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Gettign Started with the 'recent' Package

introduction.Rmd

Introduction

This package implements the methods of Fellows et. al. 2022 for the calculation of incidence using recency assays such as LAg-Avidity. Because treated individuals and elite controllers are known to ave high false positivity rates on recency assays, individuals with low viral load or a previous HIV+ diagnosis are screened out. A previous diagnosis can be indicated by either a self-reported previous positive test or through the detection of ARV antibodies.

Data

The package includes example data, which we will be using in this vignette.

library(recent)
data("assay_data")
head(assay_data[1:7])
#>      recent   hiv undiagnosed elite_cntr       tslt ever_hiv_test   weights
#> 2139  FALSE  TRUE       FALSE       TRUE 1232.54154          TRUE  72.32627
#> 6918     NA FALSE       FALSE      FALSE         NA         FALSE 195.87990
#> 5448     NA FALSE       FALSE      FALSE   73.66674          TRUE  87.99275
#> 3277     NA FALSE       FALSE      FALSE   54.07090          TRUE  77.74728
#> 3179     NA FALSE       FALSE      FALSE   25.08993          TRUE 117.30507
#> 3983     NA FALSE       FALSE      FALSE         NA         FALSE 137.49198
  1. recent: Logical. Did the subject test recent on the assay.
  2. hiv: Logical. Is the subject HIV+.
  3. undiagnosed: Logical. Has the subject never received a positive HIV test.
  4. elite_cntr: Logical. Viral load test <1,000.
  5. tslt: Numeric. Time since last HIV test in months.
  6. ever_hiv_test: Logical. Has the subject never been tested for HIV.
  7. weights: Numeric. Survey Weights.
  8. btwt001-btwt206: Numeric. Jackknife replicate weights.

Incidence Estimation

Incidence is calculated using the rita_incidence function. The frr and assay_surv are the reference FRR and reference probability of testing recent by day since seroconversion. Here we use values calculated for the LAg-Avidity assay. The default recency period (tau) is 2 years.

inc <- rita_incidence(
    recent=assay_data$recent,
    undiagnosed=assay_data$undiagnosed,
    low_viral=assay_data$elite_cntr,
    hiv=assay_data$hiv,
    weights=assay_data$weights,
    tslt=assay_data$tslt,
    ever_hiv_test=assay_data$ever_hiv_test,
    frr = lag_avidity_frr()[1],
    assay_surv = lag_avidity_survival(2 * 365)
  )
 knitr::kable(inc)
incidence residual_frr omega_rs omega_s P(R|S) P(S|H) P(H)
0.0151573 0.0008827 0.2921342 1.093136 0.072833 0.195313 0.2480427

The function outputs the following values:

  1. incidence: The incidence.
  2. residual_frr: The false recency rate accounting for the screening process.
  3. omega_rs: The mean duration of recency up to tau accounting for the screening process.
  4. P(R|S) : The proportion of screened in individual who test recent.
  5. P(S|H) : The proportion of HIV+ individuals that are screened in.
  6. P(H) : HIV prevalence.

By default the function estimates the time to diagnosis distribution using testing history information among undiagnosed HIV+ cases. If there are few of these, or their reliability is questionable, the HIV- population can be used instead by specifying ``.

inc <- rita_incidence(
    recent=assay_data$recent,
    undiagnosed=assay_data$undiagnosed,
    low_viral=assay_data$elite_cntr,
    hiv=assay_data$hiv,
    weights=assay_data$weights,
    tslt=assay_data$tslt,
    ever_hiv_test=assay_data$ever_hiv_test,
    frr = lag_avidity_frr()[1],
    assay_surv = lag_avidity_survival(2 * 365),
    test_history_population = "negative"
  )
knitr::kable(inc)
incidence residual_frr omega_rs omega_s P(R|S) P(S|H) P(H)
0.0171628 0.0009378 0.2572542 0.8308294 0.072833 0.195313 0.2480427

Bootstrap Confidence Intervals

Calculation of confidence intervals using survey replicate weights is supported.

rep_weights <-  dplyr::select(assay_data, dplyr::contains("btwt"))
ri <- rita_bootstrap(
    recent=assay_data$recent,
    undiagnosed=assay_data$undiagnosed,
    low_viral=assay_data$elite_cntr,
    hiv=assay_data$hiv,
    weights=assay_data$weights,
    tslt=assay_data$tslt,
    ever_hiv_test=assay_data$ever_hiv_test,
    rep_weights = rep_weights,
    rep_weight_type = "JK1"
  )
#> Warning in svrepdesign.default(repweights = rep_weights, weights = weights, :
#> scale (n-1)/n not provided: guessing n=number of replicates
knitr::kable(ri)
estimate std_error lower_bound upper_bound
incidence 0.0151573 0.0110182 -0.0064379 0.0367525
residual_frr 0.0008827 0.0001937 0.0005030 0.0012623
omega_rs 0.2921342 0.0193668 0.2541759 0.3300925
omega_s 1.0931364 0.1296151 0.8390954 1.3471774
P(R|S) 0.0728330 0.0471441 -0.0195676 0.1652337
P(S|H) 0.1953130 0.0255741 0.1451886 0.2454374
P(H) 0.2480427 0.0146154 0.2193971 0.2766884

Numerous jackknife and bootstrap weight types are supported. See survey::svrrepdesign for details.