Article

Leveraging Longitudinal Patient-Reported Outcomes Trajectories to Predict Survival in Non-Small-Cell Lung Cancer

2025-01-28

Abstract excerpt

<h4>Purpose</h4> Despite their potential, patient-reported outcomes (PROs) are often underutilized in clinical decision-making, especially when improvements in PROs do not align with clinical outcomes. This misalignment may result from insufficient analytical methods that overlook the temporal dynamics and substantial variability of PROs data. To address these gaps, we developed a novel approach to investigate the...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: dfdf0c7c-f103-5974-9342-ec64fcd39d51
DOI: 10.1101/2025.01.27.25321050

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Leveraging Longitudinal Patient-Reported Outcomes Trajectories to Predict Survival in Non-Small-Cell Lung CancerDOI 10.1101/2025.01.27.25321050Read

Global Transportability of Clinical Trial Outcomes to Real-World Lung Cancer Populations A case Study using Lung-MAP S1400ISemantic proximity Read

Independent burden and functional status trajectories define prognostic phenotypes in stage IV NSCLCSemantic proximity Read

Correlation Between Surrogate End Points and Overall Survival in a Multi-institutional Clinicogenomic Cohort of Patients With Non-Small Cell Lung or Colorectal Cancer.Semantic proximity Read

Real-World Data and Evidence in Lung Cancer: A Review of Recent DevelopmentsSemantic proximity Read

Transportability of patient outcomes from a US clinical trial to real-world populations - a case study using Lung-MAP S1400I (NCT02785952)Semantic proximity Read

Calculating epidemiological outcomes from simulated longitudinal dataSemantic proximity Read

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung CancerSemantic proximity Read

ShinyEvents: harmonizing longitudinal data for real world survival estimationSemantic proximity Read

Real-world progression-free survival (rwPFS) and the impact of PD-L1 and smoking in driver-mutated non-small cell lung cancer (NSCLC) treated with immunotherapy.Semantic proximity Read

Temporal Learning with Dynamic Range (TLDR) for Modeling Recurrent Exposure and Treatment OutcomesSemantic proximity Read

Methodological Challenges using Routine Clinical Care Data for Real-World Evidence: a Rapid Review utilizing a systematic literature search and focus group discussionSemantic proximity Read

Integrating stakeholder perspectives in modeling routine data for therapeutic decision-makingSemantic proximity Read