Weighted log rank type statistics for comparing survival curves when there is a time lag in the effectiveness of treatment
Abstract
In certain long-term treatment trials, one expects some lag period before the treatment is fully effective. This paper presents two weighted log rank type statistics designed to have good efficiency over a wide range of lags: a maximin efficiency robust statistic and a simplified version of this statistic. Both statistics can be computed as easily as the log rank statistic. Asymptotic efficiency calculations, supported by small sample simulations, show that the two proposed statistics are substantially more efficient than the conventional log rank statistic in certain lag situations with comparatively little efficiency loss relative to the log rank statistic when no lag exists. We recommend the maximin statistic for situations where a lag is expected but cannot be specified precisely in advance.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A stratified constant piecewise weighted log-rank test (weight 0 for weeks 0 to 8 after randomization and weight 1 after that period) was used to compare the trial groups because both groups received the same treatments during the bridging period.
“We used a stratified constant piecewise weighted log-rank test (in which a weight of 0 was assigned for the log-rank statistic for weeks 0 to 8 after randomization and a weight of 1 after that period) 22,23 to compare the trial groups, because both groups received the same treatments during the bridging period.”
From Cilta-cel or Standard Care in Lenalidomide-Refractory Multiple Myeloma. (2023) · cites this paper as [23]
Cited by
Sorted by citations
Cilta-cel or Standard Care in Lenalidomide-Refractory Multiple Myeloma.
San-Miguel, J., Dhakal, B., Yong, K., Spencer, A., Anguille, S., Mateos, M.-V., Fernández de Larrea, C., Martínez-López, J., Moreau, P., Touzeau, C., et al. (2023) · New England Journal of Medicine · doi:10.1056/nejmoa2303379 · Cited by 827 · see citing works
Galsky, M., Mortazavi, A., Milowsky, M., George, S., Gupta, S., Fleming, M. T., Dang, L., Geynisman, D., Walling, R., Alter, R., et al. (2020) · Journal of Clinical Oncology · doi:10.1200/jco.19.03091 · Cited by 110 · see citing works
“The sample size was determined by inverting the test statistic proposed by Zucker and Lakatos.(18) With a type I error as 0.”
MethodInfluentialImproved Logrank‐Type Tests for Survival Data Using Adaptive Weights
Yang, S., & Prentice, R. L. (2009) · Biometrics · doi:10.1111/j.1541-0420.2009.01243.x · Cited by 97 · see citing works
“Examples include the linear combinations (Gastwirth, 1985; Zucker and Lakatos, 1990), maximum of several standardized tests (Tarone, 1981; Fleming and Harrington, 1984, 1991), maximum after orthogonalization (Breslow, Elder, and Berger, 1984), or χ2 test of the simultaneous null hypothesis for the…”
BackgroundA comparison of sample size methods for the logrank statistic.
Lakatos, E., & Lan, K. K. G. (1992) · Statistics in Medicine · doi:10.1002/sim.4780110205 · Cited by 99 · see citing works
Self-citationAnalysis of Accelerated Hazards Models
Chen, Y.-Q., & Wang, M.-C. (2000) · Journal of the American Statistical Association · doi:10.1080/01621459.2000.10474236 · Cited by 94 · see citing works
“trials (Gail 1985; Zucker and Lakatos 1990).”
Genetic model selection in two-phase analysis for case-control association studies
Zheng, G., & Ng, H. K. T. (2007) · Biostatistics · doi:10.1093/biostatistics/kxm039 · Cited by 81 · see citing works
“…the data is uncertain, but a family of scientifically plausible models and their normally distributed tests are available (Gastwirth, 1966, 1985; Davies, 1977; Zucker and Lakatos, 1990; Whittemore and Tu, 1998; Freidlin and others, 1999; Gastwirth and Freidlin, 2000; Shih and Whittemore, 2001).”
BackgroundDesigning therapeutic cancer vaccine trials with delayed treatment effect: Z. XU<i>ET AL.</i>
Xu, Z., Zhen, B., Park, Y., & Zhu, B. (2017) · Statistics in Medicine · doi:10.1002/sim.7157 · Cited by 76 · see citing works
“This finding is consistent with Zucker and Lakatos’s results [14], where the study power can be severely over-estimated when the true changing point t∗ is above the specified.”
BackgroundResultBiometrika Centenary: Survival analysis
Oakes, D. (2001) · Biometrika · doi:10.1093/biomet/88.1.99 · Cited by 67 · see citing works
Designing complex group sequential survival trials
Lakatos, E. (2002) · Statistics in Medicine · doi:10.1002/sim.1193 · Cited by 65 · see citing works
Self-citationPower and Sample Size Calculation for Log-rank Test with a Time Lag in Treatment Effect
Zhang, D.-W., & Quan, H. (2009) · Statistics in Medicine · doi:10.1002/sim.3501 · Cited by 48
“Zucker and Lakatos [4] proposed a maximum efficiency robust statistic to test the treatment effect with a time lag.”
InfluentialComparison of robust tests for genetic association using case-control studies
Zheng, G., Freidlin, B., & Gastwirth, J. (2006) · Institute of Mathematical Statistics eBooks · doi:10.1214/074921706000000491 · Cited by 48 · see citing works
“Although a simple algorithm when C is the class of linear combination of {Zi : i ∈ I} was given in Gastwirth [9] (see also Zucker and Lakatos [27]), the computation of ZMERT is more complicated as it is related to quadratic programming algorithms.”
MethodLikelihood ratio tests for a changepoint with survival data
Luo, X., Turnbull, B., & Clark, L. (1997) · doi:10.1093/biomet/84.3.555 · Cited by 48
Estimation of treatment effects in weighted log-rank tests
Lin, R. S., & León, L. F. (2017) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2017.09.004 · Cited by 46 · see citing works
A Monte Carlo approach for change‐point detection in the Cox proportional hazards model
Liu, M., Lu, W.-B., & Shao, Y. (2008) · Statistics in Medicine · doi:10.1002/sim.3214 · Cited by 42
“For the two-sample problem, Zucker and Lakatos [2] investigated the log-rank test statistics for a treatment lag effect.”
MethodPalo, G. D., Camerini, T., Marubini, E., Costa, A., Formelli, F., del Vecchio, M., Mariani, L., Miceli, R., Mascotti, G., Magni, A., et al. (1997) · Tumori · doi:10.1177/030089169708300603 · Cited by 40
Podgor, M. J., & Gastwirth, J. (1994) · Statistics in Medicine · doi:10.1002/sim.4780130535 · Cited by 39
Analysis of Genetic Association Studies
Zheng, G., Yang, Y., Zhu, X.-F., & Elston, R. C. (2012) · doi:10.1007/978-1-4614-2245-7 · Cited by 38
“In addition to applications of the MERT in genetics, it has applications in other areas, including survival analysis [90, 116, 356].”
MethodFlexible Weighted Log‐Rank Tests Optimal for Detecting Early and/or Late Survival Differences
Wu, L., & Gilbert, P. (2002) · Biometrics · doi:10.1111/j.0006-341x.2002.00997.x · Cited by 36
On power and efficiency robust linkage tests for affected sibs
Gastwirth, J., & Freidlin, B. (2000) · Annals of Human Genetics · doi:10.1046/j.1469-1809.2000.6450443.x · Cited by 36
“A simple algorithm to obtain the MERT is given in Gastwirth (1985) and is used by Zucker & Lakatos (1990) to analyse survival data.”
BackgroundMethodDesign and analysis of cancer screening trials
Etzioni, R., Connor, R., Prorok, P., & Self, S. (1995) · Statistical Methods in Medical Research · doi:10.1177/096228029500400102 · Cited by 35
Joo, J., Kwak, M., Chen, Z., & Zheng, G. (2009) · Statistics in Medicine · doi:10.1002/sim.3759 · Cited by 44 · see citing works
“Although this condition may not be easily verified in many applications, the simpler MERT (3) has been discussed and applied in many settings [26, 27], including genetic studies.”
Designing cancer immunotherapy trials with random treatment time-lag effect
Xu, Z.-Z., Park, Y., Zhen, B., & Zhu, B. (2018) · Statistics in Medicine · doi:10.1002/sim.7937 · Cited by 33
“For a given 𝜆2 and N, simulate the time-to-event data under the random lag duration scenario where t∗ind ∼ Uniform[1, 11] months and compute the empirical powers of the following testing methods: • GPW-Logrank test assuming a random lag time varying between 1 and 11 months, with weights w∗1(t) = 0, if t ≤ 1;w∗2(t) = (…”
BackgroundMethodInfluentialUno, H., Tian, L., Claggett, B., & Wei, L.-J. (2015) · Statistics in Medicine · doi:10.1002/sim.6591 · Cited by 33
“For example, the G tests, which are constructed assuming a class of survival distributions indexed by ρ and γ under the alternative hypothesis (Fleming and Harrington, 1991), a linear combination or the maximum of several test statistics (Tarone, 1981; Fleming and Harrington, 1984, 1991; Gastwirth, 1985; Zucker and La…”
Accelerated Hazards Model based on Parametric Families Generalized with Bernstein Polynomials
Chen, Y., Hanson, T. E., & Zhang, J.-J. (2013) · Biometrics · doi:10.1111/biom.12104 · Cited by 32
“…and accelerated failure time (e.g., Cox and Oakes, 1984) models, the accelerated hazards (AH) model (Chen and Wang, 2000) was proposed for its ability to capture the gradual effects of a treatment; this lag period often exists before a treatment is fully effective (e.g., Zucker and Lakatos, 1990).”
Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials
Chang, M., Balser, J., Roach, J., & Bliss, R. (2019) · doi:10.1201/9781351214544 · Cited by 28
Liu, S.-F., Chu, C.-H., & Rong, A. (2018) · Pharmaceutical statistics · doi:10.1002/pst.1878 · Cited by 28
A sequential testing approach to detecting multiple change points in the proportional hazards model
He, P., Fang, L., & Su, Z. (2013) · Statistics in Medicine · doi:10.1002/sim.5605 · Cited by 26
“For randomized clinical trials with two treatment arms, Zucker and Lakatos [7] investigated the logrank test statistics for a delayed treatment effect.”
MethodInterim Futility Monitoring Assessing Immune Therapies With a Potentially Delayed Treatment Effect.
Korn, E., & Freidlin, B. (2018) · Journal of Clinical Oncology · doi:10.1200/jco.2018.77.7144 · Cited by 25
A simulation study used to design the sequential monitoring plan for a clinical trial.
Brooks, M. M., Hallstrom, A., & Pecková, M. (1995) · Statistics in Medicine · doi:10.1002/sim.4780142006 · Cited by 24
Horiguchi, M., Cronin, A., Takeuchi, M., & Uno, H. (2018) · Statistics in Medicine · doi:10.1002/sim.7661 · Cited by 23 · see citing works
Tests for comparing estimated survival functions
Chauvel, C., & O'Quigley, J. (2014) · doi:10.1093/biomet/asu015 · Cited by 23
Bootstrapping a change-point Cox model for survival data
Xu, G.-J., Sen, B., & Ying, Z. (2013) · Electronic Journal of Statistics · doi:10.1214/14-ejs927 · Cited by 23
“For clinical trial data, Meinert (1986) and Zucker and Lakatos (1990) argue that the treatment effect may manifest only after a period of time.”
MethodYe, T., & Yu, M.-G. (2018) · Biometrics · doi:10.1111/biom.12916 · Cited by 20 · see citing works
“In particular, a good approximation for (t) is n−1p(1 − p) × {number of failures in [0, t]} (Schoenfeld, 1983; Zucker and Lakatos, 1990).”
BackgroundInfluentialA General Class of Change Point and Change Curve Modeling for Life Time Data
Patra, K., & Dey, D. (2002) · doi:10.1023/a:1022454909407 · Cited by 20
Statistical and practical considerations in designing of immuno-oncology trials
Mukhopadhyay, P., Huang, W.-M., Metcalfe, P., Öhrn, F., Jenner, M., & Stone, A. (2020) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2020.1815035 · Cited by 19
“Zucker and Lakatos (1990) proposed two weighted log-rank types of statistics designed to have good efficiency over a wide range of lag functions, which can be applied in situations where a delayed effect is expected but cannot be specified precisely in advance.”
BackgroundAdditive Hazards Models with Latent Treatment Effectiveness Lag Time
Chen, Y.-Q., Rohde, C., & Wang, M.-C. (2002) · doi:10.1093/biomet/89.4.917 · Cited by 19
A New Semiparametric Estimation Method for Accelerated Hazard Model
Zhang, J.-J., Peng, Y.-W. P., & Zhao, O. (2011) · Biometrics · doi:10.1111/j.1541-0420.2011.01592.x · Cited by 18
“…(AFT) model (Cox and Oakes, 1984), the AH model is particularly useful to model the effect of treatments or other covariates on the failure time distribution when the treatment effect is gradually released and there is a lag period before the treatment is fully effective (Zucker and Lakatos, 1990).”
MethodSome statistical issues in HIV vaccine trials.
Rida, W., & Lawrence, D. (1994) · Statistics in Medicine · doi:10.1002/sim.4780131922 · Cited by 18
Using Surrogate Endpoints in Adaptive Designs with Delayed Treatment Effect
Li, Q., Lin, J.-C., Liu, M., Wu, L., & Liu, Y.-Y. (2021) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2021.1938203 · Cited by 17 · see citing works
“For example, weighted log-rank test, restricted mean survival time (RMST), and other methods (Harrington and Fleming 1982; Zucker and Lakatos 1990; Yang and Prentice 2005; Fine 2007; Royston and Parmar 2011; Uno et al. 2014; Xu et al. 2017) are proposed to overcome the limitations of logrank test under nonproportional…”
MethodA New Semiparametric Estimation Method for Accelerated Hazards Mixture Cure Model
Zhang, J.-J., Peng, Y.-W. P., & Li, H.-F. (2013) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2012.09.017 · Cited by 17
“That is, there is a lag period before the treatment is fully effective (Zucker and Lakatos, 1990).”
The asymptotic distribution of MLE of treatment lag threshold
Luo, X. (1996) · doi:10.1016/0378-3758(95)00142-5 · Cited by 17
An omnibus test for several hazard alternatives in prevention randomized controlled clinical trials
Garès, V., Andrieu, S., Dupuy, J., & Savy, N. (2015) · Statistics in Medicine · doi:10.1002/sim.6366 · Cited by 16 · see citing works
A Two-Stage Failure Model for Bayesian Change Point Analysis
Lin, J. (2008) · IEEE Transactions on Reliability · doi:10.1109/tr.2008.923484 · Cited by 15
Change-Point Analysis of Survival Data with Application in Clinical Trials
Chen, X., & Baron, M. I. (2014) · doi:10.4236/ojs.2014.49062 · Cited by 14
“As another example, [2] describes analysis of the Physician’s Health Study for testing the effect of beta-carotene on cancer incidence.”
MethodRegression for censored survival data with lag effects
Luo, X., Turnbull, B., Cai, H., & Clark, L. (1994) · doi:10.1080/03610929408831455 · Cited by 14
Is it time for the weighted log-rank test to play a more important role in confirmatory trials?
Su, Z., & Zhu, M. (2017) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2017.09.007 · Cited by 13
Etzioni, R., & Gulati, R. (2013) · Medical Care · doi:10.1097/mlr.0b013e31828a7e1a · Cited by 13
Kundu, M., & Sarkar, J. (2021) · Statistics in Medicine · doi:10.1002/sim.8905 · Cited by 12
“The R codes to generate CDE based on Equations (10) and (11) is presented in Appendix 3.”
MethodComparison of survival distributions in clinical trials: A practical guidance
Chen, X.-T., Wang, X., Chen, K., Zheng, Y., Chappell, R. J., & Dey, J. (2020) · Clinical Trials · doi:10.1177/1740774520928614 · Cited by 12
Design and Analysis of clinical Trials in the Presence of Delayed Treatment Effect
Sit, T., Liu, M., Shnaidman, M., & Ying, Z. (2016) · Statistics in Medicine · doi:10.1002/sim.6889 · Cited by 12
Adaptive Test for Testing the Difference in Survival Distributions
Pecková, M., & Fleming, T. (2003) · Lifetime Data Analysis · doi:10.1023/a:1025822717910 · Cited by 12 · see citing works
“These include the maximin efficiency tests (Gastwirth, 1985; Zucker and Lakatos, 1990), the supremum version tests (Gill, 1980; Fleming, Harrington and O’Sullivan, 1987), the maximum of a finite cluster of statistics (Tarone, 1981; Fleming and Harrington, 1984; Lee, 1996) and the maximum of a large class of statistics…”
MethodNatarajan, R., Turnbull, B., Slate, E., & Clark, L. (1996) · Computer Methods and Programs in Biomedicine · doi:10.1016/0169-2607(96)01717-8 · Cited by 12
Bigger, J., Parides, M., Rolnitzky, L., Meier, P., Levin, B., & Egan, D. (1998) · Controlled Clinical Trials · doi:10.1016/s0197-2456(97)00124-4 · Cited by 11
Sample size calculation for logrank test and prediction of number of events over time
Lu, K. (2020) · Pharmaceutical statistics · doi:10.1002/pst.2069 · Cited by 10
Optimality of testing procedures for survival data in the nonproportional hazards setting
Arfè, A., Alexander, B. M., & Trippa, L. (2020) · Biometrics · doi:10.1111/biom.13315 · Cited by 9 · see citing works
“We considerMantel’s log-rank test and several others that account for delayed treatment effects: (i) a lagged log-rank test that ignores the first 10% of observed followup times (Zucker and Lakatos, 1990); (ii) the Fleming– Harrington G0,1 test, which gives more weight to late events (Fine, 2007); (iii) the adaptive l…”
BackgroundMethodInfluentialEstimation of delay time in survival data with delayed treatment effect
Li, W., Chen, S., & Rong, A. (2018) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2018.1534857 · Cited by 10
“Finally, for RCTs with delayed treatment effect, some research works focused on various weighted log-rank tests (He and Su, 2015; Xu et al., 2017; Zucker and Lakatos, 1990).”
MethodOn the Fleming—Harrington test for late effects in prevention randomized controlled trials
Garès, V., Andrieu, S., Dupuy, J., & Savy, N. (2017) · Journal of Statistical Theory and Practice · doi:10.1080/15598608.2017.1295889 · Cited by 10 · see citing works
Zucker, D. M. (1992) · Biometrics · doi:10.2307/2532355 · Cited by 10
Self-citationKlinglmüller, F., Fellinger, T., König, F., Friede, T., Hooker, A. C., Heinzl, H., Mittlböck, M., Brugger, J., Bardo, M., Huber, C., et al. (2025) · Statistics in Medicine · doi:10.1002/sim.70019 · Cited by 9
Assurance methods for designing a clinical trial with a delayed treatment effect
Salsbury, J. A., Oakley, J. E., Julious, S., & Hampson, L. V. (2023) · Statistics in Medicine · doi:10.1002/sim.10136 · Cited by 9 · see citing works
Ding, X., & Wu, J.-R. (2020) · Pharmaceutical statistics · doi:10.1002/pst.2003 · Cited by 9
“The sample size formula (7) for the MERT derived by Ye and Yu(5) underestimated the sample size even when δ>0.”
MethodNon-parametric estimation of relative risk in survival and associated tests
Wakounig, S., Heinze, G., & Schemper, M. (2015) · Statistical Methods in Medical Research · doi:10.1177/0962280211431022 · Cited by 9 · see citing works
Trial Design for Cancer Immunotherapy: A Methodological Toolkit
Saad, E., Coart, E., Deltuvaite-Thomas, V., Garcia-Barrado, L., Burzykowski, T., & Buyse, M. (2023) · Cancers · doi:10.3390/cancers15184669 · Cited by 8 · see citing works
Yu, C., Huang, X., Hui, N., & He, P. (2021) · Pharmaceutical statistics · doi:10.1002/pst.2092 · Cited by 8
Chu, C.-H., Liu, S.-F., & Rong, A. (2020) · Pharmaceutical statistics · doi:10.1002/pst.1976 · Cited by 8
Projecting the yearly mortality reductions due to a cancer screening programme
Liu, Z., Hanley, J., & Strumpf, E. (2013) · Journal of Medical Screening · doi:10.1177/0969141313504088 · Cited by 8 · see citing works
“2013–2:40pm] [1–8] //blrnas3/cenpro/ApplicationFiles/Journals/SAGE/3B2/MSCJ/Vol00000/130005/APPFile/SG-MSCJ130005.”
Maximin efficiency-robust tests and some extensions
Bose, S., & Slud, E. (1995) · doi:10.1016/0378-3758(94)e0091-8 · Cited by 8
“However, as Gastwirth (1985), Zucker and Lakatos (1990), and Zucker (1992) have pointed out, there are special infinite-dimensional families of (contiguous) alternatives with respect to which the minimum ARE of a MERT test will still be reasonably good (say, 60-70%).”
BackgroundCancer Immunotherapy Trial Design with Random Delayed Treatment Effect and Cure Rate
Wu, J.-R., & Wei, J. (2021) · Statistics in Medicine · doi:10.1002/sim.9258 · Cited by 7
Tang, Y.-Q. (2021) · Statistical Methods in Medical Research · doi:10.1177/0962280220988570 · Cited by 7
DESIGN OF VACCINE TRIALS DURING OUTBREAKS WITH AND WITHOUT A DELAYED VACCINATION COMPARATOR
Dean, N., Halloran, M. E., & Longini, I. (2016) · bioRxiv · doi:10.1101/074088 · Cited by 7
“The incubation and immune ramp-up periods could be explicitly included in the likelihood; if this lag is known, it can be used to construct optimal weights [Zucker and Lakatos, 1990].”
MethodHe, P., & Su, Z. (2015) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2015.08.003 · Cited by 7
A flexible approach to the crossing hazards problem
Muggeo, V., & Tagliavia, M. (2010) · Statistics in Medicine · doi:10.1002/sim.3959 · Cited by 7
“For this reason some authors have suggested to introduce weights changing sign before and after the crossing point [10, 11, 24, 25].”
BackgroundModels for survival data from cancer prevention studies
Sinha, D., Ibrahim, J. G., & Chen, M.-H. (2002) · doi:10.1111/1467-9868.00345 · Cited by 7
“Zucker and Lakatos (1990), Freedman et al. (1993) and Luo et al. (1997) have discussed several animal experiments and clinical trials for evaluating the clinical benefits of several compounds for the prevention of cancer.”
BackgroundMethodInfluentialStatistical considerations in long-term efficacy evaluation of anti-cancer therapies
Li, R.-B., Zhang, J., Wang, J.-Z., & Wang, J. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1265953 · Cited by 6
Survival model construction guided by fit and predictive strength
Chauvel, C., & O'Quigley, J. (2014) · Biometrics · doi:10.1111/biom.12611 · Cited by 6
“This model has been considered previously by several authors (Murhpy and Sen, 1991; Hastie and Tibshirani, 1990; Zucker and Lakatos, 1990; Cai and Sun, 2003; Winnett and Sasieni, 2003; Scheike and Martinussen, 2004).”
MethodWeighted logrank tests to detect a transient improvement in survivorship.
Wallenstein, S., & Berger, A. (1997) · Biometrics · doi:10.2307/2533974 · Cited by 6
“Efficiency and Robustness For the continuous case, the efficiency of a test with weight W(t) relative to the optimal test with weight W*(t) is [Zucker and Lakatos, 1990, equation (2)] f (u) W*(u)W(u)h(u) du '0 .”
BackgroundNew Test for the Comparison of Survival Curves to Detect Late Differences
Nizeyimana, I., Mwalili, S., & Orwa, G. (2023) · Journal of Probability and Statistics · doi:10.1155/2023/9945446 · Cited by 5 · see citing works
“where τ is the total time of the study, w (t) is the weight function at time t, Ri(t) is the number of items/individuals at risk at time t in the i group, R(t) is the overall number of items/individuals at risk at time t, and Ni(t) is the number of items/individuals which underwent the event of interest by time t in t…”
BackgroundGierz, K., & Park, K. (2021) · Biometrical journal. Biometrische Zeitschrift · doi:10.1002/bimj.202000262 · Cited by 5
Ramos, I. C., Qendri, V., & Al, M. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02373-5 · Cited by 5 · see citing works
“Studies discussing issues related to non-PH go back to the 1980’s and 1990’s; thus, even before the use of IO therapies [42, 52, 62, 69, 78].”
BackgroundKlinglmuller, F., Fellinger, T., Konig, F., Friede, T., Hooker, A. C., Heinzl, H., Mittlböck, M., Brugger, J., Bardo, M., Huber, C., et al. (2023) · doi:10.48550/arxiv.2310.05622 · Cited by 4
“For hypothesis testing, weighted log-rank tests have been considered that have a larger power than the unweighted test, if more weight is allocated to time points where the effect is larger [13, 14, 15, 16].”
Qian, K., & Zhou, X. (2022) · Mathematics · doi:10.3390/math10152573 · Cited by 4 · see citing works
Ngari, M., Schmitz, S., Maronga, C., Mramba, L., & Vaillant, M. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01280-3 · Cited by 4 · see citing works
The Inverse Log-Rank Test: A Versatile Procedure for Late Separating Survival Curves
Efird, J. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20247164 · Cited by 3
Kaneko, Y., & Morita, S. (2023) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2023.2244055 · Cited by 3
“Under the alternative hypothesis defined by each of the lag models, we calculate μ w and σ 2 w using λ 1 t ð Þ in Equation (5) and is the optimal weight in terms of detecting treatment effect over control (Zucker and Lakatos 1990).”
InfluentialZ max test for delayed effect in immuno-oncology clinical trials
Yang, M., Hua, Z.-W., Xue, L., & Hu, M. (2020) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543406.2019.1632873 · Cited by 3
“For example, Zucker and Lakatos (1990) proposed to use a linear combination of weighted log-rank tests or a combination of maximum efficiency to account for a broad range of lags time functions.”
BackgroundMethodA new modeling and inference approach for the Systolic Blood Pressure Intervention Trial outcomes
Yang, S., Ambrosius, W., Fine, L. J., Bress, A., Cushman, W., Raj, D., Rehman, S., & Tamariz, L. (2018) · Clinical Trials · doi:10.1177/1740774518769865 · Cited by 3
An approximate algorithm for estimating treatment lags from right censored data
Janikow, C., Cai, H., & Luo, X. (1993) · doi:10.1016/0898-1221(93)90187-z · Cited by 3
Ning, Z., Wu, J., Ding, Y., Wang, Y., Peng, Q., & Fu, L.-Y. (2023) · bioRxiv · doi:10.1109/jbhi.2023.3311808 · Cited by 2 · see citing works
“Kaplan-Meier(KM)analysis[47]withLog-rank-typetest[48]is thenperformed.”
Non-parametric treatment time-lag effect estimation
Gierz, K., Park, K., & Qiu, P. (2021) · Statistical Methods in Medical Research · doi:10.1177/09622802211032693 · Cited by 2
Adaptive lasso for accelerated hazards models
Chen, Y., Zhang, J.-J., & Xu, Y.-Y. (2018) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2018.1491579 · Cited by 2 · see citing works
The Influence of Biostatistics at the National Heart, Lung, and Blood Institute
DeMets, D. L., Wittes, J. T., & Geller, N. L. (2015) · The American Statistician · doi:10.1080/00031305.2015.1035962 · Cited by 3 · see citing works
“The American Statistician, May 2015, Vol. 69, No. 2 115 Zucker and Lakatos (1990) and Zucker (1992) proposed using weighted log-rank tests to deal with nonproportional hazards.”
Chen, Y.-Q., Yang, J., Cheng, S., & Jackson, J. (2007) · doi:10.1093/biomet/asm019 · Cited by 2
Berger, A., & Wallenstein, S. (1993) · doi:10.1016/0167-7152(93)90116-z · Cited by 2
Bayesian Inference For The Segmented Weibull Distribution
Coelho-Barros, E. A., Achcar, J., Martinez, E., Davarzani, N., & Grabsch, H. (2019) · Revista Colombiana de Estadística · doi:10.15446/rce.v42n2.76815 · Cited by 1
Bayesian optimality of testing procedures for survival data
Arfé, A., Alexander, B., & Trippa, L. (2019) · doi:10.48550/arxiv.1902.00161 · Cited by 1
“We consider Mantel’s log-rank test and several others which account for delayed treatment effects: i) a lagged log-rank test that ignores the first 10% of observed follow-up times (Zucker and Lakatos, 1990); ii) the Fleming-Harrington G test, which gives more weight to late events (Fine, 2007); iii) the adaptive log-r…”
Adaptive robust genetic association tests using case‐parents triad families
Wang, J., & Tai, J. (2015) · Biometrical journal. Biometrische Zeitschrift · doi:10.1002/bimj.201300135 · Cited by 1 · see citing works
“Later on, Zucker and Lakatos (1990) used the procedure for the comparison of treatments with time lag effects in survival analysis.”
MethodAssessing Treatment Benefit in Immuno-oncology
Buyse, M., Saad, E., Burzykowski, T., & Péron, J. (2020) · Statistics in Biosciences · doi:10.1007/s12561-020-09268-1
Evaluation of the treatment time-lag effect for survival data
Park, K., & Qiu, P. (2017) · Lifetime Data Analysis · doi:10.1007/s10985-017-9390-7
“To compare the hazard rate functions of the two groups, we conduct the hypothesis testing of (5) using both the Wald test (6), the likelihood ratio test (7), the 6 existing methods mentioned above related to (9), and the method by Zucker and Lakatos (1990). For the 6 existing methods considered, besides the regular co…”
BackgroundMethodInfluentialComparing two hazard curves when there is a treatment time‐lag effect
Zhang, X.-X., Datta, S., & Qiu, P.-H. (2024) · Statistics in Medicine · doi:10.1002/sim.10142
“In cases when the time-lag period can be pre-specified, Zucker and Lakatos 18 proposed two weighted Log-rank tests for comparing the related hazard curves.”
Hazard Model with Random Treatment Time-Lag Effect
Ye, X., & Liu, Y.-Y. (2023) · Acta Mathematica Sinica. English series · doi:10.1007/s10114-023-1391-8
- Authors
- ZUCKER, D. M., & LAKATOS, E.
- Affiliations
- National Heart, Lung, and Blood Institute (US)
- ORCID
- ZUCKER, DAVID M.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1990
- Volume
- 77
- Issue
- 4
- Pages
- 853-864
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2079219970
- Cited by
- 114
- References
- 0
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 8 · 2022: 1 · 2021: 8 · 2020: 7 · 2019: 2 · 2018: 11 · 2017: 4 · 2016: 3 · 2015: 4 · 2014: 7 · 2013: 5 · 2012: 5
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biometrika (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- survival curves, asymptotic efficiency, small sample simulation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review