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Partial-linear single-index transformation models with censored data

Lifetime Data Analysis, ISSN: 1572-9249, Vol: 30, Issue: 4, Page: 701-720
2024
  • 1
    Citations
  • 0
    Usage
  • 1
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    1
  • Captures
    1
  • Mentions
    1
    • News Mentions
      1
      • News
        1

Most Recent News

Study Data from New York University (NYU) Update Understanding of CDC and FDA (Partial-linear Single-index Transformation Models With Censored Data)

2024 MAY 10 (NewsRx) -- By a News Reporter-Staff News Editor at CDC & FDA Daily -- Researchers detail new data in CDC and FDA.

Article Description

In studies with time-to-event outcomes, multiple, inter-correlated, and time-varying covariates are commonly observed. It is of great interest to model their joint effects by allowing a flexible functional form and to delineate their relative contributions to survival risk. A class of semiparametric transformation (ST) models offers flexible specifications of the intensity function and can be a general framework to accommodate nonlinear covariate effects. In this paper, we propose a partial-linear single-index (PLSI) transformation model that reduces the dimensionality of multiple covariates into a single index and provides interpretable estimates of the covariate effects. We develop an iterative algorithm using the regression spline technique to model the nonparametric single-index function for possibly nonlinear joint effects, followed by nonparametric maximum likelihood estimation. We also propose a nonparametric testing procedure to formally examine the linearity of covariate effects. We conduct Monte Carlo simulation studies to compare the PLSI transformation model with the standard ST model and apply it to NYU Langone Health de-identified electronic health record data on COVID-19 hospitalized patients’ mortality and a Veteran’s Administration lung cancer trial.

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