Implications of Selection Bias Due to Delayed Study Entry in Clinical Genomic Studies
Authors: Samantha Brown, Jessica A. Lavery, Ronglai Shen, Axel S. Martin, Kenneth L. Kehl, Shawn M. Sweeney, Eva M. Lepisto, Hira Rizvi, Caroline G. McCarthy, Nikolaus Schultz, Jeremy L. Warner, Ben Ho Park, Philippe L. Bedard, Gregory J. Riely, Deborah Schrag, Katherine S. Panageas, Shawn Sweeney, Margaret Foti, Yekaterina Khotskaya, Michael Fiandalo, Benjamin Gross, Nikolaus Schultz, Brooke Mastrogiacomo, Mahdi Sarmardy, Marilyn Li, Adam Resnick, Angela Waanders, Jena Lilly, Richard Carvajal, Raul Rabadan, Matthew Ingham, Susan Hsaio, Jean Abraham, James Brenton, Oscar Rueda, Carlos Caldas, Mikel Valgañón, Dilrini Silva, Chris Boursnell, Raquel Garcia, Ezequiel Rodriguez, Birgit Nimmervoll, Ethan Cerami, Matthew Ducar, Priti Kumari, Neal Lindeman, Laura MacConnaill, John Orechia, Deborah Schrag, Priyanka Shivdasani, Eliezer Van Allen, Jason Johnson, Pasi Jänne, Eva Lepisto, Michael Hassett, Sindy Pimentel, Parin Sripakdeevong, Katherine Janeway, Jason M. Johnson, Matthew Meyerson, Daniel Quinn, Oya Cushing, Kevin Haigis, Diana Miller, Kenneth Kehl, Alexander Gustav, Angela Tramontano, Simon Arango Baquero, Jonathan Bell, Michelle Green, Shannon McCall, Michael Datto, Fabien Calvo, Fabrice Andre, Meurice Guillaume, Semih Dogan, Lacroix Ludovic, Jean Scoazec, Monica Ardenos, Gilles Vassal, Stefan Michels, Victor Velculescu, Alexander Baras, Christopher Gocke, Julie Brahmer, Charles Sawyers, David Solit, Stu Gardos, Mike Berger, Marc Ladanyi, Gregory Riely, Joseph Sirintrapun, Katherine Panageas, Ari Caroline, Stacy Thomas, Andrew Zarski, Ahmet Zehir, Alexia Iasonosa, John Philip, Samantha Brown, Andrew Kung, Ritika Kundra, Julia Rudolph, Jessica Lavery, Hira Rivzi, Julian Schwartz, Caroline McCarthy, Maufur Bhuiya, Axel Martin, Cynthia Chu, Raymond DuBois, Tony van de Velde, Geritt Meijer, Hugo Horlings, Harm van Tinteren, Martijn Lolkema, Les Nijman, Mariska Bierkens, Jelle Hoeve, Emilie Voest, Annemieke Hiemstra, Gabe Sonke, Jacques Craenmehr, Jan Hudecek, Kim Monkhorst, Walter Urba, Brady Bernard, Brian Piening, Carlo Bifulco, Paul Tittel, Julie Cramer, Justin Guinney, Thomas Yu, Xindi Guo, Alyssa Acebedo, Philip Gold, Neil Bailey, Sabah Kadri, Jeremy Segal, Wanjari Pankhuri, Peng Wang, Steinhardt George, Moung Christine, Laura Van't Veer, Eric Talevich, Amanda Wren, Alejandro Sweet-Cordero, Michelle Turski, Philippe Bedard, Suzanne KamelReid, Zhibin Lu, Trevor Pugh, Lillian Siu, Stuart Watt, Natasha Leighl, Celeste Yu, Lailah Ahmed, Geeta Krishna, Carlos Virtaenen, Helen Chow, Demi Plagianakos, Samantha Del Rossi, Nitthusha Singaravelan, Sevan Hakgor, Nazish Qazi, Alisha Nguyen, Natalie Stickle, Thomas Stricker, Christine Micheel, Ingrid Anderson, Leigh Jones, Lucy Wang, Christine Lovly, Michele LeNoue Newton, Ben Park, Jeremy Warner, Daniel Fabbri, Joseph Coco, Chen Ye, Sandip Chaugai, Sanjay Mishra, Yuanchu James Yang, Li Wen, Rodrigo Dienstmann, Susana Aguilar Izquierdo, Cristina Viaplana Donato, Francesco Mancuso, Umit Topaloglu, Liang Liu, Meijian Guan, Wei Zhang, Guangxu Jin, James Knight, Michael D'Eletto, E. Zeynep Ormay, Shrikant Mane, Kaya Bilguvar, Walther Zenta, Daniel Dykas
Journal: JAMA Oncology (2021)

Real-world data sets that combine clinical and genomic data may be subject to left truncation (when potential study participants are not included because they have already passed the milestone of interest at the time of study recruitment). The lapse between diagnosis and molecular testing can present analytic challenges and threaten the validity and interpretation of survival analyses.Effects of ignoring left truncation when estimating overall survival are illustrated using data from the American Association for Cancer Research (AACR) Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative (GENIE BPC), and a straightforward risk-set adjustment approach is described. Ignoring left truncation results in overestimation of overall survival: unadjusted median survival estimates from diagnosis among patients with stage IV non-small cell lung cancer or stage IV colorectal cancer were overestimated by more than 1 year.Clinicogenomic data are a valuable resource for evaluation of real-world cancer outcomes and should be analyzed using appropriate methods to maximize their potential. Analysts must become adept at application of appropriate statistical methods to ensure valid, meaningful, and generalizable research findings.