Time to Event Analysis from Sample Size Considerations to Results Interpretation in Simple Words
Source category: Real World Evidence and Big Data
Author collections: Iryna Kotenko
THE COMMUNITY ARCHIVE
Survival Analysis Papers and PresentationsPage 2
Time-to-event analyses require explicit decisions about endpoints, censoring and competing events. This collection brings together SAS conference discussions of survival models, Kaplan–Meier estimates and the data preparation supporting them.
129 papers · Showing 21–40
Visually Exploring Kaplan-Meier Curves Using SAS GTL and R survminer
Source category: Data Visualization and Reporting
Author collections: Yang Gao
Bayesian Methods in Survival Analysis: Enhancing Insights in Clinical Research
Source category: Statistics and Analytics
Author collections: Vadym Kalinichenko
Comparative Analysis: Creating Kaplan-Meier Plots Using SAS vs Python – A Step-by-Step Approach
Source category: Data Visualisation
Author collections: Mrityunjay Kumar
Kaplan-Meier Graph: a comparative study using SAS vs R
Source category: Data Visualization and Reporting
Author collections: Mrityunjay Kumar · Shashikant Kumar
Programming Considerations in Deriving Progression-Free Survival on Next-Line Therapy (PFS2)
Source category: Data Standards
Reconstruction of Individual Patient Data (IPD) from Published Kaplan-Meier Curves Using Guyot’s Algorithm: Step-by-Step Programming in R
Source category: Real World Evidence and Big Data
Author collections: Ajay Gupta
The Survival Mode
Source category: e-Posters
CDISC’s ADTTE Domain: Time-to-Event Analysis From a Programmer’s Point of View
Source category: Poster Presentations
Author collections: Vitali Gering
How to Customize Survival Curves in R and Add Interactivity
Source category: New Tools Stories
Author collections: Oleksandr Babych
Machine-Learning Approaches to Survival Analysis of Clinical Data
Source category: Analytics & Statistics
Author collections: Dorothee Childs · Christoph Bergen
Parametric Event-prediction for Time-to-event Data in Clinical Trials
Source category: Analytics, Big Data & Statistics
Author collections: Jingyuan Chen
Submission survival guidelines for Statistical Programmers
Source category: e-Posters
Author collections: Norihiko Oharu
Survival Methods for Crossover in Oncology Trials
Source category: Advanced Programming
Author collections: Brian Mosier
FDA Advisory Committee Meetings– A Statistical Programmer’s Survival Guide
Source category: Submission Standards
Author collections: Phil Hall
Generating Forest and Kaplan Meier graphs for Regulatory Submission: Comparison of SAS and R
Source category: Data Visualization and Reporting
Author collections: Girija Javvaji · Sivasankar Konda
Reconstruction of survival curves: An approach of comparing study drug curve with the standard of care digital survival curve
Source category: Applications Development
Author collections: Girish Kankipati · Jai Deep Mittapalli
A Macro for Point Estimate and Confidence Interval of the Survival Time Percentiles
Source category: ePosters
Author collections: Hengwei Liu
A SAS Macro to Perform Kaplan-Meier Multiple Imputation for Survival Analyses with Competing Events
Source category: SAS Global Forum proceedings
An Overview of Regression Approaches for the Competing Risks Data
Source category: Analytics & Statistics
Author collections: Oksana Mykhailova