Haoda Fu

The Effects of LY2405319, an FGF21 Analog, in Obese Human Subjects with Type 2 Diabetes

Fibroblast growth factor 21 (FGF21) is a recently discovered metabolic regulator. Exogenous FGF21 produces beneficial metabolic effects in animal models; however, the translation of these observations to humans has not been tested. Here, we studied the effects of LY2405319 (LY), a variant of FGF21, in a randomized, placebo-controlled, double-blind proof-of-concept trial in patients with obesity and type 2 diabetes. Patients received placebo or 3, 10, or 20 mg of LY daily for 28 days. LY treatment produced significant improvements in dyslipidemia, including decreases in low-density lipoprotein cholesterol and triglycerides and increases in high-density lipoprotein cholesterol and a shift to a potentially less atherogenic apolipoprotein concentration profile. Favorable effects on body weight, fasting insulin, and adiponectin were also detected. However, only a trend toward glucose lowering was observed. These results indicate that FGF21 is bioactive in humans and suggest that FGF21-based therapies may be effective for the treatment of selected metabolic disorders.

Evaluation of Efficacy and Safety of the Glucagon Receptor Antagonist LY2409021 in Patients With Type 2 Diabetes: 12- and 24-Week Phase 2 Studies.

OBJECTIVE Type 2 diabetes pathophysiology is characterized by dysregulated glucagon secretion. LY2409021, a potent, selective small-molecule glucagon receptor antagonist that lowers glucose was evaluated for efficacy and safety in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS The efficacy (HbA1c and glucose) and safety (serum aminotransferase) of once-daily oral administration of LY2409021 was assessed in two double-blind studies. Phase 2a study patients were randomized to 10, 30, or 60 mg of LY2409021 or placebo for 12 weeks. Phase 2b study patients were randomized to 2.5, 10, or 20 mg LY2409021 or placebo for 24 weeks. RESULTS LY2409021 produced reductions in HbA1c that were significantly different from placebo over both 12 and 24 weeks. After 12 weeks, least squares (LS) mean change from baseline in HbA1c was –0.83% (10 mg), –0.65% (30 mg), and –0.66% (60 mg) (all P < 0.05) vs. placebo, 0.11%. After 24 weeks, LS mean change from baseline in HbA1c was –0.45% (2.5 mg), –0.78% (10 mg, P < 0.05), –0.92% (20 mg, P < 0.05), and –0.15% with placebo. Increases in serum aminotransferase, fasting glucagon, and total fasting glucagon-like peptide-1 (GLP-1) were observed; levels returned to baseline after drug washout. Fasting glucose was also lowered with LY2409021 at doses associated with only modest increases in aminotransferases (mean increase in alanine aminotransferase [ALT] ≤10 units/L). The incidence of hypoglycemia in the LY2409021 groups was not statistically different from placebo. CONCLUSIONS In patients with type 2 diabetes, glucagon receptor antagonist treatment significantly lowered HbA1c and glucose levels with good overall tolerability and a low risk for hypoglycemia. Modest, reversible increases in serum aminotransferases were observed.

Short-term administration of the glucagon receptor antagonist LY2409021 lowers blood glucose in healthy people and in those with type 2 diabetes.

To describe the clinical effects of single and multiple doses of a potent, selective, orally administered, small‐molecule antagonist of the human glucagon receptor, LY2409021, in healthy subjects and in patients with type 2 diabetes.

Guidance on the implementation and reporting of a drug safety Bayesian network meta-analysis

The Drug Information Association Bayesian Scientific Working Group (BSWG) was formed in 2011 with a vision to ensure that Bayesian methods are well understood and broadly utilized for design and analysis and throughout the medical product development process, and to improve industrial, regulatory, and economic decision making. The group, composed of individuals from academia, industry, and regulatory, has as its mission to facilitate the appropriate use and contribute to the progress of Bayesian methodology. In this paper, the safety sub-team of the BSWG explores the use of Bayesian methods when applied to drug safety meta-analysis and network meta-analysis. Guidance is presented on the conduct and reporting of such analyses. We also discuss different structural model assumptions and provide discussion on prior specification. The work is illustrated through a case study involving a network meta-analysis related to the cardiovascular safety of non-steroidal anti-inflammatory drugs.

Bayesian Adaptive Dose-Finding Studies with Delayed Responses

In recent years, Bayesian response-adaptive designs have been used to improve the efficiency of learning in dose-finding studies. Many current methods for analyzing the data at the time of the interim analysis only use the data from patients who have completed the study. Therefore, data collected at intermediate time points are not used for decision making in these studies. However, in some disease areas such as diabetes and obesity, patients may need to be studied for several weeks or months for a drug effect to emerge. Additionally, slow enrollment rates can limit the number of patients who complete the study in a given period of time. Consequently, at the time of an interim analysis, there may be only a small proportion (e.g., 20%) of patients who have completed the study. In this paper, we propose a new Bayesian prediction model to incorporate all the data (from patients who have completed the study and those who have not completed) to make decisions about the study at the interim analysis. Examples of decisions made at the interim analysis include adaptive treatment allocation, dropping nonefficacious dose arms, stopping the study for positive efficacy, and stopping the study for futility. The model is able to handle incomplete longitudinal data including missing data considered missing at random (MAR). A utility-function-based decision rule is also discussed. The benefit of our new method is demonstrated through trial simulations. Three scenarios are examined, and the simulation results demonstrate that this new method outperforms traditional design with the same sample size in each of these scenarios.

Detecting outlying trials in network meta-analysis

Network meta-analysis (NMA) expands the scope of a conventional pairwise meta-analysis to simultaneously handle multiple treatment comparisons. However, some trials may appear to deviate markedly from the others and thus be inappropriate to be synthesized in the NMA. In addition, the inclusion of these trials in evidence synthesis may lead to bias in estimation. We call such trials trial-level outliers. To the best of our knowledge, while heterogeneity and inconsistency in NMA have been extensively discussed and well addressed, few previous papers have considered the proper detection and handling of trial-level outliers. In this paper, we propose several Bayesian outlier detection measures, which are then applied to a diabetes data set. Simulation studies comparing our approaches in both arm-based and contrast-based model settings are provided in two supporting appendices.

Estimating optimal treatment regimes via subgroup identification in randomized control trials and observational studies

With new treatments and novel technology available, personalized medicine has become an important piece in the new era of medical product development. Traditional statistics methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials. Motivated by the recent development of outcome weighted learning framework, we propose an alternative algorithm to search treatment assignments which has a connection with subgroup identification problems. Our method focuses on applications from clinical trials to generate easy to interpret results. This framework is able to handle two or more than two treatments from both randomized control trials and observational studies. We implement our algorithm in C++ and connect it with R. Its performance is evaluated by simulations, and we apply our method to a dataset from a diabetes study. Copyright

Interpretability of Cancer Clinical Trial Results Using Restricted Mean Survival Time as an Alternative to the Hazard Ratio

Importance In a comparative clinical study with progression-free survival (PFS) or overall survival (OS) as the end point, the hazard ratio (HR) is routinely used to design the study and to estimate the treatment effect at the end of the study. The clinical interpretation of the HR may not be straightforward, especially when the underlying model assumption is not valid. A robust procedure for study design and analysis that enables clinically meaningful interpretation of trial results is warranted. Objective To discuss issues of conventional trial design and analysis and to present alternatives to the HR using a recent immunotherapy study as an illustrative example. Design, Setting, and Participants By comparing 2 groups in a survival analysis, we discuss issues of using the HR and present the restricted mean survival time (RMST) as a summary measure of patients’ survival profile over time. We show how to use the difference or ratio in RMST between 2 groups as an alternative for designing and analyzing a clinical study with an immunotherapy study as an illustrative example. Main Outcomes and Measures Overall survival or PFS. Group contrast measures included HR, RMST difference or ratio, and the event rate difference. Results For the illustrative example, the HR procedure indicates that nivolumab significantly prolonged patient OS and was numerically better than docetaxel for PFS. However, the median PFS time of docetaxel was significantly better than that of nivolumab. Therefore, it may be difficult to use median OS and/or PFS to interpret of the HR value clinically. On the other hand, using RMST difference, nivolumab was significantly better than docetaxel for both OS and PFS. We also provide details regarding design of a future study with RMST-based measures. Conclusions and Relevance The design and analysis of a conventional cancer clinical trial can be improved by adopting a robust statistical procedure that enables clinically meaningful interpretations of the treatment effect. The RMST-based quantitative method may be used as a primary tool for future cancer trials or to help us to better understand the clinical interpretation of the HR even when its model assumption is plausible.

Incorporation of individual-patient data in network meta-analysis for multiple continuous endpoints, with application to diabetes treatment

Availability of individual patient-level data (IPD) broadens the scope of network meta-analysis (NMA) and enables us to incorporate patient-level information. Although IPD is a potential gold mine in biomedical areas, methodological development has been slow owing to limited access to such data. In this paper, we propose a Bayesian IPD NMA modeling framework for multiple continuous outcomes under both contrast-based and arm-based parameterizations. We incorporate individual covariate-by-treatment interactions to facilitate personalized decision making. Furthermore, we can find subpopulations performing well with a certain drug in terms of predictive outcomes. We also impute missing individual covariates via an MCMC algorithm. We illustrate this approach using diabetes data that include continuous bivariate efficacy outcomes and three baseline covariates and show its practical implications. Finally, we close with a discussion of our results, a review of computational challenges, and a brief description of areas for future research.

Joint modeling of progression-free survival and overall survival by a Bayesian normal induced copula estimation model.

In cancer clinical trials, in addition to time to death (i.e., overall survival), progression-related measurements such as progression-free survival and time to progression are also commonly used to evaluate treatment efficacy. It is of scientific interest and importance to understand the correlations among these measurements. In this paper, we propose a Bayesian semi-competing risks approach to jointly model progression-related measurements and overall survival. This new model is referred to as the NICE model, which stands for the normal induced copula estimation model. Correlation among these variables can be directly derived from the joint model. In addition, when correlation exists, simulation shows that the joint model is able to borrow strength from correlated measurements, and as a consequence the NICE model improves inference on both variables. The proposed model is in a Bayesian framework that enables us to use it in various Bayesian contexts, such as Bayesian adaptive design and using posterior predictive samples to simulate future trials. We conducted simulation studies to demonstrate properties of the NICE model and applied this method to a data set from chemotherapy-naive patients with extensive-stage small-cell lung cancer.