Zooming In, Then Out: Why We Must Apply Human-Centered Design to Transform Diabetes Technology.

Technological advances in devices, such as continuous glucose monitors (CGMs) or intermittently scanned continuous glucose monitors (isCGMs), do not necessarily by themselves translate to improved clinical outcomes or quality of life. Human-centered design (HCD) is an accessible, flexible process that could contribute to reducing the gap between current challenges and more optimal future solutions, by continuing to refine crucial considerations, such as usability. Starting with understanding the unmet needs of patients, cultivating novel and different collaborations, and applying humility to humanize technology are three facets underlying this approach. Human-centered design can help expand our perspective to serve as another essential tool to help further refine diabetes technology.

Collaboratively Designing an App for a More Personalized, Community-Endorsed Continuous Glucose Monitoring Onboarding Experience: An Early Study.

BACKGROUND: Acclimating to a new technology device, such as a continuous glucose monitor (CGM), can be challenging. Current resources may not sufficiently answer questions patients living with diabetes (PWD) may have. We asked how we might improve the process to onboard a PWD to CGM. Our specific aims were (1) to develop, employing a co-designing approach, a prototype of an app for facilitating onboarding to CGM and (2) to obtain early feedback on its usability. METHODS: We applied a human-centered design (HCD) approach; this process first seeks to deeply understand the unmet needs and frustrations users face. After wearing a demonstration CGM; observing PWD onboarding with health care professionals (HCPs) in clinic; and interviewing 8 PWD and 2 HCP, we developed, tested, and refined a low-fidelity prototype of a clickable app. With insights from this initial round of feedback, we then created a high-fidelity prototype with 3 key features: (1) individual entry of goals and questions; (2) a daily progress tracker for these goals; and (3) a community portal that facilitates exchange of questions and answers. We used the validated System Usability Scale (SUS) to quantify user feedback. RESULTS: Focus group participants found our early app to be usable and acceptable. Measurement of usability by the SUS yielded a score of 74, which is above average (68) reported for all applications tested, per usability.gov. CONCLUSIONS: Our early prototype app is a more personalized, additional tool that could bridge an information and support gap for patients who are new to CGM. This app could also help PWD on an ongoing basis, by evolving with them to enhance ease and engagement with diabetes self-management.

A smoothed corrected score approach for proportional hazards model with misclassified discretized covariates induced by error-contaminated continuous time-dependent exposure.

We consider the proportional hazards model in which the covariates include the discretized categories of a continuous time-dependent exposure variable measured with error. Naively ignoring the measurement error in the analysis may cause biased estimation and erroneous inference. Although various approaches have been proposed to deal with measurement error when the hazard depends linearly on the time-dependent variable, it has not yet been investigated how to correct when the hazard depends on the discretized categories of the time-dependent variable. To fill this gap in the literature, we propose a smoothed corrected score approach based on approximation of the discretized categories after smoothing the indicator function. The consistency and asymptotic normality of the proposed estimator are established. The observation times of the time-dependent variable are allowed to be informative. For comparison, we also extend to this setting two approximate approaches, the regression calibration and the risk-set regression calibration. The methods are assessed by simulation studies and by application to data from an HIV clinical trial.

Simulation Extrapolation Method for Cox Regression Model with a Mixture of Berkson and Classical Errors in the Covariates using Calibration Data.

Many biomedical or epidemiological studies often aim to assess the association between the time to an event of interest and some covariates under the Cox proportional hazards model. However, a problem is that the covariate data routinely involve measurement error, which may be of classical type, Berkson type or a combination of both types. The issue of Cox regression with error-prone covariates has been well-discussed in the statistical literature, which has focused mainly on classical error so far. This paper considers Cox regression analysis when some covariates are possibly contaminated with a mixture of Berkson and classical errors. We propose a simulation extrapolation-based method to address this problem when two replicates of the mismeasured covariates are available along with calibration data for some subjects in a subsample only. The proposed method places no assumption on the mixture percentage. Its finite-sample performance is assessed through a simulation study. It is applied to the analysis of data from an AIDS clinical trial study.

SGLT-2 Inhibitors: A New Mechanism for Glycemic Control.

Glucosuria, the presence of glucose in the urine, has long been regarded as a consequence of uncontrolled diabetes. However, glucose excretion can be induced by blocking the activity of the renal sodium-glucose cotransporter 2 (SGLT-2). This mechanism corrects hyperglycemia independently of insulin. This article provides an overview of the paradigm shift that triggered the development of the SGLT-2 inhibitor class of agents and summarizes the available evidence from clinical studies to date.

Dapagliflozin: an evidence-based review of its potential in the treatment of type-2 diabetes.

Dapagliflozin is a sodium-glucose co-transporter-2 inhibitor that lowers plasma glucose by decreasing its renal reabsorption. The resulting excretion of glucose in the urine (glucosuria) has transformed what was once solely regarded as an adverse facet of diabetes into a potential novel therapeutic strategy. Glucosuria leads to weight loss, due to a reduction in calories, which is thought to rehabilitate insulin sensitivity, at least partially. By acting independently of insulin action or secretion, dapagliflozin appears to avert or minimize two key barriers to optimal glycemic control: hypoglycemia and weight gain. From the clinical studies conducted thus far in patients with type 2 diabetes, dapagliflozin significantly decreases HbA(1c) (by ~0.5%-1%, from a baseline of 8%-9%), as well as body weight (~2-3 kg), without increased risk of hypoglycemia. Dapagliflozin thus represents a paradigm shift in the treatment of diabetes. While long-term data on safety and efficacy are forthcoming, the results published to date suggest that this agent has the potential to be another option in the treatment of diabetes treatments. This article examines the evidence currently available on the efficacy and safety of dapagliflozin.

A paradigm shift in diabetes therapy--dapagliflozin and other SGLT2 inhibitors.

Blocking sodium-glucose cotransporters (SGLTs) to decrease the reabsorption of glucose--and thus increase renal glucose excretion--represents a novel therapeutic approach to diabetes that is independent of insulin secretion or action. Preclinical and clinical studies of SGLT2 inhibitors in subjects with type 2 diabetes (T2DM), as well as genetic mutations in kidney-specific SGLT2 that result in no adverse sequelae, appear to support this strategy. These investigations reveal that increasing renal glucose excretion by inhibiting SGLT2 can lower plasma glucose levels, as well as reduce body weight. Further data from larger trials are forthcoming regarding efficacy and safety, but the results reported thus far suggest that the positive impact of SGLT2 inhibitors may be attained without producing significant adverse effects. This class of agents, including dapagliflozin, may thus hold an advantage over many currently used medications for diabetes. This review outlines the role of SGLT2 in glucose homeostasis and the evidence currently available on the potential for clinical application of these agents in diabetes.

SGLT2 inhibition--a novel strategy for diabetes treatment.

Inhibiting sodium-glucose co-transporters (SGLTs), which have a key role in the reabsorption of glucose in the kidney, has been proposed as a novel therapeutic strategy for diabetes. Genetic mutations in the kidney-specific SGLT2 isoform that result in benign renal glycosuria, as well as preclinical and clinical studies with SGLT2 inhibitors in type 2 diabetes, support the potential of this approach. These investigations indicate that elevating renal glucose excretion by suppressing SGLT2 can reduce plasma glucose levels, as well as decrease weight. Although data from ongoing Phase III trials of these agents are needed to more fully assess safety, results suggest that the beneficial effects of SGLT2 inhibition might be achieved without exerting significant side effects--an advantage over many current diabetes medications. This article discusses the role of SGLT2 in glucose homeostasis and the evidence available so far on the therapeutic potential of blocking these transporters in the treatment of diabetes.

Expected estimating equations for missing data, measurement error, and misclassification, with application to longitudinal nonignorable missing data.

Missing data, measurement error, and misclassification are three important problems in many research fields, such as epidemiological studies. It is well known that missing data and measurement error in covariates may lead to biased estimation. Misclassification may be considered as a special type of measurement error, for categorical data. Nevertheless, we treat misclassification as a different problem from measurement error because statistical models for them are different. Indeed, in the literature, methods for these three problems were generally proposed separately given that statistical modeling for them are very different. The problem is more challenging in a longitudinal study with nonignorable missing data. In this article, we consider estimation in generalized linear models under these three incomplete data models. We propose a general approach based on expected estimating equations (EEEs) to solve these three incomplete data problems in a unified fashion. This EEE approach can be easily implemented and its asymptotic covariance can be obtained by sandwich estimation. Intensive simulation studies are performed under various incomplete data settings. The proposed method is applied to a longitudinal study of oral bone density in relation to body bone density.

Numerical equivalence of imputing scores and weighted estimators in regression analysis with missing covariates.

Imputation, weighting, direct likelihood, and direct Bayesian inference (Rubin, 1976) are important approaches for missing data regression. Many useful semiparametric estimators have been developed for regression analysis of data with missing covariates or outcomes. It has been established that some semiparametric estimators are asymptotically equivalent, but it has not been shown that many are numerically the same. We applied some existing methods to a bladder cancer case-control study and noted that they were the same numerically when the observed covariates and outcomes are categorical. To understand the analytical background of this finding, we further show that when observed covariates and outcomes are categorical, some estimators are not only asymptotically equivalent but also actually numerically identical. That is, although their estimating equations are different, they lead numerically to exactly the same root. This includes a simple weighted estimator, an augmented weighted estimator, and a mean-score estimator. The numerical equivalence may elucidate the relationship between imputing scores and weighted estimation procedures.

Structured correlation in models for clustered data.

Correlation is always a concern in the analysis of clustered data. One area of interest is to develop a general correlation modelling approach for high dimensional data with unbalanced hierarchical and heterogeneous data structures, e.g. multilevel data. Commonly used correlation structures might have limitation for such situations. In this paper, we propose two extensions, multiblock and multilayer correlations. These methods are very flexible in modelling correlation and can be incorporated in many multivariate approaches, while the major discussion focuses on the applications under the generalized estimating equations (GEE) methods. The approaches are especially useful in GEE when each cluster is large and complex but the number of clusters is small. If an incorrect correlation is applied to such data, the results are less efficient. Multiblock and multilayer correlations extend GEE methods to model complicated multilevel data with arbitrary number of levels and cluster size. The extended estimating equation for correlation parameters has an orthogonal property, and the computation is very efficient. A simulation study compares the conventional methods versus the proposed methods, and it shows the gain in relative efficiency and the flexibility in modelling various structures.