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sim_parallel_3c.R
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do_sim <- function(sim_df) {
## Preparation de l'output
results <- list(
ad_models = NULL,
ad_nets = NULL,
ipd_2steps_models = NULL,
ipd_nets = NULL,
ipd_ma_models = NULL,
ipd_1step_models = NULL
)
## Transformation pour le Poisson -----------------------------------------------------------------------
sim_df$poisson_df <- map(sim_df$poisson_df, survsim4)
# Exploitation en essais separe =====================================================================
## Cox/Poisson non ajuste (AD) ------------------------------------------------------------------------------
ad_models <- sim_df %>% select(- poisson_df)
ad_models$poisson <- map(sim_df$poisson_df, ~ glm2(status ~ ns(periode, 3) + treatment + offset(offset) , family = "poisson", data = .))
results$ad_models <- ad_models
## Net meta non ajuste -------------------------------------------------------------------------------
ad_nets <- ad_models %>% mutate(
TE = map_dbl(poisson, ~ .[[1]]["treatment"]),
seTE = map_dbl(poisson, ~ sqrt(.[[2]]["treatment", "treatment"])),
treat1 = substr(comp, 1, 1),
treat2 = substr(comp, 2, 2),
poisson = NULL,
studlab = paste0(comp, essai),
essai = NULL,
comp = NULL
) %>% group_by(scenario, ttt, sig, tau) %>% nest()
ad_nets <- ad_nets %>% mutate(net = map(data, ~ netmeta2(.$TE, .$seTE, .$treat1, .$treat2, .$studlab, data = ., sm = "HR")))
results$ad_nets <- ad_nets
## Cox/Poisson ajuste (IPD) -------------------------------------------------------------------------------
ipd_2steps_models <- sim_df %>% select(- poisson_df)
ipd_2steps_models$res <- map(sim_df$poisson_df, poisson_lin_cat_glm)
ipd_2steps_models <- ipd_2steps_models %>% mutate(
poisson_lin = map(res,"lin"),
poisson_cat = map(res,"cat"),
res = NULL
)
results$ipd_2steps_models <- ipd_2steps_models
## Net meta ajuste -------------------------------------------------------------------------------
idx <- map_dbl(ipd_2steps_models$poisson_cat, ~ length(.[[1]])) ## cas ou une categorie n'existe pas
ipd_2steps_models <- ipd_2steps_models %>% filter(idx == 11) %>% mutate(
TE = map_dbl(poisson_lin, ~ .[[1]]["treatment"]),
seTE = map_dbl(poisson_lin, ~ sqrt(.[[2]]["treatment", "treatment"])),
TE_q1 = map_dbl(poisson_cat, ~ .[[1]]["treatment"]),
TE_q2 = map_dbl(poisson_cat, ~ .[[1]]["treatment"] + .[[1]]["age_clq2"]),
TE_q3 = map_dbl(poisson_cat, ~ .[[1]]["treatment"] + .[[1]]["age_clq3"]),
TE_q4 = map_dbl(poisson_cat, ~ .[[1]]["treatment"] + .[[1]]["age_clq4"]),
seTE_q1 = map_dbl(poisson_cat, ~ sqrt(.[[2]]["treatment", "treatment"])),
seTE_q2 = map_dbl(poisson_cat, ~ sqrt(.[[2]]["treatment", "treatment"] + .[[2]]["age_clq2", "age_clq2"] + .[[2]]["treatment", "age_clq2"]*2)),
seTE_q3 = map_dbl(poisson_cat, ~ sqrt(.[[2]]["treatment", "treatment"] + .[[2]]["age_clq3", "age_clq3"] + .[[2]]["treatment", "age_clq3"]*2)),
seTE_q4 = map_dbl(poisson_cat, ~ sqrt(.[[2]]["treatment", "treatment"] + .[[2]]["age_clq4", "age_clq4"] + .[[2]]["treatment", "age_clq4"]*2)),
treat1 = substr(comp, 1, 1),
treat2 = substr(comp, 2, 2),
poisson = NULL,
studlab = paste0(comp, essai),
essai = NULL,
comp = NULL
) %>% group_by(scenario, ttt, sig, tau) %>% nest()
ipd_nets <- ipd_2steps_models %>% mutate(
nets = map(data, ~netmeta_ipd(
TE = .$TE, seTE = .$seTE,
TE_q1 = .$TE_q1, seTE_q1 = .$seTE_q1,
TE_q2 = .$TE_q2, seTE_q2 = .$seTE_q2,
TE_q3 = .$TE_q3, seTE_q3 = .$seTE_q3,
TE_q4 = .$TE_q4, seTE_q4 = .$seTE_q4,
treat1 = .$treat1, treat2 = .$treat2, studlab = .$studlab)),
data = NULL
)
ipd_nets <- ipd_nets %>% mutate(
net_lin = map(nets, "lin"),
net_q1 = map(nets, "q1"),
net_q2 = map(nets, "q2"),
net_q3 = map(nets, "q3"),
net_q4 = map(nets, "q4"),
nets = NULL
)
results$ipd_nets <- ipd_nets
# Exploitation en MA ================================================================================
## Fusion pour former les MA ------------------------------------------------------------------------
ipd_ma <- sim_df %>% group_by(scenario, ttt, sig, tau, comp) %>% summarise(ma = list(bind_rows(poisson_df, .id = "essai")))
## calcul des modeles
ipd_ma_models <- ipd_ma %>% select(- ma)
ipd_ma_models$res <- map(ipd_ma$ma, poisson_lin_cat_glmer_ma)
ipd_ma_models <- ipd_ma_models %>% mutate(
poisson_lin = map(res,"lin"),
poisson_cat = map(res,"cat"),
res = NULL
)
results$ipd_ma_models <- ipd_ma_models
# Exploitation en NMA ===============================================================================
## Fusion pour former les NMA -----------------------------------------------------------------------
ipd_nma <- ipd_ma %>% fuse_nma()
## Creation du modele aleatoire (parametrisé selon Freeman)
ipd_1step_models <- ipd_nma %>% select(- nma)
ipd_1step_models$res <- map(ipd_nma$nma, poisson_lin_cat_glmer_nma)
ipd_1step_models <- ipd_1step_models %>% mutate(
poisson_lin = map(res,"lin"),
poisson_cat = map(res,"cat"),
res = NULL
)
results$ipd_1step_models <- ipd_1step_models
## retour des résultats
return(results)
}