Karen L. Price

Sural sensory action potential identifies diabetic peripheral neuropathy responders to therapy

Identifying patients with diabetic peripheral neuropathy (DPN) amenable to therapy is a challenge. To determine whether the amplitude of the sural sensory nerve action potential (sural SNAP) reflects the severity of DPN, an analysis was performed on 205 patients with DPN, identified by an abnormal vibration detection threshold (VDT), who were enrolled in a multinational clinical trial investigating ruboxistaurin (RBX) mesylate. Nerve conduction velocity and response amplitude and latency were measured and compared. VDT was significantly lower in those with preserved sural SNAPs (n = 128) than in those in whom they were absent (n = 77; 21.5 vs. 22.7 JND units, P = 0.002). Thus, preserved sural SNAP denoted less severe DPN. Logistic regression analyses evaluating baseline characteristics, HbA1c, and baseline symptom scores identified only DPN duration as a factor that might contribute to the presence of sural SNAP (P = 0.004; OR = 0.896). For patients with abnormal VDT, preserved sural SNAP identifies a patient population with less severe DPN who may respond to therapeutic intervention in clinical trials. Muscle Nerve, 2005

Clinical safety of the selective PKC-β inhibitor, ruboxistaurin

The aim of this manuscript is to report the safety profile of patients treated with ruboxistaurin mesylate (RBX; LY333531), a selective protein kinase C-β (PKC-β) inhibitor, for up to 4 years. Data from patients with diabetes (1396 RBX 32 mg/day; 1408 placebo) were combined from 11 placebo-controlled, double-masked studies. The proportion of patients who reported one or more serious adverse events was greater in the placebo group than in the RBX-treated group (23.2 versus 20.8%, respectively). There were 51 deaths (21 RBX; 30 placebo) reported in this patient cohort; none of the deaths was attributed to study drug by the investigators. Common adverse drug reactions (≥ 1/100 – < 1/10 patients) that were reported in the RBX-treated patients were dyspepsia and increased blood creatine phosphokinase. In controlled, randomised clinical trials, RBX had an adverse event profile comparable to placebo, and was well tolerated.

Cognitive and Functional Decline and Their Relationship in Patients with Mild Alzheimer's Dementia

BACKGROUND In patients with Alzheimers disease (AD), the relationship between cognitive and functional progression is not fully understood; however, functional decline has been postulated to follow cognitive decline. OBJECTIVE To assess the relationship between cognitive and functional treatment effects in mild AD dementia patients. METHODS Data of patients with mild AD were pooled from two multicenter, double-blind, Phase 3 studies. Patients were randomized to infusions of 400-mg solanezumab (n = 654), or placebo (n = 660) every 4 weeks for 18 months. Cognitive and functional outcome measures were assessed using the AD Assessment Scale-Cognitive subscale (ADAS-Cog) and the AD Cooperative Study-Activities of Daily Living (ADCS-ADL), respectively. Analyses included comparisons among normalized scales, correlations between outcome measures, and path analyses to model the relationship of treatment effect on cognition and function. RESULTS Normalized ADAS-Cog and ADCS-ADL scales showed cognitive impairment was more evident than functional impairment in mild AD. The correlation between cognition and function increased over time. Path analyses demonstrated that 87% of the treatment effect on function was driven by the treatment effect on cognition, with the remaining 13% due to direct treatment effect. CONCLUSION Findings from this study are consistent with the hypothesis that functional impairment is primarily driven by and follows cognitive decline in mild AD dementia. The cognitive treatment effect appeared to explain the majority of the functional treatment effect. It is possible that a cognitive treatment effect may be considered as a leading indicator for functional outcomes in an 18-month clinical trial for milder stages of AD.

Correlation of vibratory quantitative sensory testing and nerve conduction studies in patients with diabetes

Monitoring the course of diabetic peripheral neuropathy (DPN) remains a challenge. Besides clinical examination, nerve conduction studies (NCS) and quantitative sensory testing (QST) are the most commonly used methods for evaluating peripheral nerve function in clinical trials and population studies. In this study the correlation between vibratory QST and NCS was determined. Patients (N = 227) with diabetes mellitus participated in this multicenter, single‐visit, cross‐sectional study. QST of vibration measured with the CASE IV system was compared with a composite score of peroneal motor and tibial motor NCS and with individual attributes of peroneal, tibial, and sural nerves. The correlation between QST and composite score of NCS was 0.234 (Pearson correlation coefficient, P = 0.001). The correlations between QST and individual attributes of NCS ranged from 0.189 to 0.480 (Pearson correlation coefficients, P < 0.001). The low to moderate correlation between QST and NCS suggests that these tests cannot replace each other but are complementary. Muscle Nerve, 2007

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.

Cognitive Impairment Precedes and Predicts Functional Impairment in Mild Alzheimer's Disease

Abstract Background: The temporal relationship of cognitive deficit and functional impairment in Alzheimer’s disease (AD) is not well characterized. Recent analyses suggest cognitive decline predicts subsequent functional decline throughout AD progression. Objective: To better understand the relationship between cognitive and functional decline in mild AD using autoregressive cross-lagged (ARCL) panel analyses in several clinical trials. Methods: Data included placebo patients with mild AD pooled from two multicenter, double-blind, Phase 3 solanezumab (EXPEDITION/2) or semagacestat (IDENTITY/2) studies, and from AD patients participating in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Cognitive and functional outcomes were assessed using AD Assessment Scale-Cognitive subscale (ADAS-Cog), AD Cooperative Study-Activities of Daily Living instrumental subscale (ADCS-iADL), or Functional Activities Questionnaire (FAQ), respectively. ARCL panel analyses evaluated relationships between cognitive and functional impairment over time. Results: In EXPEDITION, ARCL panel analyses demonstrated cognitive scores significantly predicted future functional impairment at 5 of 6 time points, while functional scores predicted subsequent cognitive scores in only 1 of 6 time points. Data from IDENTITY and ADNI programs yielded consistent results whereby cognition predicted subsequent function, but not vice-versa. Conclusions: Analyses from three databases indicated cognitive decline precedes and predicts subsequent functional decline in mild AD dementia, consistent with previously proposed hypotheses, and corroborate recent publications using similar methodologies. Cognitive impairment may be used as a predictor of future functional impairment in mild AD dementia and can be considered a critical target for prevention strategies to limit future functional decline in the dementia process.

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.

Bayesian methods for design and analysis of safety trials

Safety assessment is essential throughout medical product development. There has been increased awareness of the importance of safety trials recently, in part due to recent US Food and Drug Administration guidance related to thorough assessment of cardiovascular risk in the treatment of type 2 diabetes. Bayesian methods provide great promise for improving the conduct of safety trials. In this paper, the safety subteam of the Drug Information Association Bayesian Scientific Working Group evaluates challenges associated with current methods for designing and analyzing safety trials and provides an overview of several suggested Bayesian opportunities that may increase efficiency of safety trials along with relevant case examples.

Bayesian modeling of cost-effectiveness studies with unmeasured confounding: a simulation study

Unmeasured confounding is a common problem in observational studies. Failing to account for unmeasured confounding can result in biased point estimators and poor performance of hypothesis tests and interval estimators. We provide examples of the impacts of unmeasured confounding on cost-effectiveness analyses using observational data along with a Bayesian approach to correct estimation. Assuming validation data are available, we propose a Bayesian approach to correct cost-effectiveness studies for unmeasured confounding. We consider the cases where both cost and effectiveness are assumed to have a normal distribution and when costs are gamma distributed and effectiveness is normally distributed. Simulation studies were conducted to determine the impact of ignoring the unmeasured confounder and to determine the size of the validation data required to obtain valid inferences.

Identifying Potential Adverse Events Dose-Response Relationships Via Bayesian Indirect and Mixed Treatment Comparison Models

Patients and prescribers need to be fully informed regarding the safety profile of approved medications. This includes knowledge and information regarding whether an adverse event of interest exhibits a potential dose-response relationship. In order to thoroughly evaluate whether an adverse event rate increases with increasing dose level, evidence from multiple clinical trials needs to be combined and analyzed. The various clinical trials that need to be combined often include different dose levels. If one evaluates the dose-response relationship by including only the trials with all of the common dose levels, this will lead to the exclusion of potentially several clinical trials as well as dose levels, and thus the loss of important information. Other methods, such as crudely pooling patients on the same dose level across different studies, are subject to bias due to the breakdown of randomization. It is preferable to include all studies and relevant dose levels, even if all studies do not contain the same dose levels. Bayesian methodology has been shown to be useful in the context of indirect and mixed treatment comparison methods, to combine information from different therapies in different studies in order to make treatment effect inferences. This type of approach is foundational to the models presented here, but instead of modeling different dose arms in different studies, we extend the methodology to allow for assessment of the dose-response relationship across multiple clinical trials. In this paper, we propose three Bayesian indirect/mixed treatment comparison models to assess adverse event dose-response relationships. These three models are designed to handle binary responses and time to event responses. We apply the methods to real data sets and demonstrate that our proposed methods are useful in discovering potential dose-response relationships.