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Multiple variances (Claret) #399

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Aug 12, 2024
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Multiple variances (Claret) #399

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8 changes: 4 additions & 4 deletions R/LongitudinalClaretBruno.R
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Expand Up @@ -77,10 +77,10 @@ LongitudinalClaretBruno <- function(
Parameter(name = "lm_clbr_mu_c", prior = mu_c, size = "n_arms"),
Parameter(name = "lm_clbr_mu_p", prior = mu_p, size = "n_arms"),

Parameter(name = "lm_clbr_omega_b", prior = omega_b, size = 1),
Parameter(name = "lm_clbr_omega_g", prior = omega_g, size = 1),
Parameter(name = "lm_clbr_omega_c", prior = omega_c, size = 1),
Parameter(name = "lm_clbr_omega_p", prior = omega_p, size = 1),
Parameter(name = "lm_clbr_omega_b", prior = omega_b, size = "n_studies"),
Parameter(name = "lm_clbr_omega_g", prior = omega_g, size = "n_arms"),
Parameter(name = "lm_clbr_omega_c", prior = omega_c, size = "n_arms"),
Parameter(name = "lm_clbr_omega_p", prior = omega_p, size = "n_arms"),

Parameter(name = "lm_clbr_sigma", prior = sigma, size = 1)
)
Expand Down
47 changes: 42 additions & 5 deletions R/SimLongitudinalClaretBruno.R
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Expand Up @@ -81,6 +81,12 @@ SimLongitudinalClaretBruno <- function(
link_identity = 0,
link_growth = 0
) {

if (length(omega_b) == 1) omega_b <- rep(omega_b, length(mu_b))
if (length(omega_g) == 1) omega_g <- rep(omega_g, length(mu_g))
if (length(omega_c) == 1) omega_c <- rep(omega_c, length(mu_c))
if (length(omega_p) == 1) omega_p <- rep(omega_p, length(mu_p))

.SimLongitudinalClaretBruno(
times = times,
sigma = sigma,
Expand Down Expand Up @@ -111,8 +117,23 @@ setValidity(
if (length(unique(par_lengths)) != 1) {
return("The parameters `mu_g`, `mu_c` & `mu_p` must have the same length.")
}
pairs <- list(
"omega_b" = "mu_b",
"omega_g" = "mu_g",
"omega_c" = "mu_c",
"omega_p" = "mu_p"
)
for (i in seq_along(pairs)) {
omega <- slot(object, names(pairs)[[i]])
mu <- slot(object, pairs[[i]])
if (!(length(omega) == length(mu))) {
return(
sprintf("`%s` must be length 1 or the same length as `%s`", omega, mu)
)
}
}
len_1_pars <- c(
"sigma", "omega_b", "omega_g", "omega_c", "omega_p",
"sigma",
"link_dsld", "link_ttg", "link_identity",
"link_growth"
)
Expand Down Expand Up @@ -164,10 +185,26 @@ sampleSubjects.SimLongitudinalClaretBruno <- function(object, subjects_df) {
dplyr::distinct(.data$subject, .data$arm, .data$study) |>
dplyr::mutate(study_idx = as.numeric(.data$study)) |>
dplyr::mutate(arm_idx = as.numeric(.data$arm)) |>
dplyr::mutate(ind_b = stats::rlnorm(dplyr::n(), object@mu_b[.data$study_idx], object@omega_b)) |>
dplyr::mutate(ind_g = stats::rlnorm(dplyr::n(), object@mu_g[.data$arm_idx], object@omega_g)) |>
dplyr::mutate(ind_c = stats::rlnorm(dplyr::n(), object@mu_c[.data$arm_idx], object@omega_c)) |>
dplyr::mutate(ind_p = stats::rlnorm(dplyr::n(), object@mu_p[.data$arm_idx], object@omega_p))
dplyr::mutate(ind_b = stats::rlnorm(
dplyr::n(),
object@mu_b[.data$study_idx],
object@omega_b[.data$study_idx]
)) |>
dplyr::mutate(ind_g = stats::rlnorm(
dplyr::n(),
object@mu_g[.data$arm_idx],
object@omega_g[.data$arm_idx]
)) |>
dplyr::mutate(ind_c = stats::rlnorm(
dplyr::n(),
object@mu_c[.data$arm_idx],
object@omega_c[.data$arm_idx]
)) |>
dplyr::mutate(ind_p = stats::rlnorm(
dplyr::n(),
object@mu_p[.data$arm_idx],
object@omega_p[.data$arm_idx]
))

res[, c("subject", "arm", "study", "ind_b", "ind_g", "ind_c", "ind_p")]
}
Expand Down
24 changes: 12 additions & 12 deletions inst/stan/lm-claret-bruno/model.stan
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Expand Up @@ -12,10 +12,10 @@ parameters{
vector[n_arms] lm_clbr_mu_c;
vector[n_arms] lm_clbr_mu_p;

real<lower={{ machine_double_eps }}> lm_clbr_omega_b;
real<lower={{ machine_double_eps }}> lm_clbr_omega_g;
real<lower={{ machine_double_eps }}> lm_clbr_omega_c;
real<lower={{ machine_double_eps }}> lm_clbr_omega_p;
vector<lower={{ machine_double_eps }}>[n_studies] lm_clbr_omega_b;
vector<lower={{ machine_double_eps }}>[n_arms] lm_clbr_omega_g;
vector<lower={{ machine_double_eps }}>[n_arms] lm_clbr_omega_c;
vector<lower={{ machine_double_eps }}>[n_arms] lm_clbr_omega_p;

{% if centred -%}
vector<lower={{ machine_double_eps }}>[n_subjects] lm_clbr_ind_b;
Expand Down Expand Up @@ -45,16 +45,16 @@ transformed parameters{

{% if not centred -%}
vector<lower={{ machine_double_eps }}>[n_subjects] lm_clbr_ind_b = exp(
lm_clbr_mu_b[subject_study_index] + (lm_clbr_eta_b * lm_clbr_omega_b)
lm_clbr_mu_b[subject_study_index] + (lm_clbr_eta_b .* lm_clbr_omega_b[subject_study_index])
);
vector<lower={{ machine_double_eps }}>[n_subjects] lm_clbr_ind_g = exp(
lm_clbr_mu_g[subject_arm_index] + (lm_clbr_eta_g * lm_clbr_omega_g)
lm_clbr_mu_g[subject_arm_index] + (lm_clbr_eta_g .* lm_clbr_omega_g[subject_arm_index])
);
vector<lower={{ machine_double_eps }}>[n_subjects] lm_clbr_ind_c = exp(
lm_clbr_mu_c[subject_arm_index] + (lm_clbr_eta_c * lm_clbr_omega_c)
lm_clbr_mu_c[subject_arm_index] + (lm_clbr_eta_c .* lm_clbr_omega_c[subject_arm_index])
);
vector<lower={{ machine_double_eps }}>[n_subjects] lm_clbr_ind_p = exp(
lm_clbr_mu_p[subject_arm_index] + (lm_clbr_eta_p * lm_clbr_omega_p)
lm_clbr_mu_p[subject_arm_index] + (lm_clbr_eta_p .* lm_clbr_omega_p[subject_arm_index])
);
{%- endif -%}

Expand Down Expand Up @@ -89,10 +89,10 @@ model {
// Source - lm-claret-bruno/model.stan
//
{% if centred %}
lm_clbr_ind_b ~ lognormal(lm_clbr_mu_b[subject_study_index], lm_clbr_omega_b);
lm_clbr_ind_g ~ lognormal(lm_clbr_mu_g[subject_arm_index], lm_clbr_omega_g);
lm_clbr_ind_c ~ lognormal(lm_clbr_mu_c[subject_arm_index], lm_clbr_omega_c);
lm_clbr_ind_p ~ lognormal(lm_clbr_mu_p[subject_arm_index], lm_clbr_omega_p);
lm_clbr_ind_b ~ lognormal(lm_clbr_mu_b[subject_study_index], lm_clbr_omega_b[subject_study_index]);
lm_clbr_ind_g ~ lognormal(lm_clbr_mu_g[subject_arm_index], lm_clbr_omega_g[subject_arm_index]);
lm_clbr_ind_c ~ lognormal(lm_clbr_mu_c[subject_arm_index], lm_clbr_omega_c[subject_arm_index]);
lm_clbr_ind_p ~ lognormal(lm_clbr_mu_p[subject_arm_index], lm_clbr_omega_p[subject_arm_index]);
{%- endif -%}

}
8 changes: 4 additions & 4 deletions tests/testthat/test-LongitudinalClaretBruno.R
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Expand Up @@ -97,10 +97,10 @@ test_that("Can recover known distributional parameters from a SF joint model", {
mu_g = log(c(0.9, 1.1)),
mu_c = log(c(0.45, 0.35)),
mu_p = log(c(2.4, 1.8)),
omega_b = 0.12,
omega_g = 0.12,
omega_c = 0.12,
omega_p = 0.12,
omega_b = 0.1,
omega_g = c(0.3, 0.1),
omega_c = c(0.1, 0.3),
omega_p = c(0.3, 0.1),
link_ttg = 0.3,
link_dsld = -0.02,
link_identity = 0,
Expand Down
26 changes: 21 additions & 5 deletions vignettes/statistical-specification.Rmd
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Expand Up @@ -393,10 +393,10 @@ SLD_{ij} &=
b_i \cdot \exp\left(g_i t_{ij} - \frac{p_i}{c_i} \left(1 - e^{-c_i t_{ij} }\right)\right) &
\text{if } t_{ij} \geq 0. \end{cases}\\
\\
b_i &\sim \text{LogNormal}(\mu_{bl(i)}, \omega_b) \\
g_i &\sim \text{LogNormal}(\mu_{gk(i)}, \omega_g) \\
c_i &\sim \text{LogNormal}(\mu_{ck(i)}, \omega_c) \\
p_i &\sim \text{LogNormal}(\mu_{pk(i)}, \omega_p) \\
b_i &\sim \text{LogNormal}(\mu_{bl(i)}, \omega_{b l(i)}) \\
g_i &\sim \text{LogNormal}(\mu_{gk(i)}, \omega_{g k(i)}) \\
c_i &\sim \text{LogNormal}(\mu_{ck(i)}, \omega_{c k(i)}) \\
p_i &\sim \text{LogNormal}(\mu_{pk(i)}, \omega_{p k(i)}) \\
\end{align*}
$$

Expand All @@ -414,7 +414,23 @@ Where:
* $k(i)$ is the treatment arm index for subject $i$
* $l(i)$ is the study index for subject $i$
* $\mu_{\theta k(i)}$ is the population mean for parameter $\theta$ in group $k(i)$
* $\omega_{\theta}$ is the population variance for parameter $\theta$.
* $\omega_{\theta k(i)}$ is the population variance for parameter $\theta$ in group $k(i)$


If using the non-centred parameterisation then the following alternative formulation is used:
$$
\begin{align*}
b_i &= exp(\mu_{b l(i)} + \omega_{b l(i)} * \eta_{b i}) \\
g_i &= exp(\mu_{g k(i)} + \omega_{g k(i)} * \eta_{g i}) \\
c_i &= exp(\mu_{c k(i)} + \omega_{c k(i)} * \eta_{c i}) \\
p_i &= exp(\mu_{p k(i)} + \omega_{p k(i)} * \eta_{p i}) \\
\\
\eta_{b i} &\sim N(0, 1)\\
\eta_{g i} &\sim N(0, 1) \\
\eta_{c i} &\sim N(0, 1) \\
\eta_{p i} &\sim N(0, 1) \\
\end{align*}
$$


### Derivative of the SLD Trajectory Link
Expand Down
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