inputs to the function we use vaccinated
and control
mock-up population class objects together with artificially chosen TrueEfficacy
parameter.
Note: To convert your data in to the population
class object use generatePopulation()
function from PoDBAY package. See vignette vignette("population", package = "PoDBAY")
for further details.
# Mockup vaccinated and control population class objects data(vaccinated) data(control) # Observed vaccine efficacy TrueEfficacy <- 0.53 # PoD curve parameter estimation params_et50_slope <- PoDEfficacySquaredError(TrueEfficacy, vaccinated, control, initialSlope = 6) params_et50_slope
NOTE
Once we have $et_{50}$ and $\gamma$ estimated we can proceed with $p_{max}$ estimation using PmaxEstimation
.
As the inputs to the function we use estimated $et_{50}$ and $\gamma$, control
mock-up population class object together with artificially chosen IncidenceRate
parameter.
# Incidence rate for low titer population IncidenceRate <- 0.02 # pmax estimation pmax <- PmaxEstimation(IncidenceRate, params_et50_slope, control) # combining PoD curve parameters PoDParams <- unlist(c(params_et50_slope, pmax)) PoDParams
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