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For simplicity we consider a two-stage case with a binary action set \(\{0,1\}\) at each stage and a final outcome \(U\).
A full observation without right censoring is given by the temporally ordered variable \(O\):
\[\begin{align*} O^F = \{B, X_1, A_1, X_2, A_2, X_3, U_3\}, \end{align*}\]
where \(B\) denotes the baseline variables, \(X_1, X_2, X_3\) denote the stage specific variables, \(U\) denotes the outcome/utility, and \(A_1, A_2\) denote the treatment actions.
Now, introduce non-missingness indicators \(\Delta_1, \Delta_2, \Delta_3\). Under right-censoring, an observation is given by
\[\begin{align*} O = \{B, X_1, \Delta_1, \Delta_1 A_1, \Delta_1 X_2, \Delta_1 \Delta_2, \Delta_1 \Delta_2 A_2, \Delta_1 \Delta_2 X_3, \Delta_1 \Delta_2\Delta_3, \Delta_1 \Delta_2 \Delta_3 U\}. \end{align*}\]
For convenience, \(H_1 = \{B, X_1\}\), \(H_2 = \{B, X_1, A_1, X_2\}\), and \(H_3 = \{B, X_1, A_1, X_2, A_2, X_3\}\).
The full data target parameter is still
\[\begin{aligned} \mathbb{E}\left[ \left(\prod_{k=1}^2 \frac{I\{A_k = d_k(H_k\} }{g_{0,k}(H_k, A_k)} \right) U \right], \end{aligned}\]We assume the following (strong) sequential randomization for the right-censoring process:
\[\begin{aligned} &\Delta_3 \perp U | H_3, \Delta_1 = 1, \Delta_2 = 1\\ &\Delta_2 \perp \{A_2, X_3, \Delta_3, U\}| H_2, \Delta_1 = 1 \\ &\Delta_1 \perp \{A_1, X_2, \Delta_2, A_2, X_3, \Delta_3, U\} | H_1. \end{aligned}\]Under these assumptions it can be shown that our observed data target parameter is equal to the full data target parameter:
\[\begin{aligned} &\mathbb{E}\left[ \left(\prod_{k=1}^3 \frac{\Delta_k}{C_{0,k}(H_k)} \right) \left(\prod_{k=1}^2 \frac{I\{A_k = d_k(H_k\} }{g_{0,k}(H_k, A_k)} \right) U \right]\\ =&\mathbb{E}\left[ \left(\prod_{k=1}^2 \frac{I\{A_k = d_k(H_k\} }{g_{0,k}(H_k, A_k)} \right) U \right], \end{aligned}\] where \[\begin{aligned} C_{0,k}(h_k) = \mathbb{P}(\Delta_k = 1| H_k = h_k, \overline{\Delta}_k = 1) \end{aligned}\]and
\[\begin{aligned} g_{0,1}(H_1, a_1) &= \mathbb{P}(A_1 = a_1|H_1, \Delta_1 = 1) = \mathbb{P}(A_1 = a_1|H_1)\\ g_{0,2}(H_2, a_2) &= \mathbb{P}(A_2 = a_2|H_2, \Delta_1 = 1, \Delta_2 = 1) = \mathbb{P}(A_2 = a_2|H_2) \end{aligned}\] Note the observed target parameter is also identified via the g-computation formula \[\begin{aligned} \mathbb{E}[Q^d_{0,1}(H_1, d_1(H_1))], \end{aligned}\] where \[\begin{aligned} Q^d_{0,1}(h_1, d_1(h_1)) &= \mathbb{E}[Q^d_{0,2}(H_2, d_2(H_2))|H_1=h_1, \Delta_1 = 1, A_1 = d(h_1)]\\ Q^d_{0,2}(h_2, d_2(h_2)) &= \mathbb{E}[Q^d_{0,3}(H_3)|H_2 = h_2, \Delta_1 = 1, \Delta_2 = 1, A_2 = d(h_2)]\\ Q^d_{0,3}(h_3) &= \mathbb{E}[U|H_3 = h_3, \Delta_1 = 1, \Delta_2 = 2, \Delta_3 = 1]. \end{aligned}\]Note that the observed target parameter can be seen as a 5-step inverse probability weighting expression, and that the associated g-computation formula can be reduced to 3 steps. This would not be the case if additional information was collected between \(\Delta_k\) and \(A_k\).
For the same reason the (un-centralized) efficient/robust score reduces to
\[\begin{aligned} Z_1(d,& g, Q^d, C)(O) \\ &= \,Q_{1}^{d}(H_1 , d_1(H_1)) \\ &+ \sum_{r = 1}^3 \left\{ \prod_{j = 1}^{r} \frac{\Delta_j}{C_j(H_j)} \frac{I\{A_j = d_j(H_{j})\}}{g_{j}(H_j, A_j)} \right\}\left\{Q_{r+1}^{d}(H_{r+1} , d_{r+1}(H_{r+1})) - Q_{r}^{d}(H_r , d_r(H_r))\right\}, \end{aligned}\] where we set \[\begin{aligned} \frac{I\{A_3 = d_3(H_{3})\}}{g_{3}(H_3, A_3)} = 1, \qquad Q_{4}^{d}(H_{4} , d_{4}(H_{4})) = U. \end{aligned}\]The above expression for the score by direct calculation of the efficient influence function for the observed target parameter or by utilizing a monotone coarsening argument, Tsiatis (2006) or Laan and Robins (2003).
A one-step estimator based on this score is implmented in polle via policy_eval().
Assume that \(A_1\), \(A_2\), \(U\) occur at time points \(T_1 < T_2 < T_3\). Let \(C\) denote an underlying censoring time point. Then we observe time points \(\tilde T_k = \min(T_k, C)\) and non-misising indicators \(\Delta_k = \{T_k \leq C\}\). Note that with this temporal structure \(\prod_{j = 1}^k \Delta_j = \Delta_k\). For notational convenience we include \(\tilde T_k\) in the state variable \(X_k\).
Now, let \(C_k\) denote a survival model for the censoring process:
\[\begin{aligned} C_{0,k}(\tilde T_k|H_k) = \mathbb{P}(C > T_k|H_k, C \geq \tilde T_{k-1}) \end{aligned}\] \[\begin{aligned} Z_1(d,& g, Q^d, C)(O) \\ &= \,Q_{1}^{d}(H_1 , d_1(H_1)) \\ &+ \sum_{r = 1}^3 \left\{ \prod_{j = 1}^{r} \frac{\Delta_j}{C_j(\tilde T_j|H_j)} \frac{I\{A_j = d_j(H_{j})\}}{g_{j}(H_j, A_j)} \right\}\left\{Q_{r+1}^{d}(H_{r+1} , d_{r+1}(H_{r+1})) - Q_{r}^{d}(H_r , d_r(H_r))\right\}. \end{aligned}\]We note that this score adapted to handle continuous right-censroing is not the efficient score which incorporate the induced temporal structure. A study of the second order remainder terms yields that for correctly specified nuisance models for \(g_0, Q^d_0, C_0\), under reasonable convergence rate assumptions, the associated one-step estimator has asymptotic variance as induced by the influence function \(Z_1(d,g_0, Q^d_0, C_0)(O) - \mathbb{E}[Z_1(d,g_0, Q^d_0, C_0)(O)]\).
sim_single_stage_right_cens <- function(n = 2e3, zeta = c(0.7, 0.2), type = "right"){
d <- sim_single_stage(n = n)
pd <- policy_data(data = d,
action = "A",
covariates = c("Z", "L", "B"),
utility = "U")
ld <- pd$stage_data
ld[stage == 1, time := 1]
ld[stage == 2, time := 2]
ld[stage == 2, Z := d$Z]
ld[stage == 2, L := d$L]
ld[stage == 2, B := d$B]
## simulating the right censoring time
## only depending on the baseline covariate Z:
C <- c(rexp(n, 1) / exp((-1) * cbind(1, as.numeric(d$Z)) %*% zeta))
ld[stage == 1, time_c := C]
ld[stage == 2, time_c := C]
ld[, delta := time_c >= time]
ld[delta == FALSE , event := 2]
ld[delta == FALSE, A := NA]
ld[delta == FALSE & stage == 2, U := NA]
ld[delta == FALSE, U_A0 := 0]
ld[delta == FALSE, U_A1 := 0]
ld[ , tmp := shift(delta, fill = TRUE), by = list(id)]
ld <- ld[tmp == TRUE, ]
ld[ , time := pmin(time, time_c)]
ld[ , time_c := NULL]
ld[ , tmp := NULL]
ld[ , delta := NULL]
if (type == "interval"){
ld[, time2 := time]
ld[, time := shift(time, fill = 0), by = list(id)]
}
return(ld)
}set.seed(1)
ld <- sim_single_stage_right_cens(n = 5e2, type = "interval")
pd <- policy_data(data = ld, type = "long", action = "A", time = "time", time2 = "time2")pe <- policy_eval(
policy_data = pd,
policy = policy_def(1),
m_model = q_glm(~ .),
m_full_history = TRUE,
c_models = list(c_cox(formula = ~ Z_1),
c_cox(formula = ~ Z_2*A_1)),
c_full_history = TRUE
)
pe## Estimate Std.Err 2.5% 97.5% P-value
## E[U(d)] -3.092 0.443 -3.96 -2.224 2.959e-12pe <- policy_eval(
policy_data = pd,
policy_learn = policy_learn(type = "blip", control = control_blip()),
m_model = q_glm(~.),
m_full_history = FALSE,
c_models = c_cox(formula = ~ Z)
)
pe## Estimate Std.Err 2.5% 97.5% P-value
## E[U(d)]: d=blip(eta=0) 0.3275 0.2114 -0.08694 0.7419 0.1214These binaries (installable software) and packages are in development.
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