Survival Analysis With Generalized Additive Models : Part I (background and rationale)

After a really long break, I’d will resume my blogging activity. It is actually a full circle for me, since one of the first posts that kick started this blog, matured enough to be published in a peer-reviewed journal last week. In the next few posts I will use the R code included to demonstrate the survival fitting capabilities of Generalized Additive Models (GAMs) in real world datasets. The first post in this series will summarize the background, rationale and expected benefits to be realized by adopting GAMs from survival analysis.

In a nutshell, the basic ideas of the GAM approach to survival analysis are the following:

  1. One approximates the integral defining the survival function as a discrete sum using a quadrature rule
  2. One evaluates the likelihood at the nodes of the aforementioned quadrature rule
  3. A regression model is postulated for the log-hazard rate function
  4. As a result of 1-3 the survival regression problem is transformed into a Poisson regression one
  5. If penalized regression is used to fit the regression model, then GAM fitting software may be used for survival analysis

Ideas along the lines 1-4 have been re-surfacing in the literature ever since the Proportional Hazards Model was described. The mathematical derivations justifying Steps 1-4 are straightforward to follow and are detailed in the PLoS paper. The corresponding derivations for the Cox model are also described in a previous post.

Developments such as 1-4 were important in the first 10 years of the Cox model, since there were no off-the-shelf implementations of the partial (profile) likelihood approach. This limited the practical scope of proportional hazards modeling and set off a parallel computational line of research in how one could use other statistical software libraries to fit the Cox model.  In fact, the first known to the author application of a proportional model for the analysis of a National Institute of Health (NIH) randomized controlled trial used a software implementing a Poisson regression to calculate the hazard ratio. The trial was the NCDS trial that examined adequacy indices for the hemodialysis prescription (the description of the software was published 6 months prior to the clinical paper).  Many of these efforts were computationally demanding and died off as the Cox model was implemented in the various statistical packages after the late 80s and semi-parametric theory took off and provide a post-hoc justification for many of the nuances implicit in the Cox model.  Nevertheless, one can argue that in the era of the modern computer, no one really needs the Cox model. This technical report and the author’s work on a real world, complex dataset provides the personal background for my research on GAM approaches for survival data.

The GAM (or Poisson GAM, PGAM as called in the paper) is an extension of these old ideas (see the literature survey here and here). In particular, PGAM models the quantities that are modeled semi-parametrically (e.g. the baseline hazard) in the Cox model with parametric, flexible functions that are estimated by penalized regressio. One of the first applications of penalized regression for survival analysis is the Fine and Gray spline model, which is however not a PGAM model.  There are specific benefits to be realized from penalizing the Poisson regression and adopting GAMs  in the context of survival analysis:

  • Parsimony: degrees of freedom are not wasted as penalization will seek the most parsimonious representation (fewer degrees of freedom) among the many possible functions that may fit the data
  • Reduction of the analyst-degrees-of freedom: the shape of the functional relationships between survival and outcome are learned from the data. This limits the potential of someone putting a specific form for this relationship (e.g. linear v.s. quadratic) and running away with the most convenient p-value
  • Multiple time scale modelling: one can model more than one time scales in a dataset (i.e. not just the study time). This is useful when adjusting for secular trends in an observational dataset or even in a randomized trial. In particular cluster randomized trials at the community level may be immune to secular trends
  • Non-proportional hazards modeling: when the hazards are not proportional, the Cox model is not applicable. Many oncology datasets will demonstrate a deviation from proportionality (in fact we re-analyzed such a trial in the PLoS paper) . For a particular dataset, one would like to know whether the proportionality assumption is violated, and if so one would like to “adjust” for it. Such an adjustment will take the form of a time-varying hazard ratio function and these may be estimated with the PGAMs. In such a case, one can even extract an “average” hazard ratio while still estimating a time-varying deviation around it using the PGAM. However non-proportionality should shift the analyst to:
  • Alternative measures of treatment effect: These may include relative risks, absolute risk differences of even differences in the (Restricted) Mean Survival Time. Such measures are calculated from the time varying hazard ratios using statistical simulation techniques
  • Handling of correlated outcomes: Correlated outcomes may arise from center effects, multiple events in the same individual or even cluster randomization designs. The analysis of such outcomes is facilitated by the interpretation of the PGAM as a generalized linear mixed model and the introduction of the corresponding random effects and their correlation structure into the regression
  • Center effects: A variety of modeling options are available including stratified hazards, fixed or random effects
  • Subgroup analyses
  • Time varying external covariates
  • Unconditional/Conditional/Population averaged effects: The unconditional estimate is obtained by indexing the individuals with the group they are assigned to (e.g. placebo or drug in an interventional trial). The conditional estimate is obtained by introducing covariates (e.g. gender, age) into the regression to calculate effects for individuals that share these characteristics. The population effect averages over the conditional effects over all the individuals in the trial. In epidemiology it is known as the corrected group prognosis method. This was introduced in a JAMA paper almost 15 years ago as a way to generate adjusted survival probability curves
  • Handling of right censored/left truncated/uncensored data

These benefits follow directly from the  mixed model equivalence between semi-parametric, penalized regression and Generalized Mixed Linear Models. An excellent, survey may be found here, while Simon Wood’s book in the GAM implementation of the mgcv package in R contains a concise presentation of these ideas.

As it stands the method presented has no software  implementation similar to the survival package in R. Even though we provide R code to run the examples in the paper, the need for the various functions may not be entirely clear. Hence the next series of posts will go over the code and the steps required to fit the PGAM using the R programming language.

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One Response to “Survival Analysis With Generalized Additive Models : Part I (background and rationale)”

  1. Distilled News | Data Analytics & R Says:

    […] Survival Analysis With Generalized Additive Models : Part I (background and rationale) After a really long break, I’d will resume my blogging activity. It is actually a full circle for me, since one of the first posts that kick started this blog, matured enough to be published in a peer-reviewed journal last week. In the next few posts I will use the R code included to demonstrate the survival fitting capabilities of Generalized Additive Models (GAMs) in real world datasets. The first post in this series will summarize the background, rationale and expected benefits to be realized by adopting GAMs from survival analysis. […]

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