A visualization method measuring the performance of biomarkers for guiding treatment decisions

Yang, Hui; Tang, Rui; Hale, Mike; Huang, Jing

doi:10.1002/pst.1728

Cited by 5 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Performing the procedure described above for multiple t and several values for normalZ results in a three-dimensional survival surface, which can be used to create multiple plots based on summary statistics. Most of these plots have already been described in the literature, 12,13,15,40 but the authors introducing them did not use g-computation to obtain the underlying estimates. By using g-computation instead, the values represented by the graphs can be interpreted as counterfactual quantities, if the previously mentioned assumptions hold.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Denz

Timmesfeld

2023

Epidemiology

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

“…The simplest of these is a landmark survival probability plot. 13,40 To create this plot, one or multiple t is chosen. The survival probability at t is then visualized as a function of the continuous covariate.…”

Section: Methodsmentioning

confidence: 99%

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

Denz

Timmesfeld

2023

Epidemiology

View full text Add to dashboard Cite

show abstract

“…prediction model) to evaluate the degree of trastuzumab benefit in early breast cancer patients on disease-free survival [1, 2]. Statistical methodology has been proposed for the development of prediction models and their validation [3, 4] and to estimate individual predictions in a survival setting [5, 6]; however, no clear guidance has been yet reached and evaluated in a high-dimensional setting.…”

Section: Introductionmentioning

confidence: 99%

Robust estimation of the expected survival probabilities from high-dimensional Cox models with biomarker-by-treatment interactions in randomized clinical trials

Ternès

Rotolo

Michiels

2017

BMC Med Res Methodol

View full text Add to dashboard Cite

BackgroundThanks to the advances in genomics and targeted treatments, more and more prediction models based on biomarkers are being developed to predict potential benefit from treatments in a randomized clinical trial. Despite the methodological framework for the development and validation of prediction models in a high-dimensional setting is getting more and more established, no clear guidance exists yet on how to estimate expected survival probabilities in a penalized model with biomarker-by-treatment interactions.MethodsBased on a parsimonious biomarker selection in a penalized high-dimensional Cox model (lasso or adaptive lasso), we propose a unified framework to: estimate internally the predictive accuracy metrics of the developed model (using double cross-validation); estimate the individual survival probabilities at a given timepoint; construct confidence intervals thereof (analytical or bootstrap); and visualize them graphically (pointwise or smoothed with spline). We compared these strategies through a simulation study covering scenarios with or without biomarker effects. We applied the strategies to a large randomized phase III clinical trial that evaluated the effect of adding trastuzumab to chemotherapy in 1574 early breast cancer patients, for which the expression of 462 genes was measured.ResultsIn our simulations, penalized regression models using the adaptive lasso estimated the survival probability of new patients with low bias and standard error; bootstrapped confidence intervals had empirical coverage probability close to the nominal level across very different scenarios. The double cross-validation performed on the training data set closely mimicked the predictive accuracy of the selected models in external validation data. We also propose a useful visual representation of the expected survival probabilities using splines. In the breast cancer trial, the adaptive lasso penalty selected a prediction model with 4 clinical covariates, the main effects of 98 biomarkers and 24 biomarker-by-treatment interactions, but there was high variability of the expected survival probabilities, with very large confidence intervals.ConclusionBased on our simulations, we propose a unified framework for: developing a prediction model with biomarker-by-treatment interactions in a high-dimensional setting and validating it in absence of external data; accurately estimating the expected survival probability of future patients with associated confidence intervals; and graphically visualizing the developed prediction model. All the methods are implemented in the R package biospear, publicly available on the CRAN.Electronic supplementary materialThe online version of this article (doi:10.1186/s12874-017-0354-0) contains supplementary material, which is available to authorized users.

show abstract

On evaluating how well a biomarker can predict treatment response with survival data

Mboup

Blanche

Latouche

2020

Pharmaceutical Statistics

View full text Add to dashboard Cite

SummaryOne of the objectives of personalized medicine is to take treatment decisions based on a biomarker measurement. Therefore, it is often interesting to evaluate how well a biomarker can predict the response to a treatment. To do so, a popular methodology consists of using a regression model and testing for an interaction between treatment assignment and biomarker. However, the existence of an interaction is not sufficient for a biomarker to be predictive. It is only necessary. Hence, the use of the marker‐by‐treatment predictiveness curve has been recommended. In addition to evaluate how well a single continuous biomarker predicts treatment response, it can further help to define an optimal threshold. This curve displays the risk of a binary outcome as a function of the quantiles of the biomarker, for each treatment group. Methods that assume a binary outcome or rely on a proportional hazard model for a time‐to‐event outcome have been proposed to estimate this curve. In this work, we propose some extensions for censored data. They rely on a time‐dependent logistic model, and we propose to estimate this model via inverse probability of censoring weighting. We present simulations results and three applications to prostate cancer, liver cirrhosis, and lung cancer data. They suggest that a large number of events need to be observed to define a threshold with sufficient accuracy for clinical usefulness. They also illustrate that when the treatment effect varies with the time horizon which defines the outcome, then the optimal threshold also depends on this time horizon.

show abstract

A visualization method measuring the performance of biomarkers for guiding treatment decisions

Cited by 5 publications

References 24 publications

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

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

Robust estimation of the expected survival probabilities from high-dimensional Cox models with biomarker-by-treatment interactions in randomized clinical trials

On evaluating how well a biomarker can predict treatment response with survival data

Contact Info

Product

Resources

About