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Load packages

library(tidyverse)

library(coxme)
library(brms)
library(tidybayes)
library(ggridges)

library(kableExtra)
library(knitrhooks) # install with devtools::install_github("nathaneastwood/knitrhooks")

output_max_height() # a knitrhook option

options(stringsAsFactors = FALSE)

Load data

# load desiccation resistance data
DesRes <- read.csv("data/3.DesRes.csv") %>% 
  # add event (all flies died)
  mutate(EVENT = 1,
         LINE = paste0(Treatment, substr(Replicate, 2, 2)),
         ID = paste(LINE, Vial, sep = ''))

# calculate survival times
# paste time and date
DesRes$d <- paste(DesRes$Death_date, DesRes$Death_time, sep = ' ')

# experiment start time
start_timeDes <- "04/02/2017 12:00"

DesRes$survival.time <- as.numeric(strptime(DesRes$d, format = "%d/%m/%Y %H") - strptime(start_timeDes, format = "%d/%m/%Y %H"))


# load starvation resistance data
StaRes <- read.csv("data/3.StarvRes.csv") %>% 
  # add event (all flies died)
  mutate(EVENT = 1,
         LINE = paste0(Treatment, substr(Replicate, 2, 2)),
         ID = paste(LINE, Vial, sep = ''))

# calculate survival times
# paste time and date
StaRes$d <- paste(StaRes$Death_date, StaRes$Death_time, sep = ' ')

# experiment start time
start_timeSta <- "04/02/2017 12:00"

StaRes$survival.time <- as.numeric(strptime(StaRes$d, format = "%d/%m/%Y %H") - strptime(start_timeSta, format = "%d/%m/%Y %H"))

# 5 individuals have missing survival times which we will right censor at max. survival time
# Two M females (M2) were right censored as survival time not recorded
# Three P females (P2 Vial 6 and P4 vial 9) were right censored as survival time not recorded
StaRes[which(is.na(StaRes$survival.time)), 'EVENT'] <- 0
StaRes[which(is.na(StaRes$survival.time)), 'survival.time'] <- max(na.omit(StaRes$survival.time))

Inspecting the raw data

bind_rows(
  DesRes %>% 
  select(Treatment, Sex, survival.time) %>% mutate(var = 'Desiccation'),
  StaRes %>% filter(EVENT == 1) %>% 
  select(Treatment, Sex, survival.time) %>% mutate(var = 'Starvation')
) %>% 
  mutate(var2 = paste(Treatment, Sex)) %>% 
  ggplot(aes(x = survival.time, y = Sex, fill = var2)) +
  geom_boxplot() +
  scale_fill_manual(values = c("pink", "skyblue", "red", "blue"), name = "",
                    labels = c('Monogamy Females', 'Monogamy Males',
                               'Polandry Females', 'Polandry Males')) +
  labs(x = 'Survival time (hours)') +
  facet_wrap(~var, ncol = 2) +
  theme_bw() +
  NULL

Version Author Date
709456c Martin Garlovsky 2021-01-18
989e86f lukeholman 2020-12-18
96d1188 Martin Garlovsky 2020-12-13
c175be4 Martin Garlovsky 2020-12-04
df61dde Martin Garlovsky 2020-12-04

Figure 1: Survival time in hours for flies in each treatment split by sex.

Survival analysis

We modeled desiccation and starvation resistance using survival analysis. We measured time in hours until death (EVENT = 1) for single sex triads of flies housed in vials (n = 7-10 vials per replicate per sex) containing no media and silica gel beads between the cotton and parafilm enclosing the vial (desiccation resistance) or an agar media providing moisture only (starvation resistance). We monitored vials for deaths every two hours until all flies perished. For the starvation resistance assay five individuals (two M females and three P females) were right censored (EVENT = 0) at the end of the observation period as death times were not recorded or remained alive.

Kaplan-Meier survival curve

First we plot Kaplan-Meier survival curves.

survminer::ggsurvplot(survfit(Surv(survival.time, EVENT) ~ Treatment + Sex, data = DesRes),
                      conf.int = TRUE,
                      risk.table = FALSE,
                      linetype = "Sex",
                      palette = c("pink", "skyblue", "red", "blue"),
                      xlab = "Time (hours)",
                      legend = 'right',
                      legend.title = "",
                      legend.labs = c("M \u2640","M \u2642",
                                      'P \u2640','P \u2642'),
                      break.time.by = 12,
                      ggtheme = theme_bw()) 

Version Author Date
709456c Martin Garlovsky 2021-01-18
989e86f lukeholman 2020-12-18
96d1188 Martin Garlovsky 2020-12-13
df61dde Martin Garlovsky 2020-12-04
#ggsave(filename = 'figures/desiccation.pdf', width = 5.5, height = 5, dpi = 600, useDingbats = FALSE)

survminer::ggsurvplot(survfit(Surv(survival.time, EVENT) ~ Treatment + Sex, data = StaRes),
                      conf.int = TRUE,
                      risk.table = FALSE,
                      linetype = "Sex",
                      palette = c("pink", "skyblue", "red", "blue"),
                      xlab = "Time (hours)",
                      legend = 'right',
                      legend.title = "",
                      legend.labs = c("M \u2640","M \u2642",
                                      'P \u2640','P \u2642'),
                      break.time.by = 12,
                      ggtheme = theme_bw()) 

Version Author Date
709456c Martin Garlovsky 2021-01-18
989e86f lukeholman 2020-12-18
96d1188 Martin Garlovsky 2020-12-13
df61dde Martin Garlovsky 2020-12-04
#ggsave(filename = 'figures/starvation.pdf', width = 5.5, height = 5, dpi = 600, useDingbats = FALSE)

Figure 2: Kaplan-Meier survival curves for flies in each treatment split by sex. + indicates censored individuals (n = 5).

Check proportional hazards assumption

Next we need to check that the proportional hazards assumption is not violated before fitting the model, where crossing hazards (lines) indicate violation of the proportional hazards assumption. For both desiccation and starvation we see crossing hazards for the male survival curves. We will therefore fit accelerated failure time (AFT) models with a Weibull distribution (see here).

# assess proportional hazards assumption
par(mar = c(2, 2, 2, 2), mfrow = c(1, 2))
plot(survfit(Surv(survival.time, EVENT) ~ Treatment + Sex, data = DesRes), 
     lty = 1:2, lwd = 2,
     col = c("pink", "skyblue", "red", "blue"), 
     main = 'Desiccation',
     fun = "cloglog")

legend("topleft", c("M \u2640","M \u2642",'P \u2640','P \u2642'), 
       col = c("pink", "skyblue", "red", "blue"), 
       lty = 1:2, 
       lwd = 2,
       bty = 'n'
)

plot(survfit(Surv(survival.time, EVENT) ~ Treatment + Sex, data = StaRes), 
     lty = 1:2, lwd = 2,
     col = c("pink", "skyblue", "red", "blue"), 
     main = 'Starvation',
     fun = "cloglog")

Version Author Date
709456c Martin Garlovsky 2021-01-18
989e86f lukeholman 2020-12-18
df61dde Martin Garlovsky 2020-12-04
par(mfrow = c(1, 1))

Fit the survival models for desiccation and starvation resistance

We fit an accelerated failure time model in brms using family = weibull(), with time (hours) to event (death) as the response and sexual selection treatment (Treatment; Monogamy or Polyandry), Sex (female or male) and their interaction as predictors. See here for a helpful explanation on fitting survival models in brms. We also include replicate treatment as a random intercept term for each of the 8 lines and a random slope term to allow the effect of selection treatment to vary across replicate lines. We also include vial ID as a random intercept term as individuals housed in the same vial may show a correlated response.

if(!file.exists("output/des_brm.rds")){ # if the model doesn't exist fit it, else load it
  
  des_brm <- brm(survival.time | cens(1 - EVENT) ~ Treatment * Sex + (Treatment|LINE) + (1|ID),
               prior = c(set_prior("normal(0,0.5)", class = "b"),
                         set_prior("cauchy(0,0.1)", class = "sd")),
               iter = 5000, chains = 4, cores = 4,
               control = list(max_treedepth = 20,
                              adapt_delta = 0.999),
               data = DesRes, family = weibull())
  
  saveRDS(des_brm, "output/des_brm.rds")
} else {
  des_brm <- readRDS('output/des_brm.rds')
}


if(!file.exists("output/sta_brm.rds")){ # if the model doesn't exist fit it, else load it
  
  sta_brm <- brm(survival.time | cens(EVENT) ~ Treatment * Sex + (Treatment|LINE) + (1|ID),
               prior = c(set_prior("normal(0,0.5)", class = "b"),
                         set_prior("cauchy(0,0.1)", class = "sd")),
               iter = 5000, chains = 4, cores = 4,
               control = list(max_treedepth = 20,
                              adapt_delta = 0.999),
               # brm uses 0 = event, 1 = censor so need to recode
               data = StaRes %>% mutate(EVENT = if_else(EVENT == 1, 0, 1)), 
               family = weibull())
  
  saveRDS(sta_brm, "output/sta_brm.rds")
} else {
  sta_brm <- readRDS('output/sta_brm.rds')
}

Table of model parameter estimates - eclosion time

Formatted table

Taking the exponent of the coefficients gives an estimate of the multiplicative effect of the time to event compared to baseline (Monogamy females) (see here). For instance, for desiccation resistance, being male accelerates time to event by a factor of exp(-0.167) = 0.847 (95% confidence intervals = 0.795 - 0.902), i.e. Monogamy males live 0.847 times shorter than Monogamy females.

des_test <- bind_rows(
  hypothesis(des_brm, 'TreatmentP = 0')$hypothesis,
  hypothesis(des_brm, 'Sexm = 0')$hypothesis,
  hypothesis(des_brm, 'TreatmentP:Sexm = 0')$hypothesis
) %>% 
  mutate(Parameter = c('Treatment (P)', 'Sex (M)', 'Treatment (P) x Sex (M)'),
         across(2:5, round, 3)) %>% 
  relocate(Parameter, Estimate, Est.Error, CI.Lower, CI.Upper, Star)

sta_test <- bind_rows(
  hypothesis(sta_brm, 'TreatmentP = 0')$hypothesis,
  hypothesis(sta_brm, 'Sexm = 0')$hypothesis,
  hypothesis(sta_brm, 'TreatmentP:Sexm = 0')$hypothesis
) %>% 
  mutate(Parameter = c('Treatment (P)', 'Sex (M)', 'Treatment (P) x Sex (M)'),
         across(2:5, round, 3)) %>% 
  relocate(Parameter, Estimate, Est.Error, CI.Lower, CI.Upper, Star)

des_pvals <- bayestestR::p_direction(des_brm) %>% 
  as.data.frame() %>%
  mutate(vars = map_chr(str_split(Parameter, "_"), ~ .x[2]),
         p_val = 1 - pd, 
         star = ifelse(p_val < 0.05, "\\*", "")) %>%
  select(vars, p_val, star)

sta_pvals <- bayestestR::p_direction(sta_brm) %>% 
  as.data.frame() %>%
  mutate(vars = map_chr(str_split(Parameter, "_"), ~ .x[2]),
         p_val = 1 - pd, 
         star = ifelse(p_val < 0.05, "\\*", "")) %>%
  select(vars, p_val, star)

bind_rows(
  des_test %>% 
    mutate(vars = c('TreatmentP', 'Sexm', 'TreatmentP.Sexm')) %>% 
    left_join(des_pvals %>% filter(vars != 'Intercept'), 
              by = c("vars")) %>% 
    select(Parameter, Estimate, Est.Error, CI.Lower, CI.Upper, `p` = p_val, star),
  sta_test %>% 
    mutate(vars = c('TreatmentP', 'Sexm', 'TreatmentP.Sexm')) %>% 
    left_join(sta_pvals %>% filter(vars != 'Intercept'), 
              by = c("vars")) %>% 
    select(Parameter, Estimate, Est.Error, CI.Lower, CI.Upper, `p` = p_val, star)
  ) %>% 
  mutate(p = ifelse(p > 0.001, round(p, 3), '< 0.001')) %>% 
  rename(` ` = star) %>%
  kable() %>% 
  kable_styling(full_width = FALSE) %>%
  group_rows("Desiccation", 1, 3) %>%
  group_rows("Starvation", 4, 6)
Parameter Estimate Est.Error CI.Lower CI.Upper p
Desiccation
Treatment (P) 0.055 0.098 -0.139 0.256 0.269
Sex (M) -0.167 0.033 -0.229 -0.103 < 0.001 *
Treatment (P) x Sex (M) -0.097 0.046 -0.189 -0.007 0.017 *
Starvation
Treatment (P) 0.088 0.108 -0.132 0.302 0.184
Sex (M) -0.334 0.036 -0.404 -0.265 < 0.001 *
Treatment (P) x Sex (M) -0.131 0.050 -0.228 -0.031 0.005 *

Complete output from summary.brmsfit()

The shape parameter (\(1/\)scale parameter; see here) describes the change in hazard over time where:

  • \(p\) = 1: constant hazard
  • \(p\) > 1: increasing hazard over time
  • \(p\) < 1: decreasing hazard over time
Desiccation
des_brm
 Family: weibull 
  Links: mu = log; shape = identity 
Formula: survival.time | cens(1 - EVENT) ~ Treatment * Sex + (Treatment | LINE) + (1 | ID) 
   Data: DesRes (Number of observations: 438) 
Samples: 4 chains, each with iter = 5000; warmup = 2500; thin = 1;
         total post-warmup samples = 10000

Group-Level Effects: 
~ID (Number of levels: 146) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     0.11      0.01     0.09     0.13 1.00     2793     4912

~LINE (Number of levels: 8) 
                          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
sd(Intercept)                 0.11      0.04     0.05     0.22 1.00     3291
sd(TreatmentP)                0.07      0.07     0.00     0.26 1.00     3111
cor(Intercept,TreatmentP)    -0.15      0.57    -0.96     0.92 1.00     4892
                          Tail_ESS
sd(Intercept)                 5284
sd(TreatmentP)                5026
cor(Intercept,TreatmentP)     5213

Population-Level Effects: 
                Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept           3.66      0.06     3.53     3.79 1.00     2749     4013
TreatmentP          0.05      0.10    -0.14     0.26 1.00     2782     3535
Sexm               -0.17      0.03    -0.23    -0.10 1.00     2900     4317
TreatmentP:Sexm    -0.10      0.05    -0.19    -0.01 1.00     2685     4957

Family Specific Parameters: 
      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
shape     7.56      0.37     6.86     8.29 1.00     4599     6970

Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
Starvation
sta_brm
 Family: weibull 
  Links: mu = log; shape = identity 
Formula: survival.time | cens(EVENT) ~ Treatment * Sex + (Treatment | LINE) + (1 | ID) 
   Data: StaRes %>% mutate(EVENT = if_else(EVENT == 1, 0, 1 (Number of observations: 480) 
Samples: 4 chains, each with iter = 5000; warmup = 2500; thin = 1;
         total post-warmup samples = 10000

Group-Level Effects: 
~ID (Number of levels: 160) 
              Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sd(Intercept)     0.12      0.01     0.10     0.14 1.00     3767     5984

~LINE (Number of levels: 8) 
                          Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
sd(Intercept)                 0.14      0.05     0.07     0.27 1.00     3864
sd(TreatmentP)                0.08      0.08     0.00     0.27 1.00     3801
cor(Intercept,TreatmentP)    -0.24      0.56    -0.98     0.91 1.00     6686
                          Tail_ESS
sd(Intercept)                 4216
sd(TreatmentP)                4370
cor(Intercept,TreatmentP)     5591

Population-Level Effects: 
                Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept           4.07      0.08     3.92     4.23 1.00     3627     4963
TreatmentP          0.09      0.11    -0.13     0.30 1.00     4401     4844
Sexm               -0.33      0.04    -0.40    -0.27 1.00     4801     6136
TreatmentP:Sexm    -0.13      0.05    -0.23    -0.03 1.00     4838     5556

Family Specific Parameters: 
      Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
shape     5.68      0.26     5.19     6.19 1.00     5846     6682

Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).

Posterior effect size of treatment and sex on survival

# get posterior predictions
post_des <- posterior_samples(des_brm) %>% 
  as_tibble() %>%
  select(contains("b_"), -contains("Intercept")) %>% 
  mutate(draw = 1:n()) %>% 
  pivot_longer(-draw) %>% 
  mutate(key = str_remove_all(name, "b_"))

post_sta <- posterior_samples(sta_brm) %>% 
  as_tibble() %>%
  select(contains("b_"), -contains("Intercept")) %>% 
  mutate(draw = 1:n()) %>% 
  pivot_longer(-draw) %>% 
  mutate(key = str_remove_all(name, "b_"))

bind_rows(post_des %>% mutate(var = 'Desiccation'), 
          post_sta %>% mutate(var = 'Starvation')) %>%
  mutate(key = recode(key, TreatmentP = "Treatment (P)", Sexm = 'Sex (M)', `TreatmentP:Sexm` = 'Treatment (P) x Sex (M)')) %>% 
  ggplot(aes(x = value, y = key, fill = key)) + 
  geom_vline(xintercept = 0, linetype = 2) + 
  stat_halfeye(alpha = .8) +
  scale_fill_brewer(palette = "Spectral") +
  coord_cartesian(xlim = c(-0.4, 0.4)) +
  labs(x = "Effect size", y = "Model parameter") +
  facet_wrap(~var) +
  theme_ridges() +
  theme(legend.position = 'none',
        legend.title = element_blank()) +
  NULL

Version Author Date
8000504 Martin Garlovsky 2021-01-19
709456c Martin Garlovsky 2021-01-18
989e86f lukeholman 2020-12-18
96d1188 Martin Garlovsky 2020-12-13
7d4b609 Martin Garlovsky 2020-12-05

sessionInfo()
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

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

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

other attached packages:
 [1] knitrhooks_0.0.4 knitr_1.31       kableExtra_1.3.1 ggridges_0.5.3  
 [5] tidybayes_2.3.1  brms_2.14.4      Rcpp_1.0.6       coxme_2.2-16    
 [9] bdsmatrix_1.3-4  survival_3.2-7   forcats_0.5.1    stringr_1.4.0   
[13] dplyr_1.0.3      purrr_0.3.4      readr_1.4.0      tidyr_1.1.2     
[17] tibble_3.0.5     ggplot2_3.3.3    tidyverse_1.3.0  workflowr_1.6.2 

loaded via a namespace (and not attached):
  [1] readxl_1.3.1         backports_1.2.1      plyr_1.8.6          
  [4] igraph_1.2.6         splines_4.0.3        svUnit_1.0.3        
  [7] crosstalk_1.1.1      rstantools_2.1.1     inline_0.3.17       
 [10] digest_0.6.27        htmltools_0.5.1.1    rsconnect_0.8.16    
 [13] fansi_0.4.2          magrittr_2.0.1       openxlsx_4.2.3      
 [16] modelr_0.1.8         RcppParallel_5.0.2   matrixStats_0.57.0  
 [19] xts_0.12.1           prettyunits_1.1.1    colorspace_2.0-0    
 [22] rvest_0.3.6          ggdist_2.4.0         haven_2.3.1         
 [25] xfun_0.20            callr_3.5.1          crayon_1.3.4        
 [28] jsonlite_1.7.2       lme4_1.1-26          zoo_1.8-8           
 [31] glue_1.4.2           survminer_0.4.8      gtable_0.3.0        
 [34] webshot_0.5.2        V8_3.4.0             distributional_0.2.1
 [37] car_3.0-10           pkgbuild_1.2.0       rstan_2.21.1        
 [40] abind_1.4-5          scales_1.1.1         mvtnorm_1.1-1       
 [43] DBI_1.1.1            rstatix_0.6.0        miniUI_0.1.1.1      
 [46] viridisLite_0.3.0    xtable_1.8-4         foreign_0.8-81      
 [49] km.ci_0.5-2          stats4_4.0.3         StanHeaders_2.21.0-7
 [52] DT_0.17              htmlwidgets_1.5.3    httr_1.4.2          
 [55] threejs_0.3.3        RColorBrewer_1.1-2   arrayhelpers_1.1-0  
 [58] ellipsis_0.3.1       pkgconfig_2.0.3      loo_2.4.1           
 [61] farver_2.0.3         dbplyr_2.0.0         labeling_0.4.2      
 [64] tidyselect_1.1.0     rlang_0.4.10         reshape2_1.4.4      
 [67] later_1.1.0.1        munsell_0.5.0        cellranger_1.1.0    
 [70] tools_4.0.3          cli_2.2.0            generics_0.1.0      
 [73] broom_0.7.3          evaluate_0.14        fastmap_1.1.0       
 [76] yaml_2.2.1           processx_3.4.5       fs_1.5.0            
 [79] zip_2.1.1            survMisc_0.5.5       nlme_3.1-151        
 [82] whisker_0.4          mime_0.9             projpred_2.0.2      
 [85] xml2_1.3.2           compiler_4.0.3       bayesplot_1.8.0     
 [88] shinythemes_1.2.0    rstudioapi_0.13      gamm4_0.2-6         
 [91] curl_4.3             ggsignif_0.6.0       reprex_1.0.0        
 [94] statmod_1.4.35       stringi_1.5.3        highr_0.8           
 [97] ps_1.5.0             Brobdingnag_1.2-6    lattice_0.20-41     
[100] Matrix_1.3-2         nloptr_1.2.2.2       markdown_1.1        
[103] KMsurv_0.1-5         shinyjs_2.0.0        vctrs_0.3.6         
[106] pillar_1.4.7         lifecycle_0.2.0      bridgesampling_1.0-0
[109] insight_0.12.0       data.table_1.13.6    httpuv_1.5.5        
[112] R6_2.5.0             promises_1.1.1       rio_0.5.16          
[115] gridExtra_2.3        codetools_0.2-18     boot_1.3-26         
[118] colourpicker_1.1.0   MASS_7.3-53          gtools_3.8.2        
[121] assertthat_0.2.1     rprojroot_2.0.2      withr_2.4.1         
[124] shinystan_2.5.0      bayestestR_0.8.2     mgcv_1.8-33         
[127] parallel_4.0.3       hms_1.0.0            grid_4.0.3          
[130] coda_0.19-4          minqa_1.2.4          rmarkdown_2.6       
[133] carData_3.0-4        ggpubr_0.4.0         git2r_0.28.0        
[136] shiny_1.6.0          lubridate_1.7.9.2    base64enc_0.1-3     
[139] dygraphs_1.1.1.6