Last updated: 2019-03-20

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This creates the exposure data for the dose-response analysis

Subset exposure data

no HHV-8, EBV, or HSV (no, late, and limited infections)
Exclude family AZ in HHV-6 because no infection and all 0 viral loads

exposure_data = subset(virusMeltedDataDemoAllInfant,
              times >= infantdob & ((infantInfection == 0) | (infantInfection == 1 & times <= infantInfDate)) &
                idpar != "P" & !Virus %in% c("ORL_HHV8", "ORL_HSV", "ORL_EBV") & 
                !(FamilyID == "AZ" & Virus == "ORL_HHV6")) %>%
  mutate(
    virus = str_split_fixed(Virus, "_", n = 2)[,2],
    virus = if_else(virus == "HHV6", "HHV-6", virus)
    )

#merge later
age_data = subset(virusMeltedDataDemoAllInfant, idpar == "P") %>% 
  group_by(FamilyID) %>%
  summarize(enrollment_age = as.numeric(difftime(min(times), unique(infantdob))))

Create time variables

Set up the time variable (days), relative to infantdob, eventually turn into weeks

infant_dates = subset(virusMeltedDataDemoAllInfant, idpar == "P" & 
                        !Virus %in% c("ORL_HHV8", "ORL_HSV", "ORL_EBV")) %>%
  group_by(FamilyID, Virus) %>%
  summarize(final_infant_date = if(infantInfection[1]) infantInfDate[1] else max(times),
            first_infant_date = infantdob[1])

exposure_times = left_join(exposure_data, infant_dates, by = c("FamilyID", "Virus")) %>%
  filter(times >= first_infant_date & times <= final_infant_date)

exposure_times$infant_days =
  with(exposure_times, as.numeric(difftime(times, first_infant_date, units = "days")))

exposure_times$days_from_final_infant =
  with(exposure_times, as.numeric(difftime(final_infant_date, times, units = "days")))

exposure_times %>% group_by(FamilyID, idpar, virus) %>% 
  arrange(infant_days) %>%
  mutate(time_diff = c(NA, diff(infant_days))) %>%
  ggplot(aes(x = days_from_final_infant, y = time_diff)) + 
  facet_wrap(~idpar) +
  geom_point() +
  geom_hline(yintercept = 7, colour = "red")

Warning: Removed 122 rows containing missing values (geom_point).

Create exposure variable by household member

# combines siblings into one exposure

primary_exposures_idpar = exposure_times %>% 
  group_by(FamilyID, idpar, virus, infant_days, momhiv, days_from_final_infant) %>%
  summarize(
    total_contributed_idpar = n(),
    who_contributed_idpar = paste(str_split_fixed(PatientID, "-", n = 2)[,2], collapse = ", "),
    exposure = log10(sum(10^count, na.rm = T)),
    infected = infantInfection[1],
    final_infant_day = as.numeric(difftime(final_infant_date[1], first_infant_date[1], units = "days"))
    )  %>% 
  group_by(FamilyID, virus, idpar) %>%
  mutate(
    unique_id = paste(FamilyID, virus, idpar, sep = "-"),
    min_time_from_end = min(days_from_final_infant),
    exposure = if_else(exposure <= 1, 0, exposure)
  )

with(primary_exposures_idpar, ftable(idpar, total_contributed_idpar))

      total_contributed_idpar    1    2    3
idpar                                       
M                             1606    0    0
S                             1057  110  158

Combine exposure data and create outcome

Here, we leave counts (exposures) at times relative to infant birth, and create the outcome variable describing infant infection status in the following week.

The outcome is variable is defined so that the infectious exposure occured 4-14 days prior to infected detection.

# make a new dataset organized by time before swab, use new days, this is for household
# create outcome variable

all_exposures_raw = primary_exposures_idpar %>% 
  rename(count = exposure) %>%
  filter(days_from_final_infant > 0) %>%  # these are either censored cases or infections (negative = post-infection)
  group_by(FamilyID, idpar, virus) %>% 
  mutate(
    final_exposure = days_from_final_infant == min(days_from_final_infant),
    infectious_1wk = if_else(days_from_final_infant <= 14 & final_exposure & infected == 1, 1, 0)
    )

testthat::expect_equal(min(subset(all_exposures_raw, infected == 0)$days_from_final_infant),
                       expected = 7, 
                       info = "check if all uninfected measurements are at least a week from final measurement (ie, no infection one week later)")

testthat::expect_equal(min(subset(all_exposures_raw, infected == 1)$days_from_final_infant),
                       expected = 4, 
                       info = "check if all infected measurements > 4")

Combine exposures into weekly variable

Create weekly categories

wk_cuts = 0:ceiling(max(primary_exposures_idpar$infant_days)/7) * 7
wk_labels = head(wk_cuts, -1)/7
all_exposures_raw$infant_wks = cut(all_exposures_raw$infant_days, include.lowest = T, ordered_result = T,
                                 breaks = wk_cuts, labels = wk_labels)

all_exposures_raw$final_infant_wk = as.numeric(as.character(cut(all_exposures_raw$final_infant_day, 
                                                   include.lowest = T, ordered_result = T,
                                 breaks = 0:100 * 7, labels = F))) - 1

testthat::expect_equal(min(all_exposures_raw$final_infant_wk) ,
                       expected = 0, 
                       info = "check infant_wk rescale")

all_exposures_raw$infant_wks = as.numeric(as.character(all_exposures_raw$infant_wks))

ggplot(arrange(all_exposures_raw, infant_days), aes(y = infant_wks, x = infant_days)) +
  geom_tile()

Fill in missing weeks

All interpolation is done using the interpolation of the log viral load (zoo:na.approx).
map_df was used so that the data is summarized by a refactored infant_wk so complete can be used to find missing weeks for a giving exposure set (which varies by infant and exposure source).

all_exposures = map_df(unique(all_exposures_raw$unique_id), function(uid){
  temp_data = subset(ungroup(all_exposures_raw), unique_id == uid)
  
  # refactor levels for complete()
  temp_data$infant_wks = factor(temp_data$infant_wks, 
                                levels = min(temp_data$infant_wks):max(temp_data$infant_wks)) 

  out = temp_data %>%
  group_by(FamilyID, unique_id, momhiv, virus, idpar, infant_wks, final_infant_wk) %>%
  summarize(
    count = max(count),
    infected = unique(infected),
    infectious_1wk = max(infectious_1wk),
    final_exposure = max(final_exposure)
    ) %>% 
    ungroup() %>%
    complete(infant_wks, nesting(FamilyID, momhiv, virus, idpar, final_infant_wk, 
                               infected, unique_id)) %>%
    arrange(infant_wks) %>%
    mutate(
      interpolate_src = if_else(is.na(count), unique(temp_data$idpar), ""),
      infant_wks = as.numeric(as.character(infant_wks)),
      infectious_1wk = na.locf(infectious_1wk), # should only be zero, tested after
      final_exposure = na.locf(final_exposure), # same as above
      count = na.approx(count)
      )
  if(nrow(temp_data) == 1) return(out)

  testthat::expect_equal(n_distinct(diff(out$infant_wks)), expected = 1, 
                         info=paste("Check infant_wks interpolation worked (common interval)", unique(out$unique_id)))
  testthat::expect_equal(unique(diff(out$infant_wks)), expected = 1, 
                         info=paste("Check infant_wks interpolation worked (interval = one)", unique(out$unique_id)))
  
  out
  
})


testthat::expect_equal(all_exposures %>% group_by(unique_id) %>% 
  summarize(test = sum(infectious_1wk), test2 = sum(final_exposure)) %>%
  filter(test > 1 | test2 > 1) %>% nrow(), expected = 0, 
  info = "Verifying infectious_1wk and final_exposure after interpolation")
  
ggplot(arrange(all_exposures, unique_id), aes(y = infant_wks, x = unique_id)) +
  geom_tile() +
  coord_flip()

all_exposures_wide = all_exposures %>%
  group_by(FamilyID, virus, infant_wks) %>%
  mutate(
    interpolate_src = str_trim(str_c(sort(unique(interpolate_src)), collapse = " "))
  ) %>%
  ungroup() %>%
  reshape2::dcast(FamilyID + virus + infant_wks + infectious_1wk + final_infant_wk +
                                   infected + momhiv + final_exposure + interpolate_src ~ idpar,
                                     data = ., value.var = "count") %>%
  mutate(
    HH = log10(10^M + 10^S),
    HH = if_else(HH <= 1, 0, HH)
    ) %>% 
  ungroup()

exposure_data = all_exposures_wide %>%
  filter(!is.na(S)  & !is.na(M)) %>%
  group_by(FamilyID, virus) %>%
  mutate(
    obs_infected = infected * max(infectious_1wk), 
    final_wk = max(infant_wks),
    outcome_time = ifelse(obs_infected, final_infant_wk, final_wk + 1)
  ) %>%
  ungroup() %>%
  left_join(age_data, by = "FamilyID")

exposure_data_long = exposure_data %>%
  gather(idpar, count, S, M, HH) 

testthat::expect_equal(exposure_data %>% group_by(FamilyID, virus) %>% 
  summarize(test = sum(infectious_1wk), test2 = sum(final_exposure)) %>%
  filter(test > 1 | test2 > 1) %>% nrow(), expected = 0, 
  info = "Verifying final exposure has at most one infectious dose per infant")

testthat::expect_equal(exposure_data_long %>% group_by(FamilyID, idpar, virus) %>% 
  summarize(test = sum(infectious_1wk), test2 = sum(final_exposure)) %>%
  filter(test > 1 | test2 > 1) %>% nrow(), expected = 0, 
  info = "Verifying final exposure has at most one infectious dose per infant")

# save the data
if(save_data) save(exposure_data, exposure_data_long, file = "data/exposure_data.RData")

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.3

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2  forcats_0.3.0   stringr_1.4.0   dplyr_0.7.8    
 [5] purrr_0.3.0     readr_1.3.1     tidyr_0.8.2     tibble_2.0.1   
 [9] ggplot2_3.1.0   tidyverse_1.2.1 zoo_1.8-4      

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 xfun_0.4         reshape2_1.4.3   haven_2.0.0     
 [5] lattice_0.20-38  testthat_2.0.1   colorspace_1.4-0 generics_0.0.2  
 [9] htmltools_0.3.6  yaml_2.2.0       rlang_0.3.1      pillar_1.3.1    
[13] withr_2.1.2      glue_1.3.0       modelr_0.1.2     readxl_1.2.0    
[17] bindr_0.1.1      plyr_1.8.4       munsell_0.5.0    gtable_0.2.0    
[21] workflowr_1.2.0  cellranger_1.1.0 rvest_0.3.2      evaluate_0.12   
[25] labeling_0.3     knitr_1.21       broom_0.5.1      Rcpp_1.0.0      
[29] backports_1.1.3  scales_1.0.0     jsonlite_1.6     fs_1.2.6        
[33] hms_0.4.2        digest_0.6.18    stringi_1.2.4    grid_3.5.1      
[37] rprojroot_1.3-2  cli_1.0.1        tools_3.5.1      magrittr_1.5    
[41] lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2 
[45] xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.8   
[49] httr_1.4.0       rstudioapi_0.9.0 R6_2.3.0         nlme_3.1-137    
[53] git2r_0.24.0     compiler_3.5.1

setup-exposure-data

Bryan Mayer

2019-03-19