start = 0) {

ev(amt = dose_mg_m2 * BSA, cmt = if (lipo) 4 else 1,

time = start, ii = interval, addl = ncyc - 1,

rate = if (tinf > 0) dose_mg_m2 * BSA / tinf else 0)

c(ev(amt = 8 * WT, cmt = 12, time = start),

ev(amt = 6 * WT, cmt = 12, time = start + 21,

ii = 21, addl = floor(weeks / 3) - 2))

sc <- SCENARIOS[[name]]

m <- mod

if (length(sc$par)) m <- param(m, sc$par)

m %>% ev(sc$ev) %>% mrgsim(end = tend, delta = 1, ...) %>% as_tibble() %>%

mutate(scenario = name)

d %>% group_by(ID) %>%

summarise(EF0 = first(LVEF), nadir = min(LVEF),

CTRCD = any(LVEF < 50 & (first(LVEF) - LVEF) >= 10),

HF = any(LVEF < 40),

GLS_drop15 = any((first(GLS) - GLS) / first(GLS) >= 0.15),

TNI_pos = any(CTNI > 40 & time < 170),

.groups = "drop")

bind_rows(lapply(c(2, 4, 5, 6, 7, 8, 10), function(ncyc) {

out <- mod %>% ev(dox_ev(60, ncyc, 21)) %>%

mrgsim(end = tend, delta = 1, nid = n) %>% as_tibble()

ctrcd(out) %>% summarise(cum_dose = 60 * ncyc,

CTRCD_pct = 100 * mean(CTRCD),

HF_pct = 100 * mean(HF))

}))

bind_rows(lapply(c("DOX480", "DOX480_WEEKLY", "DOX480_INF72", "DOX480_PLD",

"DOX480_DEX"), function(k) {

out <- sim_scenario(mod, k, tend = tend, nid = n)

idx <- sim_scenario(mod, k, tend = tend) # index patient, no ETAs

ctrcd(out) %>%

summarise(scenario = k, CTRCD_pct = 100 * mean(CTRCD),

HF_pct = 100 * mean(HF)) %>%

# index-patient exposure metrics: the point is that AUC_CH barely moves

# across these rows while peak_nuclear and peak_P53 move by 10-500x

mutate(peak_nuclear = max(idx$CHF), peak_P53 = max(idx$P53),

peak_CH = max(idx$CH), AUC_CH = max(idx$AUCH))

}))

sim_scenario(mod, name) %>%

filter(time %in% c(0, 60, 120, 170, 240, 365, 540, 730)) %>%

transmute(time, LVEF, LVEF_NOMASK, LVEF_IRREV, MYOCYTES, FUNCDEF,

FIBROSIS, MASK,

mask_points = LVEF - LVEF_NOMASK,

reversible_points = LVEF_IRREV - LVEF)

out <- sim_scenario(mod, name, nid = n)

first_day <- function(d, cond) {

d %>% group_by(ID) %>% filter({{ cond }}) %>%

summarise(day = min(time), .groups = "drop")

}

tni <- first_day(out, CTNI > 14)

gls <- out %>% group_by(ID) %>%

filter((first(GLS) - GLS) / first(GLS) >= 0.15) %>%

summarise(day_gls = min(time), .groups = "drop")

ef <- out %>% group_by(ID) %>%

filter(LVEF < 50, (first(LVEF) - LVEF) >= 10) %>%

summarise(day_ef = min(time), .groups = "drop")

list(tni = tni, gls = gls, ef = ef,

gls_lead = inner_join(gls, ef, "ID") %>%

summarise(median_lead_days = median(day_ef - day_gls)),

tni_npv = ctrcd(out) %>% group_by(TNI_pos) %>%

summarise(CTRCD_pct = 100 * mean(CTRCD), n = n()))

target <- sim_scenario(mod, "DOX240", tend = tend) %>%

filter(time == 365) %>% pull(LVEF)

res <- bind_rows(lapply(4:14, function(ncyc) {

out <- mod %>% ev(c(dox_ev(60, ncyc, 21), dex_ev(60, ncyc, 21))) %>%

mrgsim(end = tend, delta = 1) %>% as_tibble()

tibble(cum_dose = 60 * ncyc,

LVEF_12m = out$LVEF[out$time == 365])

}))

list(unprotected_240_LVEF12m = target, protected = res,

iso_effective_dose = res$cum_dose[which(res$LVEF_12m <= target)[1]])

combos <- list(

`no protection` = list(),

`statin during chemo` = list(TGT_STA = 0.75, TON_STA = 0),

`statin after chemo` = list(TGT_STA = 0.75, TON_STA = 170),

`ACEi+BB during chemo` = list(TGT_ACE = 0.8, TON_ACE = 0,

TGT_BB = 0.8, TON_BB = 0),

`ACEi+BB after chemo` = list(TGT_ACE = 0.8, TON_ACE = 170,

TGT_BB = 0.8, TON_BB = 170),

`ARNI after chemo` = list(TGT_ARNI = 0.9, TON_ARNI = 170),

`SGLT2i during chemo` = list(TGT_SGLT = 0.7, TON_SGLT = 0))

bind_rows(lapply(names(combos), function(nm) {

m <- if (length(combos[[nm]])) param(mod, combos[[nm]]) else mod

out <- m %>% ev(dox_ev(60, 8, 21)) %>%

mrgsim(end = 730, delta = 1, nid = n) %>% as_tibble()

ctrcd(out) %>% summarise(strategy = nm, CTRCD_pct = 100 * mean(CTRCD),

mean_nadir = mean(nadir))

}))

get <- function(k) {

out <- sim_scenario(mod, k, nid = n)

out %>% group_by(ID) %>%

summarise(maxfall = first(LVEF) - min(LVEF),

regain = last(LVEF) - min(LVEF), .groups = "drop") %>%

summarise(scenario = k, maxfall = mean(maxfall), regain = mean(regain))

}

res <- bind_rows(lapply(c("DOX240", "TRAS_ALONE", "DOX240_TSEQ",

"DOX240_TCONC"), get))

additive <- res$maxfall[1] + res$maxfall[2]

res %>% mutate(additive_prediction = additive,

excess = maxfall - additive)

270, 365, 540)) {

bind_rows(lapply(starts, function(t0) {

m <- param(mod, list(TGT_ACE = 0.8, TON_ACE = t0,

TGT_BB = 0.8, TON_BB = t0))

out <- m %>% ev(dox_ev(60, 8, 21)) %>%

mrgsim(end = t0 + 365, delta = 1, nid = n) %>% as_tibble()

out %>% group_by(ID) %>%

summarise(had_ctrcd = any(LVEF < 50 & (first(LVEF) - LVEF) >= 10 &

time <= 365),

EF_start = LVEF[which.min(abs(time - t0))],

EF_end = last(LVEF),

FIB_start = FIBROSIS[which.min(abs(time - t0))],

EF_base = first(LVEF), .groups = "drop") %>%

filter(had_ctrcd) %>%

summarise(start_day = t0, n = n(), EF_start = mean(EF_start),

EF_12mo_later = mean(EF_end),

recovered_pct = 100 * mean(EF_end >= 50 |

(EF_base - EF_end) < 5),

FIB_at_start = mean(FIB_start))

}))

bind_rows(lapply(c(0.12, 0.18, 0.25, 0.34, 0.45), function(fm) {

out <- param(mod, FM = fm) %>% ev(dox_ev(60, 8, 21)) %>%

mrgsim(end = 730, delta = 1, nid = n) %>% as_tibble()

ctrcd(out) %>% summarise(FM = fm, CTRCD_pct = 100 * mean(CTRCD),

mean_nadir = mean(nadir))

}))

bind_rows(lapply(names(SCENARIOS), function(k) {

out <- sim_scenario(mod, k, nid = n)

ctrcd(out) %>% summarise(scenario = k, label = SCENARIOS[[k]]$label,

CTRCD_pct = 100 * mean(CTRCD),

HF_pct = 100 * mean(HF),

GLS15_pct = 100 * mean(GLS_drop15),

mean_nadir = mean(nadir))

}))

list(dose_response = dose_response(mod, n),

schedules = compare_schedules(mod, n),

mask = decompose_mask(mod),

lead_times = lead_times(mod, n = n),

dexrazoxane_sparing = dex_sparing(mod),

upstream_vs_downstream = upstream_vs_downstream(mod, n),

trastuzumab = trastuzumab_interaction(mod, n),

reversibility_window = reversibility_window(mod, n),

metaboliser = metaboliser_status(mod, n),

population = vpop_incidence(mod, n))