Trajectoires de soins
Joseph Larmarange
24/06/2021
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.4 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(labelled)
library(questionr)
library(gtsummary)
library(viridis)## Le chargement a nécessité le package : viridisLitelibrary(scales)##
## Attachement du package : 'scales'## L'objet suivant est masqué depuis 'package:viridis':
##
## viridis_pal## L'objet suivant est masqué depuis 'package:purrr':
##
## discard## L'objet suivant est masqué depuis 'package:readr':
##
## col_factorload("care_trajectories.RData")
care_trajectories <- as_tibble(care_trajectories)
var_label(care_trajectories$sex) <- "Sexe"
val_labels(care_trajectories$sex) <- c(homme = 0, femme = 1)
var_label(care_trajectories$age) <- "Âge"
var_label(care_trajectories$education) <- "Education"
val_labels(care_trajectories$education) <- c(
primaire = 1,
secondaire = 2,
supérieur = 3
)
val_labels(care_trajectories$care_status) <- c(
"diagnostiqué, mais pas suivi" = "D",
"suivi, mais pas sous traitement" = "C",
"sous traitement, mais infection non contrôlée" = "T",
"sous traitement et infection contrôlée" = "S"
)
care_trajectories <- care_trajectories %>%
set_variable_labels(
id = "Identifiant Patient",
month = "Mois depuis la diagnostic",
care_status = "Statut dans les soins",
wealth = "Niveau de richesse",
distance_clinic = "Distance à la clinique la plus proche"
) %>%
set_value_labels(
wealth = c(bas = 1, moyen = 2, haut = 3),
distance_clinic = c("moins de 10 km" = 1, "10 km ou plus" = 2)
)Première description des données
care_trajectories %>%
unlabelled() %>%
tbl_summary()| Characteristic | N = 49,3651 |
|---|---|
| Identifiant Patient | 4,961 (2,481, 7,532) |
| Mois depuis la diagnostic | 9 (4, 16) |
| Statut dans les soins | |
| diagnostiqué, mais pas suivi | 25,374 (51%) |
| suivi, mais pas sous traitement | 5,886 (12%) |
| sous traitement, mais infection non contrôlée | 4,596 (9.3%) |
| sous traitement et infection contrôlée | 13,509 (27%) |
| Sexe | |
| homme | 17,781 (36%) |
| femme | 31,584 (64%) |
| Âge | |
| 16-29 | 16,911 (34%) |
| 30-59 | 29,365 (59%) |
| 60+ | 3,089 (6.3%) |
| Education | |
| primaire | 10,417 (21%) |
| secondaire | 19,024 (39%) |
| supérieur | 19,924 (40%) |
| Niveau de richesse | |
| bas | 15,432 (31%) |
| moyen | 20,769 (42%) |
| haut | 13,164 (27%) |
| Distance à la clinique la plus proche | |
| moins de 10 km | 26,804 (54%) |
| 10 km ou plus | 22,561 (46%) |
|
1
Median (IQR); n (%)
|
|
Le fichier contient en tout 49365 lignes (une par individu et par mois de suivi). Il correspond à 2929 individus différents.
n <- care_trajectories %>%
filter(month %in% (0:8*6)) %>%
group_by(month) %>%
count() %>%
pluck("n")
etiquettes <- paste0("M", 0:8*6, "\n(n=", n, ")")
ggplot(care_trajectories) +
aes(x = month, fill = to_factor(care_status)) +
geom_bar(width = 1, color = "gray50") +
scale_fill_viridis(discrete = TRUE, direction = -1) +
xlab("") + ylab("") +
scale_x_continuous(breaks = 0:8*6, labels = etiquettes) +
labs(fill = "Statut dans les soins") +
ggtitle("Distribution du statut dans les soins chaque mois") +
guides(fill = guide_legend(nrow = 2)) +
theme_light() +
theme(legend.position = "bottom")
ggplot(care_trajectories %>% filter(month <= 36)) +
aes(x = month, fill = to_factor(care_status)) +
geom_bar(width = 1, color = "gray50", position = "fill") +
scale_fill_viridis(discrete = TRUE, direction = -1) +
xlab("") + ylab("") +
scale_x_continuous(breaks = 0:8*6, labels = etiquettes) +
scale_y_continuous(labels = percent, breaks = 0:5/5) +
labs(fill = "Statut dans les soins") +
ggtitle("Cascade des soins observée, selon le temps depuis le diagnostic") +
guides(fill = guide_legend(nrow = 2)) +
theme_light() +
theme(legend.position = "bottom")
Analyse de survie classique
ind <- care_trajectories %>% filter(month == 0)
ind$diagnostic <- 0
ind <- ind %>%
left_join(
care_trajectories %>%
filter(care_status %in% c("C", "T", "S")) %>%
group_by(id) %>%
summarise(entree_soins = min(month)),
by = "id"
) %>%
left_join(
care_trajectories %>%
filter(care_status %in% c("T", "S")) %>%
group_by(id) %>%
summarise(initiation_tt = min(month)),
by = "id"
) %>%
left_join(
care_trajectories %>%
filter(care_status == "S") %>%
group_by(id) %>%
summarise(controle = min(month)),
by = "id"
) %>%
left_join(
care_trajectories %>%
group_by(id) %>%
summarise(suivi = max(month)),
by = "id"
)Un premier exemple : temps entre l’entrée en soins et l’initiation d’un traitement
library(survival)
library(broom)
tmp <- ind %>% filter(!is.na(entree_soins))
tmp$event <- FALSE
tmp$time <- tmp$suivi - tmp$entree_soins
tmp$event <- !is.na(tmp$initiation_tt)
tmp$time[tmp$event] <- tmp$initiation_tt[tmp$event] - tmp$entree_soins[tmp$event]
kaplan <- survfit(Surv(time, event) ~ 1 , data = tmp)
res <- tidy(kaplan, conf.int = TRUE)
ggplot(res) +
aes(x = time, y = estimate) +
geom_line()
Temps depuis le diagnostic
km <- function(date_origine, date_evenement, nom = ""){
library(survival)
library(broom)
tmp <- ind
tmp <- tmp %>% filter(!is.na(tmp[[date_origine]]))
tmp$event <- FALSE
tmp$time <- tmp$suivi - tmp[[date_origine]]
tmp$event <- !is.na(tmp[[date_evenement]])
tmp$time[tmp$event] <- tmp[[date_evenement]][tmp$event] - tmp[[date_origine]][tmp$event]
kaplan <- survfit(Surv(time, event) ~ 1 , data = tmp)
res <- tidy(kaplan, conf.int = TRUE)
res$nom <- nom
res
}depuis_diag <- bind_rows(
km("diagnostic", "entree_soins", "Entrée en soins"),
km("diagnostic", "initiation_tt", "Initiation du traitement"),
km("diagnostic", "controle", "Contrôle de l'infection")
)
depuis_diag$nom <- fct_inorder(depuis_diag$nom)ggplot(depuis_diag) +
aes(
x = time, y = 1 - estimate, colour = nom,
ymin = 1 - conf.high, ymax = 1 - conf.low, fill = nom
) +
geom_ribbon(alpha = .25, mapping = aes(colour = NULL)) +
geom_line() +
scale_x_continuous(limits = c(0, 36), breaks = 0:6*6) +
scale_y_continuous(labels = scales::percent) +
xlab("mois depuis le diagnostic") +
ylab("") + labs(color = "", fill = "") +
theme_classic() +
theme(
legend.position = "bottom",
panel.grid.major.y = element_line(colour = "grey")
)## Warning: Removed 31 row(s) containing missing values (geom_path).
depuis_prec <- bind_rows(
km("diagnostic", "entree_soins", "Entrée en soins"),
km("entree_soins", "initiation_tt", "Initiation du traitement"),
km("initiation_tt", "controle", "Contrôle de l'infection")
)
depuis_prec$nom <- fct_inorder(depuis_prec$nom)ggplot(depuis_prec) +
aes(
x = time, y = 1 - estimate, colour = nom,
ymin = 1 - conf.high, ymax = 1 - conf.low, fill = nom
) +
geom_ribbon(alpha = .25, mapping = aes(colour = NULL)) +
geom_line() +
scale_x_continuous(limits = c(0, 36), breaks = 0:6*6) +
scale_y_continuous(labels = scales::percent) +
xlab("mois depuis l'étape précédente") +
ylab("") + labs(color = "", fill = "") +
theme_classic() +
theme(
legend.position = "bottom",
panel.grid.major.y = element_line(colour = "grey")
) +
facet_grid(cols = vars(nom))## Warning: Removed 13 row(s) containing missing values (geom_path).
Selon le sexe
km_sexe <- function(date_origine, date_evenement, nom = ""){
library(survival)
library(broom)
tmp <- ind
tmp <- tmp %>% filter(!is.na(tmp[[date_origine]]))
tmp$event <- FALSE
tmp$time <- tmp$suivi - tmp[[date_origine]]
tmp$event <- !is.na(tmp[[date_evenement]])
tmp$time[tmp$event] <- tmp[[date_evenement]][tmp$event] - tmp[[date_origine]][tmp$event]
tmp$sexe <- to_factor(tmp$sex)
kaplan <- survfit(Surv(time, event) ~ sexe , data = tmp)
res <- tidy(kaplan, conf.int = TRUE)
res$nom <- nom
res
}
depuis_prec_sexe <- bind_rows(
km_sexe("diagnostic", "entree_soins", "Entrée en soins"),
km_sexe("entree_soins", "initiation_tt", "Initiation du traitement"),
km_sexe("initiation_tt", "controle", "Contrôle de l'infection")
)
depuis_prec_sexe$nom <- fct_inorder(depuis_prec_sexe$nom)
depuis_prec_sexe$sexe <- depuis_prec_sexe$strata %>%
str_sub(start = 6)ggplot(depuis_prec_sexe) +
aes(
x = time, y = 1 - estimate, colour = sexe,
ymin = 1 - conf.high, ymax = 1 - conf.low, fill = sexe
) +
geom_ribbon(alpha = .25, mapping = aes(colour = NULL)) +
geom_line() +
scale_x_continuous(limits = c(0, 36), breaks = 0:6*6) +
scale_y_continuous(labels = scales::percent) +
xlab("mois depuis l'étape précédente") +
ylab("") + labs(color = "", fill = "") +
theme_classic() +
theme(
legend.position = "bottom",
panel.grid.major.y = element_line(colour = "grey")
) +
facet_grid(cols = vars(nom))
Analyse de séquences sur l’ensemble du fichier
large <- care_trajectories %>%
select(id, month, care_status) %>%
pivot_wider(names_from = month, values_from = care_status, names_prefix = "m")
library(TraMineR, quietly = TRUE)##
## TraMineR stable version 2.2-2 (Built: 2021-06-21)## Website: http://traminer.unige.ch## Please type 'citation("TraMineR")' for citation information.seq_all <- seqdef(
large %>% select(-id),
id = large$id,
alphabet = c("D", "C", "T", "S"),
states = c("diagnostiqué", "en soins", "sous traitement", "controlé"),
cpal = viridis(4, direction = -1)
)## [>] found missing values ('NA') in sequence data## [>] preparing 2929 sequences## [>] coding void elements with '%' and missing values with '*'## [>] state coding:## [alphabet] [label] [long label]## 1 D diagnostiqué diagnostiqué## 2 C en soins en soins## 3 T sous traitement sous traitement## 4 S controlé controlé## [>] 2929 sequences in the data set## [>] min/max sequence length: 1/51seqdplot(seq_all, legend.prop = .25)
couts <- seqcost(seq_all, method = "CONSTANT")## [>] creating 4x4 substitution-cost matrix using 2 as constant valuecouts$sm[1,] <- c(0, 1, 2, 3)
couts$sm[2,] <- c(1, 0, 1, 2)
couts$sm[3,] <- c(2, 1, 0, 1)
couts$sm[4,] <- c(3, 2, 1, 0)
couts$indel <- max(couts$sm)/2
couts## $indel
## [1] 1.5
##
## $sm
## diagnostiqué-> en soins-> sous traitement-> controlé->
## diagnostiqué-> 0 1 2 3
## en soins-> 1 0 1 2
## sous traitement-> 2 1 0 1
## controlé-> 3 2 1 0dist_all <- seqdist(seq_all, method = "OM", sm = couts$sm, indel = couts$indel)## [>] 2929 sequences with 4 distinct states## [>] checking 'sm' (size and triangle inequality)## [>] 1370 distinct sequences## [>] min/max sequence lengths: 1/51## [>] computing distances using the OM metric## [>] elapsed time: 2.48 secsarbre_all <- hclust(as.dist(dist_all), method = "ward.D2")
plot(arbre_all)
library(seqhandbook)
seq_heatmap(seq_all, arbre_all)
Analyse de séquences sur 18 mois
large$seq_length <- seqlength(seq_all)
large_m18 <- large %>%
filter(seq_length >= 19) %>%
select(id, m0:m18)
seq_m18 <- seqdef(
large_m18 %>% select(-id),
id = large_m18$id,
alphabet = c("D", "C", "T", "S"),
states = c("diagnostiqué", "en soins", "sous traitement", "controlé"),
cpal = viridis(4, direction = -1)
)## [>] state coding:## [alphabet] [label] [long label]## 1 D diagnostiqué diagnostiqué## 2 C en soins en soins## 3 T sous traitement sous traitement## 4 S controlé controlé## [>] 1317 sequences in the data set## [>] min/max sequence length: 19/19seqdplot(seq_m18, legend.prop = .25)
dist_m18 <- seqdist(seq_m18, method = "OM", sm = couts$sm, indel = couts$indel)## [>] 1317 sequences with 4 distinct states## [>] checking 'sm' (size and triangle inequality)## [>] 578 distinct sequences## [>] min/max sequence lengths: 19/19## [>] computing distances using the OM metric## [>] elapsed time: 0.46 secsarbre_m18 <- hclust(as.dist(dist_m18), method = "ward.D2")
plot(arbre_m18)
seq_heatmap(seq_m18, arbre_m18)
library(WeightedCluster, quietly = TRUE)## This is WeightedCluster stable version 1.4-1 (Built: 2020-07-07)##
## To get the manuals, please run:## vignette("WeightedCluster") ## Complete manual in English## vignette("WeightedClusterFR") ## Complete manual in French## vignette("WeightedClusterPreview") ## Short preview in English##
## To cite WeightedCluster in publications please use:## Studer, Matthias (2013). WeightedCluster Library Manual: A practical## guide to creating typologies of trajectories in the social sciences## with R. LIVES Working Papers, 24. doi:## 10.12682/lives.2296-1658.2013.24seqtree_m18 <- as.seqtree(arbre_m18, seqdata = seq_m18, diss = dist_m18, ncluster = 7)
seqtreedisplay(seqtree_m18, type = "I", filename = "seqtree_m18.png", show.depth = TRUE)## [>] GraphViz version 2.38 found.
mon seqtree
large_m18$typo_cah <- cutree(arbre_m18, k = 4)pam_m18 <- wcKMedoids(dist_m18, k = 4, initialclust = arbre_m18)
large_m18$typo_pam <- pam_m18$clustering
library(gtsummary, quietly = TRUE)
large_m18 %>%
tbl_cross(row = typo_cah, col = typo_pam)| Characteristic | typo_pam | Total | |||
|---|---|---|---|---|---|
| 6 | 23 | 85 | 410 | ||
| typo_cah | |||||
| 1 | 522 | 4 | 0 | 39 | 565 |
| 2 | 0 | 3 | 330 | 3 | 336 |
| 3 | 1 | 203 | 59 | 13 | 276 |
| 4 | 23 | 0 | 5 | 112 | 140 |
| Total | 546 | 210 | 394 | 167 | 1,317 |
large_m18$ordre_cmd <- cmdscale(as.dist(dist_m18), k = 1)seqIplot(seq_m18, group = large_m18$typo_cah)
seqIplot(seq_m18, group = large_m18$typo_cah, sortv = large_m18$ordre_cmd)
seqIplot(seq_m18, group = large_m18$typo_pam, sortv = large_m18$ordre_cmd)
tab <- tibble(
indicateur = names(wcClusterQuality(dist_m18, large_m18$typo_cah)$stats),
cah = wcClusterQuality(dist_m18, large_m18$typo_cah)$stats,
pam = wcClusterQuality(dist_m18, large_m18$typo_pam)$stats
)
tab %>% gt::gt() %>% gt::fmt_number(2:3, decimals = 2, sep_mark = " ", dec_mark = ",")| indicateur | cah | pam |
|---|---|---|
| PBC | 0,70 | 0,71 |
| HG | 0,93 | 0,95 |
| HGSD | 0,93 | 0,95 |
| ASW | 0,53 | 0,57 |
| ASWw | 0,53 | 0,57 |
| CH | 938,97 | 1 023,12 |
| R2 | 0,68 | 0,70 |
| CHsq | 3 351,21 | 3 980,60 |
| R2sq | 0,88 | 0,90 |
| HC | 0,03 | 0,02 |
large_m18$groupe <- as.character(large_m18$typo_pam)
large_m18$groupe <- fct_recode(large_m18$groupe,
"Hors Soins" = "6",
"Lents" = "23",
"Rapides" = "85",
"Inaboutis" = "410"
)
large_m18$groupe <- fct_relevel(
large_m18$groupe,
"Rapides", "Lents", "Inaboutis", "Hors Soins"
)
large_m18 <- large_m18 %>%
mutate(rang_cmd = rank(ordre_cmd, ties.method = "first"))
long_m18 <- large_m18 %>%
pivot_longer(m0:m18, names_to = "month", values_to = "care_status", names_prefix = "m")
long_m18$month <- as.integer(long_m18$month)
long_m18$care_statusF <- to_factor(long_m18$care_status)
ggplot(long_m18) +
aes(x = month, y = factor(rang_cmd), fill = care_statusF) +
geom_raster() +
scale_fill_viridis(discrete = TRUE, direction = -1) +
facet_grid(rows = vars(groupe), scales = "free", space = "free") +
labs(x = NULL, y = NULL, fill = NULL) +
scale_y_discrete(labels = NULL) +
scale_x_continuous(
expand = expansion(0, 0),
breaks = 0:3*6,
labels = paste0("M", 0:3*6)
) +
guides(
fill = guide_legend(ncol = 2)
) +
theme_minimal() +
theme(
legend.position = "bottom"
)
Facteurs associés à chaque groupe
large_m18 <- large_m18 %>%
left_join(
care_trajectories %>%
filter(month == 0) %>%
select(id, sex, age, education),
by = "id"
)library(gtsummary)
theme_gtsummary_language("fr", decimal.mark = ",", big.mark = " ")## Setting theme `language: fr`large_m18 %>%
select(groupe, age, sex, education) %>%
unlabelled() %>%
tbl_summary(
by = "groupe",
digits = all_categorical() ~ c(0, 1)
) %>%
add_overall(last = TRUE) %>%
add_p()| Caractéristique | Rapides, N = 3941 | Lents, N = 2101 | Inaboutis, N = 1671 | Hors Soins, N = 5461 | Total, N = 1 3171 | p-valeur2 |
|---|---|---|---|---|---|---|
| Âge | <0,001 | |||||
| 16-29 | 96 (24,4%) | 71 (33,8%) | 62 (37,1%) | 235 (43,0%) | 464 (35,2%) | |
| 30-59 | 269 (68,3%) | 128 (61,0%) | 91 (54,5%) | 281 (51,5%) | 769 (58,4%) | |
| 60+ | 29 (7,4%) | 11 (5,2%) | 14 (8,4%) | 30 (5,5%) | 84 (6,4%) | |
| Sexe | <0,001 | |||||
| homme | 104 (26,4%) | 61 (29,0%) | 42 (25,1%) | 243 (44,5%) | 450 (34,2%) | |
| femme | 290 (73,6%) | 149 (71,0%) | 125 (74,9%) | 303 (55,5%) | 867 (65,8%) | |
| Education | 0,002 | |||||
| primaire | 63 (16,0%) | 47 (22,4%) | 30 (18,0%) | 123 (22,5%) | 263 (20,0%) | |
| secondaire | 126 (32,0%) | 83 (39,5%) | 61 (36,5%) | 212 (38,8%) | 482 (36,6%) | |
| supérieur | 205 (52,0%) | 80 (38,1%) | 76 (45,5%) | 211 (38,6%) | 572 (43,4%) | |
|
1
n (%)
2
test du khi-deux d'indépendance
|
||||||
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2large_m18 %>%
unlabelled() %>%
ggbivariate(
outcome = "groupe",
explanatory = c("age", "sex", "education")
)
large_m18 %>%
unlabelled() %>%
ggtable(
columnsX = "groupe",
columnsY = c("age", "sex", "education"),
cells = "col.prop",
fill = "std.resid",
legend = 1
)
library(nnet)
large_m18$groupe2 <- large_m18$groupe %>%
fct_relevel("Hors Soins")
regm <- multinom(
groupe2 ~ sex + age + education,
data = unlabelled(large_m18)
)## # weights: 28 (18 variable)
## initial value 1825.749674
## iter 10 value 1648.561757
## iter 20 value 1638.851444
## final value 1638.811514
## convergedregm %>%
tbl_regression()## i Multinomial models have a different underlying structure than the models
## gtsummary was designed for. Other gtsummary functions designed to work with
## tbl_regression objects may yield unexpected results.| Caractéristique | log(OR)1 | 95% CI1 | p-valeur |
|---|---|---|---|
| Rapides | |||
| Sexe | |||
| homme | — | — | |
| femme | 0,88 | 0,59 – 1,2 | <0,001 |
| Âge | |||
| 16-29 | — | — | |
| 30-59 | 0,82 | 0,50 – 1,1 | <0,001 |
| 60+ | 0,79 | 0,16 – 1,4 | 0,013 |
| Education | |||
| primaire | — | — | |
| secondaire | 0,18 | -0,21 – 0,56 | 0,4 |
| supérieur | 0,35 | -0,04 – 0,75 | 0,080 |
| Lents | |||
| Sexe | |||
| homme | — | — | |
| femme | 0,72 | 0,37 – 1,1 | <0,001 |
| Âge | |||
| 16-29 | — | — | |
| 30-59 | 0,56 | 0,19 – 0,93 | 0,003 |
| 60+ | 0,48 | -0,33 – 1,3 | 0,2 |
| Education | |||
| primaire | — | — | |
| secondaire | 0,05 | -0,38 – 0,48 | 0,8 |
| supérieur | -0,20 | -0,66 – 0,26 | 0,4 |
| Inaboutis | |||
| Sexe | |||
| homme | — | — | |
| femme | 0,91 | 0,52 – 1,3 | <0,001 |
| Âge | |||
| 16-29 | — | — | |
| 30-59 | 0,20 | -0,21 – 0,61 | 0,3 |
| 60+ | 0,55 | -0,22 – 1,3 | 0,2 |
| Education | |||
| primaire | — | — | |
| secondaire | 0,20 | -0,30 – 0,69 | 0,4 |
| supérieur | 0,30 | -0,23 – 0,82 | 0,3 |
|
1
OR = rapport de cotes, CI = intervalle de confiance
|
|||
regm %>%
ggcoef_multinom(exponentiate = TRUE)
regm %>%
ggcoef_multinom(exponentiate = TRUE, type = "f")
library(ggeffects)
ggeffect(regm) %>%
plot() %>%
cowplot::plot_grid(plotlist = ., ncol = 3)
Modèle à observations répétées
ct36 <- care_trajectories %>%
filter(month > 0 & month <= 36) %>%
mutate(
care_statusF = to_factor(care_status, ordered = TRUE),
monthF = to_factor(month),
sexF = to_factor(sex),
educationF = to_factor(education),
ageF = to_factor(age)
)library(geepack)
mod_td <- ordgee(
care_statusF ~ sexF + ageF + educationF + monthF,
data = ct36,
id = ct36$id
)mod_td##
## Call:
## ordgee(formula = care_statusF ~ sexF + ageF + educationF + monthF,
## id = ct36$id, data = ct36)
##
## Mean Model:
## Mean Link: logit
## Variance to Mean Relation: binomial
##
## Coefficients:
## Inter:diagnostiqué, mais pas suivi
## -2.85698477
## Inter:suivi, mais pas sous traitement
## -3.42478225
## Inter:sous traitement, mais infection non contrôlée
## -3.94640557
## sexFfemme
## 0.74197221
## ageF30-59
## 0.55766093
## ageF60+
## 0.71470374
## educationFsecondaire
## 0.15854089
## educationFsupérieur
## 0.33738618
## monthF2
## 0.05881916
## monthF3
## 0.30596665
## monthF4
## 1.02687418
## monthF5
## 1.33982178
## monthF6
## 1.54488605
## monthF7
## 1.72739094
## monthF8
## 1.85568719
## monthF9
## 1.94922032
## monthF10
## 2.01403419
## monthF11
## 2.07402293
## monthF12
## 2.13033106
## monthF13
## 2.23087200
## monthF14
## 2.31381168
## monthF15
## 2.37384862
## monthF16
## 2.43853544
## monthF17
## 2.43604013
## monthF18
## 2.44472063
## monthF19
## 2.44212744
## monthF20
## 2.45983083
## monthF21
## 2.45694848
## monthF22
## 2.53331374
## monthF23
## 2.54803629
## monthF24
## 2.54645028
## monthF25
## 2.57370257
## monthF26
## 2.61034365
## monthF27
## 2.66691710
## monthF28
## 2.75046931
## monthF29
## 2.70064394
## monthF30
## 2.70655325
## monthF31
## 2.64031072
## monthF32
## 2.65807920
## monthF33
## 2.66800514
## monthF34
## 2.69014244
## monthF35
## 2.78338141
## monthF36
## 2.77707707
##
## Scale is fixed.
##
## Correlation Model:
## Correlation Structure: independence
##
## Returned Error Value: 0
## Number of clusters: 2830 Maximum cluster size: 36s <- summary(mod_td)
print(s)##
## Call:
## ordgee(formula = care_statusF ~ sexF + ageF + educationF + monthF,
## id = ct36$id, data = ct36)
##
## Mean Model:
## Mean Link: logit
## Variance to Mean Relation: binomial
##
## Coefficients:
## estimate san.se
## Inter:diagnostiqué, mais pas suivi -2.85698477 0.12723763
## Inter:suivi, mais pas sous traitement -3.42478225 0.12893566
## Inter:sous traitement, mais infection non contrôlée -3.94640557 0.13203443
## sexFfemme 0.74197221 0.08677773
## ageF30-59 0.55766093 0.08900306
## ageF60+ 0.71470374 0.19302434
## educationFsecondaire 0.15854089 0.09879946
## educationFsupérieur 0.33738618 0.10862919
## monthF2 0.05881916 0.04051162
## monthF3 0.30596665 0.04752640
## monthF4 1.02687418 0.06099815
## monthF5 1.33982178 0.06505731
## monthF6 1.54488605 0.06763666
## monthF7 1.72739094 0.06841053
## monthF8 1.85568719 0.06964169
## monthF9 1.94922032 0.07031151
## monthF10 2.01403419 0.07147048
## monthF11 2.07402293 0.07358797
## monthF12 2.13033106 0.07563332
## monthF13 2.23087200 0.07782793
## monthF14 2.31381168 0.07990717
## monthF15 2.37384862 0.08030596
## monthF16 2.43853544 0.08078078
## monthF17 2.43604013 0.08215885
## monthF18 2.44472063 0.08277468
## monthF19 2.44212744 0.08630783
## monthF20 2.45983083 0.09519804
## monthF21 2.45694848 0.10028608
## monthF22 2.53331374 0.10246209
## monthF23 2.54803629 0.10467272
## monthF24 2.54645028 0.10609955
## monthF25 2.57370257 0.10686565
## monthF26 2.61034365 0.10980383
## monthF27 2.66691710 0.11156270
## monthF28 2.75046931 0.11406879
## monthF29 2.70064394 0.11551039
## monthF30 2.70655325 0.11748402
## monthF31 2.64031072 0.12448719
## monthF32 2.65807920 0.13290673
## monthF33 2.66800514 0.13906969
## monthF34 2.69014244 0.14819048
## monthF35 2.78338141 0.15959135
## monthF36 2.77707707 0.17498887
## wald p
## Inter:diagnostiqué, mais pas suivi 504.178987 0.000000e+00
## Inter:suivi, mais pas sous traitement 705.537046 0.000000e+00
## Inter:sous traitement, mais infection non contrôlée 893.365222 0.000000e+00
## sexFfemme 73.106953 0.000000e+00
## ageF30-59 39.258215 3.713028e-10
## ageF60+ 13.709701 2.133496e-04
## educationFsecondaire 2.574978 1.085656e-01
## educationFsupérieur 9.646317 1.897316e-03
## monthF2 2.108038 1.465271e-01
## monthF3 41.445600 1.211971e-10
## monthF4 283.400872 0.000000e+00
## monthF5 424.132789 0.000000e+00
## monthF6 521.709272 0.000000e+00
## monthF7 637.580908 0.000000e+00
## monthF8 710.020643 0.000000e+00
## monthF9 768.544591 0.000000e+00
## monthF10 794.109398 0.000000e+00
## monthF11 794.352958 0.000000e+00
## monthF12 793.355643 0.000000e+00
## monthF13 821.633841 0.000000e+00
## monthF14 838.464136 0.000000e+00
## monthF15 873.796797 0.000000e+00
## monthF16 911.259455 0.000000e+00
## monthF17 879.144218 0.000000e+00
## monthF18 872.295140 0.000000e+00
## monthF19 800.638107 0.000000e+00
## monthF20 667.658601 0.000000e+00
## monthF21 600.220482 0.000000e+00
## monthF22 611.296033 0.000000e+00
## monthF23 592.576175 0.000000e+00
## monthF24 576.027663 0.000000e+00
## monthF25 580.016601 0.000000e+00
## monthF26 565.145668 0.000000e+00
## monthF27 571.453592 0.000000e+00
## monthF28 581.406585 0.000000e+00
## monthF29 546.628851 0.000000e+00
## monthF30 530.732309 0.000000e+00
## monthF31 449.842803 0.000000e+00
## monthF32 399.983308 0.000000e+00
## monthF33 368.051277 0.000000e+00
## monthF34 329.541348 0.000000e+00
## monthF35 304.177256 0.000000e+00
## monthF36 251.857560 0.000000e+00
##
## Scale is fixed.
##
## Correlation Model:
## Correlation Structure: independence
##
## Returned Error Value: 0
## Number of clusters: 2830 Maximum cluster size: 36library(gtsummary)
tbl_regression(mod_td)## ! `broom::tidy()` failed to tidy the model.## x No tidy method for objects of class geese## Warning: Parameters can't be retrieved for objects of class 'simpleError'.## ! `tidy_parameters()` also failed.## x Sorry, `model_parameters()` does currently not work for objects of class 'geese'.## ! `broom::tidy()` failed to tidy the model.## x No tidy method for objects of class geese## Warning: Parameters can't be retrieved for objects of class 'simpleError'.## ! `tidy_parameters()` also failed.## x Sorry, `model_parameters()` does currently not work for objects of class 'geese'.## x There was an error calling `tidy_fun()`. Most likely, this is because the
## function supplied in `tidy_fun=` was misspelled, does not exist, is not
## compatible with your object, or was missing necessary arguments (e.g. `conf.level=` or `conf.int=`). See error message below.## Error: Error in tidy_fun(model, ...): Unable to tidy `x`.library(broom)
tidy(mod_td)## Error: No tidy method for objects of class geeselibrary(parameters)
model_parameters(mod_td)## Warning: Parameters can't be retrieved for objects of class 'simpleError'.## Error: Sorry, `model_parameters()` does currently not work for objects of class 'geese'.library(broom.helpers)##
## Attachement du package : 'broom.helpers'## Les objets suivants sont masqués depuis 'package:gtsummary':
##
## all_continuous, all_contraststidy_plus_plus(mod_td)## ! `broom::tidy()` failed to tidy the model.## x No tidy method for objects of class geese## Warning: Parameters can't be retrieved for objects of class 'simpleError'.## ! `tidy_parameters()` also failed.## x Sorry, `model_parameters()` does currently not work for objects of class 'geese'.## ! `broom::tidy()` failed to tidy the model.## x No tidy method for objects of class geese## Warning: Parameters can't be retrieved for objects of class 'simpleError'.## ! `tidy_parameters()` also failed.## x Sorry, `model_parameters()` does currently not work for objects of class 'geese'.## x There was an error calling `tidy_fun()`. Most likely, this is because the
## function supplied in `tidy_fun=` was misspelled, does not exist, is not
## compatible with your object, or was missing necessary arguments (e.g. `conf.level=` or `conf.int=`). See error message below.## Error: Error in tidy_fun(model, ...): Unable to tidy `x`.source(url("https://raw.githubusercontent.com/larmarange/JLutils/master/R/tidy_model.R"))
tidy_model(mod_td)## Error in 1:ncol(co): l'argument est de longueur nullecoef(mod_td)## NULLconfint(mod_td)## Error in vcov.default(object): object does not have variance-covariance matrixs <- summary(mod_td)
res <- s$mean
res$term <- rownames(res)
alpha <- .95
mult <- qnorm((1 + alpha) / 2)
res$conf.low <- res$estimate - mult * res$san.se
res$conf.high <- res$estimate + mult * res$san.se
res$estimate <- exp(res$estimate)
res$conf.low <- exp(res$conf.low)
res$conf.high <- exp(res$conf.high)library(scales)
res %>%
mutate(
estimate = number(estimate, accuracy = .01),
p = pvalue(p),
ci = paste0(
"[",
number(conf.low, accuracy = .1),
"-",
number(conf.high, accuracy = .1),
"]"
)
) %>%
filter(!str_detect(term, "Inter")) %>%
select(Facteur = term, OR = estimate, "95%CI" = ci, "p-valeur"= p) %>%
knitr::kable(row.names = FALSE, align = "lrcr")| Facteur | OR | 95%CI | p-valeur |
|---|---|---|---|
| sexFfemme | 2.10 | [1.8-2.5] | <0.001 |
| ageF30-59 | 1.75 | [1.5-2.1] | <0.001 |
| ageF60+ | 2.04 | [1.4-3.0] | <0.001 |
| educationFsecondaire | 1.17 | [1.0-1.4] | 0.109 |
| educationFsupérieur | 1.40 | [1.1-1.7] | 0.002 |
| monthF2 | 1.06 | [1.0-1.1] | 0.147 |
| monthF3 | 1.36 | [1.2-1.5] | <0.001 |
| monthF4 | 2.79 | [2.5-3.1] | <0.001 |
| monthF5 | 3.82 | [3.4-4.3] | <0.001 |
| monthF6 | 4.69 | [4.1-5.4] | <0.001 |
| monthF7 | 5.63 | [4.9-6.4] | <0.001 |
| monthF8 | 6.40 | [5.6-7.3] | <0.001 |
| monthF9 | 7.02 | [6.1-8.1] | <0.001 |
| monthF10 | 7.49 | [6.5-8.6] | <0.001 |
| monthF11 | 7.96 | [6.9-9.2] | <0.001 |
| monthF12 | 8.42 | [7.3-9.8] | <0.001 |
| monthF13 | 9.31 | [8.0-10.8] | <0.001 |
| monthF14 | 10.11 | [8.6-11.8] | <0.001 |
| monthF15 | 10.74 | [9.2-12.6] | <0.001 |
| monthF16 | 11.46 | [9.8-13.4] | <0.001 |
| monthF17 | 11.43 | [9.7-13.4] | <0.001 |
| monthF18 | 11.53 | [9.8-13.6] | <0.001 |
| monthF19 | 11.50 | [9.7-13.6] | <0.001 |
| monthF20 | 11.70 | [9.7-14.1] | <0.001 |
| monthF21 | 11.67 | [9.6-14.2] | <0.001 |
| monthF22 | 12.60 | [10.3-15.4] | <0.001 |
| monthF23 | 12.78 | [10.4-15.7] | <0.001 |
| monthF24 | 12.76 | [10.4-15.7] | <0.001 |
| monthF25 | 13.11 | [10.6-16.2] | <0.001 |
| monthF26 | 13.60 | [11.0-16.9] | <0.001 |
| monthF27 | 14.40 | [11.6-17.9] | <0.001 |
| monthF28 | 15.65 | [12.5-19.6] | <0.001 |
| monthF29 | 14.89 | [11.9-18.7] | <0.001 |
| monthF30 | 14.98 | [11.9-18.9] | <0.001 |
| monthF31 | 14.02 | [11.0-17.9] | <0.001 |
| monthF32 | 14.27 | [11.0-18.5] | <0.001 |
| monthF33 | 14.41 | [11.0-18.9] | <0.001 |
| monthF34 | 14.73 | [11.0-19.7] | <0.001 |
| monthF35 | 16.17 | [11.8-22.1] | <0.001 |
| monthF36 | 16.07 | [11.4-22.6] | <0.001 |
library(GGally)
res %>%
filter(!str_detect(term, "Inter")) %>%
ggcoef_plot(y = "term", facet_row = NULL, exponentiate = TRUE)