README.md

In wilsoncai1992/onestep.survival: One-step TMLE for survival analysis

onestep.survival

The onestep.survival R package is a tool for estimating counterfactual survival curve under static or dynamic interventions on treatment (exposure), while at the same time adjust for measured counfounding. Targeted Maximum Likelihood Estimate (TMLE) approach is employed to create a doubly robust and semi-parametrically efficient estimator. Machine Learning algorithms (SuperLearner) are implemented to all stages of the estimation.

Currently implemented estimators include:

1. One-step TMLE for the whole survival curve
2. One-step TMLE for survival at a specific end point
3. Iterative TMLE for survival at a specific end point

Installation

To install the development version (requires the devtools package):

install.packages('SuperLearner')
install.packages('tmle')
install.packages('Matrix')
devtools::install_github('wilsoncai1992/survtmle2')
devtools::install_github('wilsoncai1992/onestep.survival')

Documentation

• To see all available package documentation:

?onestep.survival
help(package = 'onestep.survival')

Brief overview

Data structure

The data input of all methods in the package should be an R data.frame in the following survival long data format:

#   ID W A T.tilde delta
# 1  1 0 0      95     1
# 2  2 1 1       1     0
# 3  3 0 0     215     1
# 4  4 1 1      15     1
# 5  5 0 0      73     1
# 6  6 0 0      15     1

Usage

# simulate data
library(simcausal)
D <- DAG.empty()
D <- D +
  node("W", distr = "rbinom", size = 1, prob = .5) +
  node("A", distr = "rbinom", size = 1, prob = .35 + .4*W) +
  # Time to failure has an exponential distribution:
  node("Trexp", distr = "rexp", rate = 1 + .5*W - .5*A) +
  # Time to censoring has a weibull distribution:
  node("Cweib", distr = "rweibull", shape = .7 - .2*W, scale = 1) +
  # Actual time to failure is scaled by 100:
  node("T", distr = "rconst", const = round(Trexp*100,0)) +
  node("C", distr = "rconst", const = round(Cweib*100, 0)) +
  # Observed random variable (follow-up time):
  node("T.tilde", distr = "rconst", const = ifelse(T <= C , T, C)) +
  # Observed random variable (censoring indicator, 1 - failure event, 0 - censored):
  node("delta", distr = "rconst", const = ifelse(T <= C , 1, 0))
setD <- set.DAG(D)

# Simulate the data from the above data generating distribution:
dat <- sim(setD, n=1e2)
# subset into observed dataset
library(dplyr)
# only grab ID, W's, A, T.tilde, Delta
Wname <- grep('W', colnames(dat), value = TRUE)
dat <- dat[,c('ID', Wname, 'A', "T.tilde", "delta")]
head(dat)
# check positivity
check_positivity(dat)

library(onestep.survival)
# iterative TMLE: each time separately
dW <- rep(1, nrow(dat))
# dW <- rep(0, nrow(dat))
iterative_tmle <- survtmle_multi_t(dat = dat, dW = dW,
                                  SL.ftime = c("SL.glm","SL.mean","SL.step"),
                                  SL.ctime = c("SL.glm","SL.mean"),
                                  SL.trt = c("SL.glm","SL.mean","SL.step"))
plot(iterative_tmle, add = TRUE)

# one-step TMLE: target entire curve
dW <- rep(1, nrow(dat))
# dW <- rep(0, nrow(dat))
onestep_whole_curve <- surv_onestep(dat = dat, dW = dW,
                                    g.SL.Lib = c("SL.glm","SL.mean","SL.step"),
                                    Delta.SL.Lib = c("SL.glm","SL.mean", 'SL.gam'),
                                    ht.SL.Lib = c("SL.glm","SL.mean","SL.step", 'SL.gam'))
plot(onestep_whole_curve, add = TRUE)

Citation

To cite onestep.survival in publications, please use:

Cai W, van der Laan MJ (2016). One-step TMLE for time-to-event outcomes. Working paper.

Funding

Copyright

This software is distributed under the GPL-2 license.

Community Guidelines

Feedback, bug reports (and fixes!), and feature requests are welcome; file issues or seek support here.

wilsoncai1992/onestep.survival documentation built on May 29, 2019, 11:58 a.m.