Chenyang Zhang1, Guosheng Yin2. 1. Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, SAR, China. 2. Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, SAR, China. gyin@hku.hk.
Abstract
BACKGROUND: We propose a new measure of treatment effect based on the expected reduction in the number of patients to treat (RNT) which is defined as the difference of the reciprocals of clinical measures of interest between two arms. Compared with the conventional number needed to treat (NNT), RNT shows superiority with both binary and time-to-event endpoints in randomized controlled trials (RCTs). METHODS: Five real RCTs, two with binary endpoints and three with survival endpoints, are used to illustrate the concept of RNT and compare the performances between RNT and NNT. For survival endpoints, we propose two versions of RNT: one is based on the survival rate and the other is based on the restricted mean survival time (RMST). Hypothetical scenarios are also constructed to explore the advantages and disadvantages of RNT and NNT. RESULTS: Because there is no baseline for computation of NNT, it fails to differentiate treatment effect in the absolute scale. In contrast, RNT conveys more information than NNT due to its reversed order of differencing and inverting. For survival endpoints, two versions of RNT calculated as the difference of the reciprocals of survival rates and RMSTs are complementary to each other. The RMST-based RNT can capture the entire follow-up profile and thus is clinically more intuitive and meaningful, as it inherits the time-to-event characteristics for survival endpoints instead of using truncated binary endpoints at a specific time point. CONCLUSIONS: The RNT can serve as an alternative measure for quantifying treatment effect in RCTs, which complements NNT to help patients and clinicians better understand the magnitude of treatment benefit.
BACKGROUND: We propose a new measure of treatment effect based on the expected reduction in the number of patients to treat (RNT) which is defined as the difference of the reciprocals of clinical measures of interest between two arms. Compared with the conventional number needed to treat (NNT), RNT shows superiority with both binary and time-to-event endpoints in randomized controlled trials (RCTs). METHODS: Five real RCTs, two with binary endpoints and three with survival endpoints, are used to illustrate the concept of RNT and compare the performances between RNT and NNT. For survival endpoints, we propose two versions of RNT: one is based on the survival rate and the other is based on the restricted mean survival time (RMST). Hypothetical scenarios are also constructed to explore the advantages and disadvantages of RNT and NNT. RESULTS: Because there is no baseline for computation of NNT, it fails to differentiate treatment effect in the absolute scale. In contrast, RNT conveys more information than NNT due to its reversed order of differencing and inverting. For survival endpoints, two versions of RNT calculated as the difference of the reciprocals of survival rates and RMSTs are complementary to each other. The RMST-based RNT can capture the entire follow-up profile and thus is clinically more intuitive and meaningful, as it inherits the time-to-event characteristics for survival endpoints instead of using truncated binary endpoints at a specific time point. CONCLUSIONS: The RNT can serve as an alternative measure for quantifying treatment effect in RCTs, which complements NNT to help patients and clinicians better understand the magnitude of treatment benefit.
Entities:
Keywords:
Absolute risk reduction; Number needed to treat; Randomized controlled trials; Restricted mean survival time; Treatment effect
Authors: Hajime Uno; Janet Wittes; Haoda Fu; Scott D Solomon; Brian Claggett; Lu Tian; Tianxi Cai; Marc A Pfeffer; Scott R Evans; Lee-Jen Wei Journal: Ann Intern Med Date: 2015-07-21 Impact factor: 25.391
Authors: James Y W Lau; Yuanyuan Yu; Raymond S Y Tang; Heyson C H Chan; Hon-Chi Yip; Shannon M Chan; Sally W Y Luk; Sunny H Wong; Louis H S Lau; Rashid N Lui; Ting T Chan; Joyce W Y Mak; Francis K L Chan; Joseph J Y Sung Journal: N Engl J Med Date: 2020-04-02 Impact factor: 91.245