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  • 1
    In: Diabetes Care, American Diabetes Association, Vol. 44, No. 2 ( 2021-02-01), p. 511-518
    Abstract: We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS A total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression. RESULTS Faster HbA1c progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles (R2 = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8–10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07–0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role. CONCLUSIONS Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression.
    Type of Medium: Online Resource
    ISSN: 0149-5992 , 1935-5548
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2021
    detail.hit.zdb_id: 1490520-6
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  • 2
    In: Diabetes Care, American Diabetes Association, Vol. 41, No. 9 ( 2018-09-01), p. 1844-1853
    Abstract: The choice of therapy for type 2 diabetes after metformin is guided by overall estimates of glycemic response and side effects seen in large cohorts. A stratified approach to therapy would aim to improve on this by identifying subgroups of patients whose glycemic response or risk of side effects differs markedly. We assessed whether simple clinical characteristics could identify patients with differing glycemic response and side effects with sulfonylureas and thiazolidinediones. RESEARCH DESIGN AND METHODS We studied 22,379 patients starting sulfonylurea or thiazolidinedione therapy in the U.K. Clinical Practice Research Datalink (CPRD) to identify features associated with increased 1-year HbA1c fall with one therapy class and reduced fall with the second. We then assessed whether prespecified patient subgroups defined by the differential clinical factors showed differing 5-year glycemic response and side effects with sulfonylureas and thiazolidinediones using individual randomized trial data from ADOPT (A Diabetes Outcome Progression Trial) (first-line therapy, n = 2,725) and RECORD (Rosiglitazone Evaluated for Cardiovascular Outcomes and Regulation of Glycemia in Diabetes) (second-line therapy, n = 2,222). Further replication was conducted using routine clinical data from GoDARTS (Genetics of Diabetes Audit and Research in Tayside Scotland) (n = 1,977). RESULTS In CPRD, male sex and lower BMI were associated with greater glycemic response with sulfonylureas and a lesser response with thiazolidinediones (both P & lt; 0.001). In ADOPT and RECORD, nonobese males had a greater overall HbA1c reduction with sulfonylureas than with thiazolidinediones (P & lt; 0.001); in contrast, obese females had a greater HbA1c reduction with thiazolidinediones than with sulfonylureas (P & lt; 0.001). Weight gain and edema risk with thiazolidinediones were greatest in obese females; however, hypoglycemia risk with sulfonylureas was similar across all subgroups. CONCLUSIONS Patient subgroups defined by sex and BMI have different patterns of benefits and risks on thiazolidinedione and sulfonylurea therapy. Subgroup-specific estimates can inform discussion about the choice of therapy after metformin for an individual patient. Our approach using routine and shared trial data provides a framework for future stratification research in type 2 diabetes.
    Type of Medium: Online Resource
    ISSN: 0149-5992 , 1935-5548
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2018
    detail.hit.zdb_id: 1490520-6
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  • 3
    In: Biometrical Journal, Wiley, Vol. 64, No. 2 ( 2022-02), p. 343-360
    Abstract: Randomized clinical trials in oncology typically utilize time‐to‐event endpoints such as progression‐free survival or overall survival as their primary efficacy endpoints, and the most commonly used statistical test to analyze these endpoints is the log‐rank test. The power of the log‐rank test depends on the behavior of the hazard ratio of the treatment arm to the control arm. Under the assumption of proportional hazards, the log‐rank test is asymptotically fully efficient. However, this proportionality assumption does not hold true if there is a delayed treatment effect. Cancer immunology has evolved over time and several cancer vaccines are available in the market for treating existing cancers. This includes sipuleucel‐T for metastatic hormone‐refractory prostate cancer, nivolumab for metastatic melanoma, and pembrolizumab for advanced nonsmall‐cell lung cancer. As cancer vaccines require some time to elicit an immune response, a delayed treatment effect is observed, resulting in a violation of the proportional hazards assumption. Thus, the traditional log‐rank test may not be optimal for testing immuno‐oncology drugs in randomized clinical trials. Moreover, the new immuno‐oncology compounds have been shown to be very effective in prolonging overall survival. Therefore, it is desirable to implement a group sequential design with the possibility of early stopping for overwhelming efficacy. In this paper, we investigate the max‐combo test, which utilizes the maximum of two weighted log‐rank statistics, as a robust alternative to the log‐rank test. The new test is implemented for two‐stage designs with possible early stopping at the interim analysis time point. Two classes of weights are investigated for the max‐combo test: the Fleming and Harrington (1981) weights and the Magirr and Burman (2019) modest  weights.
    Type of Medium: Online Resource
    ISSN: 0323-3847 , 1521-4036
    URL: Issue
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    Language: English
    Publisher: Wiley
    Publication Date: 2022
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    SSG: 12
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  • 4
    Online Resource
    Online Resource
    Wiley ; 2010
    In:  Statistics in Medicine Vol. 29, No. 7-8 ( 2010-03-30), p. 743-759
    In: Statistics in Medicine, Wiley, Vol. 29, No. 7-8 ( 2010-03-30), p. 743-759
    Abstract: Confirmatory clinical trials comparing the efficacy of a new treatment with an active control typically aim at demonstrating either superiority or non‐inferiority. In the latter case, the objective is to show that the experimental treatment is not worse than the active control by more than a pre‐specified non‐inferiority margin. We consider two classes of group‐sequential designs that combine the superiority and non‐inferiority objectives: non‐adaptive designs with fixed group sizes and adaptive designs where future group sizes may be based on the observed treatment effect. For both classes, we derive group‐sequential designs meeting error probability constraints that have the lowest possible expected sample size averaged over a set of values of the treatment effect. These optimized designs provide an efficient means of reducing expected sample size under a range of treatment effects, even when the separate objectives of proving superiority and non‐inferiority would require quite different fixed sample sizes. We also present error spending versions of group‐sequential designs that are easily implementable and can handle unpredictable group sizes or information levels. We find the adaptive choice of group sizes to yield some modest efficiency gains; alternatively, expected sample size may be reduced by adding another interim analysis to a non‐adaptive group‐sequential design. Copyright © 2010 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
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    Language: English
    Publisher: Wiley
    Publication Date: 2010
    detail.hit.zdb_id: 1491221-1
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  • 5
    Online Resource
    Online Resource
    Wiley ; 2015
    In:  Statistics in Medicine Vol. 34, No. 29 ( 2015-12-20), p. 3793-3810
    In: Statistics in Medicine, Wiley, Vol. 34, No. 29 ( 2015-12-20), p. 3793-3810
    Abstract: We consider sample size re‐estimation in a clinical trial, in particular when there is a significant delay before the measurement of patient response. Mehta and Pocock have proposed methods in which sample size is increased when interim results fall in a ‘promising zone’ where it is deemed worthwhile to increase conditional power by adding more subjects. Our analysis reveals potential pitfalls in applying this approach. Mehta and Pocock use results of Chen, DeMets and Lan to identify when increasing sample size, but applying a conventional level  α significance test at the end of the trial does not inflate the type I error rate: we have found the greatest gains in power per additional observation are liable to lie outside the region defined by this method. Mehta and Pocock increase sample size to achieve a particular conditional power, calculated under the current estimate of treatment effect: this leads to high increases in sample size for a small range of interim outcomes, whereas we have found it more efficient to make moderate increases in sample size over a wider range of cases. If the aforementioned pitfalls are avoided, we believe the broad framework proposed by Mehta and Pocock is valuable for clinical trial design. Working in this framework, we propose sample size rules that apply explicitly the principle of adding observations when they are most beneficial. The resulting trial designs are closely related to efficient group sequential tests for a delayed response proposed by Hampson and Jennison. Copyright © 2015 John Wiley & Sons, Ltd.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2015
    detail.hit.zdb_id: 1491221-1
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  • 6
    Online Resource
    Online Resource
    Wiley ; 1999
    In:  Statistics in Medicine Vol. 18, No. 13 ( 1999-07-15), p. 1575-1585
    In: Statistics in Medicine, Wiley, Vol. 18, No. 13 ( 1999-07-15), p. 1575-1585
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 1999
    detail.hit.zdb_id: 1491221-1
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  • 7
    Online Resource
    Online Resource
    Wiley ; 2015
    In:  Statistics in Medicine Vol. 34, No. 1 ( 2015-01-15), p. 39-58
    In: Statistics in Medicine, Wiley, Vol. 34, No. 1 ( 2015-01-15), p. 39-58
    Abstract: We consider seamless phase II/III clinical trials that compare K treatments with a common control in phase II then test the most promising treatment against control in phase III. The final hypothesis test for the selected treatment can use data from both phases, subject to controlling the familywise type I error rate. We show that the choice of method for conducting the final hypothesis test has a substantial impact on the power to demonstrate that an effective treatment is superior to control. To understand these differences in power, we derive decision rules maximizing power for particular configurations of treatment effects. A rule with such an optimal frequentist property is found as the solution to a multivariate Bayes decision problem. The optimal rules that we derive depend on the assumed configuration of treatment means. However, we are able to identify two decision rules with robust efficiency: a rule using a weighted average of the phase II and phase III data on the selected treatment and control, and a closed testing procedure using an inverse normal combination rule and a Dunnett test for intersection hypotheses. For the first of these rules, we find the optimal division of a given total sample size between phases II and III. We also assess the value of using phase II data in the final analysis and find that for many plausible scenarios, between 50% and 70% of the phase II numbers on the selected treatment and control would need to be added to the phase III sample size in order to achieve the same increase in power. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2015
    detail.hit.zdb_id: 1491221-1
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  • 8
    Online Resource
    Online Resource
    Wiley ; 2019
    In:  Statistics in Medicine Vol. 38, No. 30 ( 2019-12-30), p. 5670-5671
    In: Statistics in Medicine, Wiley, Vol. 38, No. 30 ( 2019-12-30), p. 5670-5671
    Type of Medium: Online Resource
    ISSN: 0277-6715 , 1097-0258
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1491221-1
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  • 9
    In: The Lancet, Elsevier BV, Vol. 394, No. 10216 ( 2019-12), p. 2263-2270
    Type of Medium: Online Resource
    ISSN: 0140-6736
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
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    detail.hit.zdb_id: 3306-6
    detail.hit.zdb_id: 1476593-7
    SSG: 5,21
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  • 10
    Online Resource
    Online Resource
    Elsevier BV ; 2011
    In:  Computational Statistics & Data Analysis Vol. 55, No. 1 ( 2011-1), p. 338-352
    In: Computational Statistics & Data Analysis, Elsevier BV, Vol. 55, No. 1 ( 2011-1), p. 338-352
    Type of Medium: Online Resource
    ISSN: 0167-9473
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2011
    detail.hit.zdb_id: 1478763-5
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