Measures of prediction error for survival data with longitudinal covariates

2011 ◽  
Vol 53 (2) ◽  
pp. 275-293 ◽  
Author(s):  
Rotraut Schoop ◽  
Martin Schumacher ◽  
Erika Graf
Keyword(s):  
Survival Data ◽  
Prediction Error ◽  
Longitudinal Covariates

How to Assess Prognostic Models for Survival Data: A Case Study in Oncology

2003 ◽  
Vol 42 (05) ◽  
pp. 564-571 ◽  
Author(s):  
M. Schumacher ◽  
E. Graf ◽  
T. Gerds
Keyword(s):  
Test Data ◽  
Survival Data ◽  
Prediction Error ◽  
Classification Scheme ◽  
Neural Nets ◽  
Brier Score ◽  
Data Set ◽  
Independent Test ◽  
Artificial Neural

Summary Objectives: A lack of generally applicable tools for the assessment of predictions for survival data has to be recognized. Prediction error curves based on the Brier score that have been suggested as a sensible approach are illustrated by means of a case study. Methods: The concept of predictions made in terms of conditional survival probabilities given the patient’s covariates is introduced. Such predictions are derived from various statistical models for survival data including artificial neural networks. The idea of how the prediction error of a prognostic classification scheme can be followed over time is illustrated with the data of two studies on the prognosis of node positive breast cancer patients, one of them serving as an independent test data set. Results and Conclusions: The Brier score as a function of time is shown to be a valuable tool for assessing the predictive performance of prognostic classification schemes for survival data incorporating censored observations. Comparison with the prediction based on the pooled Kaplan Meier estimator yields a benchmark value for any classification scheme incorporating patient’s covariate measurements. The problem of an overoptimistic assessment of prediction error caused by data-driven modelling as it is, for example, done with artificial neural nets can be circumvented by an assessment in an independent test data set.

Discrete‐time survival data with longitudinal covariates

2020 ◽  
Vol 39 (29) ◽  
pp. 4372-4385
Author(s):  
Chi‐Chung Wen ◽  
Yi‐Hau Chen
Keyword(s):  
Discrete Time ◽  
Survival Data ◽  
Longitudinal Covariates

scholarly journals Predicting Factors Affecting the First Recurrence of Epithelial Ovarian Cancer Using Random Survival Forest

2021 ◽  
Author(s):  
Maryam Deldar ◽  
Robab Anbiaee ◽  
Kourosh Sayehmiri
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Survival Data ◽  
Prediction Error ◽  
Life Quality ◽  
Metastatic Tumor ◽  
Relative Importance ◽  
Factors Affecting ◽  
First Recurrence ◽  
Random Survival Forest

Predicting survival time has many Effective implications in life quality management for the remainder of the patient's life. Also, survival data are highly variable and make accurate predictions difficult or impossible. Random Survival Forest by repeated tree construction on Bootstrap samples and averaging on the results of these trees reduce the prediction error and cause further generalization of these results. In this retrospective study, the records of 141 patients with epithelial ovarian cancer who were referred to the oncology and radiotherapy ward of Imam Hossein Hospital in Tehran from 2007 to 2018 were used. Random Survival Forest was fitted to the data to investigate the key factors affecting the first recurrence of epithelial ovarian cancer. The mean age of the patients in our study was 52 (23-82) years and the median time to the first recurrence in these was 17 (0.5-127) months, respectively. According to RSF results, using variable importance criterion (VIMP) metastatic tumor with relative importance 2.665 and also using minimal (MD) by depth 2.349, tumor stage with relative importance 1.993 and depth 2.678, and maximum platelet count with relative importance 2.132 and depth 2.683 were the most important variables affecting in the first recurrence of Epithelial Ovarian Cancer. One of the disadvantages of classical methods is the inappropriate fitting of many variables and the need for specific assumptions. More advanced methods such as RSF without the need for any specific assumptions with less prediction error can well explain event variations when exposed to high-dimensional data.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

Temporal dynamics of real-world emotion are more strongly linked to prediction error than outcome.

2020 ◽  
Vol 149 (9) ◽  
pp. 1755-1766 ◽  
Author(s):  
William J. Villano ◽  
A. Ross Otto ◽  
C. E. Chiemeka Ezie ◽  
Roderick Gillis ◽  
Aaron S. Heller
Keyword(s):  
Real World ◽  
Prediction Error ◽  
Temporal Dynamics
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