Browsing by Subject "Subgroups"

Now showing 1 - 5 of 5
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    A Bayesian phase I/II biomarker-based design for identifying subgroup-specific optimal dose for immunotherapy
    (Sage, 2022) Guo, Beibei; Zang, Yong; Biostatistics and Health Data Science, School of Medicine
    Immunotherapy is an innovative treatment that enlists the patient’s immune system to battle tumors. The optimal dose for treating patients with an immunotherapeutic agent may differ according to their biomarker status. In this article, we propose a biomarker-based phase I/II dose-finding design for identifying subgroup-specific optimal dose for immunotherapy (BSOI) that jointly models the immune response, toxicity, and efficacy outcomes. We propose parsimonious yet flexible models to borrow information across different types of outcomes and subgroups. We quantify the desirability of the dose using a utility function and adopt a two-stage dose-finding algorithm to find the optimal dose for each subgroup. Simulation studies show that the BSOI design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.
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    BIPSE: A Biomarker-based Phase I/II Design for Immunotherapy Trials with Progression-free Survival Endpoint
    (Wiley, 2022) Guo, Beibei; Zang, Yong; Biostatistics and Health Data Science, School of Medicine
    A Bayesian biomarker-based phase I/II design (BIPSE) is presented for immunotherapy trials with a progression-free survival (PFS) endpoint. The objective is to identify the subgroup-specific optimal dose, defined as the dose with the best risk-benefit tradeoff in each biomarker subgroup. We jointly model the immune response, toxicity outcome, and PFS with information borrowing across subgroups. A plateau model is used to describe the marginal distribution of the immune response. Conditional on the immune response, we model toxicity using probit regression and model PFS using the mixture cure rate model. During the trial, based on the accumulating data, we continuously update model estimates and adaptively randomize patients to doses with high desirability within each subgroup. Simulation studies show that the BIPSE design has desirable operating characteristics in selecting the subgroup-specific optimal doses and allocating patients to those optimal doses, and outperforms conventional designs.
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    Cognitively defined Alzheimer's dementia subgroups have distinct atrophy patterns
    (Wiley, 2024) Crane, Paul K.; Groot, Colin; Ossenkoppele, Rik; Mukherjee, Shubhabrata; Choi, Seo-Eun; Lee, Michael; Scollard, Phoebe; Gibbons, Laura E.; Sanders, R. Elizabeth; Trittschuh, Emily; Saykin, Andrew J.; Mez, Jesse; Nakano, Connie; Mac Donald, Christine; Sohi, Harkirat; Alzheimer’s Disease Neuroimaging Initiative; Risacher, Shannon; Medicine, School of Medicine
    Introduction: We sought to determine structural magnetic resonance imaging (MRI) characteristics across subgroups defined based on relative cognitive domain impairments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and to compare cognitively defined to imaging-defined subgroups. Methods: We used data from 584 people with Alzheimer's disease (AD) (461 amyloid positive, 123 unknown amyloid status) and 118 amyloid-negative controls. We used voxel-based morphometry to compare gray matter volume (GMV) for each group compared to controls and to AD-Memory. Results: There was pronounced bilateral lower medial temporal lobe atrophy with relative cortical sparing for AD-Memory, lower left hemisphere GMV for AD-Language, anterior lower GMV for AD-Executive, and posterior lower GMV for AD-Visuospatial. Formal asymmetry comparisons showed substantially more asymmetry in the AD-Language group than any other group (p = 1.15 × 10-10 ). For overlap between imaging-defined and cognitively defined subgroups, AD-Memory matched up with an imaging-defined limbic predominant group. Discussion: MRI findings differ across cognitively defined AD subgroups.
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    Incidence of cognitively defined late-onset Alzheimer's dementia subgroups from a prospective cohort study
    (Elsevier, 2017-12) Crane, Paul K.; Trittschuh, Emily; Mukherjee, Shubhabrata; Saykin, Andrew J.; Sanders, Elizabeth; Larson, Eric B.; McCurry, Susan M.; McCormick, Wayne; Bowen, James D.; Grabowski, Thomas; Moore, Mackenzie; Gross, Alden L.; Keene, Dirk; Bird, Thomas E.; Gibbons, Laura E.; Mez, Jesse; Radiology and Imaging Sciences, School of Medicine
    INTRODUCTION: There may be biologically relevant heterogeneity within typical late-onset Alzheimer's dementia. METHODS: We analyzed cognitive data from people with incident late-onset Alzheimer's dementia from a prospective cohort study. We determined individual averages across memory, visuospatial functioning, language, and executive functioning. We identified domains with substantial impairments relative to that average. We compared demographic, neuropathology, and genetic findings across groups defined by relative impairments. RESULTS: During 32,286 person-years of follow-up, 869 people developed Alzheimer's dementia. There were 393 (48%) with no domain with substantial relative impairments. Some participants had isolated relative impairments in memory (148, 18%), visuospatial functioning (117, 14%), language (71, 9%), and executive functioning (66, 8%). The group with isolated relative memory impairments had higher proportions with ≥ APOE ε4 allele, more extensive Alzheimer's-related neuropathology, and higher proportions with other Alzheimer's dementia genetic risk variants. DISCUSSION: A cognitive subgrouping strategy may identify biologically distinct subsets of people with Alzheimer's dementia.
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    Optimal sequential enrichment designs for phase II clinical trials
    (Wiley, 2017-01-15) Zang, Yong; Yuan, Ying; Biostatistics, School of Public Health
    In the early phase development of molecularly targeted agents (MTAs), a commonly encountered situation is that the MTA is expected to be more effective for a certain biomarker subgroup, say marker-positive patients, but there is no adequate evidence to show that the MTA does not work for the other subgroup, that is, marker-negative patients. After establishing that marker-positive patients benefit from the treatment, it is often of great clinical interest to determine whether the treatment benefit extends to marker-negative patients. The authors propose optimal sequential enrichment (OSE) designs to address this practical issue in the context of phase II clinical trials. The OSE designs evaluate the treatment effect first in marker-positive patients and then in marker-negative patients if needed. The designs are optimal in the sense that they minimize the expected sample size or the maximum sample size under the null hypothesis that the MTA is futile. An efficient, accurate optimization algorithm is proposed to find the optimal design parameters. One important advantage of the OSE design is that the go/no-go interim decision rules are specified prior to the trial conduct, which makes the design particularly easy to use in practice. A simulation study shows that the OSE designs perform well and are ethically more desirable than the commonly used marker-stratified design. The OSE design is applied to an endometrial carcinoma trial.