{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T22:24:08Z","timestamp":1777501448794,"version":"3.51.4"},"reference-count":63,"publisher":"Informa UK Limited","content-domain":{"domain":["www.tandfonline.com"],"crossmark-restriction":true},"short-container-title":["Journal of the American Statistical Association"],"DOI":"10.1080\/01621459.2026.2657052","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:16:07Z","timestamp":1777389367000},"page":"1-30","update-policy":"https:\/\/doi.org\/10.1080\/tandf_crossmark_01","source":"Crossref","is-referenced-by-count":0,"title":["An Online Meta-Level Adaptive Design Framework with Targeted Learning Inference: Applications to Evaluating and Utilizing Surrogate Outcomes in Adaptive Designs"],"prefix":"10.1080","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9106-1672","authenticated-orcid":false,"given":"Wenxin","family":"Zhang","sequence":"first","affiliation":[{"name":"Division of Biostatistics, University of California","place":["Berkeley"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aaron","family":"Hudson","sequence":"additional","affiliation":[{"name":"Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center"},{"name":"Department of Biostatistics, University of Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maya","family":"Petersen","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, University of California","place":["Berkeley"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"van der Laan","sequence":"additional","affiliation":[{"name":"Division of Biostatistics, University of California","place":["Berkeley"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"301","published-online":{"date-parts":[[2026,4,28]]},"reference":[{"key":"e_1_3_1_2_1","unstructured":"Atkinson A. C. Biswas A. and Pronzato L. (2011) Covariate-balanced response-adaptive designs for clinical trials with continuous responses that target allocation probabilities Technical Report NI11042-DAE Isaac Newton Institute for Mathematical Sciences Cambridge UK."},{"key":"e_1_3_1_3_1","unstructured":"Bibaut A. Dimakopoulou M. Kallus N. Chambaz A. and van der Laan M. (2021) \u2018Post-contextual-bandit inference\u2019 Advances in Neural Information Processing Systems 34 28548\u201328559."},{"key":"e_1_3_1_4_1","doi-asserted-by":"crossref","unstructured":"Brown B. M. (1971) \u2018Martingale central limit theorems\u2019 The Annals of Mathematical Statistics 42 59\u201366.","DOI":"10.1214\/aoms\/1177693494"},{"key":"e_1_3_1_5_1","doi-asserted-by":"crossref","unstructured":"Bubeck S. Cesa-Bianchi N. et\u00a0al. (2012) \u2018Regret analysis of stochastic and nonstochastic multi-armed bandit problems\u2019 Foundations and Trends in Machine Learning 5(1) 1\u2013122.","DOI":"10.1561\/2200000024"},{"key":"e_1_3_1_6_1","doi-asserted-by":"crossref","unstructured":"Buyse M. Molenberghs G. Burzykowski T. Renard D. and Geys H. (2000) \u2018The validation of surrogate endpoints in meta-analyses of randomized experiments\u2019 Biostatistics 1(1) 49\u201367.","DOI":"10.1093\/biostatistics\/1.1.49"},{"key":"e_1_3_1_7_1","doi-asserted-by":"crossref","unstructured":"Chambaz A. and van der Laan M. J. (2014) \u2018Inference in targeted group-sequential covariate-adjusted randomized clinical trials\u2019 Scandinavian Journal of Statistics 41(1) 104\u2013140.","DOI":"10.1111\/sjos.12013"},{"key":"e_1_3_1_8_1","doi-asserted-by":"crossref","unstructured":"Chambaz A. Zheng W. and van der Laan M. J. (2017) \u2018Targeted sequential design for targeted learning inference of the optimal treatment rule and its mean reward\u2019 The Annals of Statistics 45(6) 2537\u20132564.","DOI":"10.1214\/16-AOS1534"},{"key":"e_1_3_1_9_1","doi-asserted-by":"crossref","unstructured":"Cheung S. H. Zhang L.-X. Hu F. and Chan W. S. (2014) \u2018Covariate-adjusted response-adaptive designs for generalized linear models\u2019 Journal of Statistical Planning and Inference 149 152\u2013161.","DOI":"10.1016\/j.jspi.2014.02.006"},{"key":"e_1_3_1_10_1","doi-asserted-by":"crossref","unstructured":"Chow S.-C. and Chang M. (2008) \u2018Adaptive design methods in clinical trials\u2013a review\u2019 Orphanet Journal of Rare Diseases 3(1) 11.","DOI":"10.1186\/1750-1172-3-11"},{"key":"e_1_3_1_11_1","doi-asserted-by":"crossref","unstructured":"Daniels M. J. and Hughes M. D. (1997) \u2018Meta-analysis for the evaluation of potential surrogate markers\u2019 Statistics in Medicine 16(17) 1965\u20131982.","DOI":"10.1002\/(SICI)1097-0258(19970915)16:17<1965::AID-SIM630>3.0.CO;2-M"},{"key":"e_1_3_1_12_1","doi-asserted-by":"crossref","unstructured":"Duan W. Ba S. and Zhang C. (2021) Online experimentation with surrogate metrics: Guidelines and a case study in \u2018Proceedings of the 14th ACM International Conference on Web Search and Data Mining\u2019 pp. 193\u2013201.","DOI":"10.1145\/3437963.3441737"},{"key":"e_1_3_1_13_1","doi-asserted-by":"crossref","unstructured":"Elliott M. R. (2023) \u2018Surrogate endpoints in clinical trials\u2019 Annual Review of Statistics and its Application 10 75\u201396.","DOI":"10.1146\/annurev-statistics-032921-035359"},{"key":"e_1_3_1_14_1","doi-asserted-by":"crossref","unstructured":"Freedman L. S. Graubard B. I. and Schatzkin A. (1992) \u2018Statistical validation of intermediate endpoints for chronic diseases\u2019 Statistics in Medicine 11(2) 167\u2013178.","DOI":"10.1002\/sim.4780110204"},{"key":"e_1_3_1_15_1","doi-asserted-by":"crossref","unstructured":"Gao J. Hu F. and Ma W. (2024) \u2018Response-adaptive randomization procedure in clinical trials with surrogate endpoints\u2019 Statistics in Medicine 43(30) 5911\u20135921.","DOI":"10.1002\/sim.10286"},{"key":"e_1_3_1_16_1","doi-asserted-by":"crossref","unstructured":"Geng E. H. Odeny T. A. Montoya L. M. Iguna S. Kulzer J. L. Adhiambo H. F. Eshun-Wilson I. Akama E. Nyandieka E. Guz\u00e9 M. A. Shade S. Packel L. Fox B. Camlin C. Thirumurthy H. Lyons C. Bukusi E. A. and Petersen M. L. (2023) \u2018Adaptive strategies for retention in care among persons living with hiv\u2019 NEJM Evidence 2(4) EVIDoa2200076.","DOI":"10.1056\/EVIDoa2200076"},{"key":"e_1_3_1_17_1","doi-asserted-by":"crossref","unstructured":"Gilbert P. B. and Hudgens M. G. (2008) \u2018Evaluating candidate principal surrogate endpoints\u2019 Biometrics 64(4) 1146\u20131154.","DOI":"10.1111\/j.1541-0420.2008.01014.x"},{"key":"e_1_3_1_18_1","unstructured":"Gill R. D. van der Laan M. J. and Wellner J. A. (1995) \u2018Inefficient estimators of the bivariate survival function for three models\u2019 Annales de l\u2019Institut Henri Poincar\u00e9 Probabilit\u00e9s et Statistiques 31(3) 545\u2013597."},{"key":"e_1_3_1_19_1","doi-asserted-by":"crossref","unstructured":"Hadad V. Hirshberg D. A. Zhan R. Wager S. and Athey S. (2021) \u2018Confidence intervals for policy evaluation in adaptive experiments\u2019 Proceedings of the National Academy of Sciences 118(15) e2014602118.","DOI":"10.1073\/pnas.2014602118"},{"key":"e_1_3_1_20_1","doi-asserted-by":"crossref","unstructured":"Hsu J. Y. Kennedy E. H. Roy J. A. Stephens-Shields A. J. Small D. S. and Joffe M. M. (2015) \u2018Surrogate markers for time-varying treatments and outcomes\u2019 Clinical Trials 12(4) 309\u2013316.","DOI":"10.1177\/1740774515583500"},{"key":"e_1_3_1_21_1","doi-asserted-by":"crossref","unstructured":"Hu F. and Rosenberger W. F. (2006) The Theory of Response-Adaptive Randomization in Clinical Trials John Wiley & Sons.","DOI":"10.1002\/047005588X"},{"key":"e_1_3_1_22_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.2014.903846"},{"key":"e_1_3_1_23_1","doi-asserted-by":"crossref","unstructured":"Huang X. Ning J. Li Y. Estey E. Issa J.-P. and Berry D. A. (2009) \u2018Using short-term response information to facilitate adaptive randomization for survival clinical trials\u2019 Statistics in Medicine 28(12) 1680\u20131689.","DOI":"10.1002\/sim.3578"},{"key":"e_1_3_1_24_1","doi-asserted-by":"crossref","unstructured":"Larsen N. Stallrich J. Sengupta S. Deng A. Kohavi R. and Stevens N. T. (2024) \u2018Statistical challenges in online controlled experiments: A review of a\/b testing methodology\u2019 The American Statistician 78(2) 135\u2013149.","DOI":"10.1080\/00031305.2023.2257237"},{"key":"e_1_3_1_25_1","doi-asserted-by":"publisher","DOI":"10.1017\/9781108571401"},{"key":"e_1_3_1_26_1","doi-asserted-by":"crossref","unstructured":"Lin D. Fleming T. and De Gruttola V. (1997) \u2018Estimating the proportion of treatment effect explained by a surrogate marker\u2019 Statistics in Medicine 16(13) 1515\u20131527.","DOI":"10.1002\/(SICI)1097-0258(19970715)16:13<1515::AID-SIM572>3.0.CO;2-1"},{"key":"e_1_3_1_27_1","doi-asserted-by":"crossref","unstructured":"Luedtke A. R. and van der Laan M. J. (2016a) \u2018Statistical inference for the mean outcome under a possibly non-unique optimal treatment strategy\u2019 The Annals of Statistics 44(2) 713\u2013742.","DOI":"10.1214\/15-AOS1384"},{"key":"e_1_3_1_28_1","doi-asserted-by":"crossref","unstructured":"Luedtke A. R. and van der Laan M. J. (2016b) \u2018Super-learning of an optimal dynamic treatment rule\u2019 The International Journal of Biostatistics 12(1) 305\u2013332.","DOI":"10.1515\/ijb-2015-0052"},{"key":"e_1_3_1_29_1","unstructured":"Malenica I. Bibaut A. and van der Laan M. J. (2021) \u2018Adaptive sequential design for a single time-series\u2019 arXiv preprint arXiv:2102.00102 ."},{"key":"e_1_3_1_30_1","doi-asserted-by":"crossref","unstructured":"Malenica I. Coyle J. R. van der Laan M. J. and Petersen M. L. (2024) \u2018Adaptive sequential surveillance with network and temporal dependence\u2019 Biometrics 80(1) ujad007.","DOI":"10.1093\/biomtc\/ujad007"},{"key":"e_1_3_1_31_1","doi-asserted-by":"crossref","unstructured":"McDonald T. M. Maystre L. Lalmas M. Russo D. and Ciosek K. (2023) Impatient bandits: Optimizing recommendations for the long-term without delay in \u2018Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining\u2019 pp. 1687\u20131697.","DOI":"10.1145\/3580305.3599386"},{"key":"e_1_3_1_32_1","doi-asserted-by":"crossref","unstructured":"Montoya L. M. van der Laan M. J. Luedtke A. R. Skeem J. L. Coyle J. R. and Petersen M. L. (2021) \u2018The optimal dynamic treatment rule superlearner: considerations performance and application to criminal justice interventions\u2019 The International Journal of Biostatistics 19(1) 217\u2013238.","DOI":"10.1515\/ijb-2020-0127"},{"key":"e_1_3_1_33_1","doi-asserted-by":"crossref","unstructured":"Murphy S. A. (2003) \u2018Optimal dynamic treatment regimes\u2019 Journal of the Royal Statistical Society Series B: Statistical Methodology 65(2) 331\u2013355.","DOI":"10.1111\/1467-9868.00389"},{"key":"e_1_3_1_34_1","doi-asserted-by":"crossref","unstructured":"Nowacki A. S. Zhao W. and Palesch Y. Y. (2017) \u2018A surrogate-primary replacement algorithm for response-adaptive randomization in stroke clinical trials\u2019 Statistical Methods in Medical Research 26(3) 1078\u20131092.","DOI":"10.1177\/0962280214567142"},{"key":"e_1_3_1_35_1","unstructured":"Pearl J. (2000) Causality: Models Reasoning and Inference Cambridge University Press."},{"key":"e_1_3_1_36_1","doi-asserted-by":"crossref","unstructured":"Prentice R. L. (1989) \u2018Surrogate endpoints in clinical trials: definition and operational criteria\u2019 Statistics in Medicine 8(4) 431\u2013440.","DOI":"10.1002\/sim.4780080407"},{"key":"e_1_3_1_37_1","unstructured":"Richardson L. Zito A. Greaves D. and Soriano J. (2023) \u2018Pareto optimal proxy metrics\u2019 arXiv preprint arXiv:2307.01000 ."},{"key":"e_1_3_1_38_1","doi-asserted-by":"crossref","unstructured":"Robbins H. (1952) \u2018Some aspects of the sequential design of experiments\u2019 Bulletin of the American Mathematical Society 58(5) 527\u2013535.","DOI":"10.1090\/S0002-9904-1952-09620-8"},{"key":"e_1_3_1_39_1","doi-asserted-by":"crossref","unstructured":"Robertson D. S. Lee K. M. L\u00f3pez-Kolkovska B. C. and Villar S. S. (2023) \u2018Response-adaptive randomization in clinical trials: From myths to practical considerations\u2019 Statistical Science 38(2) 185\u2013208.","DOI":"10.1214\/22-STS865"},{"key":"e_1_3_1_40_1","doi-asserted-by":"crossref","unstructured":"Robins J. M. (2004) Optimal structural nested models for optimal sequential decisions in \u2018Proceedings of the Second Seattle Symposium in Biostatistics\u2019 Springer pp. 189\u2013326.","DOI":"10.1007\/978-1-4419-9076-1_11"},{"key":"e_1_3_1_41_1","doi-asserted-by":"crossref","unstructured":"Robins J. M. and Greenland S. (1992) \u2018Identifiability and exchangeability for direct and indirect effects\u2019 Epidemiology 3(2) 143\u2013155.","DOI":"10.1097\/00001648-199203000-00013"},{"key":"e_1_3_1_42_1","doi-asserted-by":"crossref","unstructured":"Rosenberger W. F. and Sverdlov O. (2008) \u2018Handling covariates in the design of clinical trials\u2019 Statistical Science 23(3) 404\u2013419.","DOI":"10.1214\/08-STS269"},{"key":"e_1_3_1_43_1","doi-asserted-by":"crossref","unstructured":"Rosenberger W. F. Vidyashankar A. and Agarwal D. K. (2001) \u2018Covariate-adjusted response-adaptive designs for binary response\u2019 Journal of Biopharmaceutical Statistics 11(4) 227\u2013236.","DOI":"10.1081\/BIP-120008846"},{"key":"e_1_3_1_44_1","doi-asserted-by":"crossref","unstructured":"Simchi-Levi D. and Wang C. (2023) Multi-armed bandit experimental design: Online decision-making and adaptive inference in \u2018International Conference on Artificial Intelligence and Statistics\u2019 PMLR pp. 3086\u20133097.","DOI":"10.2139\/ssrn.4224969"},{"key":"e_1_3_1_45_1","doi-asserted-by":"crossref","unstructured":"Tamura R. N. Faries D. E. Andersen J. S. and Heiligenstein J. H. (1994) \u2018A case study of an adaptive clinical trial in the treatment of out-patients with depressive disorder\u2019 Journal of the American Statistical Association 89(427) 768\u2013776.","DOI":"10.1080\/01621459.1994.10476810"},{"key":"e_1_3_1_46_1","doi-asserted-by":"crossref","unstructured":"Thompson W. R. (1933) \u2018On the likelihood that one unknown probability exceeds another in view of the evidence of two samples\u2019 Biometrika 25(3-4) 285\u2013294.","DOI":"10.1093\/biomet\/25.3-4.285"},{"key":"e_1_3_1_47_1","doi-asserted-by":"crossref","unstructured":"van der Laan M. J. (2006) \u2018Statistical inference for variable importance\u2019 The International Journal of Biostatistics 2(1).","DOI":"10.2202\/1557-4679.1008"},{"key":"e_1_3_1_48_1","unstructured":"van der Laan M. J. (2008) \u2018The construction and analysis of adaptive group sequential designs\u2019 U.C. Berkeley Division of Biostatistics. Working Paper 232. https:\/\/biostats.bepress.com\/ucbbiostat\/paper232."},{"key":"e_1_3_1_49_1","unstructured":"van der Laan M. J. (2023) \u2018Higher order spline highly adaptive lasso estimators of functional parameters: Pointwise asymptotic normality and uniform convergence rates\u2019 arXiv preprint arXiv:2301.13354 ."},{"key":"e_1_3_1_50_1","doi-asserted-by":"crossref","unstructured":"van der Laan M. J. and Luedtke A. R. (2015) \u2018Targeted learning of the mean outcome under an optimal dynamic treatment rule\u2019 Journal of Causal Inference 3(1) 61\u201395.","DOI":"10.1515\/jci-2013-0022"},{"key":"e_1_3_1_51_1","unstructured":"van der Laan M. J. and Malenica I. (2018) \u2018Robust estimation of data-dependent causal effects based on observing a single time-series\u2019 arXiv preprint arXiv:1809.00734 ."},{"key":"e_1_3_1_52_1","doi-asserted-by":"crossref","unstructured":"van der Laan M. J. Polley E. C. and Hubbard A. E. (2007) \u2018Super learner\u2019 Statistical Applications in Genetics and Molecular Biology 6(1).","DOI":"10.2202\/1544-6115.1309"},{"key":"e_1_3_1_53_1","doi-asserted-by":"crossref","unstructured":"van der Laan M. J. and Rose S. (2011) Targeted Learning: Causal Inference for Observational and Experimental Data Springer.","DOI":"10.1007\/978-1-4419-9782-1"},{"key":"e_1_3_1_54_1","doi-asserted-by":"crossref","unstructured":"van der Laan M. J. and Rose S. (2018) Targeted Learning in Data Science Springer.","DOI":"10.1007\/978-3-319-65304-4"},{"key":"e_1_3_1_55_1","doi-asserted-by":"crossref","unstructured":"van der Laan M. J. and Rubin D. (2006) \u2018Targeted maximum likelihood learning\u2019 The International Journal of Biostatistics 2(1).","DOI":"10.2202\/1557-4679.1043"},{"key":"e_1_3_1_56_1","doi-asserted-by":"crossref","unstructured":"VanderWeele T. J. (2013) \u2018Surrogate measures and consistent surrogates\u2019 Biometrics 69(3) 561\u2013565.","DOI":"10.1111\/biom.12071"},{"key":"e_1_3_1_57_1","doi-asserted-by":"crossref","unstructured":"Weir C. J. and Taylor R. S. (2022) \u2018Informed decision-making: Statistical methodology for surrogacy evaluation and its role in licensing and reimbursement assessments\u2019 Pharmaceutical Statistics 21(4) 740\u2013756.","DOI":"10.1002\/pst.2219"},{"key":"e_1_3_1_58_1","doi-asserted-by":"crossref","unstructured":"Yang J. Eckles D. Dhillon P. and Aral S. (2024) \u2018Targeting for long-term outcomes\u2019 Management Science 70(6) 3841\u20133855.","DOI":"10.1287\/mnsc.2023.4881"},{"key":"e_1_3_1_59_1","doi-asserted-by":"crossref","unstructured":"Zhan R. Hadad V. Hirshberg D. A. and Athey S. (2021) Off-policy evaluation via adaptive weighting with data from contextual bandits in \u2018Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining\u2019 pp. 2125\u20132135.","DOI":"10.1145\/3447548.3467456"},{"key":"e_1_3_1_60_1","unstructured":"Zhang K. Janson L. and Murphy S. (2021) \u2018Statistical inference with m-estimators on adaptively collected data\u2019 Advances in Neural Information Processing Systems 34 7460\u20137471."},{"key":"e_1_3_1_61_1","doi-asserted-by":"crossref","unstructured":"Zhang L.-X. Hu F. Cheung S. H. and Chan W. S. (2007) \u2018Asymptotic properties of covariate-adjusted response-adaptive designs\u2019 The Annals of Statistics 35(3) 1166\u20131182.","DOI":"10.1214\/009053606000001424"},{"key":"e_1_3_1_62_1","doi-asserted-by":"crossref","unstructured":"Zhao W. Ma W. Wang F. and Hu F. (2022) \u2018Incorporating covariates information in adaptive clinical trials for precision medicine\u2019 Pharmaceutical Statistics 21(1) 176\u2013195.","DOI":"10.1002\/pst.2160"},{"key":"e_1_3_1_63_1","doi-asserted-by":"crossref","unstructured":"Zheng W. and van der Laan M. J. (2011) Cross-Validated Targeted Minimum-Loss-Based Estimation Springer New York New York NY pp. 459\u2013474.","DOI":"10.1007\/978-1-4419-9782-1_27"},{"key":"e_1_3_1_64_1","doi-asserted-by":"crossref","unstructured":"Zhu H. (2015) \u2018Covariate-adjusted response adaptive designs incorporating covariates with and without treatment interactions\u2019 Canadian Journal of Statistics 43(4) 534\u2013553.","DOI":"10.1002\/cjs.11260"}],"container-title":["Journal of the American Statistical Association"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.tandfonline.com\/doi\/pdf\/10.1080\/01621459.2026.2657052","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T15:16:40Z","timestamp":1777389400000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.tandfonline.com\/doi\/full\/10.1080\/01621459.2026.2657052"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,28]]},"references-count":63,"alternative-id":["10.1080\/01621459.2026.2657052"],"URL":"https:\/\/doi.org\/10.1080\/01621459.2026.2657052","relation":{},"ISSN":["0162-1459","1537-274X"],"issn-type":[{"value":"0162-1459","type":"print"},{"value":"1537-274X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,28]]},"assertion":[{"value":"The publishing and review policy for this title is described in its Aims & Scope.","order":1,"name":"peerreview_statement","label":"Peer Review Statement"},{"value":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=uasa20","URL":"http:\/\/www.tandfonline.com\/action\/journalInformation?show=aimsScope&journalCode=uasa20","order":2,"name":"aims_and_scope_url","label":"Aim & Scope"},{"value":"2025-03-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-10","order":1,"name":"revised","label":"Revised","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}