Adjusting win statistics for dependent censoring

For composite outcomes whose components can be prioritized on clinical importance, the win ratio, the net benefit and the win odds apply that order in comparing patients pairwise to produce wins and subsequently win proportions. Because these three statistics are derived using the same win proportions and they test the same hypothesis of equal win probabilities in the two treatment groups, we refer to them as win statistics. These methods, particularly the win ratio and the net benefit, have received increasing attention in methodological research and in design and analysis of clinical trials. For time‐to‐event outcomes, however, censoring may introduce bias. Previous work has shown that inverse‐probability‐of‐censoring weighting (IPCW) can correct the win ratio for bias from independent censoring. The present article uses the IPCW approach to adjust win statistics for dependent censoring that can be predicted by baseline covariates and/or time‐dependent covariates (producing the CovIPCW‐adjusted win statistics). Theoretically and with examples and simulations, we show that the CovIPCW‐adjusted win statistics are unbiased estimators of treatment effect in the presence of dependent censoring.

[1]  H. Quan,et al.  Some Meaningful Weighted Log-Rank and Weighted Win Loss Statistics , 2020 .

[2]  D. Hoaglin,et al.  The inverse-probability-of-censoring weighting (IPCW) adjusted win ratio statistic: an unbiased estimator in the presence of independent censoring , 2020, Journal of Biopharmaceutical Statistics.

[3]  Lei Peng The use of the win odds in the design of non-inferiority clinical trials , 2020, Journal of biopharmaceutical statistics.

[4]  J. Verbeeck,et al.  Evaluation of inferential methods for the net benefit and win ratio statistics , 2020, Journal of biopharmaceutical statistics.

[5]  D. Hoaglin,et al.  The Win Ratio: On Interpretation and Handling of Ties , 2019, Statistics in Biopharmaceutical Research.

[6]  G. Koch,et al.  Adjusted win ratio with stratification: Calculation methods and interpretation , 2019, Statistical methods in medical research.

[7]  D. Hoaglin,et al.  The win ratio: Impact of censoring and follow‐up time and use with nonproportional hazards , 2019, Pharmaceutical statistics.

[8]  G. Molenberghs,et al.  Generalized pairwise comparison methods to analyze (non)prioritized composite endpoints , 2019, Statistics in medicine.

[9]  S. Pocock,et al.  Statistical Appraisal of 6 Recent Clinical Trials in Cardiology: JACC State-of-the-Art Review. , 2019, Journal of the American College of Cardiology.

[10]  M. Ellison,et al.  Statistical methods for survival trial design: with applications to cancer clinical trial using R , 2018, Journal of Biopharmaceutical Statistics.

[11]  D. Schoenfeld,et al.  Graphing the Win Ratio and its components over time , 2018, Statistics in medicine.

[12]  Sanjiv J. Shah,et al.  Tafamidis Treatment for Patients with Transthyretin Amyloid Cardiomyopathy , 2018, The New England journal of medicine.

[13]  L. Mao,et al.  On the alternative hypotheses for the win ratio , 2018, Biometrics.

[14]  Jianrong Wu Statistical Methods for Survival Trial Design , 2018 .

[15]  Duolao Wang,et al.  The stratified win ratio , 2018, Journal of biopharmaceutical statistics.

[16]  M. Hughes,et al.  Estimation of the cumulative incidence function under multiple dependent and independent censoring mechanisms , 2018, Lifetime Data Analysis.

[17]  M. Buyse,et al.  An extension of generalized pairwise comparisons for prioritized outcomes in the presence of censoring , 2018, Statistical methods in medical research.

[18]  M. V. van Noorden,et al.  Correcting for dependent censoring in routine outcome monitoring data by applying the inverse probability censoring weighted estimator , 2018, Statistical methods in medical research.

[19]  Xiaodong Luo,et al.  Weighted win loss approach for analyzing prioritized outcomes , 2017, Statistics in medicine.

[20]  M. Vandemeulebroecke,et al.  A generalized analytic solution to the win ratio to analyze a composite endpoint considering the clinical importance order among components , 2016, Pharmaceutical statistics.

[21]  D. Oakes On the win-ratio statistic in clinical trials with multiple types of event , 2016 .

[22]  M. Buyse,et al.  The Net Chance of a Longer Survival as a Patient-Oriented Measure of Treatment Benefit in Randomized Clinical Trials. , 2016, JAMA oncology.

[23]  S. Pocock,et al.  A win ratio approach to comparing continuous non‐normal outcomes in clinical trials , 2016, Pharmaceutical statistics.

[24]  W. Tsai,et al.  An alternative approach to confidence interval estimation for the win ratio statistic , 2015, Biometrics.

[25]  S. Pocock,et al.  The win ratio: a new approach to the analysis of composite endpoints in clinical trials based on clinical priorities , 2011, European heart journal.

[26]  M. Buyse Generalized pairwise comparisons of prioritized outcomes in the two‐sample problem , 2010, Statistics in medicine.

[27]  M. Pfeffer,et al.  Effects of candesartan on mortality and morbidity in patients with chronic heart failure: the CHARM-Overall programme , 2003, The Lancet.

[28]  J. Robins,et al.  Correcting for Noncompliance and Dependent Censoring in an AIDS Clinical Trial with Inverse Probability of Censoring Weighted (IPCW) Log‐Rank Tests , 2000, Biometrics.

[29]  D. Schoenfeld,et al.  Combining mortality and longitudinal measures in clinical trials. , 1999, Statistics in medicine.

[30]  J. Klein,et al.  Survival Analysis: Techniques for Censored and Truncated Data , 1997 .

[31]  J. Lachin,et al.  Large sample inference for a win ratio analysis of a composite outcome based on prioritized components. , 2016, Biostatistics.

[32]  Salim Yusuf,et al.  Predictors of mortality and morbidity in patients with chronic heart failure. , 2006, European heart journal.

[33]  M. Pagano,et al.  Survival analysis. , 1996, Nutrition.

[34]  B. Efron The two sample problem with censored data , 1967 .