Understanding the predictive value of continuous markers for censored survival data using a likelihood ratio approach

Visualizing the (Causal) Effect of a Continuous Variable on a Time-To-Event Outcome

Visualization is a key aspect of communicating the results of any study aiming to estimate causal effects. In studies with time-to-event outcomes, the most popular visualization approach is depicting survival curves stratified by the variable of interest. This approach cannot be used when the variable of interest is continuous. Simple workarounds, such as categorizing the continuous covariate and plotting survival curves for each category, can result in misleading depictions of the main effects. Instead, we propose a new graphic, the survival area plot, to directly depict the survival probability over time and as a function of a continuous covariate simultaneously. This plot utilizes g-computation based on a suitable time-to-event model to obtain the relevant estimates. Through the use of g-computation, those estimates can be adjusted for confounding without additional effort, allowing a causal interpretation under the standard causal identifiability assumptions. If those assumptions are not met, the proposed plot may still be used to depict noncausal associations. We illustrate and compare the proposed graphics to simpler alternatives using data from a large German observational study investigating the effect of the Ankle-Brachial Index on survival. To facilitate the usage of these plots, we additionally developed the contsurvplot R-package, which includes all methods discussed in this paper.

Commentary: Statistical analysis of 2 x 2 tables in Biomarker studies 1) The Four "indices of test validity"

Abstract/SummaryDiagnostic statistics such as sensitivity and specificity are widely used in the assessment of biomarkers. Interpretation of these and other statistics derived from a 2 x 2 table can be complex. The properties of the commonly used statistics are discussed. The object is to provide help in their interpretation for authors designing studies and the subsequent reporting of results and to referees and others who assess such papers.