Survival Models
Survival models apply to animals and plants as well as inanimate objects such as machine parts or electrical components. Survival models arise when there is an event of interest for a group of subjects, machine component, or other item that is
- certain to occur after some amount of time,
- but only measured for a fixed period of time, during which the event may not have occurred for all subjects.
For example, one might wish to estimate the the distribution of time to failure for solid state drives in a data center, but only measure drives for a two year period, after which some number will have failed and some will still be in service.
Survival models are often used comparatively, such as comparing time to death of patients diagnosed with stage one liver cancer under a new treatment and a standard treatment (pure controls are not allowed when there is an effective existing treatment for a serious condition). During a two year trial, some patients will die and others will survive.
Survival models may involve covariates, such as the factory at which a component is manufactured, the day on which it is manufactured, and the amount of usage it gets. A clinical trial might be adjusted for the sex and age of a cancer patient or the hospital at which treatment is received.
Survival models come in two main flavors, parametric and semi-parametric. In a parametric model, the survival time of a subject is modeled explicitly using a parametric probability distribution. There is a great deal of flexibility in how the parametric probability distribution is constructed. The sections below consider exponential and Weibull distributed survival times.
Rather than explicitly modeling a parametric survival probability, semi-parametric survival models instead model the relative effect on survival of covariates. The final sections of this chapter consider the proportional hazards survival model.
Exponential survival model
The exponential distribution is commonly used in survival models where there is a constant risk of failure that does not go up the longer a subject survives. This is because the exponential distribution is memoryless in sense that if \(T \sim \textrm{exponential}(\lambda)\) for some rate \(\lambda > 0,\) then \[\begin{equation*} \Pr[T > t] = \Pr[T > t + t' \mid T > t']. \end{equation*}\] If component survival times are distributed exponentially, it means the distribution of time to failure is the same no matter how long the item has already survived. This can be a reasonable assumption for electronic components, but is not a reasonable model for animal survival.
The exponential survival model has a single parameter for the rate, which assumes all subjects have the same distribution of failure time (this assumption is relaxed in the next section by introducing per-subject covariates). With the rate parameterization, the expected survival time for a component with survival time represented as the random variable \(T\) is \[\begin{equation*} \mathbb{E}[T \mid \lambda] = \frac{1}{\lambda}. \end{equation*}\] The exponential distribution is sometimes parameterized in terms of a scale (i.e., inverse rate) \(\beta = 1 / \lambda\).
The data for a survival model consists of two components. First, there is a vector \(t \in (0, \infty)^N\) of \(N\) observed failure times. Second, there is a censoring time \(t^{\textrm{cens}}\) such that failure times greater than \(t^{\textrm{cens}}\) are not observed. The censoring time assumption imposes a constraint which requires \(t_n < t^{\textrm{cens}}\) for all \(n \in 1{:}N.\) For the censored subjects, the only thing required in the model is their total count, \(N^\textrm{cens}\) (their covariates are also required for models with covariates).
The model for the observed failure times is exponential, so that for \(n \in 1{:}N,\) \[\begin{equation*} t_n \sim \textrm{exponential}(\lambda). \end{equation*}\]
The model for the censored failure times is also exponential. All that is known of a censored item is that its failure time is greater than the censoring time, so each censored item contributes a factor to the likelihood of \[\begin{equation*} \Pr[T > t^{\textrm{cens}}] = 1 - F_T(t^{\textrm{cens}}), \end{equation*}\] where \(F_T\) is the cumulative distribution function (cdf) of survival time \(T\) (\(F_X(x) = \Pr[X \leq x]\) is standard notation for the cdf of a random variable \(X\)). The function \(1 - F_T(t)\) is the complementary cumulative distribution function (ccdf), and it is used directly to define the likelihood \[\begin{eqnarray*} p(t, t^{\textrm{cens}}, N^{\textrm{cens}} \mid \lambda) & = & \prod_{n=1}^N \textrm{exponential}(t_n \mid \lambda) \cdot \prod_{n=1}^{N^{\textrm{cens}}} \textrm{exponentialCCDF}(t^{\textrm{cens}} \mid \lambda) \\ & = & \prod_{n=1}^N \textrm{exponential}(t_n \mid \lambda) \cdot \textrm{exponentialCCDF}(t^{\textrm{cens}} \mid \lambda)^{N^{\textrm{cens}}}. \end{eqnarray*}\]
On the log scale, that’s \[\begin{eqnarray*} \log p(t, t^{\textrm{cens}}, N^{\textrm{cens}} \mid \lambda) & = & \sum_{n=1}^N \log \textrm{exponential}(t_n \mid \lambda) \\ & & { } + N^{\textrm{cens}} \cdot \log \textrm{exponentialCCDF}(t^{\textrm{cens}} \mid \lambda). \end{eqnarray*}\]
The model can be completed with a standard lognormal prior on \(\lambda,\) \[\begin{equation*} \lambda \sim \textrm{lognormal}(0, 1), \end{equation*}\] which is reasonable if failure times are in the range of 0.1 to 10 time units, because that’s roughly the 95% central interval for a variable distributed \(\textrm{lognormal}(0, 1)\). In general, the range of the prior (and likelihood!) should be adjusted with prior knowledge of expected failure times.
Stan program
The data for a simple survival analysis without covariates can be coded as follows.
data {
int<lower=0> N;
vector[N] t;
int<lower=0> N_cens;
real<lower=0> t_cens;
}In this program, N is the number of uncensored observations and t contains the times of the uncensored observations. There are a further N_cens items that are right censored at time t_cens. Right censoring means that if the time to failure is greater than
t_cens, it is only observed that the part survived until time t_cens. In the case where there are no covariates, the model only needs the number of censored items because they all share the same censoring time.
There is a single rate parameter, the inverse of which is the expected time to failure.
parameters {
real<lower=0> lambda;
}The exponential survival model and the prior are coded directly using vectorized distribution and ccdf statements. This both simplifies the code and makes it more computationally efficient by sharing computation across instances.
model {
t ~ exponential(lambda);
target += N_cens * exponential_lccdf(t_cens | lambda);
lambda ~ lognormal(0, 1);
}The likelihood for rate lambda is just the density of exponential distribution for observed failure time. The Stan code is vectorized, modeling each entry of the vector t as a having an exponential distribution with rate lambda. This data model could have been written as
for (n in 1:N) {
t[n] ~ exponential(lambda);
}The log likelihood contribution given censored items is the number of censored items times the log complementary cumulative distribution function (lccdf) at the censoring time of the exponential distribution with rate lambda. The log likelihood terms arising from the censored events could have been added to the target log density one at a time,
for (n in 1:N)
target += exponential_lccdf(t_cens | lambda);to define the same log density, but it is much more efficient computationally to multiply by a constant than do a handful of sequential additions.
Weibull survival model
The Weibull distribution is a popular alternative to the exponential distribution in cases where there is a decreasing probability of survival as a subject gets older. The Weibull distribution models this by generalizing the exponential distribution to include a power-law trend.
The Weibull distribution is parameterized by a shape \(\alpha > 0\) and scale \(\sigma > 0.\) For an outcome \(t \geq 0\), the Weibull distribution’s probability density function is \[\begin{equation*} \textrm{Weibull}(t \mid \alpha, \sigma) = \frac{\alpha}{\sigma} \cdot \left( \frac{t}{\sigma} \right)^{\alpha - 1} \cdot \exp\left(-\left(\frac{t}{\sigma}\right)^{\alpha}\right). \end{equation*}\] In contrast, recall that the exponential distribution can be expressed using a rate (inverse scale) parameter \(\beta > 0\) with probability density function \[\begin{equation*} \textrm{exponential}(t \mid \beta) = \beta \cdot \exp(-\beta \cdot t). \end{equation*}\] When \(\alpha = 1,\) the Weibull distribution reduces to an exponential distribution, \[\begin{equation*} \textrm{Weibull}(t \mid 1, \sigma) = \textrm{exponential}\!\left(t \,\bigg|\, \frac{1}{\sigma}\right). \end{equation*}\] In other words, the Weibull is a continuous expansion of the exponential distribution.
If \(T \sim \textrm{Weibull}(\alpha, \sigma),\) then the expected survival time is \[\begin{equation*} \mathbb{E}[T] = \sigma \cdot \Gamma\!\left(1 + \frac{1}{\alpha}\right), \end{equation*}\] where the \(\Gamma\) function is the continuous completion of the factorial function (i.e., \(\Gamma(1 + n) = n!\ \) for \(n \in \mathbb{N}\)). As \(\alpha \rightarrow 0\) for a fixed \(\sigma\) or as \(\sigma \rightarrow \infty\) for a fixed \(\alpha\), the expected survival time goes to infinity.
There are three regimes of the Weibull distribution.
\(\alpha < 1.\) A subject is more likely to fail early. When \(\alpha < 1,\) the Weibull density approaches infinity as \(t \rightarrow 0.\)
\(\alpha = 1.\) The Weibull distribution reduces to the exponential distribution, with a constant rate of failure over time. When \(\alpha = 1,\) the Weibull distribution approaches \(\sigma\) as \(t \rightarrow 0.\)
\(\alpha > 1.\) Subjects are less likely to fail early. When \(\alpha < 1,\) the Weibull density approaches zero as \(t \rightarrow 0.\)
With \(\alpha \leq 1,\) the mode is zero (\(t = 0\)), whereas with \(\alpha > 1,\) the mode is nonzero (\(t > 0\)).
Stan program
With Stan, one can just swap the exponential distribution for the Weibull distribution with the appropriate parameters and the model remains essentially the same. Recall the exponential model’s parameters and model block.
parameters {
real<lower=0> beta;
}
model {
t ~ exponential(beta);
target += N_cens * exponential_lccdf(t_cens | beta);
beta ~ lognormal(0, 1);
}The Stan program for the Weibull model just swaps in the Weibull distribution and complementary cumulative distribution function with shape (alpha) and scale (sigma) parameters.
parameters {
real<lower=0> alpha;
real<lower=0> sigma;
}
model {
t ~ weibull(alpha, sigma);
target += N_cens * weibull_lccdf(t_cens | alpha, sigma);
alpha ~ lognormal(0, 1);
sigma ~ lognormal(0, 1);
}As usual, if more is known about expected survival times, alpha and sigma should be given more informative priors.
Survival with covariates
Suppose that for each of \(n \in 1{:}N\) items observed, both censored and uncensored, there is a covariate (row) vector \(x_n \in \mathbb{R}^K.\) For example, a clinical trial may include the age (or a one-hot encoding of an age group) and the sex of a participant; an electronic component might include a one-hot encoding of the factory at which it was manufactured and a covariate for the load under which it has been run.
Survival with covariates replaces what is essentially a simple regression with only an intercept \(\lambda\) with a generalized linear model with a log link, where the rate for item \(n\) is \[\begin{equation*} \lambda_n = \exp(x_n \cdot \beta), \end{equation*}\] where \(\beta \in \mathbb{R}^K\) is a \(K\)-vector of regression coefficients. Thus \[\begin{equation*} t_n \sim \textrm{exponential}(\lambda_n). \end{equation*}\] The censored items have probability \[\begin{equation*} \Pr[n\textrm{-th censored}] = \textrm{exponentialCCDF}(t^{\textrm{cens}} \mid x^{\textrm{cens}}_n \cdot \beta). \end{equation*}\]
The covariates form an \(N \times K\) data matrix, \(x \in \mathbb{R}^{N \times K}\). An intercept can be introduced by adding a column of 1 values to \(x\).
A Stan program for the exponential survival model with covariates is as follows. It relies on the fact that the order of failure times (t and t_cens) corresponds to the ordering of items in the covariate matrices (x and x_cens).
data {
int<lower=0> N;
vector[N] t;
int<lower=0> N_cens;
real<lower=0> t_cens;
int<lower=0> K;
matrix[N, K] x;
matrix[N_cens, K] x_cens;
}
parameters {
vector[K] gamma;
}
model {
gamma ~ normal(0, 2);
t ~ exponential(exp(x * gamma));
target += exponential_lccdf(t_cens | exp(x_cens * gamma));
}Both the distribution statement for uncensored times and the log density increment statement for censored times are vectorized, one in terms of the exponential distribution and one in terms of the log complementary cumulative distribution function.
Hazard and survival functions
Suppose \(T\) is a random variable representing a survival time, with a smooth cumulative distribution function \[\begin{equation*} F_T(t) = \Pr[T \leq t], \end{equation*}\] so that its probability density function is \[\begin{equation*} p_T(t) = \frac{\textrm{d}}{\textrm{d}t} F_T(t). \end{equation*}\]
The survival function \(S(t)\) is the probability of surviving until at least time \(t\), which is just the complementary cumulative distribution function (ccdf) of the survival random variable \(T\), \[\begin{equation*} S(t) = 1 - F_T(t). \end{equation*}\] The survival function appeared in the Stan model in the previous section as the likelihood for items that did not fail during the period of the experiment (i.e., the censored failure times for the items that survived through the trial period).
The hazard function \(h(t)\) is the instantaneous risk of not surviving past time \(t\) assuming survival until time \(t\), which is given by \[\begin{equation*} h(t) = \frac{p_T(t)}{S(t)} = \frac{p_T(t)}{1 - F_T(t)}. \end{equation*}\] The cumulative hazard function \(H(t)\) is defined to be the accumulated hazard over time, \[\begin{equation*} H(t) = \int_0^t h(u) \, \textrm{d}u. \end{equation*}\]
The hazard function and survival function are related through the differential equation \[\begin{eqnarray*} h(t) & = & -\frac{\textrm{d}}{\textrm{d}t} \log S(t). \\[4pt] & = & -\frac{1}{S(t)} \frac{\textrm{d}}{\textrm{d}t} S(t) \\[4pt] & = & \frac{1}{S(t)} \frac{\textrm{d}}{\textrm{d}t} -(1 - F_Y(t)) \\[4pt] & = & \frac{1}{S(t)} \frac{\textrm{d}}{\textrm{d}t} (F_Y(t) - 1) \\[4pt] & = & \frac{1}{S(t)} \frac{\textrm{d}}{\textrm{d}t} F_Y(t) \\[4pt] & = & \frac{p_T(t)}{S(t)}. \end{eqnarray*}\]
If \(T \sim \textrm{exponential}(\beta)\) has an exponential distribution, then its hazard function is constant, \[\begin{eqnarray*} h(t \mid \beta) & = & \frac{p_T(t \mid \beta)}{S(t \mid \beta)} \\[4pt] & = & \frac{\textrm{exponential}(t \mid \beta)}{1 - \textrm{exponentialCCDF}(t \mid \beta)} \\[4pt] & = & \frac{\beta \cdot \exp(-\beta \cdot t)} {1 - (1 - \exp(-\beta \cdot t))} \\[4pt] & = & \frac{\beta \cdot \exp(-\beta \cdot t)} {\exp(-\beta \cdot t)} \\[4pt] & = & \beta. \end{eqnarray*}\] The exponential distribution is the only distribution of survival times with a constant hazard function.
If \(T \sim \textrm{Weibull}(\alpha, \sigma),\) then its hazard function is \[\begin{eqnarray*} h(t \mid \alpha, \sigma) & = & \frac{p_T(t \mid \alpha, \sigma)}{S(t \mid \alpha, \sigma)} \\[4pt] & = & \frac{\textrm{Weibull}(t \mid \alpha, \sigma}{1 - \textrm{WeibullCCDF}(t \mid \alpha, \sigma)} \\[4pt] & = & \frac{\frac{\alpha}{\sigma} \cdot \left( \frac{t}{\sigma} \right)^{\alpha - 1} \cdot \exp\left(-\left(\frac{t}{\sigma} \right)^\alpha\right)} {1 - \left(1 - \exp\left(-\left(\frac{t}{\sigma}\right)^\alpha \right)\right)} \\[4pt] & = & \frac{\alpha}{\sigma} \cdot \left( \frac{t}{\sigma} \right)^{\alpha - 1}. \end{eqnarray*}\]
If \(\alpha = 1\) the hazard is constant over time (which also follows from the fact that the Weibull distribution reduces to the exponential distribution when \(\alpha = 1\)). When \(\alpha > 1,\) the hazard grows as time passes, whereas when \(\alpha < 1,\) it decreases as time passes.
Proportional hazards model
The exponential model is parametric in that is specifies an explicit parametric form for the distribution of survival times. Cox (1972) introduced a semi-parametric survival model specified directly in terms of a hazard function \(h(t)\) rather than in terms of a distribution over survival times. Cox’s model is semi-parametric in that it does not model the full hazard function, instead modeling only the proportional differences in hazards among subjects.
Let \(x_n \in \mathbb{R}^K\) be a (row) vector of covariates for subject \(n\) so that the full covariate data matrix is \(x \in \mathbb{R}^{N \times K}\). In Cox’s model, the hazard function for subject \(n\) is defined conditionally in terms of their covariates \(x_n\) and the parameter vector \(\gamma \in \mathbb{R}^K\) as \[\begin{equation*} h(t \mid x_n, \beta) = h_0(t) \cdot \exp(x_n \cdot \gamma), \end{equation*}\] where \(h_0(t)\) is a shared baseline hazard function and \(x_n \cdot \gamma = \sum_{k=1}^K x_{n, k} \cdot \beta_k\) is a row vector-vector product.
In the semi-parametric, proportional hazards model, the baseline hazard function \(h_0(t)\) is not modeled. This is why it is called “semi-parametric.” Only the factor \(\exp(x_n \cdot \gamma),\) which determines how individual \(n\) varies by a proportion from the baseline hazard, is modeled. This is why it’s called “proportional hazards.”
Cox’s proportional hazards model is not fully generative. There is no way to generate the times of failure because the baseline hazard function \(h_0(t)\) is unmodeled; if the baseline hazard were known, failure times could be generated. Cox’s proportional hazards model is generative for the ordering of failures conditional on a number of censored items. Proportional hazard models may also include parametric or non-parametric model for the baseline hazard function1.
Partial likelihood function
Cox’s proportional specification of the hazard function is insufficient to generate random variates because the baseline hazard function \(h_0(t)\) is unknown. On the other hand, the proportional specification is sufficient to generate a partial likelihood that accounts for the order of the survival times.
The hazard function \(h(t \mid x_n, \beta) = h_0(t) \cdot \exp(x_n \cdot \beta)\) for subject \(n\) represents the instantaneous probability that subject \(n\) fails at time \(t\) given that it has survived until time \(t.\) The probability that subject \(n\) is the first to fail among \(N\) subjects is thus proportional to subject \(n\)’s hazard function, \[\begin{equation*} \Pr[n \textrm{ first to fail at time } t] \propto h(t \mid x_n, \beta). \end{equation*}\] Normalizing yields \[\begin{eqnarray*} \Pr[n \textrm{ first to fail at time } t] & = & \frac{h(t \mid x_n, \beta)} {\sum_{n' = 1}^N h(t \mid x_{n'}, \beta)} \\[4pt] & = & \frac{h_0(t) \cdot \exp(x_n \cdot \beta)} {\sum_{n' = 1}^N h_0(t) \cdot \exp(x_{n'} \cdot \beta)} \\[4pt] & = & \frac{\exp(x_n \cdot \beta)} {\sum_{n' = 1}^N \exp(x_{n'} \cdot \beta)}. \end{eqnarray*}\]
Suppose there are \(N\) subjects with strictly ordered survival times \(t_1 < t_2 < \cdots < t_N\) and covariate (row) vectors \(x_1, \ldots, x_N\). Let \(t^{\textrm{cens}}\) be the (right) censoring time and let \(N^{\textrm{obs}}\) be the largest value of \(n\) such that \(t_n \leq t^{\textrm{cens}}\). This means \(N^{\textrm{obs}}\) is the number of subjects whose failure time was observed. The ordering is for convenient indexing and does not cause any loss of generality—survival times can simply be sorted into the necessary order.
With failure times sorted in decreasing order, the partial likelihood for each observed subject \(n \in 1{:}N^{\textrm{obs}}\) can be expressed as \[\begin{equation*} \Pr[n \textrm{ first to fail among } n, n + 1, \ldots N] = \frac{\exp(x_n \cdot \beta)} {\sum_{n' = n}^N \exp(x_{n'} \cdot \beta)}. \end{equation*}\] The group of items for comparison and hence the summation is over all items, including those with observed and censored failure times.
The partial likelihood, defined in this form by Breslow (1975), is just the product of the partial likelihoods for the observed subjects (i.e., excluding subjects whose failure time is censored). \[\begin{equation*} \Pr[\textrm{observed failures ordered } 1, \ldots, N^{\textrm{obs}} | x, \beta] = \prod_{n = 1}^{N^{\textrm{obs}}} \frac{\exp(x_n \cdot \beta)} {\sum_{n' = n}^N \exp(x_{n'} \cdot \beta)}. \end{equation*}\] On the log scale, \[\begin{eqnarray*} \log \Pr[\textrm{obs.\ fail ordered } 1, \ldots, N^{\textrm{obs}} | x, \beta] & = & \sum_{n = 1}^{N^{\textrm{obs}}} \log \left( \frac{\exp(x_n \cdot \beta)} {\sum_{n' = n}^N \exp(x_{n'} \cdot \beta)} \right) \\[4pt] & = & x_n \cdot \beta - \log \sum_{n' = n}^N \exp(x_{n'} \cdot \beta) \\ & = & x_n \cdot \beta - \textrm{logSumExp}_{n' = n}^N \ x_{n'} \cdot \beta, \end{eqnarray*}\] where \[\begin{equation*} \textrm{logSumExp}_{n = a}^b \ x_n = \log \sum_{n = a}^b \exp(x_n) \end{equation*}\] is implemented so as to preserve numerical precision.
This likelihood follows the same approach to ranking as that developed by Plackett (1975) for estimating the probability of the order of the first few finishers in a horse race.
A simple normal prior on the components of \(\beta\) completes the model, \[\begin{equation*} \beta \sim \textrm{normal}(0, 2). \end{equation*}\] This should be scaled based on knowledge of the predictors.
Stan program
To simplify the Stan program, the survival times for uncensored events are sorted into decreasing order (unlike in the mathematical presentation, where they were sorted into ascending order). The covariates for censored and uncensored observations are separated into two matrices.
data {
int<lower=0> K; // num covariates
int<lower=0> N; // num uncensored obs
vector[N] t; // event time (non-strict decreasing)
matrix[N, K] x; // covariates for uncensored obs
int N_c; // num censored obs
real<lower=t[N]> t_c; // censoring time
matrix[N_c, K] x_c; // covariates for censored obs
}The parameters are just the coefficients.
parameters {
vector[K] beta; // slopes (no intercept)
}The prior is a simple independent centered normal distribution on each element of the parameter vector, which is vectorized in the Stan code.
model {
beta ~ normal(0, 2);
...The log likelihood is implemented so as to minimize duplicated effort. The first order of business is to calculate the linear predictors, which is done separately for the subjects whose event time is observed and those for which the event time is censored.
vector[N] log_theta = x * beta;
vector[N_c] log_theta_c = x_c * beta;These vectors are computed using efficient matrix-vector multiplies. The log of exponential values of the censored covariates times the coefficients is reused in the denominator of each factor, which on the log scale, starts with the log sum of exponentials of the censored items’ linear predictors.
real log_denom = log_sum_exp(log_theta_c);Then, for each observed survival time, going backwards from the latest to the earliest, the denominator can be incremented (which turns into a log sum of exponentials on the log scale), and then the target is updated with its likelihood contribution.
for (n in 1:N) {
log_denom = log_sum_exp(log_denom, log_theta[n]);
target += log_theta[n] - log_denom; // log likelihood
}The running log sum of exponentials is why the list is iterated in reverse order of survival times. It allows the log denominator to be accumulated one term at a time. The condition that the survival times are sorted into decreasing order is not checked. It could be checked very easily in the transformed data block by adding the following code.
transformed data {
for (n in 2:N) {
if (!(t[n] < t[n - 1])) {
reject("times must be strictly decreasing, but found"
"!(t[", n, "] < t[, ", (n - 1), "])");
}
}
}Stan model for tied survival times
Technically, for continuous survival times, the probability of two survival times being identical will be zero. Nevertheless, real data sets often round survival times, for instance to the nearest day or week in a multi-year clinical trial. The technically “correct” thing to do in the face of unknown survival times in a range would be to treat their order as unknown and infer it. But considering all \(N!\) permutations for a set of \(N\) subjects with tied survival times is not tractable. As an alternative, Efron (1977) introduced an approximate partial likelihood with better properties than a random permutation while not being quite as good as considering all permutations. Efron’s model averages the contributions as if they truly did occur simultaneously.
In the interest of completeness, here is the Stan code for an implementation of Efron’s estimator. It uses two user-defined functions. The first calculates how many different survival times occur in the data.
functions {
int num_unique_starts(vector t) {
if (size(t) == 0) return 0;
int us = 1;
for (n in 2:size(t)) {
if (t[n] != t[n - 1]) us += 1;
}
return us;
}This is then used to compute the value J to send into the function that computes the position in the array of failure times where each new failure time starts, plus an end point that goes one past the target. This is a standard way in Stan to code ragged arrays.
array[] int unique_starts(vector t, int J) {
array[J + 1] int starts;
if (J == 0) return starts;
starts[1] = 1;
int pos = 2;
for (n in 2:size(t)) {
if (t[n] != t[n - 1]) {
starts[pos] = n;
pos += 1;
}
}
starts[J + 1] = size(t) + 1;
return starts;
}
}The data format is exactly the same as for the model in the previous section, but in this case, the transformed data block is used to cache some precomputations required for the model, namely the ragged array grouping elements that share the same survival time.
transformed data {
int<lower=0> J = num_unique_starts(t);
array[J + 1] int<lower=0> starts = unique_starts(t, J);
}For each unique survival time j in 1:J, the subjects indexed from starts[j] to starts[j + 1] - 1 (inclusive) share the same survival time. The number of elements with survival time j is thus (starts[j + 1] - 1) - starts[j] + 1, or just starts[j + 1] - starts[j].
The parameters and prior are also the same—just a vector beta of coefficients with a centered normal prior. Although it starts with the same caching of results for later, and uses the same accumulator for the denominator, the overall partial likelihood is much more involved, and depends on the user-defined functions defining the transformed data variables J and starts.
vector[N] log_theta = x * beta;
vector[N_c] log_theta_c = x_c * beta;
real log_denom_lhs = log_sum_exp(log_theta_c);
for (j in 1:J) {
int start = starts[j];
int end = starts[j + 1] - 1;
int len = end - start + 1;
real log_len = log(len);
real numerator = sum(log_theta[start:end]);
log_denom_lhs = log_sum_exp(log_denom_lhs,
log_sum_exp(log_theta[start:end]));
vector[len] diff;
for (ell in 1:len) {
diff[ell] = log_diff_exp(log_denom_lhs,
log(ell - 1) - log_len
+ log_sum_exp(log_theta[start:end]));
}
target += numerator - sum(diff);
}The special function log_diff_exp is defined as
\[\begin{equation*} \textrm{logDiffExp}(u, v) = \log(\exp(u) - \exp(v)). \end{equation*}\]
Because of how J and starts are constructed, the length len will always be strictly positive so that the log is well defined.
References
Footnotes
Cox mentioned in his seminal paper that modeling the baseline hazard function would improve statistical efficiency, but he did not do it for computational reasons.↩︎