Last updated: 2019-04-08

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Here, we calculate some of the initial (pre-model) results from the infant cohort and exposure characteristcs. - Demographics - Initial survial curves - Exposure assessment

Demographics

Infant ages

exposure_data %>%
  select(FamilyID, enrollment_age) %>%
  distinct() %>%
  summarize(
        N = n(),
        enroll_median_age_days = median(enrollment_age),
        IQR = paste(quantile(enrollment_age, c(0.25, 0.75)), collapse = ", "),
        range_days = paste(range(enrollment_age), collapse = ", ")
        ) %>%
  kable() %>% kable_styling(full_width = F)
N enroll_median_age_days IQR range_days
32 2 1, 3 0, 9

Mom HIV

exposure_data %>% select(FamilyID, momhiv) %>% 
  distinct() %>%
  group_by(momhiv) %>%
  summarize(N = n()) %>% 
  kable() %>% kable_styling(full_width = F)
momhiv N
neg 15
pos 17

Survival analysis

exposure_data %>% 
  group_by(virus, FamilyID) %>% 
  summarize(obs_infected = max(infectious_1wk),
            is_infected = max(infected)) %>% group_by(virus) %>%
  summarize(
    total_infants = n_distinct(FamilyID),
    total_infected = sum(is_infected),
    total_outcome = sum(obs_infected)
    ) %>%
  kable() %>%
  kable_styling(full_width = F)
virus total_infants total_infected total_outcome
CMV 30 20 16
HHV-6 31 24 23 
surv_data = exposure_data %>% 
  group_by(FamilyID, virus, momhiv, final_infant_wk) %>%
  summarize(
    infected = max(infected)
  )

surv_fit = surv_data %>%
  group_by(virus) %>%
  nest() %>%
  mutate(
    surv_mod = map(data, ~survfit(Surv(final_infant_wk, infected) ~ 1, data = .)),
    surv_mod_hiv = map(data, ~survfit(Surv(final_infant_wk, infected) ~ momhiv, data = .)),
    logrank = map_dbl(data, ~coin::pvalue(coin::logrank_test(Surv(final_infant_wk, infected) ~ factor(momhiv),
                                                             data = ., distribution = "exact")))
    ) %>%
  select(-data)

surv_fit %>%
  select(virus, logrank) %>%
  rename(`Mother HIV Log-rank` = logrank) %>%
  kable() %>% kable_styling(full_width = F)
virus Mother HIV Log-rank
CMV 0.9708864
HHV-6 0.3649318 
surv_res = pmap_df(surv_fit, function(virus, surv_mod, surv_mod_hiv, logrank){
   broom::tidy(surv_mod) %>%
    mutate(strata = "Pooled") %>%
    bind_rows(broom::tidy(surv_mod_hiv)) %>%
    mutate(
      virus = virus,
      momhiv = str_remove_all(strata, "momhiv=")
      ) %>%
    bind_rows(crossing(virus = virus, time = -1e-12, estimate = 1, momhiv = c("Pooled", "neg", "pos")))
})
surv_res %>%
  arrange(virus, momhiv, time) %>%
  ggplot(aes(time, estimate, colour = momhiv)) + 
  geom_step() +
  geom_point(aes(shape = n.censor > 0)) +
  scale_shape_manual(guide = F, values = c(-1, 3)) +
  scale_x_continuous("Weeks after infant birth", breaks = 0:10 * 10) +
  scale_y_continuous("Proportion uninfected", expand = c(0.01, 0)) +
  geom_vline(xintercept = 52, colour = "black", linetype = "dashed") +
  scale_color_discrete("", breaks = c("neg", "pos", "Pooled"), 
                     labels = c("Mother HIV-", "Mother HIV+", "Pooled")) +
  geom_text(data= surv_fit, aes(label = str_c("p = ", round(logrank, 2))), 
            x = Inf, y = Inf, colour = "black", vjust = 1.2, hjust = 1.2) +
  facet_wrap(~virus) +
  theme_bw() +
  theme(legend.position = "top")

Version Author Date
5af6494 Bryan Mayer 2019-03-20

Exposure Analysis

exposure_data_long %>% 
  group_by(virus, idpar) %>% 
  summarize(total = n()) 
# A tibble: 6 x 3
# Groups:   virus [?]
  virus idpar total
  <chr> <chr> <int>
1 CMV   HH      807
2 CMV   M       807
3 CMV   S       807
4 HHV-6 HH      673
5 HHV-6 M       673
6 HHV-6 S       673
exposure_plots = map(c("CMV", "HHV-6"), function(v){
  surv_cens = exposure_data %>% 
    subset(virus == v) %>%
    group_by(FamilyID, virus, final_infant_wk) %>%
  summarize(
    cens = all(obs_infected == 0),
    final_time = if(!all(cens)) unique(final_infant_wk) else max(infant_wks)
  )
  
  exposure_data_long %>%
    subset(virus == v) %>%
    group_by(FamilyID) %>%
    ggplot(aes(x = infant_wks, y = count, colour = idpar)) + 
    geom_point() + 
    facet_wrap(~FamilyID) + 
    geom_vline(data = surv_cens, aes(xintercept = final_time, linetype = factor(cens))) +
    scale_linetype_discrete(guide = F) +
    ggtitle(v)
  
})


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.4

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   survminer_0.4.3  ggpubr_0.2       magrittr_1.5    
 [5] survival_2.43-3  kableExtra_1.1.0 forcats_0.3.0    stringr_1.4.0   
 [9] dplyr_0.7.8      purrr_0.3.0      readr_1.3.1      tidyr_0.8.2     
[13] tibble_2.0.1     ggplot2_3.1.0    tidyverse_1.2.1 

loaded via a namespace (and not attached):
 [1] httr_1.4.0        jsonlite_1.6      viridisLite_0.3.0
 [4] splines_3.5.1     modelr_0.1.2      assertthat_0.2.0 
 [7] highr_0.7         stats4_3.5.1      coin_1.2-2       
[10] cellranger_1.1.0  yaml_2.2.0        pillar_1.3.1     
[13] backports_1.1.3   lattice_0.20-38   glue_1.3.0       
[16] digest_0.6.18     rvest_0.3.2       colorspace_1.4-0 
[19] sandwich_2.5-0    cmprsk_2.2-7      htmltools_0.3.6  
[22] Matrix_1.2-15     plyr_1.8.4        pkgconfig_2.0.2  
[25] broom_0.5.1       haven_2.0.0       xtable_1.8-3     
[28] mvtnorm_1.0-8     scales_1.0.0      webshot_0.5.1    
[31] km.ci_0.5-2       whisker_0.3-2     KMsurv_0.1-5     
[34] git2r_0.24.0      generics_0.0.2    TH.data_1.0-10   
[37] withr_2.1.2       lazyeval_0.2.1    cli_1.0.1        
[40] crayon_1.3.4      readxl_1.2.0      evaluate_0.12    
[43] fansi_0.4.0       fs_1.2.6          nlme_3.1-137     
[46] MASS_7.3-51.1     xml2_1.2.0        tools_3.5.1      
[49] data.table_1.12.0 hms_0.4.2         multcomp_1.4-8   
[52] munsell_0.5.0     compiler_3.5.1    rlang_0.3.1      
[55] grid_3.5.1        rstudioapi_0.9.0  labeling_0.3     
[58] rmarkdown_1.8     codetools_0.2-16  gtable_0.2.0     
[61] R6_2.3.0          gridExtra_2.3     zoo_1.8-4        
[64] lubridate_1.7.4   knitr_1.21        utf8_1.1.4       
[67] survMisc_0.5.5    bindr_0.1.1       workflowr_1.2.0  
[70] rprojroot_1.3-2   modeltools_0.2-22 stringi_1.2.4    
[73] Rcpp_1.0.0        tidyselect_0.2.5  xfun_0.4