{"doi":"10.1198/sbr.2009.0059","title":"Stopping Boundaries of Flexible Sample Size Design With Flexible Trial Monitoring","abstract":null,"journal":"Statistics in Biopharmaceutical Research","year":2010,"id":589464,"datarank":0.10680464969685698,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0028325726128651607,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0028325726128651607,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"citer_count":1,"citers_with_citation_signal":1,"citers_with_endowment":1,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1508173,"name":"Zhenming Shun","orcid":null,"position":1,"is_corresponding":false},{"id":1367605,"name":"Yijia Feng","orcid":null,"position":2,"is_corresponding":false},{"id":282570,"name":"Yi He","orcid":"0000-0002-9503-0565","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Stopping Boundaries of Flexible Sample Size Design With Flexible Trial Monitoring","abstract":"In the group sequential (GS) approach with a fixed sample size design, the Type I error is controlled by the additivity of exit spending values. However, in a flexible sample size design where the sample size will be recalculated using the interim data, the overall Type I error rate can be inflated. Therefore, the predefined GS stopping boundaries have to be adjusted to maintain the Type I error level at each interim analysis and the at the overall level. The modified α spending function adjusted for sample size reestimation (SSR) is proposed to maintain the Type I error level. We use a unified approach and mathematically quantify the Type I error with and without sample size adjustment constraints. As a result, stopping boundaries can be obtained by inversely solving the exact Type I error functions. This unified approach, using Brownian motion theory, can be applied to normal, survival, and binary endpoints. Extensive simulations show the adjusted stopping boundaries can control the Type I error at each analysis and at the overall level.","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"20725694","pmcid":null,"openalex_id":"https://openalex.org/W2047140783","authors":[],"funders":[],"total_grants":0,"fwci":0.2845,"citation_percentile":0.62313157,"influential_citations":0,"citation_trend":[{"year":2013,"count":1}],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://www.tandfonline.com/doi/pdf/10.1198/sbr.2009.0059","host_type":"publisher"},{"url":"https://doi.org/10.1198/sbr.2009.0059","host_type":"journal"}],"fields_of_study":["Statistical Methods in Clinical Trials","Optimal Experimental Design Methods"],"mesh_terms":[],"keywords":["Sample size determination","Type I and type II errors","Statistics","Mathematics","Interim analysis","Sample (material)","Early stopping","Binary number","Interim","Computer science","Artificial intelligence"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Decent work and economic growth"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-23T18:50:03.622124Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}