Deriving Phantom Records in BDS Datasets To Support the FDA’s Standard Safety Figures for Missing Data Analysis
Source category: Data Standards, Governance & Implementation
Author collections: Guowei Wu · Ruchitbhai Patel
THE COMMUNITY ARCHIVE
Missing Data Papers and Presentations
Missing observations affect both data preparation and the interpretation of results. Explore conference papers on imputation, incomplete longitudinal data and sensitivity analyses, with attention to the assumptions made by each method.
83 papers · Showing 1–20
Estimand-Aligned Multiple Imputation Strategies in CNS Trials with Complex Intercurrent Events
Source category: Analytics & Statistics
From Missing to Meaningful: Validating Multiple Imputation Techniques for Reliable Analysis
Source category: Coding Tips & Tricks
Innovative Strategies for Managing Missing Data in Real-World Data: Insights from APAC Global Capability Centres
Source category: Poster Presentations
Author collections: Likhita Kolli
Is Complexity Worth It? A Case Study Comparing Multiple Imputation Strategies
Source category: Poster Presentations
Author collections: Elnaz Ghadimi · David Bushnell
Leveraging Large Language Models for Missing Data Imputation and Interpretation of Real-World Evidence Outputs
Source category: Real-world Evidence & Data Visualisation
Missing Data in PROC MCMC
Source category: AI, Analytics & Statistics
Author collections: Danny Modlin
Tipping Point Analysis in R and SAS: A Comparative Approach to Handling Missing Data in Clinical Research
Source category: Poster Presentations
Approaches to Developing Multiple Imputation ADaM Datasets
Source category: Strategic Implementation & Innovation
Author collections: Kang Xie
Contributions to CAMIS PHUSE Project in 2025: Tobit Regression, Reference-based Multiple Imputation and Tipping Point Analysis
Source category: Analytics & Statistics (AS)
Author collections: Yannick Vandendijck
Handling Missing Data in External Control Arms: Best Practices, Recommendations, and SAS Code Examples
Source category: Statistics and Analytics
Last Observation Carried Forward (LOCF) in Longitudinal Clinical Studies: Adopting a Functional Approach to Imputing Missing Values Using PROC FCMP
Source category: Pharma and Life Sciences
Author collections: Troy Hughes
Last Observation Carried Forward (LOCF) in Longitudinal Clinical Studies: Adopting a Functional Approach to Imputing Missing Values Using PROC FCMP, the SAS® Function Compiler
Source category: Solutions Development
Author collections: Troy Hughes
Last Observation Carried Forward (LOCF) in Longitudinal Patient Studies: A Functional Approach to Imputing Missing Values Using PROC FCMP, the SAS® Function Compiler
Source category: Advanced Programming
Author collections: Troy Hughes
Last Observation Carried Forward (LOCF) in Longitudinal Pharmaceutical and Clinical Trials Studies: Adopting a Functional Approach to Imputing Missing Values Using PROC FCMP, the SAS Function Compiler
Source category: Data Collection, Management, and Manipulation (DCMM)
File inside source ZIP: 61-Troy-Hughes-supporting-document.pdf
Author collections: Troy Hughes
Leveraging SAS® DATA Step to Evaluate Government Programs by Repurposing Administrative Data: Strategies for Narrowing Data Scope, Imputing Missing Data, Mitigating Bias, and Streamlining Reporting
Source category: Solutions Development
Author collections: Bert Cisneros · Richard Rivera
Mind the Gaps: Automating Multiple Imputation in Clinical Trial Workflows
Source category: Pharma and Life Sciences
Author collections: Jiangang Cai
Multiple Imputation in Composite Endpoints – It’s Not That Hard!
Source category: Analytics & Statistics
Author collections: Ashley Mao
Multiple Imputation Techniques in the Context of ADaM Datasets
Source category: Data Standards
Author collections: Shunbing Zhao
Multiple Imputations Overview of Missing Data Problem, Solution Methods and SAS Implementation
Source category: Analytics & Statistics (AS)