Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'OTHER', 'observationalModel': 'OTHER'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 500}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2019-01-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-09', 'completionDateStruct': {'date': '2024-12-31', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-09-12', 'studyFirstSubmitDate': '2019-05-23', 'studyFirstSubmitQcDate': '2019-05-23', 'lastUpdatePostDateStruct': {'date': '2025-09-17', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2019-05-28', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-12-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Overall survival', 'timeFrame': '2 years', 'description': 'time from randomization to death'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['sample size, survival, randomized clinical trials, statistical test'], 'conditions': ['Clinical Trials']}, 'referencesModule': {'references': [{'pmid': '34242412', 'type': 'BACKGROUND', 'citation': 'Dinart D, Bellera C, Rondeau V. Sample size estimation for cancer randomized trials in the presence of heterogeneous populations. Biometrics. 2022 Dec;78(4):1662-1673. doi: 10.1111/biom.13527. Epub 2021 Sep 3.'}, {'pmid': '38334044', 'type': 'BACKGROUND', 'citation': 'Dinart D, Bellera C, Rondeau V. Sample size estimation for recurrent event data using multifrailty and multilevel survival models. J Biopharm Stat. 2025 Mar;35(2):241-256. doi: 10.1080/10543406.2024.2310306. Epub 2024 Feb 9.'}, {'pmid': '39014905', 'type': 'BACKGROUND', 'citation': 'Dinart D, Rondeau V, Bellera C. Sample Size Estimation Using a Partially Clustered Frailty Model for Biomarker-Strategy Designs With Multiple Treatments. Pharm Stat. 2024 Nov-Dec;23(6):1084-1094. doi: 10.1002/pst.2407. Epub 2024 Jul 16.'}]}, 'descriptionModule': {'briefSummary': 'Most of randomized clinical trials (RCT) using time-to-event criteria as the primary endpoint are designed, powered and analyzed based on an hypothetical hazard ratio (HR) corresponding to the targeted effect size between experimental and control arms. Usually, one assumes that populations are homogeneous within each treatment arm, that is, within each arm, (i) the baseline risk is identical for all patients, and (ii) the treatment effect is identical for all patients. This assumption however may not hold in all circumstances. This project aims at providing a statistical method for the estimation of sample size in RCT, in the presence of heterogenous populations, such as assuming populations with distinct underlying baseline risks or assuming different treatment effects.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Any comparative clinical trial for which modern statistical methods are required to estimate sample size, in particular, it can be clinical trials :\n\n* with heterogeneous populations,\n* with recurrent event data,\n* involving a biomarker-Strategy Designs With Multiple Treatments.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Any comparative clinical trial for which modern statistical methods are required to estimate sample size'}, 'identificationModule': {'nctId': 'NCT03964402', 'briefTitle': 'Sample Size for Multivariate Time-to-event Data', 'organization': {'class': 'OTHER', 'fullName': 'Institut Bergonié'}, 'officialTitle': 'Sample Size Determination in Heterogeneous Populations for Multivariate Time-to-event Data', 'orgStudyIdInfo': {'id': 'IB2018-SAMPLE-SIZE-SURVIVAL'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Clinical trial', 'description': 'Corresponds to a comparative clinical trial for which our new statistical methods will be applied to estimate sample size'}]}, 'contactsLocationsModule': {'locations': [{'zip': '33400', 'city': 'Bordeaux', 'country': 'France', 'facility': 'Institut Bergonié, Comprehensive Cancer Center', 'geoPoint': {'lat': 44.84124, 'lon': -0.58046}}], 'overallOfficials': [{'name': 'Carine Bellera, PhD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Institut Bergonié'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Institut Bergonié', 'class': 'OTHER'}, 'collaborators': [{'name': 'Institut National de la Santé Et de la Recherche Médicale, France', 'class': 'OTHER_GOV'}], 'responsibleParty': {'type': 'SPONSOR'}}}}