Last updated: 2020-02-01

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Knit directory: HHVtransmission/

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File Version Author Date Message
Rmd 93301d6 Bryan 2020-02-01 wflow_publish(c("analysis/_site.yml“,”analysis/about.Rmd“,”analysis/index.Rmd",
Rmd a1d2e7b Bryan 2020-01-24 update with documentation
html a1d2e7b Bryan 2020-01-24 update with documentation
Rmd af2a4c1 Bryan 2019-12-30 all updates after co-author review; edits to tables/figs; ID75
html af2a4c1 Bryan 2019-12-30 all updates after co-author review; edits to tables/figs; ID75
html ce0f229 Bryan Mayer 2019-07-09 analysis through first-final draft
Rmd 3bc1e7c Bryan Mayer 2019-07-04 updated analysis through exposure overview
html 3bc1e7c Bryan Mayer 2019-07-04 updated analysis through exposure overview
html 39e3dc0 Bryan Mayer 2019-06-07 update through transmission risk
Rmd c96d292 Bryan Mayer 2019-04-12 updated through exposure assessment
html c96d292 Bryan Mayer 2019-04-12 updated through exposure assessment
html 5af6494 Bryan Mayer 2019-03-21 Build site.
Rmd 05626ad Bryan Mayer 2019-03-21 wflow_publish(c(“analysis/about.Rmd”, “analysis/index.Rmd”,

This Rmarkdown script creates the exposure data for the dose-response analysis.

[1] "Data was not updated or saved on this compile."

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_times = virusMeltedDataDemoAllInfant %>%
  subset(idpar != "P" & !virus %in% c("HHV-8", "HSV", "EBV") & 
           !(FamilyID == "AZ" & virus == "HHV-6")
         )

#merge later, relic from old code
age_data = select(exposure_times, FamilyID, enrollment_age) %>%
  distinct()

Create exposure variable by household member

There are multiple secondary children (S) to aggregate.

multS_exposure = exposure_times %>% 
  subset(idpar == "S") %>%
  group_by(FamilyID, virus) %>%
  mutate(
    idpar2 = str_split_fixed(PatientID, "-", n = 2)[,2],
    total_SC = n_distinct(idpar2)
  ) %>%
  subset(total_SC > 1) %>%
  group_by(FamilyID, virus) %>%
  mutate(
    pvalue = kruskal.test(count ~ idpar2)$p.value
  ) 

multS_exposure %>%
  group_by(FamilyID, virus, idpar2, pvalue) %>%
  summarize(
    mean_VL = mean(count),
    total = n(),
    mean_n = stat_paste(mean_VL, total, digits = 2, trailing_zeros = F)
  ) %>%
  select(FamilyID, virus, idpar2, mean_n, pvalue) %>%
  ungroup() %>%
  spread(idpar2, mean_n, fill = "---") %>%
  mutate(flag = pvalue < 0.1) %>%
  arrange(virus, pvalue, FamilyID) %>%
  select(everything(), pvalue, flag) %>%
  kable(digits = 3) %>%
  kable_styling(full_width = F)
FamilyID virus pvalue S1 S2 S3 flag
AS CMV 0.000 0.2 (43) 3.73 (48) 1.48 (47) TRUE
BE CMV 0.000 1.16 (40) 3.19 (38) 0 (44) TRUE
BA CMV 0.000 0.9 (26) 3.48 (47) 0.26 (21) TRUE
AC CMV 0.007 0.47 (5) 4.25 (5) TRUE
AY CMV 0.056 1.02 (37) 3.53 (1) TRUE
AB CMV 0.102 3 (16) 7.75 (1) FALSE
AM CMV 0.149 0 (4) 1.16 (7) FALSE
AT CMV 0.221 4.46 (2) 3.19 (1) FALSE
BB CMV 0.476 2.77 (24) 2.74 (32) FALSE
AC HHV-6 0.000 4 (32) 3.49 (45) 2.77 (10) TRUE
AB HHV-6 0.000 4.15 (27) 5.96 (13) TRUE
BA HHV-6 0.000 3.05 (13) 2.35 (19) 0 (7) TRUE
AM HHV-6 0.000 0.97 (13) 0 (33) 0 (6) TRUE
AT HHV-6 0.002 3.48 (12) 4.12 (7) TRUE
AS HHV-6 0.078 2.38 (4) 4.08 (4) 2.42 (5) TRUE
BB HHV-6 0.221 6.04 (1) 5.11 (2) FALSE
BE HHV-6 0.359 0.06 (40) 0 (38) 0 (44) FALSE
multS_exposure %>%
  subset(pvalue < 0.1) %>%
  ggplot(aes(x = infant_days, y = count, colour = idpar2)) +
  geom_line() + geom_point() +
  facet_wrap(~virus+FamilyID) +
  theme(legend.position = "top")

Version Author Date
af2a4c1 Bryan 2019-12-30
# combines siblings into one exposure

primary_exposures_idpar = exposure_times %>% 
  group_by(FamilyID, idpar, virus, infant_days, momhiv, days_from_final_infant, final_infant_day) %>%
  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]
    )  %>% 
  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 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

Plot checks that recoding was done right (no overlap on y-axis across steps)

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()

Version Author Date
af2a4c1 Bryan 2019-12-30
c96d292 Bryan Mayer 2019-04-12

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). This could be done with group_by and nest.

First plot displays extent of left censoring Second plot shows where intepolation occured.

all_exposures = map_df(unique(all_exposures_raw$unique_id), function(uid){
  temp_data = subset(ungroup(all_exposures_raw), unique_id == uid) %>%
    mutate(first_infant_week = min(infant_wks))
  
  # refactor levels for complete()
  temp_data$infant_wks = factor(temp_data$infant_wks, 
                                levels = 0:max(temp_data$infant_wks)) 

  out = temp_data %>%
  group_by(FamilyID, unique_id, momhiv, virus, idpar, 
           infant_wks, first_infant_week, 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, 
                                 first_infant_week, final_infant_wk, 
                               infected, unique_id)) %>%
    arrange(infant_wks) %>%
    mutate(
      interpolate_idpar = if_else(is.na(count), unique(temp_data$idpar), ""),
      infant_wks = as.numeric(as.character(infant_wks)),
      infectious_1wk = na.fill(infectious_1wk,  0),
      final_exposure = na.fill(final_exposure,  0),
      count = na.approx(count, rule = 2)
      )
  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)))
  if(any(out$interpolate_idpar != "")){
    testthat::expect_equal(unique(out$infectious_1wk[out$interpolate_idpar != ""]), 
                             expected = 0, 
                           info=paste("Check infectious_1wk interpolation",
                                      unique(out$unique_id)))
    testthat::expect_equal(unique(out$final_exposure[out$interpolate_idpar != ""]), 
                             expected = 0, 
                           info=paste("Check infectious_1wk interpolation",
                                      unique(out$unique_id))) 
    }

  out
  
}) %>%
  mutate(
    count = if_else(count >= lower_limit - 1, count, 0) # the 1 is a small tolerance factor
  )


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")
  

all_exposures %>%
  select(FamilyID, virus, idpar, first_infant_week) %>%
  distinct() %>%
  group_by(virus, first_infant_week, idpar) %>%
  summarize(total = n()) %>%
  ggplot(aes(x = factor(first_infant_week), y = total)) +
  geom_histogram(stat = "identity") +
  geom_text(aes(label = total), vjust = 1) +
  facet_grid(idpar~virus)
Warning: Ignoring unknown parameters: binwidth, bins, pad

Version Author Date
af2a4c1 Bryan 2019-12-30
3bc1e7c Bryan Mayer 2019-07-04
c96d292 Bryan Mayer 2019-04-12
all_exposures %>%
  group_by(FamilyID, infant_wks, virus) %>%
  summarize(
    interpolate = str_c(interpolate_idpar, collapse = "")
  ) %>%
  arrange(FamilyID, virus) %>%
  ggplot(aes(y = infant_wks, x = FamilyID, fill = factor(interpolate))) +
  geom_tile() +
  scale_fill_manual("", values = c("black", "red", "blue", "gray"),
                    breaks = c("", "S", "MS", "M"),
                    labels = c("", "Interpolate - S", 
                               "Interpolate - M,S", "interpolate - M")) +
  coord_flip() +
  labs(y = "infant weeks post-dob pre-infection") +
  theme_bw() +
  theme(legend.position = "top", axis.text.y = element_text(size = 7)) +
  facet_wrap(~virus, nrow = 2, strip.position = "right", scales = "free_y")

Version Author Date
af2a4c1 Bryan 2019-12-30
3bc1e7c Bryan Mayer 2019-07-04
all_exposures_wide = all_exposures %>%
  group_by(FamilyID, virus, infant_wks) %>%
  mutate(
    interpolate_idpar = str_trim(str_c(sort(unique(interpolate_idpar)), collapse = " "))
  ) %>%
  ungroup() %>%
  reshape2::dcast(FamilyID + virus + infant_wks + infectious_1wk + final_infant_wk +
                                   infected + momhiv + final_exposure + interpolate_idpar ~ idpar,
                                     data = ., value.var = "count") %>%
  mutate(
    HH = log10(10^M + 10^S),
    HH = if_else(HH <= lower_limit, 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")


# extra step because empty string patterns are not supported

tmp_chr = "-"
exposure_data_long = exposure_data %>%
  gather(idpar, count, S, M, HH) %>%
  mutate(
    interpolate_idpar_tmp = if_else(interpolate_idpar == "", tmp_chr, interpolate_idpar),
    interpolated = if_else(interpolate_idpar != "" & idpar == "HH", T,
                           str_detect(interpolate_idpar_tmp, idpar))
  ) %>%
  select(-interpolate_idpar_tmp)

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) {
  write_csv(exposure_data, "data/exposure_data.csv")
  write_csv(exposure_data_long, "data/exposure_data_long.csv")
}

sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.2

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] kableExtra_1.1.0 forcats_0.4.0    stringr_1.4.0    dplyr_0.8.3     
 [5] purrr_0.3.3      readr_1.3.1      tidyr_1.0.0      tibble_2.1.3    
 [9] ggplot2_3.2.1    tidyverse_1.3.0  zoo_1.8-6       

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.3        lubridate_1.7.4   lattice_0.20-38  
 [4] assertthat_0.2.1  rprojroot_1.3-2   digest_0.6.23    
 [7] R6_2.4.1          cellranger_1.1.0  plyr_1.8.4       
[10] backports_1.1.5   reprex_0.3.0      evaluate_0.14    
[13] httr_1.4.1        highr_0.8         pillar_1.4.2     
[16] rlang_0.4.2       lazyeval_0.2.2    readxl_1.3.1     
[19] rstudioapi_0.10   whisker_0.4       rmarkdown_1.17   
[22] labeling_0.3      webshot_0.5.1     munsell_0.5.0    
[25] broom_0.5.2       compiler_3.6.1    modelr_0.1.5     
[28] xfun_0.10         pkgconfig_2.0.3   htmltools_0.4.0  
[31] tidyselect_0.2.5  workflowr_1.4.0   viridisLite_0.3.0
[34] crayon_1.3.4      dbplyr_1.4.2      withr_2.1.2      
[37] grid_3.6.1        nlme_3.1-142      jsonlite_1.6     
[40] gtable_0.3.0      lifecycle_0.1.0   DBI_1.0.0        
[43] git2r_0.26.1      magrittr_1.5      scales_1.1.0     
[46] cli_1.1.0         stringi_1.4.3     farver_2.0.1     
[49] reshape2_1.4.3    fs_1.3.1          testthat_2.3.0   
[52] xml2_1.2.2        ellipsis_0.3.0    generics_0.0.2   
[55] vctrs_0.2.2       tools_3.6.1       glue_1.3.1       
[58] hms_0.5.3         yaml_2.2.0        colorspace_1.4-1 
[61] rvest_0.3.5       knitr_1.25        haven_2.2.0