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An R-Based Landscape Validation of a Competing Risk Model
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Flexible regression models for rate differences, risk differences and relative risks.

Mark W Donoghoe, Ian C Marschner

    The International Journal of Biostatistics
    |March 18, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a stable method using a combinatorial EM algorithm for generalized additive models (GAMs) to estimate rate and risk differences. This approach ensures reliable results for various effect measures in semi-parametric modeling.

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    Area of Science:

    • Biostatistics and Statistical Modeling
    • Clinical Trial Analysis
    • Epidemiology

    Background:

    • Generalized additive models (GAMs) offer flexible semi-parametric modeling for binary and count data.
    • Standard GAMs with canonical links provide odds and rate ratios, but estimating other measures like rate differences requires non-canonical links.
    • Traditional algorithms for constrained non-canonical GAMs can be numerically unstable.

    Purpose of the Study:

    • To describe and apply a combinatorial EM algorithm for fitting specific GAMs (identity link Poisson, identity link binomial, log link binomial).
    • To enable stable estimation of semi-parametrically adjusted rate differences, risk differences, and relative risks.
    • To ensure model estimates remain within the valid parameter space and allow for monotonicity constraints.

    Main Methods:

    • Application of a combinatorial Expectation-Maximization (EM) algorithm.
    • Fitting identity link Poisson, identity link binomial, and log link binomial generalized additive models.
    • Utilizing smooth regression functions based on B-splines for stable convergence and parameter space adherence.

    Main Results:

    • The combinatorial EM algorithm provides stable convergence to maximum likelihood estimates for the specified GAMs.
    • The method ensures that estimated parameters remain within the constrained parameter space.
    • Monotonicity constraints can be straightforwardly applied to smooth regression functions.

    Conclusions:

    • The described combinatorial EM algorithm offers a numerically stable and reliable approach for fitting constrained non-canonical GAMs.
    • This method facilitates the accurate estimation of various effect measures, including rate differences and risk differences, from semi-parametric models.
    • The approach was successfully illustrated using data from a clinical trial involving heart attack patients.