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Implications of Selection Bias Due to Delayed Study Entry in Clinical Genomic Studies
Samantha Brown1, Jessica A Lavery1, Ronglai Shen1
1Memorial Sloan Kettering Cancer Center, New York, New York.
JAMA Oncology
|November 4, 2021
Summary
Ignoring left truncation in clinicogenomic data overestimates survival. Proper risk-set adjustment methods are crucial for accurate analysis of real-world cancer outcomes using these valuable datasets.
Area of Science:
- Oncology
- Biostatistics
- Bioinformatics
Background:
- Real-world clinicogenomic data are vital for understanding cancer outcomes.
- Left truncation, where participants are already past the event of interest at recruitment, poses challenges.
- The time lag between diagnosis and molecular testing can invalidate survival analyses.
Purpose of the Study:
- To illustrate the impact of ignoring left truncation on survival analyses.
- To present a risk-set adjustment method for analyzing clinicogenomic data.
- To emphasize the need for appropriate statistical methods in real-world data analysis.
Main Methods:
- Utilized data from the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE BPC).
- Demonstrated the effects of ignoring left truncation on overall survival estimation.
- Described a risk-set adjustment approach to address left truncation.
Main Results:
- Ignoring left truncation led to overestimation of overall survival.
- Median survival estimates from diagnosis were overestimated by over one year for stage IV non-small cell lung cancer and stage IV colorectal cancer patients.
- The study highlights significant analytic challenges posed by left truncation.
Conclusions:
- Clinicogenomic data offer immense value for real-world cancer outcome evaluation.
- Appropriate statistical methodologies are essential for valid and meaningful analysis of clinicogenomic data.
- Researchers must master statistical techniques to ensure generalizable findings from real-world evidence.
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