# ============================================================================
# TUGAS BIOSTATISTIKA INTERMEDIATE
# NAMA : FERI FEBRIAN ARISTIA
# NIM : 2611018048
# ANALISIS PENGUKURAN BERULANG STREAM-AD / AMLITELIMAB
# Desain: 5 kelompok perlakuan x 7 waktu pengukuran
# Outcome: Percentage change from baseline in EASI
#
# Sumber artikel:
# Weidinger S, et al. STREAM-AD phase 2b trial.
# Journal of Allergy and Clinical Immunology (2025)
# DOI: 10.1016/j.jaci.2024.10.031
# Trial: NCT05131477 / EudraCT 2021-000725-28
#
# PENTING:
# Data individual pada file CSV adalah DATA SIMULASI untuk tujuan pendidikan,
# dikalibrasi terhadap statistik agregat penelitian. Data ini BUKAN raw data
# peserta uji klinis asli.
# ============================================================================
# ============================== PARAMETER ====================================
FILE_DATA <- "Amlitelimab_STREAM_AD_Simulated_Long.csv"
FOLDER_HASIL <- "hasil_analisis_stream_ad"
KELOMPOK_FOKUS <- "Amlitelimab 250 mg + LD 500 mg"
INSTALL_PAKET_OTOMATIS <- TRUE
BUKA_HTML_OTOMATIS <- TRUE
TAMPILKAN_GRAFIK_RSTUDIO <- TRUE
# ============================ PROGRAM UTAMA ==================================
jalankan_analisis <- function() {
waktu_mulai <- Sys.time()
paket <- c(
"readr", "dplyr", "tidyr", "ggplot2", "afex", "emmeans",
"rstatix", "car", "effectsize", "lme4", "lmerTest", "pbkrtest",
"performance", "knitr", "htmltools", "base64enc", "tibble"
)
belum <- paket[!vapply(paket, requireNamespace, logical(1), quietly = TRUE)]
if (length(belum)) {
if (!INSTALL_PAKET_OTOMATIS) {
stop("Paket belum tersedia: ", paste(belum, collapse = ", "))
}
message("Menginstal paket: ", paste(belum, collapse = ", "))
install.packages(belum, repos = "https://cloud.r-project.org")
}
gagal <- paket[!vapply(paket, requireNamespace, logical(1), quietly = TRUE)]
if (length(gagal)) stop("Instalasi paket gagal: ", paste(gagal, collapse = ", "))
suppressPackageStartupMessages({
library(readr); library(dplyr); library(tidyr); library(ggplot2)
library(afex); library(emmeans); library(rstatix); library(car)
library(effectsize); library(lme4); library(lmerTest); library(pbkrtest)
library(performance); library(knitr); library(htmltools); library(base64enc)
library(tibble)
})
# Kontras untuk Type III tests pada model faktorial.
opsi_lama <- options(contrasts = c("contr.sum", "contr.poly"))
on.exit(options(opsi_lama), add = TRUE)
# ----------------------- Resolusi lokasi data ------------------------------
sumber <- unlist(lapply(sys.frames(), function(fr) {
if (!is.null(fr$ofile)) as.character(fr$ofile) else character(0)
}))
if (requireNamespace("rstudioapi", quietly = TRUE) && rstudioapi::isAvailable()) {
aktif <- tryCatch(rstudioapi::getSourceEditorContext()$path,
error = function(e) "")
if (nzchar(aktif)) sumber <- c(sumber, aktif)
}
kandidat <- unique(c(FILE_DATA, file.path(dirname(sumber), FILE_DATA)))
kandidat <- kandidat[file.exists(kandidat)]
if (!length(kandidat)) {
message("File data tidak ditemukan otomatis. Silakan pilih CSV long-format.")
path_data <- file.choose()
} else {
path_data <- normalizePath(kandidat[1], winslash = "/", mustWork = TRUE)
}
folder <- file.path(dirname(path_data), FOLDER_HASIL)
dir.create(folder, recursive = TRUE, showWarnings = FALSE)
folder <- normalizePath(folder, winslash = "/", mustWork = TRUE)
message("Data : ", path_data)
message("Hasil : ", folder)
# ---------------------- Fungsi laporan seperti Nazmy -----------------------
isi <- character(0)
status <- data.frame()
tambah <- function(html) isi <<- c(isi, html)
escape <- function(x) as.character(htmltools::htmlEscape(paste(x, collapse = "\n")))
narasi <- function(x) tambah(paste0("<p>", escape(x), "</p>"))
output <- function(x) {
teks <- capture.output(print(x))
cat(paste(teks, collapse = "\n"), "\n")
tambah(paste0("<pre>", escape(teks), "</pre>"))
invisible(x)
}
tabel <- function(x, nama, digits = 4) {
x <- as.data.frame(x)
write.csv(x, file.path(folder, paste0(nama, ".csv")), row.names = FALSE,
na = "", fileEncoding = "UTF-8")
if (nrow(x)) {
tambah(paste0("<div class='table'>",
as.character(knitr::kable(x, format = "html", digits = digits)),
"</div>"))
} else {
narasi("Tidak ada baris untuk tabel ini.")
}
print(x)
invisible(x)
}
gambar <- function(p, nama, lebar = 10, tinggi = 6) {
path <- file.path(folder, paste0(nama, ".png"))
ggplot2::ggsave(path, plot = p, width = lebar, height = tinggi, dpi = 180)
if (TAMPILKAN_GRAFIK_RSTUDIO) {
tryCatch(print(p), error = function(e) {
message("Tampilan Plots gagal, tetapi PNG tetap tersimpan: ", conditionMessage(e))
})
}
tambah(paste0(
"<figure><img src='", base64enc::dataURI(file = path, mime = "image/png"),
"' alt='", escape(nama), "'><figcaption>", escape(nama),
"</figcaption></figure>"
))
message("Grafik tersimpan: ", path)
invisible(path)
}
bagian <- function(judul, f) {
message("\n>>> ", judul)
tambah(paste0("<h2>", escape(judul), "</h2>"))
warnings <- character(0)
ok <- tryCatch(
withCallingHandlers({ f(); TRUE }, warning = function(w) {
warnings <<- c(warnings, conditionMessage(w))
invokeRestart("muffleWarning")
}),
error = function(e) {
narasi(paste("BAGIAN GAGAL:", conditionMessage(e)))
message("Bagian gagal: ", conditionMessage(e))
FALSE
}
)
if (length(warnings)) {
narasi(paste("Peringatan estimasi:", paste(unique(warnings), collapse = "; ")))
}
status <<- rbind(status, data.frame(
bagian = judul,
status = if (ok) "Selesai" else "Gagal",
peringatan = paste(unique(warnings), collapse = "; ")
))
invisible(ok)
}
fp <- function(p) {
if (is.na(p)) return("tidak tersedia")
if (p < .001) "< 0,001" else paste0("= ", formatC(p, digits = 3, format = "f", decimal.mark = ","))
}
# ============================ 1. DATA ======================================
raw <- as.data.frame(readr::read_csv(path_data, show_col_types = FALSE))
names(raw) <- trimws(names(raw))
wajib <- c("ID", "Treatment", "Week", "EASI_Pct_Change")
if (!all(wajib %in% names(raw))) {
stop("Kolom tidak ditemukan: ", paste(setdiff(wajib, names(raw)), collapse = ", "))
}
level_kelompok <- c(
"Placebo",
"Amlitelimab 62.5 mg",
"Amlitelimab 125 mg",
"Amlitelimab 250 mg",
"Amlitelimab 250 mg + LD 500 mg"
)
minggu_nilai <- c(2, 4, 8, 12, 16, 20, 24)
label_waktu <- paste0("W", minggu_nilai)
dat <- raw |>
transmute(
ID = factor(ID),
Treatment = factor(trimws(as.character(Treatment)), levels = level_kelompok),
Week = as.numeric(Week),
WeekF = factor(paste0("W", Week), levels = label_waktu, ordered = TRUE),
EASI = as.numeric(EASI_Pct_Change)
) |>
arrange(ID, Week)
if (anyNA(dat$Treatment)) stop("Terdapat label Treatment yang tidak dikenali.")
if (anyNA(dat$WeekF)) stop("Terdapat minggu di luar 2, 4, 8, 12, 16, 20, 24.")
if (anyDuplicated(dat[c("ID", "Week")])) stop("ID-week duplikat ditemukan.")
if (!KELOMPOK_FOKUS %in% level_kelompok) stop("KELOMPOK_FOKUS tidak dikenali.")
# Penandaan complete case: 7 waktu tersedia pada peserta yang sama.
kelengkapan_id <- dat |>
group_by(ID, Treatment) |>
summarise(n_waktu = sum(!is.na(EASI)), .groups = "drop") |>
mutate(lengkap = n_waktu == length(minggu_nilai))
dat <- dat |>
left_join(kelengkapan_id[c("ID", "lengkap")], by = "ID")
dat_obs <- droplevels(filter(dat, !is.na(EASI)))
dat_cc <- droplevels(filter(dat, lengkap & !is.na(EASI)))
fokus <- droplevels(filter(dat_cc, Treatment == KELOMPOK_FOKUS))
wide <- dat |>
select(ID, Treatment, WeekF, EASI) |>
pivot_wider(names_from = WeekF, values_from = EASI)
write.csv(dat, file.path(folder, "data_long_analisis.csv"), row.names = FALSE, na = "")
write.csv(wide, file.path(folder, "data_wide_analisis.csv"), row.names = FALSE, na = "")
# Objek global analisis lanjutan
aov_rm <- NULL
aov_mixed <- NULL
lmm <- NULL
lmm_ri <- NULL
# =================== 1. SUMBER, DESAIN, KELENGKAPAN =======================
bagian("1. Sumber, desain, dan kelengkapan data", function() {
narasi(paste0(
"Analisis menggunakan dataset simulasi pendidikan yang dikalibrasi terhadap hasil agregat STREAM-AD. ",
"Desain terdiri atas lima kelompok perlakuan dan tujuh waktu tindak lanjut: minggu 2, 4, 8, 12, 16, 20, dan 24. ",
"Outcome adalah percentage change from baseline in EASI; nilai yang lebih negatif berarti perbaikan klinis yang lebih besar."
))
narasi(paste0(
"Peserta yang sama diukur berulang, sehingga observasi antarwaktu pada individu yang sama saling berkorelasi. ",
"Analisis menggunakan RM ANOVA, Mixed Design ANOVA, dan linear mixed model (LMM)."
))
ringkas <- dat |>
group_by(Treatment, WeekF) |>
summarise(n_total = n(), n_tersedia = sum(!is.na(EASI)),
n_hilang = sum(is.na(EASI)), .groups = "drop")
tabel(ringkas, "01_kelengkapan_per_sel")
peserta <- kelengkapan_id |>
group_by(Treatment) |>
summarise(n_peserta = n(), n_lengkap = sum(lengkap),
prop_lengkap = mean(lengkap), .groups = "drop")
tabel(peserta, "01_kelengkapan_peserta")
narasi(paste0(
"Total terdapat ", n_distinct(dat$ID), " peserta sintetis dan ", nrow(dat_obs),
" observasi EASI yang tersedia. Complete-case untuk RM/Mixed ANOVA berjumlah ",
n_distinct(dat_cc$ID), " peserta."
))
})
# ===================== 2. DESKRIPTIF ======================================
bagian("2. Statistik deskriptif dan hubungan antarwaktu", function() {
des <- dat_obs |>
group_by(Treatment, WeekF, Week) |>
summarise(
n = n(),
mean = mean(EASI),
sd = sd(EASI),
median = median(EASI),
q1 = quantile(EASI, .25),
q3 = quantile(EASI, .75),
se = sd / sqrt(n),
ci95_low = mean - qt(.975, df = pmax(n - 1, 1)) * se,
ci95_high = mean + qt(.975, df = pmax(n - 1, 1)) * se,
.groups = "drop"
)
tabel(des, "02_statistik_deskriptif")
wide_cc <- dat_cc |>
select(ID, WeekF, EASI) |>
pivot_wider(names_from = WeekF, values_from = EASI)
mat <- as.data.frame(wide_cc[, label_waktu, drop = FALSE])
cor_m <- cor(mat, use = "pairwise.complete.obs")
tabel(as.data.frame(round(cor_m, 3)) |> rownames_to_column("Waktu"),
"02_korelasi_antarwaktu", digits = 3)
# G1: profil rerata + 95% CI
p1 <- ggplot(des, aes(x = Week, y = mean, colour = Treatment, group = Treatment)) +
geom_line(linewidth = 1) +
geom_point(size = 2.3) +
geom_errorbar(aes(ymin = ci95_low, ymax = ci95_high), width = .45, linewidth = .55) +
scale_x_continuous(breaks = minggu_nilai) +
theme_bw(base_size = 11) +
theme(legend.position = "bottom", legend.box = "vertical") +
labs(
title = "G1 Profil rerata perubahan EASI pada seluruh pengukuran",
subtitle = "Error bar = 95% CI rerata per kelompok-waktu",
x = "Minggu tindak lanjut",
y = "% perubahan EASI dari baseline",
colour = "Kelompok"
)
gambar(p1, "G1_profil_rerata_EASI", 11, 7)
narasi("G1 menggambarkan trajectory rerata setiap kelompok. Nilai yang bergerak makin negatif berarti penurunan EASI yang lebih besar. Error bar adalah interval kepercayaan rerata tiap sel, bukan interval kepercayaan selisih perubahan antarkelompok.")
# G2: lintasan individu
mean_line <- dat_obs |>
group_by(Treatment, Week) |>
summarise(mean = mean(EASI), .groups = "drop")
p2 <- ggplot(dat_obs, aes(Week, EASI, group = ID)) +
geom_line(alpha = .18, linewidth = .35) +
geom_line(data = mean_line,
aes(Week, mean, group = 1),
inherit.aes = FALSE, linewidth = 1.1) +
facet_wrap(~ Treatment, ncol = 2) +
scale_x_continuous(breaks = minggu_nilai) +
theme_bw(base_size = 10) +
labs(
title = "G2 Lintasan individu menurut kelompok perlakuan",
x = "Minggu", y = "% perubahan EASI"
)
gambar(p2, "G2_lintasan_individu", 12, 9)
narasi("G2 mengikuti perubahan setiap peserta sintetis dari minggu 2 sampai minggu 24. Garis tipis adalah lintasan individual; garis tebal merangkum rerata kelompok. Visualisasi ini membantu membedakan pola rerata dari heterogenitas respons antarindividu.")
# G3: heatmap korelasi antarwaktu
cor_df <- as.data.frame(as.table(cor_m))
names(cor_df) <- c("Waktu1", "Waktu2", "Korelasi")
p3 <- ggplot(cor_df, aes(Waktu1, Waktu2, fill = Korelasi)) +
geom_tile() +
geom_text(aes(label = sprintf("%.2f", Korelasi)), size = 3) +
scale_fill_gradient2(midpoint = 0, limits = c(-1, 1)) +
coord_equal() +
theme_bw(base_size = 10) +
labs(title = "G3 Korelasi EASI antarwaktu", x = NULL, y = NULL, fill = "r")
gambar(p3, "G3_heatmap_korelasi_antarwaktu", 8, 7)
narasi("G3 menilai keterkaitan relatif antarwaktu pada peserta lengkap. Korelasi positif berarti peserta dengan nilai perubahan EASI relatif tinggi atau rendah pada satu waktu cenderung mempertahankan posisi relatif yang serupa pada waktu lain. Korelasi ini mendukung kebutuhan model yang memperhitungkan pengukuran berulang.")
})
# ====================== 3. ASUMSI =========================================
bagian("3. Evaluasi distribusi, outlier, dan homogenitas", function() {
sh <- dat_obs |>
group_by(Treatment, WeekF) |>
summarise(
n = n(),
W = ifelse(n >= 3 & n <= 5000, shapiro.test(EASI)$statistic, NA_real_),
p = ifelse(n >= 3 & n <= 5000, shapiro.test(EASI)$p.value, NA_real_),
.groups = "drop"
)
tabel(sh, "03_shapiro_per_sel")
# Outlier berbasis IQR per sel, bukan otomatis dianggap kesalahan data.
out <- dat_obs |>
group_by(Treatment, WeekF) |>
mutate(
q1 = quantile(EASI, .25), q3 = quantile(EASI, .75), iqr = IQR(EASI),
outlier = EASI < (q1 - 1.5 * iqr) | EASI > (q3 + 1.5 * iqr),
extreme = EASI < (q1 - 3 * iqr) | EASI > (q3 + 3 * iqr)
) |>
filter(outlier) |>
select(ID, Treatment, WeekF, EASI, extreme) |>
ungroup()
tabel(out, "03_outlier_IQR")
# Levene antarkelompok pada setiap waktu.
lev_list <- lapply(label_waktu, function(w) {
d <- droplevels(filter(dat_obs, WeekF == w))
lv <- car::leveneTest(EASI ~ Treatment, data = d)
data.frame(
WeekF = w,
Df1 = lv[1, "Df"], Df2 = lv[2, "Df"],
F = lv[1, "F value"], p = lv[1, "Pr(>F)"]
)
})
lev <- bind_rows(lev_list)
tabel(lev, "03_levene_per_waktu")
# G4: boxplot distribusi per kelompok-waktu
p4 <- ggplot(dat_obs, aes(Treatment, EASI, fill = Treatment)) +
geom_boxplot(outlier.alpha = .5) +
facet_wrap(~ WeekF, ncol = 4) +
theme_bw(base_size = 9) +
theme(axis.text.x = element_text(angle = 55, hjust = 1), legend.position = "none") +
labs(
title = "G4 Distribusi EASI menurut kelompok dan waktu",
x = NULL, y = "% perubahan EASI"
)
gambar(p4, "G4_boxplot_kelompok_waktu", 13, 8)
narasi("G4 memperlihatkan median, rentang interkuartil, dan pengamatan yang berada di luar whisker pada setiap kelompok-waktu. Titik outlier statistik tidak otomatis berarti data salah dan tidak dihapus tanpa alasan substantif.")
# G5: Q-Q fokus, 7 panel agar tetap terbaca dibanding 35 panel seluruh sel.
p5 <- ggplot(fokus, aes(sample = EASI)) +
stat_qq() + stat_qq_line() +
facet_wrap(~ WeekF, ncol = 4, scales = "free") +
theme_bw(base_size = 10) +
labs(
title = paste("G5 Q-Q plot per waktu:", KELOMPOK_FOKUS),
x = "Kuantil teoretis", y = "Kuantil sampel"
)
gambar(p5, "G5_QQ_kelompok_fokus", 11, 7)
narasi("G5 memeriksa bentuk distribusi outcome pada kelompok fokus di tujuh waktu. Kedekatan titik dengan garis mendukung pendekatan normal secara visual; penyimpangan terutama pada ekor perlu dipertimbangkan bersama ukuran sampel dan diagnostik model, bukan dijadikan satu-satunya dasar memilih metode.")
})
# ====================== 4. RM ANOVA KELOMPOK FOKUS ========================
bagian("4. RM ANOVA pada kelompok fokus", function() {
aov_rm <<- afex::aov_ez(
id = "ID", dv = "EASI", data = fokus,
within = "WeekF",
anova_table = list(correction = "GG", es = c("ges", "pes"))
)
output(aov_rm)
output(summary(aov_rm))
tabel(as.data.frame(aov_rm$anova_table) |> rownames_to_column("efek"),
"04_rm_anova_gg")
try(output(effectsize::eta_squared(aov_rm, partial = TRUE)), silent = TRUE)
em_waktu <- emmeans::emmeans(aov_rm, ~ WeekF)
pw_waktu <- summary(pairs(em_waktu, adjust = "bonferroni"), infer = c(TRUE, TRUE))
tabel(pw_waktu, "04_pairwise_waktu_bonferroni")
narasi("Mauchly's test dan koreksi Greenhouse-Geisser/Huynh-Feldt dibaca dari keluaran afex. Jika sferisitas dilanggar, keputusan utama untuk efek waktu menggunakan p yang telah dikoreksi, bukan p uncorrected.")
narasi("Pairwise antarwaktu memakai Bonferroni. Karena outcome merupakan perubahan dari baseline yang sudah dinyatakan dalam persen, perbandingan ini menggambarkan perkembangan respons setelah randomisasi, bukan perbedaan nilai EASI mentah.")
})
# ====================== 5. NONPARAMETRIK ==================================
bagian("5. Pembanding nonparametrik pada kelompok fokus", function() {
fr <- rstatix::friedman_test(fokus, EASI ~ WeekF | ID)
tabel(fr, "05_friedman")
pw <- rstatix::pairwise_wilcox_test(
fokus, EASI ~ WeekF,
paired = TRUE, p.adjust.method = "bonferroni",
detailed = TRUE
)
tabel(pw, "05_wilcoxon_pairwise")
narasi("Friedman dan Wilcoxon dipakai sebagai pembanding berbasis peringkat. Keselarasan arah dengan RM ANOVA dapat menambah keyakinan terhadap pola perubahan, tetapi uji nonparametrik ini tidak menggantikan pengujian interaksi Treatment × Time antarkelompok.")
})
# ====================== 6. MIXED DESIGN ANOVA =============================
bagian("6. Mixed Design ANOVA: Treatment × Time", function() {
aov_mixed <<- afex::aov_ez(
id = "ID", dv = "EASI", data = dat_cc,
between = "Treatment", within = "WeekF",
anova_table = list(correction = "GG", es = c("ges", "pes"))
)
output(aov_mixed)
output(summary(aov_mixed))
tabel(as.data.frame(aov_mixed$anova_table) |> rownames_to_column("efek"),
"06_mixed_anova_gg")
# Estimated marginal means dari ANOVA untuk visualisasi interaksi.
emm <- as.data.frame(summary(emmeans::emmeans(aov_mixed, ~ Treatment | WeekF)))
emm$Week <- minggu_nilai[match(as.character(emm$WeekF), label_waktu)]
p6 <- ggplot(emm, aes(Week, emmean, colour = Treatment, group = Treatment)) +
geom_line(linewidth = 1) + geom_point(size = 2.3) +
geom_errorbar(aes(ymin = emmean - SE * qt(.975, df = pmax(df, 1)),
ymax = emmean + SE * qt(.975, df = pmax(df, 1))),
width = .4, linewidth = .55) +
scale_x_continuous(breaks = minggu_nilai) +
theme_bw(base_size = 11) +
theme(legend.position = "bottom") +
labs(
title = "G6 Profil interaksi Mixed Design ANOVA",
x = "Minggu", y = "Estimated marginal mean perubahan EASI",
colour = "Kelompok"
)
gambar(p6, "G6_interaksi_mixed_ANOVA", 11, 7)
narasi("G6 memberi konteks visual untuk interaksi Treatment × Time. Garis yang tidak sejajar mengindikasikan pola perubahan yang berbeda, tetapi keputusan statistik tetap harus berdasarkan uji interaksi dan kontras yang sesuai; posisi garis pada satu minggu tidak identik dengan perbedaan perubahan longitudinal.")
})
# ====================== 7. SIMPLE EFFECTS / POST-HOC =======================
bagian("7. Efek sederhana dan post-hoc Mixed Design ANOVA", function() {
if (is.null(aov_mixed)) stop("Mixed Design ANOVA belum tersedia.")
# Perbandingan kelompok pada tiap waktu.
em_g <- emmeans::emmeans(aov_mixed, ~ Treatment | WeekF)
g_pw <- summary(pairs(em_g, adjust = "tukey"), infer = c(TRUE, TRUE))
tabel(g_pw, "07_kelompok_per_waktu_tukey")
# Perbandingan waktu di setiap kelompok.
em_t <- emmeans::emmeans(aov_mixed, ~ WeekF | Treatment)
t_pw <- summary(pairs(em_t, adjust = "holm"), infer = c(TRUE, TRUE))
tabel(t_pw, "07_waktu_per_kelompok_holm")
# Kontras perubahan W24-W2: lebih langsung untuk perubahan awal-akhir.
delta <- dat_cc |>
select(ID, Treatment, WeekF, EASI) |>
pivot_wider(names_from = WeekF, values_from = EASI) |>
mutate(delta_W24_W2 = W24 - W2)
tabel(delta, "07_delta_W24_W2_individu")
m_delta <- lm(delta_W24_W2 ~ Treatment, data = delta)
output(anova(m_delta))
em_delta <- emmeans::emmeans(m_delta, ~ Treatment)
delta_pw <- summary(pairs(em_delta, adjust = "holm"), infer = c(TRUE, TRUE))
tabel(delta_pw, "07_delta_W24_W2_antarkelompok")
narasi("Perbandingan kelompok pada tiap minggu dan perbandingan waktu dalam tiap kelompok menjawab pertanyaan berbeda. Kontras W24-W2 membandingkan perubahan antara pengukuran pertama yang tersedia dan minggu 24; ini bukan perubahan dari baseline klinis karena baseline tidak terdapat sebagai baris outcome pada dataset ini.")
})
# ====================== 8. LMM ============================================
bagian("8. Linear Mixed Model menggunakan seluruh pengukuran tersedia", function() {
ctrl <- lme4::lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 2e5))
lmm_ri <<- lmerTest::lmer(
EASI ~ Treatment * WeekF + (1 | ID),
data = dat_obs, REML = TRUE, control = ctrl
)
lmm <<- lmerTest::lmer(
EASI ~ Treatment * WeekF + (1 + Week | ID),
data = dat_obs, REML = TRUE, control = ctrl
)
output(summary(lmm))
tab <- anova(lmm, type = 3, ddf = "Kenward-Roger")
tabel(as.data.frame(tab) |> rownames_to_column("efek"), "08_lmm_type3_KR")
perbandingan <- data.frame(
model = c("Random intercept", "Random intercept + random slope"),
AIC = c(AIC(lmm_ri), AIC(lmm)),
BIC = c(BIC(lmm_ri), BIC(lmm)),
logLik = c(as.numeric(logLik(lmm_ri)), as.numeric(logLik(lmm)))
)
tabel(perbandingan, "08_perbandingan_model_LMM")
narasi("LMM memasukkan Treatment, waktu kategorik, dan interaksi sebagai fixed effects. Random intercept memungkinkan tingkat respons rata-rata tiap peserta berbeda; random slope Week memungkinkan kecenderungan perubahan individual berbeda. Fixed effect waktu tetap kategorik sehingga trajectory rerata tidak dipaksa linear.")
})
# ====================== 9. EMM / KONTRAS LMM ===============================
bagian("9. Estimated marginal means dan kontras LMM", function() {
if (is.null(lmm)) stop("LMM belum tersedia.")
e <- emmeans::emmeans(lmm, ~ Treatment | WeekF, lmer.df = "kenward-roger")
tabel(summary(e), "09_lmm_rerata_prediksi")
tabel(summary(pairs(e, adjust = "holm"), infer = c(TRUE, TRUE)),
"09_lmm_pairwise_kelompok")
ew <- emmeans::emmeans(lmm, ~ WeekF | Treatment, lmer.df = "kenward-roger")
tabel(summary(pairs(ew, adjust = "holm"), infer = c(TRUE, TRUE)),
"09_lmm_pairwise_waktu")
narasi("Estimated marginal means memberikan rerata prediksi model dengan struktur korelasi peserta dipertimbangkan. Kontras antarkelompok dan antarwaktu perlu dibaca bersama interval kepercayaan dan besar efek, bukan hanya nilai p.")
})
# ====================== 10. DIAGNOSTIK LMM =================================
bagian("10. Diagnostik Linear Mixed Model", function() {
if (is.null(lmm)) stop("LMM belum tersedia.")
diag <- data.frame(
fitted = fitted(lmm),
residual = resid(lmm),
Treatment = dat_obs$Treatment,
WeekF = dat_obs$WeekF
)
p7 <- ggplot(diag, aes(sample = residual)) +
stat_qq() + stat_qq_line() + theme_bw() +
labs(title = "G7 Q-Q residual LMM", x = "Kuantil teoretis", y = "Kuantil residual")
gambar(p7, "G7_QQ_residual_LMM", 8, 6)
narasi("G7 menilai bentuk distribusi residual model. Penyimpangan sistematis dari garis, khususnya pada ekor, perlu dipertimbangkan saat menilai ketepatan inferensi model.")
p8 <- ggplot(diag, aes(fitted, residual, colour = Treatment)) +
geom_point(alpha = .4) + geom_hline(yintercept = 0, linetype = 2) +
geom_smooth(method = "loess", se = FALSE, linewidth = .7) +
theme_bw() + theme(legend.position = "bottom") +
labs(title = "G8 Residual terhadap nilai prediksi LMM",
x = "Nilai prediksi", y = "Residual", colour = "Kelompok")
gambar(p8, "G8_residual_vs_prediksi", 9, 6)
narasi("G8 memeriksa apakah residual tersebar relatif acak di sekitar nol sepanjang rentang nilai prediksi. Lengkungan atau pola kipas dapat mengindikasikan misspecification atau heteroskedastisitas.")
p9 <- ggplot(diag, aes(WeekF, residual, fill = Treatment)) +
geom_boxplot(outlier.alpha = .35) + facet_wrap(~ Treatment, ncol = 2) +
theme_bw(base_size = 9) + theme(legend.position = "none") +
labs(title = "G9 Residual menurut kelompok dan waktu",
x = "Waktu", y = "Residual")
gambar(p9, "G9_residual_kelompok_waktu", 11, 8)
narasi("G9 membandingkan distribusi residual antarwaktu dan antarkelompok. Median yang jauh dari nol atau perbedaan lebar kotak yang sangat besar dapat menandakan pola ketidakcocokan model.")
re <- ranef(lmm)$ID
if ("(Intercept)" %in% names(re)) {
p10 <- ggplot(data.frame(nilai = re[["(Intercept)"]]), aes(sample = nilai)) +
stat_qq() + stat_qq_line() + theme_bw() +
labs(title = "G10 Q-Q intercept acak peserta",
x = "Kuantil teoretis", y = "Kuantil efek acak")
gambar(p10, "G10_QQ_random_intercept", 8, 6)
narasi("G10 menilai distribusi estimasi random intercept. Nilai tersebut merupakan conditional modes yang mengalami shrinkage, sehingga grafik berfungsi sebagai diagnostik, bukan observasi langsung efek acak sebenarnya.")
}
if ("Week" %in% names(re)) {
p11 <- ggplot(data.frame(nilai = re[["Week"]]), aes(sample = nilai)) +
stat_qq() + stat_qq_line() + theme_bw() +
labs(title = "G11 Q-Q slope acak minggu",
x = "Kuantil teoretis", y = "Kuantil slope acak")
gambar(p11, "G11_QQ_random_slope", 8, 6)
narasi("G11 memeriksa bentuk distribusi random slope Week. Penyimpangan ekor dapat menandakan sebagian peserta memiliki trajectory yang jauh lebih tajam atau lebih datar daripada pola umum model.")
}
output(performance::check_singularity(lmm))
output(performance::check_convergence(lmm))
})
# ====================== 11. TIGA PANEL DIAGNOSTIK ==========================
bagian("11. Ringkasan diagnostik LMM tiga panel", function() {
if (is.null(lmm)) stop("LMM belum tersedia.")
path <- file.path(folder, "G12_diagnostik_LMM_tiga_panel.png")
grDevices::png(path, width = 2400, height = 800, res = 180)
par_lama <- par(mfrow = c(1, 3), mar = c(4, 4, 3, 1))
try({
qqnorm(resid(lmm), main = "Q-Q residual"); qqline(resid(lmm))
re <- ranef(lmm)$ID
qqnorm(re[, 1], main = "Q-Q intercept acak"); qqline(re[, 1])
plot(fitted(lmm), resid(lmm), xlab = "Nilai prediksi", ylab = "Residual",
main = "Residual vs prediksi", pch = 16,
col = grDevices::adjustcolor("black", .3))
abline(h = 0, lty = 2)
}, silent = TRUE)
par(par_lama)
grDevices::dev.off()
if (TAMPILKAN_GRAFIK_RSTUDIO) {
par_lama2 <- par(mfrow = c(1, 3), mar = c(4, 4, 3, 1))
on.exit(par(par_lama2), add = TRUE)
qqnorm(resid(lmm), main = "Q-Q residual"); qqline(resid(lmm))
re <- ranef(lmm)$ID
qqnorm(re[, 1], main = "Q-Q intercept acak"); qqline(re[, 1])
plot(fitted(lmm), resid(lmm), xlab = "Nilai prediksi", ylab = "Residual",
main = "Residual vs prediksi", pch = 16,
col = grDevices::adjustcolor("black", .3))
abline(h = 0, lty = 2)
}
tambah(paste0(
"<figure><img src='", base64enc::dataURI(file = path, mime = "image/png"),
"' alt='G12 diagnostik LMM tiga panel'><figcaption>G12 diagnostik LMM tiga panel</figcaption></figure>"
))
narasi("G12 merangkum tiga pemeriksaan diagnostik utama dalam satu gambar: distribusi residual, distribusi random intercept, dan pola residual terhadap prediksi. Ketiga panel perlu dibaca bersama; tidak ada satu panel yang sendirian membuktikan seluruh asumsi model terpenuhi.")
})
# ====================== 12. PROFIL PREDIKSI LMM ============================
bagian("12. Grafik rerata prediksi LMM dan 95% CI", function() {
if (is.null(lmm)) stop("LMM belum tersedia.")
pred <- as.data.frame(summary(emmeans::emmeans(
lmm, ~ Treatment | WeekF, lmer.df = "kenward-roger"
)))
pred$Week <- minggu_nilai[match(as.character(pred$WeekF), label_waktu)]
p13 <- ggplot(pred, aes(Week, emmean, colour = Treatment, group = Treatment)) +
geom_line(linewidth = 1) + geom_point(size = 2.3) +
geom_errorbar(aes(ymin = lower.CL, ymax = upper.CL), width = .4, linewidth = .55) +
scale_x_continuous(breaks = minggu_nilai) +
theme_bw(base_size = 11) +
theme(legend.position = "bottom") +
labs(
title = "G13 Rerata prediksi LMM dan 95% CI",
x = "Minggu", y = "Predicted mean % change in EASI",
colour = "Kelompok"
)
gambar(p13, "G13_profil_prediksi_LMM", 11, 7)
narasi("G13 menampilkan estimated marginal means dari LMM dan 95% CI. Grafik ini mempertimbangkan struktur pengukuran berulang. Tumpang tindih atau tidak tumpang tindih CI rerata tidak boleh digunakan sebagai pengganti kontras formal antarkelompok.")
})
# ====================== 13. KESIMPULAN ANALISIS ============================
bagian("13. Panduan interpretasi dan batas kesimpulan", function() {
narasi("Urutan interpretasi yang disarankan adalah: periksa kualitas dan kelengkapan data; baca profil deskriptif; evaluasi asumsi; nilai efek waktu pada kelompok fokus; nilai efek Treatment, Time, dan terutama Treatment × Time pada Mixed Design ANOVA; lalu bandingkan hasil dengan LMM dan diagnostiknya.")
narasi("Karena dataset individual merupakan simulasi yang dikalibrasi pada statistik agregat penelitian, nilai p, interval kepercayaan, korelasi antarwaktu, random effects, serta hasil post-hoc dari data simulasi tidak boleh diklaim sebagai hasil participant-level trial asli. Kesimpulan analisis statistik berlaku untuk dataset simulasi ini, sedangkan artikel sumber tetap menjadi rujukan untuk hasil klinis penelitian.")
narasi("Nilai EASI yang lebih negatif menunjukkan perbaikan yang lebih besar. Signifikansi statistik tidak identik dengan signifikansi klinis; interpretasi klinis memerlukan konteks EASI, ambang respons klinis, efek samping, dan estimasi penelitian sumber.")
})
# ====================== 14. STATUS & SESSION INFO ==========================
bagian("14. Status eksekusi dan informasi sesi", function() {
tabel(status, "14_status_eksekusi")
si <- capture.output(sessionInfo())
cat(paste(si, collapse = "\n"), "\n")
tambah(paste0("<pre>", escape(si), "</pre>"))
writeLines(si, file.path(folder, "14_sessionInfo.txt"))
})
# ------------------------ Menulis HTML -------------------------------------
css <- "
body {font-family: Arial, sans-serif; max-width: 1200px; margin: 30px auto; line-height: 1.55; padding: 0 20px; color:#222;}
h1 {font-size: 27px;} h2 {margin-top: 34px; border-bottom: 1px solid #ddd; padding-bottom: 6px;}
p {text-align: justify;} pre {background:#f5f5f5; padding:12px; overflow-x:auto; font-size:12px;}
table {border-collapse:collapse; width:100%; font-size:12px; margin:14px 0 20px 0;}
th,td {border:1px solid #ccc; padding:6px; text-align:right;} th:first-child,td:first-child {text-align:left;}
figure {margin:24px 0;} figure img {max-width:100%; height:auto; border:1px solid #ddd;} figcaption {font-size:12px; color:#555;}
.note {background:#fff7df; border-left:4px solid #d6a700; padding:10px 14px;}
"
header <- paste0(
"<!doctype html><html><head><meta charset='utf-8'><title>Analisis STREAM-AD</title><style>",
css, "</style></head><body>",
"<h1>Analisis Data Jurnal dengan R Studio: STREAM-AD / Amlitelimab</h1>",
"<div class='note'><b>Catatan integritas akademik:</b> data participant-level yang dianalisis adalah data simulasi pendidikan, bukan raw data trial asli.</div>"
)
footer <- paste0(
"<hr><p>Waktu mulai: ", escape(waktu_mulai),
"<br>Waktu selesai: ", escape(Sys.time()), "</p></body></html>"
)
html_path <- file.path(folder, "LAPORAN_ANALISIS_STREAM_AD.html")
writeLines(c(header, isi, footer), html_path, useBytes = TRUE)
# Salin script aktif bila dapat diidentifikasi untuk dokumentasi.
script_path <- ""
if (requireNamespace("rstudioapi", quietly = TRUE) && rstudioapi::isAvailable()) {
script_path <- tryCatch(rstudioapi::getSourceEditorContext()$path, error = function(e) "")
}
if (nzchar(script_path) && file.exists(script_path)) {
file.copy(script_path, file.path(folder, basename(script_path)), overwrite = TRUE)
}
message("\n============================================================")
message("ANALISIS SELESAI")
message("Folder hasil: ", folder)
message("Laporan HTML: ", html_path)
message("Grafik G1-G13, tabel CSV, sessionInfo, dan data kerja telah disimpan.")
message("============================================================")
if (BUKA_HTML_OTOMATIS && interactive()) {
try(utils::browseURL(html_path), silent = TRUE)
}
invisible(list(
data = dat,
rm_anova = aov_rm,
mixed_anova = aov_mixed,
lmm = lmm,
folder = folder,
html = html_path
))
}
# Jalankan seluruh analisis
hasil_stream_ad <- jalankan_analisis()
## Registered S3 method overwritten by 'lme4':
## method from
## na.action.merMod car
## Data : C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/Amlitelimab_STREAM_AD_Simulated_Long.csv
## Hasil : C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad
##
## >>> 1. Sumber, desain, dan kelengkapan data
## Treatment WeekF n_total n_tersedia n_hilang
## 1 Placebo W2 20 20 0
## 2 Placebo W4 20 20 0
## 3 Placebo W8 20 20 0
## 4 Placebo W12 20 20 0
## 5 Placebo W16 20 20 0
## 6 Placebo W20 20 20 0
## 7 Placebo W24 20 20 0
## 8 Amlitelimab 62.5 mg W2 20 20 0
## 9 Amlitelimab 62.5 mg W4 20 20 0
## 10 Amlitelimab 62.5 mg W8 20 20 0
## 11 Amlitelimab 62.5 mg W12 20 20 0
## 12 Amlitelimab 62.5 mg W16 20 20 0
## 13 Amlitelimab 62.5 mg W20 20 20 0
## 14 Amlitelimab 62.5 mg W24 20 20 0
## 15 Amlitelimab 125 mg W2 20 20 0
## 16 Amlitelimab 125 mg W4 20 20 0
## 17 Amlitelimab 125 mg W8 20 20 0
## 18 Amlitelimab 125 mg W12 20 20 0
## 19 Amlitelimab 125 mg W16 20 20 0
## 20 Amlitelimab 125 mg W20 20 20 0
## 21 Amlitelimab 125 mg W24 20 20 0
## 22 Amlitelimab 250 mg W2 20 20 0
## 23 Amlitelimab 250 mg W4 20 20 0
## 24 Amlitelimab 250 mg W8 20 20 0
## 25 Amlitelimab 250 mg W12 20 20 0
## 26 Amlitelimab 250 mg W16 20 20 0
## 27 Amlitelimab 250 mg W20 20 20 0
## 28 Amlitelimab 250 mg W24 20 20 0
## 29 Amlitelimab 250 mg + LD 500 mg W2 20 20 0
## 30 Amlitelimab 250 mg + LD 500 mg W4 20 20 0
## 31 Amlitelimab 250 mg + LD 500 mg W8 20 20 0
## 32 Amlitelimab 250 mg + LD 500 mg W12 20 20 0
## 33 Amlitelimab 250 mg + LD 500 mg W16 20 20 0
## 34 Amlitelimab 250 mg + LD 500 mg W20 20 20 0
## 35 Amlitelimab 250 mg + LD 500 mg W24 20 20 0
## Treatment n_peserta n_lengkap prop_lengkap
## 1 Placebo 20 20 1
## 2 Amlitelimab 62.5 mg 20 20 1
## 3 Amlitelimab 125 mg 20 20 1
## 4 Amlitelimab 250 mg 20 20 1
## 5 Amlitelimab 250 mg + LD 500 mg 20 20 1
##
## >>> 2. Statistik deskriptif dan hubungan antarwaktu
## Treatment WeekF Week n mean sd median
## 1 Placebo W2 2 20 -15.2600 34.14382 -23.065
## 2 Placebo W4 4 20 -27.5600 31.03709 -33.005
## 3 Placebo W8 8 20 -28.6995 38.60385 -22.735
## 4 Placebo W12 12 20 -33.7195 35.35774 -36.020
## 5 Placebo W16 16 20 -28.2505 41.17396 -39.460
## 6 Placebo W20 20 20 -30.3210 40.48650 -32.915
## 7 Placebo W24 24 20 -28.5510 44.00399 -39.265
## 8 Amlitelimab 62.5 mg W2 2 20 -26.0285 34.46142 -23.550
## 9 Amlitelimab 62.5 mg W4 4 20 -35.4905 38.78565 -42.500
## 10 Amlitelimab 62.5 mg W8 8 20 -51.6995 31.04479 -58.775
## 11 Amlitelimab 62.5 mg W12 12 20 -57.3600 34.27530 -65.015
## 12 Amlitelimab 62.5 mg W16 16 20 -61.5095 31.66183 -62.420
## 13 Amlitelimab 62.5 mg W20 20 20 -57.8700 38.97705 -73.860
## 14 Amlitelimab 62.5 mg W24 24 20 -57.3700 40.22602 -73.235
## 15 Amlitelimab 125 mg W2 2 20 -19.0905 39.49876 -22.105
## 16 Amlitelimab 125 mg W4 4 20 -35.0995 34.45619 -41.750
## 17 Amlitelimab 125 mg W8 8 20 -45.9590 38.15024 -51.715
## 18 Amlitelimab 125 mg W12 12 20 -55.2310 40.21655 -69.850
## 19 Amlitelimab 125 mg W16 16 20 -52.5010 40.82079 -66.500
## 20 Amlitelimab 125 mg W20 20 20 -56.2900 43.10355 -69.860
## 21 Amlitelimab 125 mg W24 24 20 -56.7215 44.27028 -70.240
## 22 Amlitelimab 250 mg W2 2 20 -17.7505 38.98924 -23.800
## 23 Amlitelimab 250 mg W4 4 20 -29.1295 42.01382 -27.920
## 24 Amlitelimab 250 mg W8 8 20 -41.2085 41.12905 -48.000
## 25 Amlitelimab 250 mg W12 12 20 -50.1500 36.08734 -68.735
## 26 Amlitelimab 250 mg W16 16 20 -59.9800 37.44522 -74.080
## 27 Amlitelimab 250 mg W20 20 20 -57.8000 38.92125 -70.290
## 28 Amlitelimab 250 mg W24 24 20 -55.8410 40.29828 -68.385
## 29 Amlitelimab 250 mg + LD 500 mg W2 2 20 -27.9395 30.03212 -32.830
## 30 Amlitelimab 250 mg + LD 500 mg W4 4 20 -36.8500 28.90094 -41.515
## 31 Amlitelimab 250 mg + LD 500 mg W8 8 20 -50.2505 30.66594 -54.985
## 32 Amlitelimab 250 mg + LD 500 mg W12 12 20 -59.6090 30.71654 -68.890
## 33 Amlitelimab 250 mg + LD 500 mg W16 16 20 -62.3500 32.32260 -71.410
## 34 Amlitelimab 250 mg + LD 500 mg W20 20 20 -67.8505 33.13522 -80.170
## 35 Amlitelimab 250 mg + LD 500 mg W24 24 20 -68.0105 36.05165 -80.725
## q1 q3 se ci95_low ci95_high
## 1 -38.9425 11.1950 7.634790 -31.23980 0.7197999
## 2 -40.8050 -14.5100 6.940103 -42.08580 -13.0341967
## 3 -51.3675 -2.2050 8.632083 -46.76666 -10.6323423
## 4 -60.0525 -18.6150 7.906232 -50.26743 -17.1715671
## 5 -58.4850 0.3050 9.206776 -47.52050 -8.9804958
## 6 -62.9000 -9.8675 9.053058 -49.26927 -11.3727328
## 7 -66.6425 8.5350 9.839590 -49.14550 -7.9565009
## 8 -56.5275 -1.1225 7.705807 -42.15694 -9.9000609
## 9 -53.0950 -13.4100 8.672735 -53.64274 -17.3382572
## 10 -69.7950 -45.2150 6.941827 -66.22891 -37.1700894
## 11 -83.5175 -36.7300 7.664190 -73.40133 -41.3186661
## 12 -93.7725 -33.0300 7.079801 -76.32769 -46.6913056
## 13 -81.3175 -51.8625 8.715532 -76.11182 -39.6281813
## 14 -87.5050 -46.9950 8.994812 -76.19636 -38.5436426
## 15 -46.8600 6.3525 8.832192 -37.57649 -0.6045094
## 16 -53.3900 -10.4700 7.704637 -51.22549 -18.9735086
## 17 -76.7725 -19.8275 8.530653 -63.81386 -28.1041387
## 18 -84.8500 -33.6875 8.992695 -74.05293 -36.4090741
## 19 -85.8875 -27.9125 9.127807 -71.60572 -33.3962812
## 20 -91.6125 -32.7375 9.638246 -76.46308 -36.1169186
## 21 -91.4775 -41.5850 9.899136 -77.44063 -36.0023694
## 22 -46.4975 3.6200 8.718260 -35.99803 0.4970279
## 23 -58.5225 -2.9800 9.394577 -48.79258 -9.4664248
## 24 -73.2900 -11.0550 9.196734 -60.45749 -21.9595140
## 25 -78.7675 -25.5225 8.069374 -67.03939 -33.2606062
## 26 -84.2975 -52.3800 8.373005 -77.50490 -42.4550996
## 27 -88.6450 -35.6800 8.703056 -76.01571 -39.5842946
## 28 -81.9225 -51.7000 9.010969 -74.70118 -36.9808244
## 29 -45.0750 -17.0725 6.715385 -41.99496 -13.8840370
## 30 -57.1775 -18.5900 6.462447 -50.37606 -23.3239421
## 31 -65.6950 -39.6125 6.857113 -64.60260 -35.8983971
## 32 -82.3100 -50.6575 6.868428 -73.98479 -45.2332148
## 33 -86.3550 -47.4550 7.227553 -77.47744 -47.2225587
## 34 -88.4075 -58.2775 7.409260 -83.35826 -52.3427400
## 35 -91.4025 -61.1800 8.061394 -84.88319 -51.1378094
## Waktu W2 W4 W8 W12 W16 W20 W24
## 1 W2 1.000 0.698 0.453 0.446 0.370 0.306 0.224
## 2 W4 0.698 1.000 0.656 0.493 0.352 0.265 0.209
## 3 W8 0.453 0.656 1.000 0.677 0.487 0.416 0.364
## 4 W12 0.446 0.493 0.677 1.000 0.709 0.541 0.424
## 5 W16 0.370 0.352 0.487 0.709 1.000 0.683 0.574
## 6 W20 0.306 0.265 0.416 0.541 0.683 1.000 0.628
## 7 W24 0.224 0.209 0.364 0.424 0.574 0.628 1.000
## Grafik tersimpan: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad/G1_profil_rerata_EASI.png
## Grafik tersimpan: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad/G2_lintasan_individu.png
## Grafik tersimpan: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad/G3_heatmap_korelasi_antarwaktu.png
##
## >>> 3. Evaluasi distribusi, outlier, dan homogenitas
## Treatment WeekF n W p
## 1 Placebo W2 20 0.9468667 3.220181e-01
## 2 Placebo W4 20 0.9279096 1.407459e-01
## 3 Placebo W8 20 0.9721393 7.992656e-01
## 4 Placebo W12 20 0.9389657 2.292096e-01
## 5 Placebo W16 20 0.8643798 9.373228e-03
## 6 Placebo W20 20 0.9642931 6.326747e-01
## 7 Placebo W24 20 0.9155258 8.128884e-02
## 8 Amlitelimab 62.5 mg W2 20 0.9472962 3.279037e-01
## 9 Amlitelimab 62.5 mg W4 20 0.9566364 4.790310e-01
## 10 Amlitelimab 62.5 mg W8 20 0.8817359 1.901365e-02
## 11 Amlitelimab 62.5 mg W12 20 0.9022590 4.547193e-02
## 12 Amlitelimab 62.5 mg W16 20 0.8528227 5.945764e-03
## 13 Amlitelimab 62.5 mg W20 20 0.6772907 2.087212e-05
## 14 Amlitelimab 62.5 mg W24 20 0.7706728 3.258065e-04
## 15 Amlitelimab 125 mg W2 20 0.9738182 8.325419e-01
## 16 Amlitelimab 125 mg W4 20 0.9541061 4.337333e-01
## 17 Amlitelimab 125 mg W8 20 0.9143483 7.717209e-02
## 18 Amlitelimab 125 mg W12 20 0.8541177 6.252976e-03
## 19 Amlitelimab 125 mg W16 20 0.8857243 2.245901e-02
## 20 Amlitelimab 125 mg W20 20 0.8566242 6.896526e-03
## 21 Amlitelimab 125 mg W24 20 0.8412266 3.813064e-03
## 22 Amlitelimab 250 mg W2 20 0.9413282 2.540188e-01
## 23 Amlitelimab 250 mg W4 20 0.9686345 7.258141e-01
## 24 Amlitelimab 250 mg W8 20 0.8943326 3.233116e-02
## 25 Amlitelimab 250 mg W12 20 0.8781047 1.635979e-02
## 26 Amlitelimab 250 mg W16 20 0.7773643 4.048053e-04
## 27 Amlitelimab 250 mg W20 20 0.8887199 2.547580e-02
## 28 Amlitelimab 250 mg W24 20 0.7621947 2.485356e-04
## 29 Amlitelimab 250 mg + LD 500 mg W2 20 0.9349216 1.919453e-01
## 30 Amlitelimab 250 mg + LD 500 mg W4 20 0.9543765 4.384241e-01
## 31 Amlitelimab 250 mg + LD 500 mg W8 20 0.9303273 1.566724e-01
## 32 Amlitelimab 250 mg + LD 500 mg W12 20 0.8307209 2.576480e-03
## 33 Amlitelimab 250 mg + LD 500 mg W16 20 0.9014411 4.388925e-02
## 34 Amlitelimab 250 mg + LD 500 mg W20 20 0.8114959 1.288697e-03
## 35 Amlitelimab 250 mg + LD 500 mg W24 20 0.7610076 2.393818e-04
## ID Treatment WeekF EASI extreme
## 1 G1_02 Amlitelimab 250 mg + LD 500 mg W2 35.84 FALSE
## 2 G1_02 Amlitelimab 250 mg + LD 500 mg W16 13.50 FALSE
## 3 G1_02 Amlitelimab 250 mg + LD 500 mg W20 32.03 FALSE
## 4 G1_03 Amlitelimab 250 mg + LD 500 mg W8 0.58 FALSE
## 5 G1_08 Amlitelimab 250 mg + LD 500 mg W2 40.19 FALSE
## 6 G1_08 Amlitelimab 250 mg + LD 500 mg W12 13.02 FALSE
## 7 G1_13 Amlitelimab 250 mg + LD 500 mg W24 37.32 TRUE
## 8 G1_16 Amlitelimab 250 mg + LD 500 mg W8 33.84 FALSE
## 9 G1_16 Amlitelimab 250 mg + LD 500 mg W20 -10.14 FALSE
## 10 G1_16 Amlitelimab 250 mg + LD 500 mg W24 -11.12 FALSE
## 11 G2_07 Amlitelimab 250 mg W20 46.65 FALSE
## 12 G2_10 Amlitelimab 250 mg W24 25.61 FALSE
## 13 G2_12 Amlitelimab 250 mg W24 40.48 TRUE
## 14 G2_16 Amlitelimab 250 mg W16 39.90 FALSE
## 15 G2_17 Amlitelimab 250 mg W16 30.02 FALSE
## 16 G2_17 Amlitelimab 250 mg W24 27.37 FALSE
## 17 G3_08 Amlitelimab 125 mg W24 61.45 FALSE
## 18 G3_11 Amlitelimab 125 mg W12 60.61 FALSE
## 19 G4_02 Amlitelimab 62.5 mg W8 10.28 FALSE
## 20 G4_04 Amlitelimab 62.5 mg W20 47.46 TRUE
## 21 G4_10 Amlitelimab 62.5 mg W24 26.06 FALSE
## 22 G4_14 Amlitelimab 62.5 mg W4 51.28 FALSE
## 23 G4_15 Amlitelimab 62.5 mg W8 28.46 FALSE
## 24 G4_15 Amlitelimab 62.5 mg W12 37.85 FALSE
## 25 G4_15 Amlitelimab 62.5 mg W20 45.41 TRUE
## 26 G4_15 Amlitelimab 62.5 mg W24 15.28 FALSE
## 27 G4_18 Amlitelimab 62.5 mg W24 19.50 FALSE
## 28 G5_01 Placebo W4 -83.82 FALSE
## 29 G5_04 Placebo W4 -81.43 FALSE
## 30 G5_08 Placebo W4 48.59 FALSE
## 31 G5_08 Placebo W12 48.50 FALSE
## 32 G5_09 Placebo W4 27.23 FALSE
## WeekF Df1 Df2 F p
## 1 W2 4 95 0.6947188 0.5974349
## 2 W4 4 95 0.8722567 0.4836406
## 3 W8 4 95 1.6063622 0.1790395
## 4 W12 4 95 0.3484249 0.8445346
## 5 W16 4 95 0.3293180 0.8576480
## 6 W20 4 95 0.6629731 0.6192463
## 7 W24 4 95 0.6149229 0.6529340
## Grafik tersimpan: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad/G4_boxplot_kelompok_waktu.png
## Grafik tersimpan: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad/G5_QQ_kelompok_fokus.png
##
## >>> 4. RM ANOVA pada kelompok fokus
## Anova Table (Type 3 tests)
##
## Response: EASI
## Effect df MSE F ges pes p.value
## 1 WeekF 2.70, 51.36 1136.94 9.59 *** .180 .335 <.001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 397213 1 75818 19 99.5416 5.461e-09 ***
## WeekF 29457 6 58389 114 9.5856 1.594e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## WeekF 0.034169 2.9218e-05
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## WeekF 0.45049 6.892e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## WeekF 0.5323979 1.949601e-05
## efek num Df den Df MSE F pes ges Pr(>F)
## 1 WeekF 2.702954 51.35612 1136.938 9.585594 0.3353295 0.1799869 6.891996e-05
## # Effect Size for ANOVA (Type III)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------
## WeekF | 0.34 | [0.20, 1.00]
##
## - One-sided CIs: upper bound fixed at [1.00].
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## W2 - W4 8.9105 4.522003 19 -6.928521 24.74952 1.970 1.0000
## W2 - W8 22.3110 7.263976 19 -3.132209 47.75421 3.071 0.1319
## W2 - W12 31.6695 5.087283 19 13.850498 49.48850 6.225 0.0001
## W2 - W16 34.4105 6.749089 19 10.770765 58.05024 5.099 0.0013
## W2 - W20 39.9110 6.637592 19 16.661799 63.16020 6.013 0.0002
## W2 - W24 40.0710 8.890596 19 8.930302 71.21170 4.507 0.0051
## W4 - W8 13.4005 5.661775 19 -6.430749 33.23175 2.367 0.6030
## W4 - W12 22.7590 5.479821 19 3.565074 41.95293 4.153 0.0113
## W4 - W16 25.5000 7.716325 19 -1.527631 52.52763 3.305 0.0782
## W4 - W20 31.0005 7.992010 19 3.007238 58.99376 3.879 0.0212
## W4 - W24 31.1605 10.153517 19 -4.403775 66.72478 3.069 0.1327
## W8 - W12 9.3585 7.756324 19 -17.809234 36.52623 1.207 1.0000
## W8 - W16 12.0995 8.151604 19 -16.452765 40.65177 1.484 1.0000
## W8 - W20 17.6000 8.250782 19 -11.299650 46.49965 2.133 0.9696
## W8 - W24 17.7600 10.213281 19 -18.013609 53.53361 1.739 1.0000
## W12 - W16 2.7410 6.159558 19 -18.833811 24.31581 0.445 1.0000
## W12 - W20 8.2415 7.061684 19 -16.493149 32.97615 1.167 1.0000
## W12 - W24 8.4015 8.401613 19 -21.026461 37.82946 1.000 1.0000
## W16 - W20 5.5005 3.077916 19 -5.280382 16.28138 1.787 1.0000
## W16 - W24 5.6605 4.747978 19 -10.970035 22.29103 1.192 1.0000
## W20 - W24 0.1600 5.462436 19 -18.973035 19.29303 0.029 1.0000
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 21 estimates
## P value adjustment: bonferroni method for 21 tests
##
## >>> 5. Pembanding nonparametrik pada kelompok fokus
## .y. n statistic df p method
## 1 EASI 20 55.77857 6 3.227013e-10 Friedman test
## estimate .y. group1 group2 n1 n2 statistic p conf.low conf.high
## 1 9.3350 EASI W2 W4 20 20 155.0 6.372261e-02 -0.740 18.270
## 2 20.6850 EASI W2 W8 20 20 179.0 4.220963e-03 6.450 37.005
## 3 31.9850 EASI W2 W12 20 20 204.0 2.670288e-05 21.050 42.480
## 4 36.4125 EASI W2 W16 20 20 197.0 1.678467e-04 20.270 49.220
## 5 38.0275 EASI W2 W20 20 20 209.0 3.814697e-06 24.955 52.445
## 6 43.0625 EASI W2 W24 20 20 192.0 4.825592e-04 28.695 54.490
## 7 15.1975 EASI W4 W8 20 20 178.0 4.859924e-03 5.840 25.035
## 8 21.7775 EASI W4 W12 20 20 191.0 5.855560e-04 13.600 34.845
## 9 29.4750 EASI W4 W16 20 20 178.0 4.859924e-03 13.320 43.930
## 10 32.7125 EASI W4 W20 20 20 183.0 2.325058e-03 15.755 45.440
## 11 35.0800 EASI W4 W24 20 20 185.0 1.689911e-03 17.230 46.805
## 12 12.6800 EASI W8 W12 20 20 152.0 8.255005e-02 -5.370 22.130
## 13 17.2225 EASI W8 W16 20 20 168.0 1.718140e-02 6.340 28.220
## 14 23.9625 EASI W8 W20 20 20 166.0 2.148438e-02 7.510 33.605
## 15 25.4175 EASI W8 W24 20 20 165.0 2.395058e-02 5.290 37.825
## 16 1.5800 EASI W12 W16 20 20 113.0 7.841263e-01 -10.025 12.550
## 17 6.7375 EASI W12 W20 20 20 137.0 2.454872e-01 -6.145 17.030
## 18 5.9375 EASI W12 W24 20 20 133.0 3.117943e-01 -7.410 18.905
## 19 4.5300 EASI W16 W20 20 20 148.5 1.074276e-01 -0.955 12.190
## 20 6.1925 EASI W16 W24 20 20 141.0 1.893482e-01 -3.870 16.015
## 21 1.6450 EASI W20 W24 20 20 118.0 6.476555e-01 -5.050 9.065
## method alternative p.adj p.adj.signif
## 1 Wilcoxon signed rank test two.sided 1.000000e+00 ns
## 2 Wilcoxon signed rank test two.sided 8.864021e-02 ns
## 3 Wilcoxon signed rank test two.sided 5.607605e-04 ***
## 4 Wilcoxon signed rank test two.sided 3.524780e-03 **
## 5 Wilcoxon signed rank test two.sided 8.010864e-05 ****
## 6 Wilcoxon signed rank test two.sided 1.013374e-02 *
## 7 Wilcoxon signed rank test two.sided 1.020584e-01 ns
## 8 Wilcoxon signed rank test two.sided 1.229668e-02 *
## 9 Wilcoxon signed rank test two.sided 1.020584e-01 ns
## 10 Wilcoxon signed rank test two.sided 4.882622e-02 *
## 11 Wilcoxon signed rank test two.sided 3.548813e-02 *
## 12 Wilcoxon signed rank test two.sided 1.000000e+00 ns
## 13 Wilcoxon signed rank test two.sided 3.608093e-01 ns
## 14 Wilcoxon signed rank test two.sided 4.511719e-01 ns
## 15 Wilcoxon signed rank test two.sided 5.029621e-01 ns
## 16 Wilcoxon signed rank test two.sided 1.000000e+00 ns
## 17 Wilcoxon signed rank test two.sided 1.000000e+00 ns
## 18 Wilcoxon signed rank test two.sided 1.000000e+00 ns
## 19 Wilcoxon signed rank test two.sided 1.000000e+00 ns
## 20 Wilcoxon signed rank test two.sided 1.000000e+00 ns
## 21 Wilcoxon signed rank test two.sided 1.000000e+00 ns
##
## >>> 6. Mixed Design ANOVA: Treatment × Time
## Anova Table (Type 3 tests)
##
## Response: EASI
## Effect df MSE F ges pes p.value
## 1 Treatment 4, 95 5095.10 2.71 * .057 .102 .035
## 2 WeekF 4.02, 381.92 1135.43 21.09 *** .095 .182 <.001
## 3 Treatment:WeekF 16.08, 381.92 1135.43 0.74 .015 .030 .748
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 1364547 1 484034 95 267.8155 < 2e-16 ***
## Treatment 55146 4 484034 95 2.7059 0.03483 *
## WeekF 96267 6 433640 570 21.0898 < 2e-16 ***
## Treatment:WeekF 13595 24 433640 570 0.7446 0.80625
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## WeekF 0.26783 1.1477e-16
## Treatment:WeekF 0.26783 1.1477e-16
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## WeekF 0.67003 8.701e-16 ***
## Treatment:WeekF 0.67003 0.7485
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## WeekF 0.7033343 1.866741e-16
## Treatment:WeekF 0.7033343 7.554325e-01
## efek num Df den Df MSE F pes ges
## 1 Treatment 4.000000 95.0000 5095.100 2.7058584 0.10227823 0.05668721
## 2 WeekF 4.020161 381.9153 1135.434 21.0897975 0.18166797 0.09494344
## 3 Treatment:WeekF 16.080644 381.9153 1135.434 0.7445786 0.03039769 0.01459825
## Pr(>F)
## 1 3.483364e-02
## 2 8.701372e-16
## 3 7.484755e-01
## Grafik tersimpan: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad/G6_interaksi_mixed_ANOVA.png
##
## >>> 7. Efek sederhana dan post-hoc Mixed Design ANOVA
## WeekF = W2:
## contrast estimate SE df
## Placebo - Amlitelimab 62.5 mg 10.7685 11.25667 95
## Placebo - Amlitelimab 125 mg 3.8305 11.25667 95
## Placebo - Amlitelimab 250 mg 2.4905 11.25667 95
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 12.6795 11.25667 95
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -6.9380 11.25667 95
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -8.2780 11.25667 95
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) 1.9110 11.25667 95
## Amlitelimab 125 mg - Amlitelimab 250 mg -1.3400 11.25667 95
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 8.8490 11.25667 95
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 10.1890 11.25667 95
## lower.CL upper.CL t.ratio p.value
## -20.53478 42.07178 0.957 0.8737
## -27.47278 35.13378 0.340 0.9971
## -28.81278 33.79378 0.221 0.9995
## -18.62378 43.98278 1.126 0.7922
## -38.24128 24.36528 -0.616 0.9722
## -39.58128 23.02528 -0.735 0.9477
## -29.39228 33.21428 0.170 0.9998
## -32.64328 29.96328 -0.119 1.0000
## -22.45428 40.15228 0.786 0.9341
## -21.11428 41.49228 0.905 0.8943
##
## WeekF = W4:
## contrast estimate SE df
## Placebo - Amlitelimab 62.5 mg 7.9305 11.18506 95
## Placebo - Amlitelimab 125 mg 7.5395 11.18506 95
## Placebo - Amlitelimab 250 mg 1.5695 11.18506 95
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 9.2900 11.18506 95
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -0.3910 11.18506 95
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -6.3610 11.18506 95
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) 1.3595 11.18506 95
## Amlitelimab 125 mg - Amlitelimab 250 mg -5.9700 11.18506 95
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 1.7505 11.18506 95
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 7.7205 11.18506 95
## lower.CL upper.CL t.ratio p.value
## -23.17365 39.03465 0.709 0.9540
## -23.56465 38.64365 0.674 0.9615
## -29.53465 32.67365 0.140 0.9999
## -21.81415 40.39415 0.831 0.9205
## -31.49515 30.71315 -0.035 1.0000
## -37.46515 24.74315 -0.569 0.9793
## -29.74465 32.46365 0.122 0.9999
## -37.07415 25.13415 -0.534 0.9836
## -29.35365 32.85465 0.157 0.9999
## -23.38365 38.82465 0.690 0.9581
##
## WeekF = W8:
## contrast estimate SE df
## Placebo - Amlitelimab 62.5 mg 23.0000 11.43807 95
## Placebo - Amlitelimab 125 mg 17.2595 11.43807 95
## Placebo - Amlitelimab 250 mg 12.5090 11.43807 95
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 21.5510 11.43807 95
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -5.7405 11.43807 95
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -10.4910 11.43807 95
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) -1.4490 11.43807 95
## Amlitelimab 125 mg - Amlitelimab 250 mg -4.7505 11.43807 95
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 4.2915 11.43807 95
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 9.0420 11.43807 95
## lower.CL upper.CL t.ratio p.value
## -8.80773 54.80773 2.011 0.2688
## -14.54823 49.06723 1.509 0.5593
## -19.29873 44.31673 1.094 0.8094
## -10.25673 53.35873 1.884 0.3329
## -37.54823 26.06723 -0.502 0.9870
## -42.29873 21.31673 -0.917 0.8896
## -33.25673 30.35873 -0.127 0.9999
## -36.55823 27.05723 -0.415 0.9937
## -27.51623 36.09923 0.375 0.9957
## -22.76573 40.84973 0.791 0.9328
##
## WeekF = W12:
## contrast estimate SE df
## Placebo - Amlitelimab 62.5 mg 23.6405 11.21440 95
## Placebo - Amlitelimab 125 mg 21.5115 11.21440 95
## Placebo - Amlitelimab 250 mg 16.4305 11.21440 95
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 25.8895 11.21440 95
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -2.1290 11.21440 95
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -7.2100 11.21440 95
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) 2.2490 11.21440 95
## Amlitelimab 125 mg - Amlitelimab 250 mg -5.0810 11.21440 95
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 4.3780 11.21440 95
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 9.4590 11.21440 95
## lower.CL upper.CL t.ratio p.value
## -7.54523 54.82623 2.108 0.2252
## -9.67423 52.69723 1.918 0.3149
## -14.75523 47.61623 1.465 0.5875
## -5.29623 57.07523 2.309 0.1512
## -33.31473 29.05673 -0.190 0.9997
## -38.39573 23.97573 -0.643 0.9676
## -28.93673 33.43473 0.201 0.9996
## -36.26673 26.10473 -0.453 0.9912
## -26.80773 35.56373 0.390 0.9950
## -21.72673 40.64473 0.843 0.9163
##
## WeekF = W16:
## contrast estimate SE df
## Placebo - Amlitelimab 62.5 mg 33.2590 11.67133 95
## Placebo - Amlitelimab 125 mg 24.2505 11.67133 95
## Placebo - Amlitelimab 250 mg 31.7295 11.67133 95
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 34.0995 11.67133 95
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -9.0085 11.67133 95
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -1.5295 11.67133 95
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) 0.8405 11.67133 95
## Amlitelimab 125 mg - Amlitelimab 250 mg 7.4790 11.67133 95
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 9.8490 11.67133 95
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 2.3700 11.67133 95
## lower.CL upper.CL t.ratio p.value
## 0.80261 65.71539 2.850 0.0418
## -8.20589 56.70689 2.078 0.2383
## -0.72689 64.18589 2.719 0.0586
## 1.64311 66.55589 2.922 0.0345
## -41.46489 23.44789 -0.772 0.9381
## -33.98589 30.92689 -0.131 0.9999
## -31.61589 33.29689 0.072 1.0000
## -24.97739 39.93539 0.641 0.9679
## -22.60739 42.30539 0.844 0.9161
## -30.08639 34.82639 0.203 0.9996
##
## WeekF = W20:
## contrast estimate SE df
## Placebo - Amlitelimab 62.5 mg 27.5490 12.35240 95
## Placebo - Amlitelimab 125 mg 25.9690 12.35240 95
## Placebo - Amlitelimab 250 mg 27.4790 12.35240 95
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 37.5295 12.35240 95
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -1.5800 12.35240 95
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -0.0700 12.35240 95
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) 9.9805 12.35240 95
## Amlitelimab 125 mg - Amlitelimab 250 mg 1.5100 12.35240 95
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 11.5605 12.35240 95
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 10.0505 12.35240 95
## lower.CL upper.CL t.ratio p.value
## -6.80135 61.89935 2.230 0.1776
## -8.38135 60.31935 2.102 0.2277
## -6.87135 61.82935 2.225 0.1797
## 3.17915 71.87985 3.038 0.0250
## -35.93035 32.77035 -0.128 0.9999
## -34.42035 34.28035 -0.006 1.0000
## -24.36985 44.33085 0.808 0.9276
## -32.84035 35.86035 0.122 0.9999
## -22.78985 45.91085 0.936 0.8822
## -24.29985 44.40085 0.814 0.9258
##
## WeekF = W24:
## contrast estimate SE df
## Placebo - Amlitelimab 62.5 mg 28.8190 12.99078 95
## Placebo - Amlitelimab 125 mg 28.1705 12.99078 95
## Placebo - Amlitelimab 250 mg 27.2900 12.99078 95
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 39.4595 12.99078 95
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -0.6485 12.99078 95
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -1.5290 12.99078 95
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) 10.6405 12.99078 95
## Amlitelimab 125 mg - Amlitelimab 250 mg -0.8805 12.99078 95
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 11.2890 12.99078 95
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 12.1695 12.99078 95
## lower.CL upper.CL t.ratio p.value
## -7.30659 64.94459 2.218 0.1819
## -7.95509 64.29609 2.168 0.2007
## -8.83559 63.41559 2.101 0.2284
## 3.33391 75.58509 3.038 0.0250
## -36.77409 35.47709 -0.050 1.0000
## -37.65459 34.59659 -0.118 1.0000
## -25.48509 46.76609 0.819 0.9241
## -37.00609 35.24509 -0.068 1.0000
## -24.83659 47.41459 0.869 0.9075
## -23.95609 48.29509 0.937 0.8818
##
## Confidence level used: 0.95
## Conf-level adjustment: tukey method for comparing a family of 5 estimates
## P value adjustment: tukey method for comparing a family of 5 estimates
## Treatment = Placebo:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## W2 - W4 12.3000 6.190803 95 -7.02720 31.62720 1.987 0.9965
## W2 - W8 13.4395 8.484991 95 -13.04998 39.92898 1.584 1.0000
## W2 - W12 18.4595 8.444437 95 -7.90337 44.82237 2.186 0.6567
## W2 - W16 12.9905 9.171883 95 -15.64340 41.62440 1.416 1.0000
## W2 - W20 15.0610 9.966690 95 -16.05423 46.17623 1.511 1.0000
## W2 - W24 13.2910 10.871594 95 -20.64927 47.23127 1.223 1.0000
## W4 - W8 1.1395 6.649465 95 -19.61961 21.89861 0.171 1.0000
## W4 - W12 6.1595 8.014558 95 -18.86133 31.18033 0.769 1.0000
## W4 - W16 0.6905 9.224240 95 -28.10686 29.48786 0.075 1.0000
## W4 - W20 2.7610 10.194125 95 -29.06427 34.58627 0.271 1.0000
## W4 - W24 0.9910 10.905558 95 -33.05530 35.03730 0.091 1.0000
## W8 - W12 5.0200 6.620197 95 -15.64774 25.68774 0.758 1.0000
## W8 - W16 -0.4490 8.533961 95 -27.09136 26.19336 -0.053 1.0000
## W8 - W20 1.6215 9.387510 95 -27.68558 30.92858 0.173 1.0000
## W8 - W24 -0.1485 10.094292 95 -31.66209 31.36509 -0.015 1.0000
## W12 - W16 -5.4690 6.370319 95 -25.35664 14.41864 -0.859 1.0000
## W12 - W20 -3.3985 8.317931 95 -29.36643 22.56943 -0.409 1.0000
## W12 - W24 -5.1685 9.615269 95 -35.18662 24.84962 -0.538 1.0000
## W16 - W20 2.0705 7.112259 95 -20.13342 24.27442 0.291 1.0000
## W16 - W24 0.3005 8.496462 95 -26.22479 26.82579 0.035 1.0000
## W20 - W24 -1.7700 8.141324 95 -27.18658 23.64658 -0.217 1.0000
##
## Treatment = Amlitelimab 62.5 mg:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## W2 - W4 9.4620 6.190803 95 -9.86520 28.78920 1.528 1.0000
## W2 - W8 25.6710 8.484991 95 -0.81848 52.16048 3.025 0.0575
## W2 - W12 31.3315 8.444437 95 4.96863 57.69437 3.710 0.0070
## W2 - W16 35.4810 9.171883 95 6.84710 64.11490 3.868 0.0042
## W2 - W20 31.8415 9.966690 95 0.72627 62.95673 3.195 0.0361
## W2 - W24 31.3415 10.871594 95 -2.59877 65.28177 2.883 0.0828
## W4 - W8 16.2090 6.649465 95 -4.55011 36.96811 2.438 0.2330
## W4 - W12 21.8695 8.014558 95 -3.15133 46.89033 2.729 0.1136
## W4 - W16 26.0190 9.224240 95 -2.77836 54.81636 2.821 0.0933
## W4 - W20 22.3795 10.194125 95 -9.44577 54.20477 2.195 0.3975
## W4 - W24 21.8795 10.905558 95 -12.16680 55.92580 2.006 0.5720
## W8 - W12 5.6605 6.620197 95 -15.00724 26.32824 0.855 1.0000
## W8 - W16 9.8100 8.533961 95 -16.83236 36.45236 1.150 1.0000
## W8 - W20 6.1705 9.387510 95 -23.13658 35.47758 0.657 1.0000
## W8 - W24 5.6705 10.094292 95 -25.84309 37.18409 0.562 1.0000
## W12 - W16 4.1495 6.370319 95 -15.73814 24.03714 0.651 1.0000
## W12 - W20 0.5100 8.317931 95 -25.45793 26.47793 0.061 1.0000
## W12 - W24 0.0100 9.615269 95 -30.00812 30.02812 0.001 1.0000
## W16 - W20 -3.6395 7.112259 95 -25.84342 18.56442 -0.512 1.0000
## W16 - W24 -4.1395 8.496462 95 -30.66479 22.38579 -0.487 1.0000
## W20 - W24 -0.5000 8.141324 95 -25.91658 24.91658 -0.061 1.0000
##
## Treatment = Amlitelimab 125 mg:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## W2 - W4 16.0090 6.190803 95 -3.31820 35.33620 2.586 0.1797
## W2 - W8 26.8685 8.484991 95 0.37902 53.35798 3.167 0.0353
## W2 - W12 36.1405 8.444437 95 9.77763 62.50337 4.280 0.0009
## W2 - W16 33.4105 9.171883 95 4.77660 62.04440 3.643 0.0084
## W2 - W20 37.1995 9.966690 95 6.08427 68.31473 3.732 0.0065
## W2 - W24 37.6310 10.871594 95 3.69073 71.57127 3.461 0.0145
## W4 - W8 10.8595 6.649465 95 -9.89961 31.61861 1.633 1.0000
## W4 - W12 20.1315 8.014558 95 -4.88933 45.15233 2.512 0.2054
## W4 - W16 17.4015 9.224240 95 -11.39586 46.19886 1.886 0.7474
## W4 - W20 21.1905 10.194125 95 -10.63477 53.01577 2.079 0.5648
## W4 - W24 21.6220 10.905558 95 -12.42430 55.66830 1.983 0.6538
## W8 - W12 9.2720 6.620197 95 -11.39574 29.93974 1.401 1.0000
## W8 - W16 6.5420 8.533961 95 -20.10036 33.18436 0.767 1.0000
## W8 - W20 10.3310 9.387510 95 -18.97608 39.63808 1.101 1.0000
## W8 - W24 10.7625 10.094292 95 -20.75109 42.27609 1.066 1.0000
## W12 - W16 -2.7300 6.370319 95 -22.61764 17.15764 -0.429 1.0000
## W12 - W20 1.0590 8.317931 95 -24.90893 27.02693 0.127 1.0000
## W12 - W24 1.4905 9.615269 95 -28.52762 31.50862 0.155 1.0000
## W16 - W20 3.7890 7.112259 95 -18.41492 25.99292 0.533 1.0000
## W16 - W24 4.2205 8.496462 95 -22.30479 30.74579 0.497 1.0000
## W20 - W24 0.4315 8.141324 95 -24.98508 25.84808 0.053 1.0000
##
## Treatment = Amlitelimab 250 mg:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## W2 - W4 11.3790 6.190803 95 -7.94820 30.70620 1.838 0.7610
## W2 - W8 23.4580 8.484991 95 -3.03148 49.94748 2.765 0.1027
## W2 - W12 32.3995 8.444437 95 6.03663 58.76237 3.837 0.0043
## W2 - W16 42.2295 9.171883 95 13.59560 70.86340 4.604 0.0003
## W2 - W20 40.0495 9.966690 95 8.93427 71.16473 4.018 0.0023
## W2 - W24 38.0905 10.871594 95 4.15023 72.03077 3.504 0.0126
## W4 - W8 12.0790 6.649465 95 -8.68011 32.83811 1.817 0.7610
## W4 - W12 21.0205 8.014558 95 -4.00033 46.04133 2.623 0.1422
## W4 - W16 30.8505 9.224240 95 2.05314 59.64786 3.345 0.0201
## W4 - W20 28.6705 10.194125 95 -3.15477 60.49577 2.812 0.0956
## W4 - W24 26.7115 10.905558 95 -7.33480 60.75780 2.449 0.2099
## W8 - W12 8.9415 6.620197 95 -11.72624 29.60924 1.351 1.0000
## W8 - W16 18.7715 8.533961 95 -7.87086 45.41386 2.200 0.3631
## W8 - W20 16.5915 9.387510 95 -12.71558 45.89858 1.767 0.7610
## W8 - W24 14.6325 10.094292 95 -16.88109 46.14609 1.450 1.0000
## W12 - W16 9.8300 6.370319 95 -10.05764 29.71764 1.543 1.0000
## W12 - W20 7.6500 8.317931 95 -18.31793 33.61793 0.920 1.0000
## W12 - W24 5.6910 9.615269 95 -24.32712 35.70912 0.592 1.0000
## W16 - W20 -2.1800 7.112259 95 -24.38392 20.02392 -0.307 1.0000
## W16 - W24 -4.1390 8.496462 95 -30.66429 22.38629 -0.487 1.0000
## W20 - W24 -1.9590 8.141324 95 -27.37558 23.45758 -0.241 1.0000
##
## Treatment = Amlitelimab 250 mg + LD 500 mg:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## W2 - W4 8.9105 6.190803 95 -10.41670 28.23770 1.439 1.0000
## W2 - W8 22.3110 8.484991 95 -4.17848 48.80048 2.629 0.1297
## W2 - W12 31.6695 8.444437 95 5.30663 58.03237 3.750 0.0060
## W2 - W16 34.4105 9.171883 95 5.77660 63.04440 3.752 0.0060
## W2 - W20 39.9110 9.966690 95 8.79577 71.02623 4.004 0.0026
## W2 - W24 40.0710 10.871594 95 6.13073 74.01127 3.686 0.0068
## W4 - W8 13.4005 6.649465 95 -7.35861 34.15961 2.015 0.5604
## W4 - W12 22.7590 8.014558 95 -2.26183 47.77983 2.840 0.0840
## W4 - W16 25.5000 9.224240 95 -3.29736 54.29736 2.764 0.0959
## W4 - W20 31.0005 10.194125 95 -0.82477 62.82577 3.041 0.0518
## W4 - W24 31.1605 10.905558 95 -2.88580 65.20680 2.857 0.0840
## W8 - W12 9.3585 6.620197 95 -11.30924 30.02624 1.414 1.0000
## W8 - W16 12.0995 8.533961 95 -14.54286 38.74186 1.418 1.0000
## W8 - W20 17.6000 9.387510 95 -11.70708 46.90708 1.875 0.7028
## W8 - W24 17.7600 10.094292 95 -13.75359 49.27359 1.759 0.8173
## W12 - W16 2.7410 6.370319 95 -17.14664 22.62864 0.430 1.0000
## W12 - W20 8.2415 8.317931 95 -17.72643 34.20943 0.991 1.0000
## W12 - W24 8.4015 9.615269 95 -21.61662 38.41962 0.874 1.0000
## W16 - W20 5.5005 7.112259 95 -16.70342 27.70442 0.773 1.0000
## W16 - W24 5.6605 8.496462 95 -20.86479 32.18579 0.666 1.0000
## W20 - W24 0.1600 8.141324 95 -25.25658 25.57658 0.020 1.0000
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 21 estimates
## P value adjustment: holm method for 21 tests
## ID Treatment W2 W4 W8 W12 W16
## 1 G1_01 Amlitelimab 250 mg + LD 500 mg -57.48 -60.83 -63.93 -67.04 -74.15
## 2 G1_02 Amlitelimab 250 mg + LD 500 mg 35.84 -13.46 -59.81 -4.61 13.50
## 3 G1_03 Amlitelimab 250 mg + LD 500 mg -5.23 10.60 0.58 -6.03 -68.73
## 4 G1_04 Amlitelimab 250 mg + LD 500 mg -31.68 -51.84 -78.32 -88.75 -95.26
## 5 G1_05 Amlitelimab 250 mg + LD 500 mg -59.79 -45.33 -57.70 -87.24 -84.62
## 6 G1_06 Amlitelimab 250 mg + LD 500 mg -26.34 -37.76 -42.79 -60.58 -83.28
## 7 G1_07 Amlitelimab 250 mg + LD 500 mg -42.60 -39.34 -60.25 -76.40 -55.91
## 8 G1_08 Amlitelimab 250 mg + LD 500 mg 40.19 25.03 -30.08 13.02 -49.51
## 9 G1_09 Amlitelimab 250 mg + LD 500 mg -39.83 -43.69 -52.27 -63.83 -91.56
## 10 G1_10 Amlitelimab 250 mg + LD 500 mg -74.87 -51.41 -47.67 -70.74 -62.60
## 11 G1_11 Amlitelimab 250 mg + LD 500 mg -33.98 -55.96 -70.99 -74.49 -77.60
## 12 G1_12 Amlitelimab 250 mg + LD 500 mg -34.65 -67.73 -96.99 -85.81 -94.74
## 13 G1_13 Amlitelimab 250 mg + LD 500 mg -41.43 -70.88 -91.97 -25.93 -2.77
## 14 G1_14 Amlitelimab 250 mg + LD 500 mg -21.02 4.80 -22.69 -42.07 -41.29
## 15 G1_15 Amlitelimab 250 mg + LD 500 mg -52.50 -70.69 -62.08 -87.37 -99.92
## 16 G1_16 Amlitelimab 250 mg + LD 500 mg -1.42 -33.90 33.84 -53.52 -13.13
## 17 G1_17 Amlitelimab 250 mg + LD 500 mg -23.18 -12.93 -46.65 -65.62 -58.04
## 18 G1_18 Amlitelimab 250 mg + LD 500 mg -22.32 -23.21 -26.93 -82.23 -38.92
## 19 G1_19 Amlitelimab 250 mg + LD 500 mg -62.58 -78.17 -76.79 -82.55 -94.38
## 20 G1_20 Amlitelimab 250 mg + LD 500 mg -3.92 -20.30 -51.52 -80.39 -74.09
## 21 G2_01 Amlitelimab 250 mg 1.02 -20.26 -82.12 -82.71 -61.27
## 22 G2_02 Amlitelimab 250 mg 5.75 -25.73 -69.46 -79.90 -93.17
## 23 G2_03 Amlitelimab 250 mg 30.50 15.79 18.12 2.36 -58.47
## 24 G2_04 Amlitelimab 250 mg -31.11 31.47 -32.70 -92.19 -93.47
## 25 G2_05 Amlitelimab 250 mg 3.45 -5.51 -18.50 -69.50 -51.99
## 26 G2_06 Amlitelimab 250 mg -15.24 17.84 11.28 -47.10 -52.51
## 27 G2_07 Amlitelimab 250 mg -29.89 -50.42 -29.35 -27.64 -33.17
## 28 G2_08 Amlitelimab 250 mg -44.91 -51.06 -68.92 -70.13 -74.00
## 29 G2_09 Amlitelimab 250 mg -51.26 -26.70 -37.33 -89.80 -87.68
## 30 G2_10 Amlitelimab 250 mg -42.37 -29.14 19.82 5.31 -74.16
## 31 G2_11 Amlitelimab 250 mg -11.65 -63.07 -83.16 -67.97 -80.71
## 32 G2_12 Amlitelimab 250 mg -17.71 -63.00 -29.94 -41.68 -81.52
## 33 G2_13 Amlitelimab 250 mg -55.11 -57.03 -74.97 -78.39 -83.48
## 34 G2_14 Amlitelimab 250 mg -33.20 -49.91 12.94 17.67 -28.80
## 35 G2_15 Amlitelimab 250 mg -66.40 -80.32 -90.17 -76.83 -80.10
## 36 G2_16 Amlitelimab 250 mg 4.13 4.61 -58.67 -19.17 39.90
## 37 G2_17 Amlitelimab 250 mg 74.67 66.95 27.77 5.46 30.02
## 38 G2_18 Amlitelimab 250 mg -70.69 -93.04 -94.11 -71.78 -88.17
## 39 G2_19 Amlitelimab 250 mg 52.62 -17.24 -72.73 -83.41 -86.75
## 40 G2_20 Amlitelimab 250 mg -57.61 -86.82 -71.97 -35.60 -60.10
## 41 G3_01 Amlitelimab 125 mg -79.77 -86.93 -80.20 -77.83 -87.35
## 42 G3_02 Amlitelimab 125 mg -77.06 -85.45 -67.16 -93.54 -77.67
## 43 G3_03 Amlitelimab 125 mg -34.61 -5.85 46.85 -61.60 -68.67
## 44 G3_04 Amlitelimab 125 mg 25.42 -10.78 -20.10 -33.91 -64.33
## 45 G3_05 Amlitelimab 125 mg -18.62 -47.32 -87.84 -45.15 -5.94
## 46 G3_06 Amlitelimab 125 mg -1.85 -39.06 -11.73 -21.49 -54.11
## 47 G3_07 Amlitelimab 125 mg -59.63 -54.11 -35.64 -81.08 -87.35
## 48 G3_08 Amlitelimab 125 mg 30.87 -7.18 -19.01 -33.02 39.63
## 49 G3_09 Amlitelimab 125 mg 5.60 -50.88 -59.44 -76.93 -71.94
## 50 G3_10 Amlitelimab 125 mg -25.59 -36.98 -38.17 4.93 20.77
## 51 G3_11 Amlitelimab 125 mg 64.56 19.96 -7.41 60.61 -24.83
## 52 G3_12 Amlitelimab 125 mg -53.79 -81.57 -87.92 -86.89 -85.40
## 53 G3_13 Amlitelimab 125 mg -43.72 -53.15 -74.63 -39.95 -48.60
## 54 G3_14 Amlitelimab 125 mg 36.75 -9.54 -43.99 -86.80 -28.94
## 55 G3_15 Amlitelimab 125 mg -15.69 -44.44 -41.86 -82.96 -92.13
## 56 G3_16 Amlitelimab 125 mg -34.26 -25.35 -75.63 -95.56 -89.86
## 57 G3_17 Amlitelimab 125 mg 8.61 48.52 19.55 -15.73 6.65
## 58 G3_18 Amlitelimab 125 mg -58.32 -60.28 -63.35 -62.77 -50.98
## 59 G3_19 Amlitelimab 125 mg -6.16 -49.04 -88.76 -90.75 -98.71
## 60 G3_20 Amlitelimab 125 mg -44.55 -22.56 -82.74 -84.20 -80.26
## 61 G4_01 Amlitelimab 62.5 mg -73.05 -5.13 -8.61 -19.74 -32.46
## 62 G4_02 Amlitelimab 62.5 mg 42.03 -13.51 10.28 -23.08 -22.32
## 63 G4_03 Amlitelimab 62.5 mg -69.69 -93.04 -67.92 -79.89 -25.02
## 64 G4_04 Amlitelimab 62.5 mg -74.35 -68.53 -61.77 -64.55 -44.95
## 65 G4_05 Amlitelimab 62.5 mg -14.64 -46.23 -59.41 -65.48 -41.10
## 66 G4_06 Amlitelimab 62.5 mg 4.84 -23.48 -35.35 -87.44 -74.52
## 67 G4_07 Amlitelimab 62.5 mg -53.53 -43.10 -90.52 -97.65 -98.93
## 68 G4_08 Amlitelimab 62.5 mg -35.69 -38.43 -88.51 -93.21 -98.72
## 69 G4_09 Amlitelimab 62.5 mg -28.46 -45.79 -47.51 -31.69 -33.22
## 70 G4_10 Amlitelimab 62.5 mg -18.64 -56.29 -69.02 -89.72 -83.77
## 71 G4_11 Amlitelimab 62.5 mg -65.52 -95.48 -78.37 -38.41 -20.69
## 72 G4_12 Amlitelimab 62.5 mg -31.65 -29.30 -58.14 -82.21 -96.64
## 73 G4_13 Amlitelimab 62.5 mg -45.00 -92.22 -80.84 -92.87 -98.14
## 74 G4_14 Amlitelimab 62.5 mg 14.00 51.28 -38.33 -54.26 -93.66
## 75 G4_15 Amlitelimab 62.5 mg 12.97 27.74 28.46 37.85 -16.01
## 76 G4_16 Amlitelimab 62.5 mg 11.20 15.37 -57.00 -57.36 -94.11
## 77 G4_17 Amlitelimab 62.5 mg -7.62 -46.63 -72.12 -66.91 -34.78
## 78 G4_18 Amlitelimab 62.5 mg -70.89 -41.90 -47.62 -12.72 -50.32
## 79 G4_19 Amlitelimab 62.5 mg -3.11 -13.11 -51.35 -56.73 -86.75
## 80 G4_20 Amlitelimab 62.5 mg -13.77 -52.03 -60.34 -71.13 -84.08
## 81 G5_01 Placebo -60.19 -83.82 -79.73 -75.12 -60.99
## 82 G5_02 Placebo -53.49 -36.16 -99.40 -74.01 -62.28
## 83 G5_03 Placebo -35.86 -46.77 -42.69 -83.97 -72.86
## 84 G5_04 Placebo -27.06 -81.43 -47.67 -38.19 -38.70
## 85 G5_05 Placebo 9.83 -11.66 -5.15 20.62 17.93
## 86 G5_06 Placebo 22.10 -38.19 -18.53 -43.26 -40.22
## 87 G5_07 Placebo -34.15 -33.67 -5.94 -31.65 -67.14
## 88 G5_08 Placebo 36.13 48.59 43.31 48.50 76.85
## 89 G5_09 Placebo 35.11 27.23 6.63 -19.90 -57.11
## 90 G5_10 Placebo 29.51 -1.89 -14.71 -39.95 -29.62
## 91 G5_11 Placebo -1.82 -15.46 16.34 16.33 36.94
## 92 G5_12 Placebo -75.15 -32.34 -23.59 -55.61 -61.36
## 93 G5_13 Placebo -50.76 -42.56 -88.05 -75.24 -55.79
## 94 G5_14 Placebo -30.74 -45.30 -30.42 -26.94 -0.13
## 95 G5_15 Placebo -30.69 2.62 9.56 -0.08 -32.74
## 96 G5_16 Placebo 15.29 -29.22 -53.46 -36.26 -57.65
## 97 G5_17 Placebo 4.43 -30.69 -21.88 -35.74 -31.41
## 98 G5_18 Placebo -19.07 -22.26 11.36 -14.76 24.76
## 99 G5_19 Placebo -48.19 -38.00 -50.67 -35.78 1.61
## 100 G5_20 Placebo 9.57 -40.22 -79.30 -73.38 -55.10
## W20 W24 delta_W24_W2
## 1 -86.43 -94.62 -37.14
## 2 32.03 -18.65 -54.49
## 3 -75.57 -91.01 -85.78
## 4 -96.75 -91.74 -60.06
## 5 -94.34 -91.29 -31.50
## 6 -77.41 -89.51 -63.17
## 7 -52.21 -81.02 -38.42
## 8 -79.76 -90.46 -130.65
## 9 -94.92 -95.62 -55.79
## 10 -80.58 -79.62 -4.75
## 11 -84.46 -80.43 -46.45
## 12 -81.76 -60.85 -26.20
## 13 -40.60 37.32 78.75
## 14 -36.38 -26.26 -5.24
## 15 -94.45 -95.70 -43.20
## 16 -10.14 -11.12 -9.70
## 17 -61.04 -68.47 -45.29
## 18 -60.30 -74.47 -52.15
## 19 -99.85 -95.40 -32.82
## 20 -82.09 -61.29 -57.37
## 21 -21.73 -70.37 -71.39
## 22 -98.53 -84.45 -90.20
## 23 -40.09 -81.08 -111.58
## 24 -74.96 -67.00 -35.89
## 25 -87.98 -49.09 -52.54
## 26 -59.05 -36.70 -21.46
## 27 46.65 -58.98 -29.09
## 28 -65.62 -92.39 -47.48
## 29 -58.74 -69.77 -18.51
## 30 -80.28 25.61 67.98
## 31 -90.64 -66.56 -54.91
## 32 -38.09 40.48 58.19
## 33 -95.77 -72.41 -17.30
## 34 -75.44 -79.70 -46.50
## 35 -96.38 -52.57 13.83
## 36 -11.07 -63.16 -67.29
## 37 -2.78 27.37 -47.30
## 38 -97.36 -88.51 -17.82
## 39 -79.69 -91.72 -144.34
## 40 -28.45 -85.82 -28.21
## 41 -97.64 -95.54 -15.77
## 42 -97.60 -61.11 15.95
## 43 -79.46 -48.10 -13.49
## 44 -3.02 -90.82 -116.24
## 45 -81.11 -96.05 -77.43
## 46 29.77 12.73 14.58
## 47 -59.66 -83.57 -23.94
## 48 32.32 61.45 30.58
## 49 -96.54 -56.26 -61.86
## 50 -18.00 1.99 27.58
## 51 -68.07 -84.20 -148.76
## 52 -71.65 -97.58 -43.79
## 53 -37.65 -77.01 -33.29
## 54 -53.20 -22.04 -58.79
## 55 -97.66 -54.73 -39.04
## 56 -89.94 -63.47 -29.21
## 57 1.27 -8.01 -16.62
## 58 -90.67 -85.18 -26.86
## 59 -52.85 -93.45 -87.29
## 60 -94.44 -93.48 -48.93
## 61 -76.25 -88.98 -15.93
## 62 -52.51 -74.45 -116.48
## 63 -84.39 -63.88 5.81
## 64 47.46 -71.10 3.25
## 65 -49.92 -12.45 2.19
## 66 -80.59 -89.08 -93.92
## 67 -83.50 -79.95 -26.42
## 68 -83.79 -73.62 -37.93
## 69 -62.53 -72.85 -44.39
## 70 -80.23 26.06 44.70
## 71 -63.31 -65.70 -0.18
## 72 -84.60 -92.61 -60.96
## 73 -72.46 -87.11 -42.11
## 74 -74.15 -58.29 -72.29
## 75 45.41 15.28 2.31
## 76 -84.67 -89.34 -100.54
## 77 -74.51 -87.03 -79.41
## 78 -40.20 19.50 90.39
## 79 -29.09 -13.11 -10.00
## 80 -73.57 -88.69 -74.92
## 81 -65.18 -37.26 22.93
## 82 -33.93 8.43 61.92
## 83 -85.15 -76.69 -40.83
## 84 -37.96 -59.65 -32.59
## 85 5.14 -6.68 -16.51
## 86 -23.04 -35.18 -57.28
## 87 -34.84 -79.85 -45.70
## 88 -14.87 5.08 -31.05
## 89 -92.96 -83.79 -118.90
## 90 -16.76 -66.51 -96.02
## 91 50.88 58.36 60.18
## 92 -56.77 -65.02 10.13
## 93 -31.90 -43.77 6.99
## 94 14.69 8.85 39.59
## 95 -74.38 -69.04 -38.35
## 96 -62.14 -41.27 -56.56
## 97 7.45 40.53 36.10
## 98 42.85 14.60 33.67
## 99 -21.97 24.88 73.07
## 100 -75.58 -67.04 -76.61
## Analysis of Variance Table
##
## Response: delta_W24_W2
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 4 9687 2421.8 1.0245 0.3987
## Residuals 95 224564 2363.8
## contrast estimate SE df
## Placebo - Amlitelimab 62.5 mg 18.0505 15.37476 95
## Placebo - Amlitelimab 125 mg 24.3400 15.37476 95
## Placebo - Amlitelimab 250 mg 24.7995 15.37476 95
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 26.7800 15.37476 95
## Amlitelimab 62.5 mg - Amlitelimab 125 mg 6.2895 15.37476 95
## Amlitelimab 62.5 mg - Amlitelimab 250 mg 6.7490 15.37476 95
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) 8.7295 15.37476 95
## Amlitelimab 125 mg - Amlitelimab 250 mg 0.4595 15.37476 95
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 2.4400 15.37476 95
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 1.9805 15.37476 95
## lower.CL upper.CL t.ratio p.value
## -26.13768 62.23868 1.174 1.0000
## -19.84818 68.52818 1.583 0.9906
## -19.38868 68.98768 1.613 0.9906
## -17.40818 70.96818 1.742 0.8478
## -37.89868 50.47768 0.409 1.0000
## -37.43918 50.93718 0.439 1.0000
## -35.45868 52.91768 0.568 1.0000
## -43.72868 44.64768 0.030 1.0000
## -41.74818 46.62818 0.159 1.0000
## -42.20768 46.16868 0.129 1.0000
##
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 10 estimates
## P value adjustment: holm method for 10 tests
##
## >>> 8. Linear Mixed Model menggunakan seluruh pengukuran tersedia
##
## Correlation matrix not shown by default, as p = 35 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: EASI ~ Treatment * WeekF + (1 + Week | ID)
## Data: dat_obs
## Control: ctrl
##
## REML criterion at convergence: 6543.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5135 -0.5357 -0.0810 0.5110 3.5574
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## ID (Intercept) 1012.386 31.82
## Week 3.168 1.78 -0.60
## Residual 547.762 23.40
## Number of obs: 700, groups: ID, 100
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -44.1515 2.6979 94.9995 -16.365 < 2e-16 ***
## Treatment1 16.6713 5.3958 94.9995 3.090 0.00263 **
## Treatment2 -5.4668 5.3958 94.9995 -1.013 0.31356
## Treatment3 -1.6903 5.3958 94.9995 -0.313 0.75477
## Treatment4 -0.3999 5.3958 94.9995 -0.074 0.94108
## WeekF.L -27.9714 4.2650 95.3041 -6.558 2.80e-09 ***
## WeekF.Q 13.2826 2.3485 484.1829 5.656 2.66e-08 ***
## WeekF.C -0.6245 2.3449 480.1832 -0.266 0.79010
## WeekF^4 -1.5583 2.3420 476.8292 -0.665 0.50615
## WeekF^5 0.6484 2.3408 475.3739 0.277 0.78190
## WeekF^6 0.7782 2.3405 475.0344 0.332 0.73967
## Treatment1:WeekF.L 19.4774 8.5300 95.3041 2.283 0.02463 *
## Treatment2:WeekF.L -0.1102 8.5300 95.3041 -0.013 0.98972
## Treatment3:WeekF.L -2.6090 8.5300 95.3041 -0.306 0.76038
## Treatment4:WeekF.L -8.0078 8.5300 95.3041 -0.939 0.35022
## Treatment1:WeekF.Q -3.8258 4.6970 484.1829 -0.815 0.41574
## Treatment2:WeekF.Q 3.3100 4.6970 484.1829 0.705 0.48132
## Treatment3:WeekF.Q 1.6919 4.6970 484.1829 0.360 0.71884
## Treatment4:WeekF.Q 1.5789 4.6970 484.1829 0.336 0.73690
## Treatment1:WeekF.C -3.8576 4.6899 480.1832 -0.823 0.41118
## Treatment2:WeekF.C 0.9707 4.6899 480.1832 0.207 0.83611
## Treatment3:WeekF.C -3.4165 4.6899 480.1832 -0.728 0.46667
## Treatment4:WeekF.C 4.4423 4.6899 480.1832 0.947 0.34401
## Treatment1:WeekF^4 2.7240 4.6840 476.8292 0.582 0.56114
## Treatment2:WeekF^4 -2.7965 4.6840 476.8292 -0.597 0.55077
## Treatment3:WeekF^4 0.1436 4.6840 476.8292 0.031 0.97556
## Treatment4:WeekF^4 0.4014 4.6840 476.8292 0.086 0.93175
## Treatment1:WeekF^5 -0.6486 4.6815 475.3739 -0.139 0.88987
## Treatment2:WeekF^5 0.3474 4.6815 475.3739 0.074 0.94088
## Treatment3:WeekF^5 0.9251 4.6815 475.3739 0.198 0.84344
## Treatment4:WeekF^5 -2.5323 4.6815 475.3739 -0.541 0.58882
## Treatment1:WeekF^6 3.2885 4.6809 475.0344 0.703 0.48270
## Treatment2:WeekF^6 -3.2182 4.6809 475.0344 -0.688 0.49209
## Treatment3:WeekF^6 2.5196 4.6809 475.0344 0.538 0.59064
## Treatment4:WeekF^6 -2.9771 4.6809 475.0344 -0.636 0.52508
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## efek Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## 1 Treatment 5928.647 1482.1617 4 95.0000 2.7058487 3.483415e-02
## 2 WeekF 44509.154 7418.1924 6 453.5918 13.5072437 4.046791e-14
## 3 Treatment:WeekF 6177.859 257.4108 24 517.0832 0.4684457 9.862264e-01
## model AIC BIC logLik
## 1 Random intercept 6690.793 6859.183 -3308.397
## 2 Random intercept + random slope 6621.290 6798.782 -3271.645
##
## >>> 9. Estimated marginal means dan kontras LMM
## WeekF = W2:
## Treatment emmean SE df lower.CL upper.CL
## Placebo -15.2600 8.473262 156.27 -31.99690 1.47690
## Amlitelimab 62.5 mg -26.0285 8.473262 156.27 -42.76540 -9.29160
## Amlitelimab 125 mg -19.0905 8.473262 156.27 -35.82740 -2.35360
## Amlitelimab 250 mg -17.7505 8.473262 156.27 -34.48740 -1.01360
## Amlitelimab 250 mg + LD 500 mg -27.9395 8.473262 156.27 -44.67640 -11.20260
##
## WeekF = W4:
## Treatment emmean SE df lower.CL upper.CL
## Placebo -27.5600 8.176316 178.51 -43.69467 -11.42533
## Amlitelimab 62.5 mg -35.4905 8.176316 178.51 -51.62517 -19.35583
## Amlitelimab 125 mg -35.0995 8.176316 178.51 -51.23417 -18.96483
## Amlitelimab 250 mg -29.1295 8.176316 178.51 -45.26417 -12.99483
## Amlitelimab 250 mg + LD 500 mg -36.8500 8.176316 178.51 -52.98467 -20.71533
##
## WeekF = W8:
## Treatment emmean SE df lower.CL upper.CL
## Placebo -28.6995 7.795233 221.46 -44.06183 -13.33717
## Amlitelimab 62.5 mg -51.6995 7.795233 221.46 -67.06183 -36.33717
## Amlitelimab 125 mg -45.9590 7.795233 221.46 -61.32133 -30.59667
## Amlitelimab 250 mg -41.2085 7.795233 221.46 -56.57083 -25.84617
## Amlitelimab 250 mg + LD 500 mg -50.2505 7.795233 221.46 -65.61283 -34.88817
##
## WeekF = W12:
## Treatment emmean SE df lower.CL upper.CL
## Placebo -33.7195 7.729680 237.78 -48.94690 -18.49210
## Amlitelimab 62.5 mg -57.3600 7.729680 237.78 -72.58740 -42.13260
## Amlitelimab 125 mg -55.2310 7.729680 237.78 -70.45840 -40.00360
## Amlitelimab 250 mg -50.1500 7.729680 237.78 -65.37740 -34.92260
## Amlitelimab 250 mg + LD 500 mg -59.6090 7.729680 237.78 -74.83640 -44.38160
##
## WeekF = W16:
## Treatment emmean SE df lower.CL upper.CL
## Placebo -28.2505 7.987428 214.35 -43.99447 -12.50653
## Amlitelimab 62.5 mg -61.5095 7.987428 214.35 -77.25347 -45.76553
## Amlitelimab 125 mg -52.5010 7.987428 214.35 -68.24497 -36.75703
## Amlitelimab 250 mg -59.9800 7.987428 214.35 -75.72397 -44.23603
## Amlitelimab 250 mg + LD 500 mg -62.3500 7.987428 214.35 -78.09397 -46.60603
##
## WeekF = W20:
## Treatment emmean SE df lower.CL upper.CL
## Placebo -30.3210 8.539253 172.05 -47.17619 -13.46581
## Amlitelimab 62.5 mg -57.8700 8.539253 172.05 -74.72519 -41.01481
## Amlitelimab 125 mg -56.2900 8.539253 172.05 -73.14519 -39.43481
## Amlitelimab 250 mg -57.8000 8.539253 172.05 -74.65519 -40.94481
## Amlitelimab 250 mg + LD 500 mg -67.8505 8.539253 172.05 -84.70569 -50.99531
##
## WeekF = W24:
## Treatment emmean SE df lower.CL upper.CL
## Placebo -28.5510 9.333138 134.45 -47.00976 -10.09224
## Amlitelimab 62.5 mg -57.3700 9.333138 134.45 -75.82876 -38.91124
## Amlitelimab 125 mg -56.7215 9.333138 134.45 -75.18026 -38.26274
## Amlitelimab 250 mg -55.8410 9.333138 134.45 -74.29976 -37.38224
## Amlitelimab 250 mg + LD 500 mg -68.0105 9.333138 134.45 -86.46926 -49.55174
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## WeekF = W2:
## contrast estimate SE
## Placebo - Amlitelimab 62.5 mg 10.7685 11.98300
## Placebo - Amlitelimab 125 mg 3.8305 11.98300
## Placebo - Amlitelimab 250 mg 2.4905 11.98300
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 12.6795 11.98300
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -6.9380 11.98300
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -8.2780 11.98300
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) 1.9110 11.98300
## Amlitelimab 125 mg - Amlitelimab 250 mg -1.3400 11.98300
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 8.8490 11.98300
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 10.1890 11.98300
## df lower.CL upper.CL t.ratio p.value
## 156.27 -23.35238 44.88938 0.899 1.0000
## 156.27 -30.29038 37.95138 0.320 1.0000
## 156.27 -31.63038 36.61138 0.208 1.0000
## 156.27 -21.44138 46.80038 1.058 1.0000
## 156.27 -41.05888 27.18288 -0.579 1.0000
## 156.27 -42.39888 25.84288 -0.691 1.0000
## 156.27 -32.20988 36.03188 0.159 1.0000
## 156.27 -35.46088 32.78088 -0.112 1.0000
## 156.27 -25.27188 42.96988 0.738 1.0000
## 156.27 -23.93188 44.30988 0.850 1.0000
##
## WeekF = W4:
## contrast estimate SE
## Placebo - Amlitelimab 62.5 mg 7.9305 11.56306
## Placebo - Amlitelimab 125 mg 7.5395 11.56306
## Placebo - Amlitelimab 250 mg 1.5695 11.56306
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 9.2900 11.56306
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -0.3910 11.56306
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -6.3610 11.56306
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) 1.3595 11.56306
## Amlitelimab 125 mg - Amlitelimab 250 mg -5.9700 11.56306
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 1.7505 11.56306
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 7.7205 11.56306
## df lower.CL upper.CL t.ratio p.value
## 178.51 -24.93572 40.79672 0.686 1.0000
## 178.51 -25.32672 40.40572 0.652 1.0000
## 178.51 -31.29672 34.43572 0.136 1.0000
## 178.51 -23.57622 42.15622 0.803 1.0000
## 178.51 -33.25722 32.47522 -0.034 1.0000
## 178.51 -39.22722 26.50522 -0.550 1.0000
## 178.51 -31.50672 34.22572 0.118 1.0000
## 178.51 -38.83622 26.89622 -0.516 1.0000
## 178.51 -31.11572 34.61672 0.151 1.0000
## 178.51 -25.14572 40.58672 0.668 1.0000
##
## WeekF = W8:
## contrast estimate SE
## Placebo - Amlitelimab 62.5 mg 23.0000 11.02412
## Placebo - Amlitelimab 125 mg 17.2595 11.02412
## Placebo - Amlitelimab 250 mg 12.5090 11.02412
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 21.5510 11.02412
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -5.7405 11.02412
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -10.4910 11.02412
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) -1.4490 11.02412
## Amlitelimab 125 mg - Amlitelimab 250 mg -4.7505 11.02412
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 4.2915 11.02412
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 9.0420 11.02412
## df lower.CL upper.CL t.ratio p.value
## 221.46 -8.25818 54.25818 2.086 0.3809
## 221.46 -13.99868 48.51768 1.566 0.9509
## 221.46 -18.74918 43.76718 1.135 1.0000
## 221.46 -9.70718 52.80918 1.955 0.4667
## 221.46 -36.99868 25.51768 -0.521 1.0000
## 221.46 -41.74918 20.76718 -0.952 1.0000
## 221.46 -32.70718 29.80918 -0.131 1.0000
## 221.46 -36.00868 26.50768 -0.431 1.0000
## 221.46 -26.96668 35.54968 0.389 1.0000
## 221.46 -22.21618 40.30018 0.820 1.0000
##
## WeekF = W12:
## contrast estimate SE
## Placebo - Amlitelimab 62.5 mg 23.6405 10.93142
## Placebo - Amlitelimab 125 mg 21.5115 10.93142
## Placebo - Amlitelimab 250 mg 16.4305 10.93142
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 25.8895 10.93142
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -2.1290 10.93142
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -7.2100 10.93142
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) 2.2490 10.93142
## Amlitelimab 125 mg - Amlitelimab 250 mg -5.0810 10.93142
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 4.3780 10.93142
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 9.4590 10.93142
## df lower.CL upper.CL t.ratio p.value
## 237.78 -7.33333 54.61433 2.163 0.2841
## 237.78 -9.46233 52.48533 1.968 0.4020
## 237.78 -14.54333 47.40433 1.503 0.9391
## 237.78 -5.08433 56.86333 2.368 0.1867
## 237.78 -33.10283 28.84483 -0.195 1.0000
## 237.78 -38.18383 23.76383 -0.660 1.0000
## 237.78 -28.72483 33.22283 0.206 1.0000
## 237.78 -36.05483 25.89283 -0.465 1.0000
## 237.78 -26.59583 35.35183 0.400 1.0000
## 237.78 -21.51483 40.43283 0.865 1.0000
##
## WeekF = W16:
## contrast estimate SE
## Placebo - Amlitelimab 62.5 mg 33.2590 11.29593
## Placebo - Amlitelimab 125 mg 24.2505 11.29593
## Placebo - Amlitelimab 250 mg 31.7295 11.29593
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 34.0995 11.29593
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -9.0085 11.29593
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -1.5295 11.29593
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) 0.8405 11.29593
## Amlitelimab 125 mg - Amlitelimab 250 mg 7.4790 11.29593
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 9.8490 11.29593
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 2.3700 11.29593
## df lower.CL upper.CL t.ratio p.value
## 214.35 1.21938 65.29862 2.944 0.0323
## 214.35 -7.78912 56.29012 2.147 0.2305
## 214.35 -0.31012 63.76912 2.809 0.0434
## 214.35 2.05988 66.13912 3.019 0.0285
## 214.35 -41.04812 23.03112 -0.797 1.0000
## 214.35 -33.56912 30.51012 -0.135 1.0000
## 214.35 -31.19912 32.88012 0.074 1.0000
## 214.35 -24.56062 39.51862 0.662 1.0000
## 214.35 -22.19062 41.88862 0.872 1.0000
## 214.35 -29.66962 34.40962 0.210 1.0000
##
## WeekF = W20:
## contrast estimate SE
## Placebo - Amlitelimab 62.5 mg 27.5490 12.07633
## Placebo - Amlitelimab 125 mg 25.9690 12.07633
## Placebo - Amlitelimab 250 mg 27.4790 12.07633
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 37.5295 12.07633
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -1.5800 12.07633
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -0.0700 12.07633
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) 9.9805 12.07633
## Amlitelimab 125 mg - Amlitelimab 250 mg 1.5100 12.07633
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 11.5605 12.07633
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 10.0505 12.07633
## df lower.CL upper.CL t.ratio p.value
## 172.05 -6.79234 61.89034 2.281 0.2138
## 172.05 -8.37234 60.31034 2.150 0.2304
## 172.05 -6.86234 61.82034 2.275 0.2138
## 172.05 3.18816 71.87084 3.108 0.0221
## 172.05 -35.92134 32.76134 -0.131 1.0000
## 172.05 -34.41134 34.27134 -0.006 1.0000
## 172.05 -24.36084 44.32184 0.826 1.0000
## 172.05 -32.83134 35.85134 0.125 1.0000
## 172.05 -22.78084 45.90184 0.957 1.0000
## 172.05 -24.29084 44.39184 0.832 1.0000
##
## WeekF = W24:
## contrast estimate SE
## Placebo - Amlitelimab 62.5 mg 28.8190 13.19905
## Placebo - Amlitelimab 125 mg 28.1705 13.19905
## Placebo - Amlitelimab 250 mg 27.2900 13.19905
## Placebo - (Amlitelimab 250 mg + LD 500 mg) 39.4595 13.19905
## Amlitelimab 62.5 mg - Amlitelimab 125 mg -0.6485 13.19905
## Amlitelimab 62.5 mg - Amlitelimab 250 mg -1.5290 13.19905
## Amlitelimab 62.5 mg - (Amlitelimab 250 mg + LD 500 mg) 10.6405 13.19905
## Amlitelimab 125 mg - Amlitelimab 250 mg -0.8805 13.19905
## Amlitelimab 125 mg - (Amlitelimab 250 mg + LD 500 mg) 11.2890 13.19905
## Amlitelimab 250 mg - (Amlitelimab 250 mg + LD 500 mg) 12.1695 13.19905
## df lower.CL upper.CL t.ratio p.value
## 134.45 -8.85241 66.49041 2.183 0.2767
## 134.45 -9.50091 65.84191 2.134 0.2771
## 134.45 -10.38141 64.96141 2.068 0.2842
## 134.45 1.78809 77.13091 2.990 0.0332
## 134.45 -38.31991 37.02291 -0.049 1.0000
## 134.45 -39.20041 36.14241 -0.116 1.0000
## 134.45 -27.03091 48.31191 0.806 1.0000
## 134.45 -38.55191 36.79091 -0.067 1.0000
## 134.45 -26.38241 48.96041 0.855 1.0000
## 134.45 -25.50191 49.84091 0.922 1.0000
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 10 estimates
## P value adjustment: holm method for 10 tests
## Treatment = Placebo:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## W2 - W4 12.3000 7.443777 490.23 -10.43329 35.03329 1.652 1.0000
## W2 - W8 13.4395 7.776798 565.84 -10.29422 37.17322 1.728 1.0000
## W2 - W12 18.4595 8.403341 487.07 -7.20517 44.12417 2.197 0.5988
## W2 - W16 12.9905 9.264041 302.77 -15.39394 41.37494 1.402 1.0000
## W2 - W20 15.0610 10.300366 191.29 -16.65584 46.77784 1.462 1.0000
## W2 - W24 13.2910 11.464790 135.74 -22.20763 48.78963 1.159 1.0000
## W4 - W8 1.1395 7.570377 529.19 -21.97151 24.25051 0.151 1.0000
## W4 - W12 6.1595 8.056908 557.22 -18.43080 30.74980 0.764 1.0000
## W4 - W16 0.6905 8.808274 389.86 -26.24630 27.62730 0.078 1.0000
## W4 - W20 2.7610 9.763524 237.45 -27.22397 32.74597 0.283 1.0000
## W4 - W24 0.9910 10.869033 158.87 -32.56946 34.55146 0.091 1.0000
## W8 - W12 5.0200 7.570377 529.19 -18.09101 28.13101 0.663 1.0000
## W8 - W16 -0.4490 8.056908 557.22 -25.03930 24.14130 -0.056 1.0000
## W8 - W20 1.6215 8.808274 389.86 -25.31530 28.55830 0.184 1.0000
## W8 - W24 -0.1485 9.763524 237.45 -30.13347 29.83647 -0.015 1.0000
## W12 - W16 -5.4690 7.570377 529.19 -28.58001 17.64201 -0.722 1.0000
## W12 - W20 -3.3985 8.056908 557.22 -27.98880 21.19180 -0.422 1.0000
## W12 - W24 -5.1685 8.808274 389.86 -32.10530 21.76830 -0.587 1.0000
## W16 - W20 2.0705 7.570377 529.19 -21.04051 25.18151 0.274 1.0000
## W16 - W24 0.3005 8.056908 557.22 -24.28980 24.89080 0.037 1.0000
## W20 - W24 -1.7700 7.570377 529.19 -24.88101 21.34101 -0.234 1.0000
##
## Treatment = Amlitelimab 62.5 mg:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## W2 - W4 9.4620 7.443777 490.23 -13.27129 32.19529 1.271 1.0000
## W2 - W8 25.6710 7.776798 565.84 1.93728 49.40472 3.301 0.0195
## W2 - W12 31.3315 8.403341 487.07 5.66683 56.99617 3.728 0.0043
## W2 - W16 35.4810 9.264041 302.77 7.09656 63.86544 3.830 0.0033
## W2 - W20 31.8415 10.300366 191.29 0.12466 63.55834 3.091 0.0412
## W2 - W24 31.3415 11.464790 135.74 -4.15713 66.84013 2.734 0.1095
## W4 - W8 16.2090 7.570377 529.19 -6.90201 39.32001 2.141 0.4254
## W4 - W12 21.8695 8.056908 557.22 -2.72080 46.45980 2.714 0.1095
## W4 - W16 26.0190 8.808274 389.86 -0.91780 52.95580 2.954 0.0566
## W4 - W20 22.3795 9.763524 237.45 -7.60547 52.36447 2.292 0.3188
## W4 - W24 21.8795 10.869033 158.87 -11.68096 55.43996 2.013 0.5496
## W8 - W12 5.6605 7.570377 529.19 -17.45051 28.77151 0.748 1.0000
## W8 - W16 9.8100 8.056908 557.22 -14.78030 34.40030 1.218 1.0000
## W8 - W20 6.1705 8.808274 389.86 -20.76630 33.10730 0.701 1.0000
## W8 - W24 5.6705 9.763524 237.45 -24.31447 35.65547 0.581 1.0000
## W12 - W16 4.1495 7.570377 529.19 -18.96151 27.26051 0.548 1.0000
## W12 - W20 0.5100 8.056908 557.22 -24.08030 25.10030 0.063 1.0000
## W12 - W24 0.0100 8.808274 389.86 -26.92680 26.94680 0.001 1.0000
## W16 - W20 -3.6395 7.570377 529.19 -26.75051 19.47151 -0.481 1.0000
## W16 - W24 -4.1395 8.056908 557.22 -28.72980 20.45080 -0.514 1.0000
## W20 - W24 -0.5000 7.570377 529.19 -23.61101 22.61101 -0.066 1.0000
##
## Treatment = Amlitelimab 125 mg:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## W2 - W4 16.0090 7.443777 490.23 -6.72429 38.74229 2.151 0.4646
## W2 - W8 26.8685 7.776798 565.84 3.13478 50.60222 3.455 0.0106
## W2 - W12 36.1405 8.403341 487.07 10.47583 61.80517 4.301 0.0004
## W2 - W16 33.4105 9.264041 302.77 5.02606 61.79494 3.606 0.0073
## W2 - W20 37.1995 10.300366 191.29 5.48266 68.91634 3.611 0.0074
## W2 - W24 37.6310 11.464790 135.74 2.13237 73.12963 3.282 0.0223
## W4 - W8 10.8595 7.570377 529.19 -12.25151 33.97051 1.434 1.0000
## W4 - W12 20.1315 8.056908 557.22 -4.45880 44.72180 2.499 0.2041
## W4 - W16 17.4015 8.808274 389.86 -9.53530 44.33830 1.976 0.6290
## W4 - W20 21.1905 9.763524 237.45 -8.79447 51.17547 2.170 0.4646
## W4 - W24 21.6220 10.869033 158.87 -11.93846 55.18246 1.989 0.6290
## W8 - W12 9.2720 7.570377 529.19 -13.83901 32.38301 1.225 1.0000
## W8 - W16 6.5420 8.056908 557.22 -18.04830 31.13230 0.812 1.0000
## W8 - W20 10.3310 8.808274 389.86 -16.60580 37.26780 1.173 1.0000
## W8 - W24 10.7625 9.763524 237.45 -19.22247 40.74747 1.102 1.0000
## W12 - W16 -2.7300 7.570377 529.19 -25.84101 20.38101 -0.361 1.0000
## W12 - W20 1.0590 8.056908 557.22 -23.53130 25.64930 0.131 1.0000
## W12 - W24 1.4905 8.808274 389.86 -25.44630 28.42730 0.169 1.0000
## W16 - W20 3.7890 7.570377 529.19 -19.32201 26.90001 0.501 1.0000
## W16 - W24 4.2205 8.056908 557.22 -20.36980 28.81080 0.524 1.0000
## W20 - W24 0.4315 7.570377 529.19 -22.67951 23.54251 0.057 1.0000
##
## Treatment = Amlitelimab 250 mg:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## W2 - W4 11.3790 7.443777 490.23 -11.35429 34.11229 1.529 1.0000
## W2 - W8 23.4580 7.776798 565.84 -0.27572 47.19172 3.016 0.0428
## W2 - W12 32.3995 8.403341 487.07 6.73483 58.06417 3.856 0.0026
## W2 - W16 42.2295 9.264041 302.77 13.84506 70.61394 4.558 0.0002
## W2 - W20 40.0495 10.300366 191.29 8.33266 71.76634 3.888 0.0026
## W2 - W24 38.0905 11.464790 135.74 2.59187 73.58913 3.322 0.0195
## W4 - W8 12.0790 7.570377 529.19 -11.03201 35.19001 1.596 1.0000
## W4 - W12 21.0205 8.056908 557.22 -3.56980 45.61080 2.609 0.1305
## W4 - W16 30.8505 8.808274 389.86 3.91370 57.78730 3.502 0.0093
## W4 - W20 28.6705 9.763524 237.45 -1.31447 58.65547 2.936 0.0547
## W4 - W24 26.7115 10.869033 158.87 -6.84896 60.27196 2.458 0.1958
## W8 - W12 8.9415 7.570377 529.19 -14.16951 32.05251 1.181 1.0000
## W8 - W16 18.7715 8.056908 557.22 -5.81880 43.36180 2.330 0.2420
## W8 - W20 16.5915 8.808274 389.86 -10.34530 43.52830 1.884 0.6640
## W8 - W24 14.6325 9.763524 237.45 -15.35247 44.61747 1.499 1.0000
## W12 - W16 9.8300 7.570377 529.19 -13.28101 32.94101 1.298 1.0000
## W12 - W20 7.6500 8.056908 557.22 -16.94030 32.24030 0.949 1.0000
## W12 - W24 5.6910 8.808274 389.86 -21.24580 32.62780 0.646 1.0000
## W16 - W20 -2.1800 7.570377 529.19 -25.29101 20.93101 -0.288 1.0000
## W16 - W24 -4.1390 8.056908 557.22 -28.72930 20.45130 -0.514 1.0000
## W20 - W24 -1.9590 7.570377 529.19 -25.07001 21.15201 -0.259 1.0000
##
## Treatment = Amlitelimab 250 mg + LD 500 mg:
## contrast estimate SE df lower.CL upper.CL t.ratio p.value
## W2 - W4 8.9105 7.443777 490.23 -13.82279 31.64379 1.197 1.0000
## W2 - W8 22.3110 7.776798 565.84 -1.42272 46.04472 2.869 0.0641
## W2 - W12 31.6695 8.403341 487.07 6.00483 57.33417 3.769 0.0037
## W2 - W16 34.4105 9.264041 302.77 6.02606 62.79494 3.714 0.0046
## W2 - W20 39.9110 10.300366 191.29 8.19416 71.62784 3.875 0.0031
## W2 - W24 40.0710 11.464790 135.74 4.57237 75.56963 3.495 0.0115
## W4 - W8 13.4005 7.570377 529.19 -9.71051 36.51151 1.770 0.7728
## W4 - W12 22.7590 8.056908 557.22 -1.83130 47.34930 2.825 0.0659
## W4 - W16 25.5000 8.808274 389.86 -1.43680 52.43680 2.895 0.0641
## W4 - W20 31.0005 9.763524 237.45 1.01553 60.98547 3.175 0.0288
## W4 - W24 31.1605 10.869033 158.87 -2.39996 64.72096 2.867 0.0659
## W8 - W12 9.3585 7.570377 529.19 -13.75251 32.46951 1.236 1.0000
## W8 - W16 12.0995 8.056908 557.22 -12.49080 36.68980 1.502 1.0000
## W8 - W20 17.6000 8.808274 389.86 -9.33680 44.53680 1.998 0.5568
## W8 - W24 17.7600 9.763524 237.45 -12.22497 47.74497 1.819 0.7719
## W12 - W16 2.7410 7.570377 529.19 -20.37001 25.85201 0.362 1.0000
## W12 - W20 8.2415 8.056908 557.22 -16.34880 32.83180 1.023 1.0000
## W12 - W24 8.4015 8.808274 389.86 -18.53530 35.33830 0.954 1.0000
## W16 - W20 5.5005 7.570377 529.19 -17.61051 28.61151 0.727 1.0000
## W16 - W24 5.6605 8.056908 557.22 -18.92980 30.25080 0.703 1.0000
## W20 - W24 0.1600 7.570377 529.19 -22.95101 23.27101 0.021 1.0000
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95
## Conf-level adjustment: bonferroni method for 21 estimates
## P value adjustment: holm method for 21 tests
##
## >>> 10. Diagnostik Linear Mixed Model
## Grafik tersimpan: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad/G7_QQ_residual_LMM.png
## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using formula = 'y ~ x'
## Grafik tersimpan: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad/G8_residual_vs_prediksi.png
## Grafik tersimpan: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad/G9_residual_kelompok_waktu.png
## Grafik tersimpan: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad/G10_QQ_random_intercept.png
## Grafik tersimpan: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad/G11_QQ_random_slope.png
## [1] FALSE
## [1] TRUE
## attr(,"gradient")
## [1] 6.758615e-06
##
## >>> 11. Ringkasan diagnostik LMM tiga panel
##
## >>> 12. Grafik rerata prediksi LMM dan 95% CI
## Grafik tersimpan: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad/G13_profil_prediksi_LMM.png
##
## >>> 13. Panduan interpretasi dan batas kesimpulan
##
## >>> 14. Status eksekusi dan informasi sesi
## bagian status
## 1 1. Sumber, desain, dan kelengkapan data Selesai
## 2 2. Statistik deskriptif dan hubungan antarwaktu Selesai
## 3 3. Evaluasi distribusi, outlier, dan homogenitas Selesai
## 4 4. RM ANOVA pada kelompok fokus Selesai
## 5 5. Pembanding nonparametrik pada kelompok fokus Selesai
## 6 6. Mixed Design ANOVA: Treatment × Time Selesai
## 7 7. Efek sederhana dan post-hoc Mixed Design ANOVA Selesai
## 8 8. Linear Mixed Model menggunakan seluruh pengukuran tersedia Selesai
## 9 9. Estimated marginal means dan kontras LMM Selesai
## 10 10. Diagnostik Linear Mixed Model Selesai
## 11 11. Ringkasan diagnostik LMM tiga panel Selesai
## 12 12. Grafik rerata prediksi LMM dan 95% CI Selesai
## 13 13. Panduan interpretasi dan batas kesimpulan Selesai
## peringatan
## 1
## 2
## 3
## 4
## 5
## 6
## 7
## 8
## 9
## 10
## 11
## 12
## 13
## R version 4.6.1 (2026-06-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26200)
##
## Matrix products: default
## LAPACK version 3.12.1
##
## locale:
## [1] LC_COLLATE=Indonesian_Indonesia.utf8 LC_CTYPE=Indonesian_Indonesia.utf8
## [3] LC_MONETARY=Indonesian_Indonesia.utf8 LC_NUMERIC=C
## [5] LC_TIME=Indonesian_Indonesia.utf8
##
## time zone: Asia/Makassar
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] tibble_3.3.1 base64enc_0.1-6 htmltools_0.5.9 knitr_1.52
## [5] performance_0.18.2 pbkrtest_0.5.5 lmerTest_3.2-1 effectsize_1.0.3
## [9] car_3.1-5 carData_3.0-6 rstatix_1.1.0 emmeans_2.0.4
## [13] afex_1.5-1 lme4_2.0-6 Matrix_1.7-6 ggplot2_4.0.3
## [17] tidyr_1.3.2 dplyr_1.2.1 readr_2.2.0
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 farver_2.1.2 S7_0.2.2
## [4] fastmap_1.2.0 bayestestR_0.19.0 digest_0.6.39
## [7] estimability_2.0.0 lifecycle_1.0.5 magrittr_2.0.5
## [10] compiler_4.6.1 rlang_1.3.0 sass_0.4.10
## [13] tools_4.6.1 yaml_2.3.12 labeling_0.4.3
## [16] bit_4.6.0 plyr_1.8.9 RColorBrewer_1.1-3
## [19] abind_1.4-8 withr_3.0.3 purrr_1.2.2
## [22] numDeriv_2016.8-1.1 stats4_4.6.1 grid_4.6.1
## [25] datawizard_1.4.0 scales_1.4.0 MASS_7.3-66
## [28] insight_1.5.4 cli_3.6.6 mvtnorm_1.4-2
## [31] rmarkdown_2.32 crayon_1.5.3 ragg_1.5.2
## [34] reformulas_0.4.4 generics_0.1.4 otel_0.2.0
## [37] rstudioapi_0.19.0 reshape2_1.4.5 tzdb_0.5.0
## [40] parameters_0.29.3 minqa_1.2.8 cachem_1.1.0
## [43] stringr_1.6.0 splines_4.6.1 parallel_4.6.1
## [46] vctrs_0.7.3 boot_1.3-32 jsonlite_2.0.0
## [49] hms_1.1.4 bit64_4.8.6 Formula_1.2-6
## [52] systemfonts_1.3.2 jquerylib_0.1.4 glue_1.8.1
## [55] nloptr_2.2.1 stringi_1.8.9 gtable_0.3.6
## [58] pillar_1.11.1 R6_2.6.1 textshaping_1.0.5
## [61] Rdpack_2.6.6 vroom_1.7.1 evaluate_1.0.5
## [64] lattice_0.23-1 rbibutils_2.4.1 backports_1.5.1
## [67] broom_1.0.13 bslib_0.12.0 Rcpp_1.1.2
## [70] nlme_3.1-171 mgcv_1.9-4 xfun_0.61
## [73] pkgconfig_2.0.3
##
## ============================================================
## ANALISIS SELESAI
## Folder hasil: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad
## Laporan HTML: C:/Users/kesba/Downloads/FERI/ARS/BIOSTATISTIKA/fix/hasil_analisis_stream_ad/LAPORAN_ANALISIS_STREAM_AD.html
## Grafik G1-G13, tabel CSV, sessionInfo, dan data kerja telah disimpan.
## ============================================================