Statistical Inference for Machine Learning Inverse Probability Weighting with Survival Outcomes

09/01/2017
by   Iván Díaz, et al.
0

We present an inverse probability weighted estimator for survival analysis under informative right censoring. Our estimator has the novel property that it converges to a normal variable at n^1/2 rate for a large class of censoring probability estimators, including many data-adaptive (e.g., machine learning) prediction methods. We present the formula of the asymptotic variance of the estimator, which allows the computation of asymptotically correct confidence intervals and p-values under data-adaptive estimation of the censoring and treatment probabilities. We demonstrate the asymptotic properties of the estimator in simulation studies, and illustrate its use in a phase III clinical trial for estimating the effect of a novel therapy for the treatment of breast cancer.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/11/2021

Inference for treatment-specific survival curves using machine learning

In the absence of data from a randomized trial, researchers often aim to...
research
04/20/2022

Estimating optimal individualized treatment rules with multistate processes

Multistate process data are common in studies of chronic diseases such a...
research
06/06/2022

Doubly Robust Inference for Hazard Ratio under Informative Censoring with Machine Learning

Randomized clinical trials with time-to-event outcomes have traditionall...
research
01/30/2019

Causal Proportional Hazards Estimation with a Binary Instrumental Variable

Instrumental variables (IV) are a useful tool for estimating causal effe...
research
11/13/2019

Balanced Policy Evaluation and Learning for Right Censored Data

Individualized treatment rules can lead to better health outcomes when p...
research
05/13/2021

Deep Neural Networks Guided Ensemble Learning for Point Estimation in Finite Samples

As one of the most important estimators in classical statistics, the uni...
research
02/05/2023

Optimal subsampling for the Cox proportional hazards model with massive survival data

The use of massive survival data has become common in survival analysis....

Please sign up or login with your details

Forgot password? Click here to reset