This R package provides functions to identify an optimal transformation of a potential surrogate marker such that the proportion of the treatment effect on a primary outcome can be inferred based on this identified optimal transformation. The potential surrogate may be continuous or discrete. These estimates are based on model-free definitions of the proportion of treatment effect explained and thus, do not require any correct model specification. It is also here with examples.
The goal of Optimal Surrogate Survival (OSsurvival) is to nonparametrically estimate the proportion of treatment on the primary outcome explained (PTE) by an optimal transformation of the surrogate marker measured at an earlier time. The primary outcome measured at a later time may be subject to censoring.
The goal of Calibrated Model Fusion (CMFsurrogate) approach is to estimate the proportion of treatment on the primary outcome explained (PTE) by optimally combining multiple markers. This approach is unique in that it identifies an optimal combination of the multiple surrogates without strictly relying on parametric assumptions while borrowing modeling strategies to avoid fully non-parametric estimation which is subject to the curse of dimensionality. It is also here with examples.
PTE (proportion of treatment explained) and RP (relative power) estimates to evaluate the surrogacy of a surrogate for the primary outcome
Model-free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival
Semiparametric Joint Modeling (SJM) to examine the treatment effect on a longitudinal surrogate marker
This package contains R functions to compute the conditional censoring logistic (CCL) estimator and model metrics to evaluate risk predictions using panel current status data. The CCL estimator takes advantage of the ability to transform panel current status data
into a binary outcome analysis, building on existing logistic regression estimators by incorporating monitoring time information into the working model.
For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of healthcare centers without sharing patient-level information. An interactive online shiny app (https://shiny.parse-health.org/SurvMaximin/) has been implemented to perform the proposed algorithm with user input data.
Differential Associations of Interleukin 6 Receptor Variant Across Genetic Ancestries and Implications for Targeted Therapies
Develop and validate scalable machine learning (ML) models to infer RA disease activity from EHR data linked with registries.
Github: https://github.com/wx202