Event History Regression with Pseudo-Observations: Computational Approaches and an Implementation in R

Due to tradition and ease of estimation, the vast majority of clinical and epidemiological papers with time-to-event data report hazard ratios from Cox proportional hazards regression models. Although hazard ratios are well known, they can be difficult to interpret, particularly as causal contrasts, in many settings. Nonparametric or fully parametric estimators allow for the direct estimation of more easily causally interpretable estimands such as the cumulative incidence and restricted mean survival. However, modeling these quantities as functions of covariates is limited to a few categorical covariates with nonparametric estimators, and often requires simulation or numeric integration with parametric estimators. Combining pseudo-observations based on non-parametric estimands with parametric regression on the pseudo-observations allows for the best of these two approaches and has many nice properties. In this paper, we develop a user friendly, easy to understand way of doing event history regression for the cumulative incidence and the restricted mean survival, using the pseudo-observation framework for estimation. The interface uses the well known formulation of a generalized linear model and allows for features including plotting of residuals, the use of sampling weights, and correct variance estimation

Aligning use of intensive care with patient values in the USA: past, present, and future.

For more than three decades, both medical professionals and the public have worried that many patients receive non-beneficial care in US intensive care units during their final months of life. Some of these patients wish to avoid severe cognitive and physical impairments, and protracted deaths in the hospital setting. Recognising when intensive care will not restore a person’s health, and helping patients and families embrace goals related to symptom relief, interpersonal connection, or spiritual fulfilment are central challenges of critical care practice in the USA. We review trials from the past decade of interventions designed to address these challenges, and present reasons why evaluating, comparing, and implementing these interventions have been difficult. Careful scrutiny of the design and interpretation of past trials can show why improving goal concordant care has been so elusive, and suggest new directions for the next generation of research

Aquatic turning performance of painted turtles (Chrysemys picta) and functional consequences of a rigid body design

The ability to capture prey and avoid predation in aquatic habitats depends strongly on the ability to perform unsteady maneuvers (e.g. turns), which itself depends strongly on body flexibility. Two previous studies of turning performance in rigid-bodied taxa have found either high maneuverability or high agility, but not both. However, examinations of aquatic turning performance in rigid-bodied animals have had limited taxonomic scope and, as such, the effects of many body shapes and designs on aquatic maneuverability and agility have yet to be examined. Turtles represent the oldest extant lineage of rigid-bodied vertebrates and the only aquatic rigid-bodied tetrapods. We evaluated the aquatic turning performance of painted turtles, Chrysemys picta (Schneider, 1783) using the minimum length-specific radius of the turning path (R/L) and the average turning rate (ωavg) as measures of maneuverability and agility, respectively. We filmed turtles conducting forward and backward turns in an aquatic arena. Each type of turn was executed using a different pattern of limb movements. During forward turns, turtles consistently protracted the inboard forelimb and held it stationary into the flow, while continuing to move the outboard forelimb and both hindlimbs as in rectilinear swimming. The limb movements of backward turns were more complex than those of forward turns, but involved near simultaneous retraction and protraction of contralateral fore- and hindlimbs, respectively. Forward turns had a minimum R/L of 0.0018 (the second single lowest value reported from any animal) and a maximum ωavg of 247.1°. Values of R/L for backward turns (0.0091-0.0950 L) were much less variable than that of forward turns (0.0018-1.0442 L). The maneuverability of turtles is similar to that recorded previously for rigidbodied boxfish. However, several morphological features of turtles (e.g. shell morphology and limb position) appear to increase agility relative to the body design of boxfish

Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation

There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the g-formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains `unbalanced' even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator

Propensity score weighting plus an adjusted proportional hazards model does not equal doubly robust away from the null

Recently it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model, and propensity score weighting with the intention of forming a doubly robust estimator that is unbiased in large samples when either the Cox model or the propensity score model is correctly specified. This combination does not, in general, produce a doubly robust estimator, even after regression standardization, when there is truly a causal effect. We demonstrate via simulation this lack of double robustness for the semiparametric Cox model, the Weibull proportional hazards model, and a simple proportional hazards flexible parametric model, with both the latter models fit via maximum likelihood. We provide a novel proof that the combination of propensity score weighting and a proportional hazards survival model, fit either via full or partial likelihood, is consistent under the null of no causal effect of the exposure on the outcome under particular censoring mechanisms if either the propensity score or the outcome model is correctly specified and contains all confounders. Given our results suggesting that double robustness only exists under the null, we outline two simple alternative estimators that are doubly robust for the survival difference at a given time point (in the above sense), provided the censoring mechanism can be correctly modeled, and one doubly robust method of estimation for the full survival curve. We provide R code to use these estimators for estimation and inference in the supplementary materials