scholarly journals A surrogate ℓ 0 sparse Cox's regression with applications to sparse high‐dimensional massive sample size time‐to‐event data

2019 ◽  
Vol 39 (6) ◽  
pp. 675-686 ◽  
Author(s):  
Eric S. Kawaguchi ◽  
Marc A. Suchard ◽  
Zhenqiu Liu ◽  
Gang Li
Keyword(s):  
Sample Size ◽  
High Dimensional ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Massive Sample ◽  
Cox's Regression

A semi-parametric cox’s regression model for zero-inflated left-censored time to event data

2015 ◽  
Vol 45 (7) ◽  
pp. 1969-1988 ◽  
Author(s):  
Roel Braekers ◽  
Yves Grouwels
Keyword(s):  
Regression Model ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Cox's Regression

scholarly journals GAC: Gene Associations with Clinical, a web based application

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1039
Author(s):  
Xinyan Zhang ◽  
Manali Rupji ◽  
Jeanne Kowalski
Keyword(s):  
High Dimensional Data ◽  
Interactive Visualization ◽  
High Dimensional ◽  
Forest Plot ◽  
Event Data ◽  
Time To Event ◽  
Web Based ◽  
Link Type ◽  
Time To Event Data ◽  
Clinical Associations

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC.  Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data.  In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

scholarly journals Variable Selection Methods for Right-Censored Time-to-Event Data with High-Dimensional Covariates

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Keivan Sadeghzadeh ◽  
Nasser Fard
Keyword(s):  
Failure Time ◽  
High Dimensional ◽  
Complex Data ◽  
Redundant Information ◽  
Event Data ◽  
Selection Methods ◽  
Time To Event ◽  
Time To Event Data ◽  
Preventive Actions ◽  
Censored Observations

Advancement in technology has led to greater accessibility of massive and complex data in many fields such as quality and reliability. The proper management and utilization of valuable data could significantly increase knowledge and reduce cost by preventive actions, whereas erroneous and misinterpreted data could lead to poor inference and decision making. On the other side, it has become more difficult to process the streaming high-dimensional time-to-event data in traditional application approaches, specifically in the presence of censored observations. This paper presents a multipurpose analytic model and practical nonparametric methods to analyze right-censored time-to-event data with high-dimensional covariates. In order to reduce redundant information and to facilitate practical interpretation, variable inefficiency in failure time is determined for the specific field of application. To investigate the performance of the proposed methods, these methods are compared with recent relevant approaches through numerical experiments and simulations.

scholarly journals GAC: Gene Associations with Clinical, a web based application

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1039
Author(s):  
Xinyan Zhang ◽  
Manali Rupji ◽  
Jeanne Kowalski
Keyword(s):  
High Dimensional Data ◽  
Interactive Visualization ◽  
High Dimensional ◽  
Forest Plot ◽  
Event Data ◽  
Time To Event ◽  
Web Based ◽  
Link Type ◽  
Time To Event Data ◽  
Clinical Associations

We present GAC, a shiny R based tool for interactive visualization of clinical associations based on high-dimensional data. The tool provides a web-based suite to perform supervised principal component analysis (SuperPC), an approach that uses both high-dimensional data, such as gene expression, combined with clinical data to infer clinical associations. We extended the approach to address binary outcomes, in addition to continuous and time-to-event data in our package, thereby increasing the use and flexibility of SuperPC.  Additionally, the tool provides an interactive visualization for summarizing results based on a forest plot for both binary and time-to-event data.  In summary, the GAC suite of tools provide a one stop shop for conducting statistical analysis to identify and visualize the association between a clinical outcome of interest and high-dimensional data types, such as genomic data. Our GAC package has been implemented in R and is available via http://shinygispa.winship.emory.edu/GAC/. The developmental repository is available at https://github.com/manalirupji/GAC.

Sample Size for Comparing Time-to-Event Data

2014 ◽  
pp. 664-671
Author(s):  
Hansheng Wang ◽  
Shein-Chung Chow
Keyword(s):  
Sample Size ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data

scholarly journals Discrimination-based sample size calculations for multivariable prognostic models for time-to-event data

2015 ◽  
Vol 15 (1) ◽  
Author(s):  
Rachel C. Jinks ◽  
Patrick Royston ◽  
Mahesh KB Parmar
Keyword(s):  
Sample Size ◽  
Prognostic Models ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Sample Size Calculations

scholarly journals Impact of limited sample size and follow-up on single event survival extrapolation for health technology assessment: a simulation study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jaclyn M. Beca ◽  
Kelvin K. W. Chan ◽  
David M. J. Naimark ◽  
Petros Pechlivanoglou
Keyword(s):  
Sample Size ◽  
Time Horizon ◽  
Goodness Of Fit ◽  
Decision Models ◽  
Small Samples ◽  
Event Data ◽  
Time To Event ◽  
Time To Event Data ◽  
Event Distribution

Abstract Introduction Extrapolation of time-to-event data from clinical trials is commonly used in decision models for health technology assessment (HTA). The objective of this study was to assess performance of standard parametric survival analysis techniques for extrapolation of time-to-event data for a single event from clinical trials with limited data due to small samples or short follow-up. Methods Simulated populations with 50,000 individuals were generated with an exponential hazard rate for the event of interest. A scenario consisted of 5000 repetitions with six sample size groups (30–500 patients) artificially censored after every 10% of events observed. Goodness-of-fit statistics (AIC, BIC) were used to determine the best-fitting among standard parametric distributions (exponential, Weibull, log-normal, log-logistic, generalized gamma, Gompertz). Median survival, one-year survival probability, time horizon (1% survival time, or 99th percentile of survival distribution) and restricted mean survival time (RMST) were compared to population values to assess coverage and error (e.g., mean absolute percentage error). Results The true exponential distribution was correctly identified using goodness-of-fit according to BIC more frequently compared to AIC (average 92% vs 68%). Under-coverage and large errors were observed for all outcomes when distributions were specified by AIC and for time horizon and RMST with BIC. Error in point estimates were found to be strongly associated with sample size and completeness of follow-up. Small samples produced larger average error, even with complete follow-up, than large samples with short follow-up. Correctly specifying the event distribution reduced magnitude of error in larger samples but not in smaller samples. Conclusions Limited clinical data from small samples, or short follow-up of large samples, produce large error in estimates relevant to HTA regardless of whether the correct distribution is specified. The associated uncertainty in estimated parameters may not capture the true population values. Decision models that base lifetime time horizon on the model’s extrapolated output are not likely to reliably estimate mean survival or its uncertainty. For data with an exponential event distribution, BIC more reliably identified the true distribution than AIC. These findings have important implications for health decision modelling and HTA of novel therapies seeking approval with limited evidence.

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