Computational formulas in cifcurve()

Formulas for the Kaplan–Meier estimator

Let $t_1 < t_2 < \cdots < t_D$ represent the distinct event times. For each event time $j=1,\ldots,D$, let $Y_j$ be the size of the risk set (the number of surviving observations) just prior to $t_j$.

Survival data is assumed when outcome.type = "survival". Let $d_j$ be the number of failures at $t_j$. The Kaplan–Meier estimator of the survival function $S(t)$ is as below.

Kaplan–Meier estimator

$$\hat{S}(t) = \prod_{t_j \le t} \left(1 - \frac{d_j}{Y_j} \right)$$ Note that the estimator is defined to be right-continuous, so the events at $t_j$ are included in the estimate of $\hat{S}(t_j)$.

The variance or standard error of the Kaplan–Meier estimator is often calculated with the Greenwood formula. This formula is derived from a binomial argument, so extension to the weighted case is ad hoc. Alternatively, Tsiatis (1981) proposes a slightly different formula based on a counting process argument which includes the weighted case.

Greenwood variance $$\textrm{Var}\!\left\{\hat{S}(t)\right \} = \hat{S}(t)^2\sum_{t_j \le t} \frac{d_j}{Y_j\,(Y_j - d_j)}$$

Tsiatis variance $$\textrm{Var}\!\left\{\hat{S}(t)\right \} = \hat{S}(t)^2\sum_{t_j \le t} \frac{d_j}{Y_j^2}$$

Xie and Liu (2005) considered Greenwood-type variance for the weighted Kaplan–Meier estimator. Suppose that the survival data consists of $\{T_i, d_i, w_i\}$ ($i=1,...,N$), an independent sample of right-censored survival data with weights. Let $d_j^w$ and $Y_j^w$ be the weighted number of failures and the weighted number at risk, respectively, at time $t_j$. The weighted Kaplan–Meier estimator of the survival function $S(t)$ is

$$\hat{S}(t) = \prod_{t_j \le t} \left( 1 - \frac{d_j^w}{Y_j^w} \right).$$ The variance proposed by Xie and Liu (2005) is expressed as an adjusted Greenwood formula,

Greenwood variance for weighted Kaplan–Meier $$\textrm{Var}\!\left\{\hat{S}(t)\right \} = \hat{S}(t)^2\sum_{t_j \le t} \frac{d_j^w Y_j}{M_j Y_j^w(Y_j^w - d_j^w)},$$ where $M_j$ is an adjustment factor defined as $$M_j = \frac{\left(\sum_{t_i \geq t_j} w_i \right)^2}{\sum_{t_i \geq t_j} w_i^2}.$$ The Tsiatis-type variance is calculated as follows in the same spirit.

Tsiatis variance for weighted Kaplan–Meier $$\textrm{Var}\!\left\{\hat{S}(t)\right \} = \hat{S}(t)^2\sum_{t_j \le t} \frac{d_j^w Y_j}{M_j (Y_j^w)^2}$$

Formulas for the Aalen-Johansen estimator

Competing risks (outcome.type = "competing-risk") arise in studies in which individuals are exposed to two or more mutually exclusive failure events. When a failure occurs, we observe the time to event $T$ and the cause of failure $\epsilon$. Suppose that $\epsilon=1$ and $\epsilon=2$ represent the event of interest and the competing risk, respectively. Let $d_{jk}$ be the number of failures of cause $k$ at time $t_j$, and now the total number of failures at $t_j$ is $d_j = d_{j1} + d_{j2}$.

The Aalen-Johansen estimator of CIF for cause $\epsilon=k$ is as below.

Aalen-Johansen estimator

$$\hat{F}_k(t) = \sum_{t_j \le t} \frac{d_{jk}}{Y_j}\,\hat{S}(t_{j-1}).$$

where $\hat{S}(t)$ is the overall survival function.

Two variance estimators of the Aalen-Johansen estimator are commonly used: one based on counting process theory (Aalen, 1978) and the other based on the delta method.

Aalen variance $$\begin{aligned} \textrm{Var}\!\left\{\hat{F}_k(t)\right \} &= \sum_{t_j \le t} \bigl[\hat{F}_k(t) - \hat{F}_k(t_j)\bigr]^2 \frac{d_j}{(Y_j-1)(Y_j - d_j)} \\[2pt] &\quad + \sum_{t_j \le t} \hat{S}^2(t_{j-1}) \frac{d_{jk}\,(Y_j - d_{jk})}{Y_j^2\,(Y_j-1)} \\[2pt] &\quad - 2 \sum_{t_j \le t} \bigl[\hat{F}_k(t) - \hat{F}_k(t_j)\bigr]\, \hat{S}(t_{j-1}) \frac{d_{jk}\,(Y_j - d_{jk})}{Y_j\,(Y_j - d_j)\,(Y_j-1)}. \end{aligned}$$

Delta method variance $$\begin{aligned} \textrm{Var}\!\left\{\hat{F}_k(t)\right \} &= \sum_{t_j \le t} \bigl[\hat{F}_k(t) - \hat{F}_k(t_j)\bigr]^2 \frac{d_j}{Y_j\,(Y_j - d_j)} \\[2pt] &\quad + \sum_{t_j \le t} \hat{S}^2(t_{i-1}) \frac{d_{jk}\,(Y_j - d_{jk})}{Y_j^3} \\[2pt] &\quad - 2 \sum_{t_j \le t} \bigl[\hat{F}_k(t) - \hat{F}_k(t_j)\bigr]\, \hat{S}(t_{j-1}) \frac{d_{jk}}{Y_j^2}. \end{aligned}$$

Variance based on influence functions

It is known that the Aalen-Johansen estimator can be expanded under regularity conditions as
$$n^{1/2}\{\hat F_k(t) - F_k(t)\} = n^{-1/2} \sum_{i=1}^n IF_{ik}(t) + o_p(1)$$ and the process $n^{1/2}\{\hat F_{ik}(t) - F_{ik}(t)\}$ converges weakly to a tight Gaussian process. Here $IF_{ik}(t)$ is the influence function, the contribution of $i$-th observation to the Aalen-Johansen estimator, and may be written as $$IF_{ik}(t) = \int_0^t \frac{n S(u^-)}{Y(u)}\,dM_i(u) - \int_0^t \frac{n F_k(u^-)}{Y(u)}\,dM_{ik}(u),$$

where $M_i(t)$ and $M_{ik}(t)$ is the Martingale process of the total count and the count of cause $k$ of $i$-th observation, respectively, and $Y(t)$ is the at-risk process. A consistent variance estimator for $n^{1/2}\{\hat F_k(t) - F_k(t)\}$ is $n^{-1} \sum_{i=1}^n \{\widehat{IF}_{ik}(t)\}^2$.

Confidence interval options

Standard errors The default in cifcurve() with weights=NULL is the Greenwood SE when outcome.type="survival" and the delta SE when outcome.type="competing-risk". The default in cifcurve() with weights is the SE based on influence functions. By default cifcurve() rescales the Greenwood/Tsiatis quantities so that std.err is reported on the probability scale; set report.survfit.std.err = TRUE to return the conventional log-survival SEs from survival::survfit().

Confidence intervals cifcurve() constructs intervals on the probability scale using the requested transformation: "arcsine-square root"/"arcsin"/"a" (default), "plain", "log", "log-log", or "logit". Passing "none"/"n" skips interval computation entirely. The function exponentiates back to the probability scale, clips bounds to [0, 1], and replaces undefined values with NA so that interval endpoints remain well behaved in plots and summaries.