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move the statistical estimates to a separate entry
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@avallecam
avallecam committed Oct 8, 2024
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)

```

# Statistical analysis

```r

#' goal:
#' model the kinetics of neutralising-antibody titres after antigenic SARS-CoV-2 exposure?

# load packages -----------------------------------------------------------

library(tidyverse)
library(cleanepi)
library(datatagr) # a generalization of {linelist}
# library(epikinetics)

# read data ---------------------------------------------------------------

# rawdata <- "data-raw/delta.csv"
rawdata <- "https://raw.githubusercontent.com/seroanalytics/epikinetics/refs/heads/main/inst/delta_full.rds"

dat <- read_csv(rawdata)

dat %>% glimpse()

# what these columns mean? ------------------------------------------------

#' data dictionary: https://seroanalytics.org/epikinetics/articles/data.html
#' reference paper: https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(24)00484-5/fulltext
#' location: https://github.com/seroanalytics/epikinetics/tree/main/inst

# 335 subjects where followed up
dat %>% count(pid)

## what "titre type" means? -------------------------------------------------

#' In the time series,
#' each subject had monthly serum measurements
#' for three types of antigens ("titre_type").
#'
#' Serum samples where challenged against Ancestral, Alpha and Delta antigens.
#'
#' The column "value" measures the titre of
#' the neutralizing effect of each sample against each antigen

dat %>%
dplyr::filter(pid == 2) %>%
dplyr::arrange(date) %>%
# select time invariant columns
dplyr::select(
pid, infection_history, exp_num, last_exp_date, last_vax_type,
dplyr::everything()
)

## what "censored" means? ----------------------------------------------------

# context: censored regression model
# the "value" as the outcome is censored above or below
# because the it was measured outside the limits of detection
# threshold limit below: 5
# threshold limit above: 2560

dat %>%
ggplot(aes(value, fill = as.factor(censored))) +
geom_histogram()

# datatagr ----------------------------------------------------------------

datatagr::lost_labels_action()
datatagr::get_lost_labels_action()
# datatagr::lost_labels_action(action = "error")

# cleanepi ----------------------------------------------------------------

# check sequence of events

dat_clean <- dat %>%
# arrange columns
dplyr::select(
pid, infection_history, exp_num, last_exp_date, last_vax_type,
dplyr::everything()
) %>%
# arrange rows
dplyr::arrange(
pid, infection_history, exp_num, last_exp_date, last_vax_type, date
) %>%
# cleanepi
cleanepi::check_date_sequence(
target_columns = c("last_exp_date", "date")
) %>%
# cleanepi::print_report()
cleanepi::timespan(
target_column = "last_exp_date",
end_date = "date",
span_unit = "days",
span_column_name = "t_since_last_exp",
span_remainder_unit = "days"
) %>%
# extra wrangling
mutate(
last_vax_type = forcats::fct_infreq(last_vax_type),
exp_num = forcats::as_factor(exp_num),
titre_type = forcats::fct_relevel(titre_type,"Ancestral", "Alpha"),
# censored = forcats::as_factor(censored) # in {epikinetics} censored needs to be numeric
) %>%
# tag with {datatagr}
datatagr::make_datatagr(
pid = "subject id",
infection_history = "subject infection history",
exp_num = "number of vaccine exposures",
last_exp_date = "date of last exposure",
last_vax_type = "type of vaccine in the last exposure",
date = "date of observation of titre in serum sample",
titre_type = "type of antigen challenged against serum sample",
value = "titre value",
censored = "censored titre value out of limit of detection [5 - 2560] bellow (-1) or above (+1)",
t_since_last_exp = "time interval between last vaccine exposure and observed serum sample titre"
) %>%
# validate with {datatagr}
datatagr::validate_datatagr(
pid = "numeric",
infection_history = "character",
exp_num = "factor",
last_exp_date="Date",
last_vax_type = "factor",
date = "Date",
titre_type = "factor",
value = "numeric",
censored = "numeric",
t_since_last_exp = "numeric"
) %>%
# datatagr::labels_df() %>% # this extract labels as column names [affects downstream]
identity()

dat_clean


# visualization: data to model --------------------------------------------

dat_clean %>%
mutate(log2_value = log2(value)) %>%
filter(censored == "0") %>%
# skimr::skim(log2_value)
ggplot(aes(x = t_since_last_exp, y = value)) +
geom_point() +
geom_smooth() +
scale_y_continuous(trans = "log2") +
facet_wrap(~infection_history+titre_type) +
xlim(0,150)


# model -------------------------------------------------------------------

#' In this vignette we use a dataset representing the Delta wave
#' which is installed with this package,
#' specifying a regression model that
#' just looks at the effect of infection history.
#'
#' Figure 2 from the paper shows population level fits for each wave,
#' disaggregated by infection history and titre type.
#' Here we reproduce the facets for the Delta wave.
#' Once the model has been fitted,
#' simulate population trajectories using the fitted population parameters.

dat_clean %>% class()

mod <- epikinetics::biokinetics$new(
data = dat_clean %>% data.table::as.data.table(),
covariate_formula = ~0 + infection_history
)

# WAIT this takes 14 minutes
# tictoc::tic()
delta <- mod$fit(
parallel_chains = 4,
iter_warmup = 50,
iter_sampling = 200,
threads_per_chain = 4
)
# tictoc::toc()

# this takes 10 seconds
# tictoc::tic()
res <- mod$simulate_population_trajectories()
head(res)
# tictoc::toc()

# visualize output --------------------------------------------------------

plot_data <- res

plot_data[, titre_type := forcats::fct_relevel(
titre_type,
c("Ancestral", "Alpha", "Delta"))]

ggplot(data = plot_data) +
geom_line(aes(x = t,
y = me,
colour = titre_type)) +
geom_ribbon(aes(x = t,
ymin = lo,
ymax = hi,
fill = titre_type), alpha = 0.65) +
scale_y_continuous(trans = "log2") +
labs(x = "Time since last exposure (days)",
y = expression(paste("Titre (IC"[50], ")"))) +
facet_wrap(infection_history ~ titre_type)
```

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