Reply to: "Beyond Response Rates: Interpreting Long-Term Benefit in CheckMate 743" and "Beyond Statistical

Arnaud Scherpereel1, Aaron S Mansfield1, Francesco Grossi1

  • 1Arnaud Scherpereel, MD, PhD, CHU Lille, Univ. Lille, Inserm, U1366-UMR9020-CRCLille-Cancer Research Center of Lille, OncoThAI, ONCOLille, Lille, France; Aaron S. Mansfield, MD, Mayo Clinic, Rochester, MN; Francesco Grossi, MD, Medical Oncology Division, Department of Medicine and Technological Innovation, University of Insubria, Varese, Italy; Sanjay Popat, PhD, FRCP, The Royal Marsden Hospital, London, United Kingdom; Paul Baas, MD, PhD, The Netherlands Cancer Institute and Leiden University Medical Center, Amsterdam, the Netherlands; Anna K. Nowak, PhD, MBBS, University of Western Australia, Perth, WA, Australia; Anne S. Tsao, MD, MBA, University of Texas MD Anderson Cancer Center, Houston, TX; Nobukazu Fujimoto, MD, PhD, Okayama Rosai Hospital, Okayama, Japan; Solange Peters, MD, PhD, Lausanne University Hospital, Lausanne, Switzerland; Yolanda Bautista Aragon, MD, Centro Médico Nacional Siglo XXI, Mexico City, Mexico; Toby Talbot, MD, The Sunrise Centre, Royal Cornwall Hospitals NHS Trust, Truro, United Kingdom; Dariusz Kowalski, MD, PhD, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland; Muhammet A. Kaplan, MD, Dicle University, Diyarbakir, Turkey; Andrés Felipe Cardona, MD, PhD, Luis Carlos Sarmiento Angulo Cancer Treatment and Research Center (CTIC), Bogotá, Colombia, Universidad El Bosque, Bogotá, Colombia; Raheela Soomro, MD, and Nan Hu, MS, Bristol Myers Squibb, Princeton, NJ; Adam Lee, MSc, Bristol Myers Squibb, Uxbridge, United Kingdom; Virginia Ip, PhD, and Yu-Han Hung, PhD, Bristol Myers Squibb, Princeton, NJ; and Gérard Zalcman, MD, PhD, Université Paris Cité, Bichat-Claude Bernard Hospital, AP-HP, Nord Cancer Institute, Paris, France.

Abstract

No abstract available in PubMed .

Related Concept Videos

Censoring Survival Data01:09

Censoring Survival Data

Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different reasons...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
The Mantel-Cox Log-Rank Test01:19

The Mantel-Cox Log-Rank Test

The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.