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1.
Pharm Stat ; 2020 Feb 27.
Artigo em Inglês | MEDLINE | ID: mdl-32107840

RESUMO

Generalized linear mixed models (GLMM) are commonly used to model the treatment effect over time while controlling for important clinical covariates. Standard software procedures often provide estimates of the outcome based on the mean of the covariates; however, these estimates will be biased for the true group means in the GLMM. Implementing GLMM in the frequentist framework can lead to issues of convergence. A simulation study demonstrating the use of fully Bayesian GLMM for providing unbiased estimates of group means is shown. These models are very straightforward to implement and can be used for a broad variety of outcomes (eg, binary, categorical, and count data) that arise in clinical trials. We demonstrate the proposed method on a data set from a clinical trial in diabetes.

2.
J Biopharm Stat ; 29(2): 287-305, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30359554

RESUMO

Dose titration becomes more and more common in improving drug tolerability as well as determining individualized treatment doses, thereby maximizing the benefit to patients. Dose titration starting from a lower dose and gradually increasing to a higher dose enables improved tolerability in patients as the human body may gradually adapt to adverse gastrointestinal effects. Current statistical analyses mostly focus on the outcome at the end-of-study follow-up without considering the longitudinal impact of dose titration on the outcome. Better understanding of the dynamic effect of dose titration over time is important in early-phase clinical development as it could allow to model the longitudinal trend and predict the longer term outcome more accurately. We propose a parametric model with two empirical methods of modeling the error terms for a continuous outcome with dose titrations. Simulations show that both approaches of modeling the error terms work well. We applied this method to analyze data from a few clinical studies and achieved satisfactory results.

3.
Stat Med ; 38(3): 354-362, 2019 02 10.
Artigo em Inglês | MEDLINE | ID: mdl-30264404

RESUMO

Drug development is a long, complex, and costly process. The majority of the cost arises from Phase 2 and Phase 3 clinical development. Reducing Phase 2 and Phase 3 failure rates would greatly reduce the average drug development cost. Obtaining more informative data on a candidate drug's efficacy and safety prior to moving to Phase 2 will improve Phase 2 success and, hence, reduce the overall development cost. While, typically, multiple ascending dose (MAD) study focuses on safety, this article proposes a model-based MAD design that not only can provide the desired safety information but also can provide informative efficacy data for certain endpoints in certain therapeutic areas where the parametric models for the longitudinal dose response and the impact of dose titration on a response variable are utilized. This type of MAD design allows relatively informative efficacy data available prior to Phase 2 development and, hence, can serve as a proof-of-concept study. This approach may greatly reduce the drug development cycle time without increasing the risk of Phase 2 development.

4.
J Diabetes Sci Technol ; 12(1): 155-162, 2018 01.
Artigo em Inglês | MEDLINE | ID: mdl-28466661

RESUMO

BACKGROUND: For new insulin analogs with properties that vary from human insulin, defining activity in units of human insulin based on glycemic lowering efficacy may be challenging. Here we present a new method that can be used to quantify a unit dose of an experimental insulin when the traditional euglycemic clamp method is not adequate. METHODS: Joint modeling of insulin dose and the glycemic outcome variable hemoglobin A1c (HbA1c), where both were response variables, was used to evaluate insulin unit potency for basal insulin peglispro (BIL). The data were from the Phase 3 program for BIL, which included greater than 5500 patients with type 1 or type 2 diabetes who were treated for 26 or 52 weeks with BIL or a comparator insulin. Both basal-bolus and basal insulin only studies were included, and some type 2 diabetes patients were insulin-naïve. RESULTS: The analysis showed that 1 unit of BIL, composed of 9 nmol of active ingredient, had similar or slightly greater potency compared to 1 unit insulin glargine or NPH insulin for all populations. CONCLUSIONS: Despite some limitations, the joint modeling of HbA1c and insulin dose provides a reasonable approach to estimate the relative potency of a new basal insulin versus an established basal insulin.


Assuntos
Glicemia/análise , Diabetes Mellitus Tipo 1/tratamento farmacológico , Diabetes Mellitus Tipo 2/tratamento farmacológico , Hemoglobina A Glicada/análise , Hipoglicemiantes/administração & dosagem , Insulina/administração & dosagem , Diabetes Mellitus Tipo 1/sangue , Diabetes Mellitus Tipo 2/sangue , Humanos , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Modelos Teóricos , Resultado do Tratamento
5.
J Diabetes Sci Technol ; 12(2): 325-332, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-29056082

RESUMO

BACKGROUND: The association of glucose variability (GV) with other glycemic measures is emerging as a topic of interest. The aim of this analysis is to study the correlation between GV and measures of glycemic control, such as glycated hemoglobin (HbA1c) and daily mean glucose (DMG). METHODS: Data from 5 phase 3 trials were pooled into 3 analysis groups: type 2 diabetes (T2D) treated with basal insulin only, T2D treated with basal-bolus therapy, and type 1 diabetes (T1D). A generalized boosted model was used post hoc to assess the relationship of the following variables with glycemic control parameters (HbA1c and DMG): within-day GV, between-day GV (calculated using self-monitored blood glucose and fasting blood glucose [FBG]), hypoglycemia rate, and certain baseline characteristics. RESULTS: Within-day GV (calculated using standard deviation [SD]) was found to have a significant influence on endpoints HbA1c and DMG in all 3 patient groups. Between-day GV from FBG (calculated using SD), within-day GV (calculated using coefficient of variation), and hypoglycemia rate were found to significantly influence the endpoint HbA1c in the T2D basal-only group. CONCLUSIONS: Lower within-day GV was significantly associated with improvement in DMG and HbA1c. This finding suggests that GV could be a marker in the early phases of new antihyperglycemic therapy development for predicting clinical outcomes in terms of HbA1c and DMG.


Assuntos
Glicemia , Diabetes Mellitus Tipo 1/sangue , Diabetes Mellitus Tipo 2/sangue , Hemoglobina A Glicada , Ensaios Clínicos Fase III como Assunto , Diabetes Mellitus Tipo 1/tratamento farmacológico , Diabetes Mellitus Tipo 2/tratamento farmacológico , Humanos , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Estudos Retrospectivos
6.
J Biopharm Stat ; 27(4): 584-594, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-27010524

RESUMO

For regulatory purposes, in a trial comparing two active treatments, a hypothesis such as noninferiority or superiority must be prespecified even when there is little known about how they compare against each other or when the objective is simply to identify the best. In this article, we extend an alternative classification methodology, the classification approach of Qu et al. (Statistics in Medicine, 30:3488-3495), to compare two active treatments when outcomes are binary and time-to-event variables. This method based on estimation approach instead of hypothesis testing can be useful when little prior information is available on which treatment has better efficacy. The entire decision space is divided into eight distinct possible outcomes based on predefined lower and upper non-inferiority margins, and the conclusion will be drawn according to the location of the confidence interval for relative risk or hazard ratio (or its logarithm transformation). We demonstrate theoretically that this method controls the misclassification rate at the specified level. We also illustrate the method by simulations and using data from a Phase 3 first-line nonsmall cell lung cancer study.


Assuntos
Ensaios Clínicos Fase III como Assunto , Projetos de Pesquisa , Intervalos de Confiança , Humanos , Modelos de Riscos Proporcionais , Risco , Resultado do Tratamento
7.
Diabetes Obes Metab ; 18(11): 1093-1097, 2016 11.
Artigo em Inglês | MEDLINE | ID: mdl-27484021

RESUMO

Basal insulin peglispro (BIL) is a novel basal insulin with hepato-preferential action, resulting from reduced peripheral effects. This report summarizes hypoglycaemia data from five BIL phase III studies with insulin glargine as the comparator, including three double-blind trials. Prespecified pooled analyses (n = 4927) included: patients with type 2 diabetes (T2D) receiving basal insulin only, those with T2D on basal-bolus therapy, and those with type 1 diabetes (T1D). BIL treatment resulted in a 36-45% lower nocturnal hypoglycaemia rate compared with glargine, despite greater reduction in glycated haemoglobin (HbA1c) and higher basal insulin dosing. The total hypoglycaemia rate was similar in patients with T2D on basal treatment only, trended towards being higher (10%) in patients with T2D on basal-bolus treatment (p = .053), and was 15% higher (p < .001) with BIL versus glargine in patients with T1D, with more daytime hypoglycaemia in the T1D and T2D groups who were receiving basal-bolus therapy. In T1D, during the maintenance treatment period (26-52 weeks), the total hypoglycaemia rate was not significantly different. There were no differences in severe hypoglycaemia in the T1D or T2D pooled analyses. BIL versus glargine treatment resulted in greater HbA1c reduction with less nocturnal hypoglycaemia in all patient populations, higher daytime hypoglycaemia with basal-bolus therapy in the T1D and T2D groups, and an associated increase in total hypoglycaemia in the patients with T1D.


Assuntos
Ritmo Circadiano/efeitos dos fármacos , Diabetes Mellitus Tipo 1/tratamento farmacológico , Diabetes Mellitus Tipo 2/tratamento farmacológico , Hipoglicemia/induzido quimicamente , Hipoglicemia/epidemiologia , Insulina Glargina/efeitos adversos , Insulina Lispro/análogos & derivados , Polietilenoglicóis/efeitos adversos , Adulto , Idoso , Glicemia/efeitos dos fármacos , Glicemia/metabolismo , Diabetes Mellitus Tipo 1/sangue , Diabetes Mellitus Tipo 2/sangue , Método Duplo-Cego , Feminino , Hemoglobina A Glicada/efeitos dos fármacos , Hemoglobina A Glicada/metabolismo , Humanos , Insulina Glargina/administração & dosagem , Insulina Lispro/administração & dosagem , Insulina Lispro/efeitos adversos , Masculino , Pessoa de Meia-Idade , Polietilenoglicóis/administração & dosagem , Estudos Retrospectivos
8.
Cardiovasc Diabetol ; 15: 78, 2016 May 17.
Artigo em Inglês | MEDLINE | ID: mdl-27188479

RESUMO

BACKGROUND: To identify possible differences in cardiovascular (CV) risk among different insulin therapies, we performed pre-specified meta-analyses across the clinical program for basal insulin peglispro (BIL), in patients randomized to treatment with BIL or comparator insulin [glargine (IG) or NPH]. METHODS: One phase 2 (12-week) and 6 phase 3 (26 to 78-week) randomized studies of BIL compared to IG or NPH, in patients with type 1 or type 2 diabetes, were included. The participants were diverse with respect to demographics, baseline glycemic control, and concomitant disease or medications, but treatment groups were comparable in each study. For any potential CV or neurovascular event, relevant medical information was provided to a blinded external clinical events committee (C5Research, Cleveland Clinic, Cleveland, OH, USA) for adjudication. Cox regression analysis was used to compare treatment groups. The primary endpoint was a composite of adjudicated MACE+ [CV death, myocardial infarction (MI), stroke, or hospitalization for unstable angina]. RESULTS: The pooled population included 5862 patients in the safety evaluation, with randomization to BIL:IG:NPH of 3578:2072:212. Mean age was 54.1 years, 27 % had type 1 diabetes, 56 % were male, and 88 % were white. Baseline demographic and clinical characteristics, including use of statins or other lipid-lowering drugs, were comparable between BIL and comparators. A total of 83 patients experienced at least 1 MACE+ and 70 patients experienced at least 1 MACE (CV death, MI, or stroke). Overall, there were no treatment-associated differences in time to MACE+ [hazard ratio (HR) for BIL versus comparator insulin (95 % CI): 0.82 (0.53-1.27)] or MACE [0.83 (0.51-1.33)]. In 4297 patients with type 2 diabetes, there were 71 MACE+ events [HR: 1.02 (95 % CI: 0.63-1.65), p = 0.94]. In 1565 patients with type 1 diabetes, there were only 12 MACE+ [0.24 (0.07-0.85), p = 0.027]. There were no differences in all-cause death between BIL and comparators. Sub-group analyses did not identify any sub-population with increased risk with BIL versus comparator insulins. CONCLUSIONS: Treatment with BIL versus comparator insulin in patients with type 1 diabetes or type 2 diabetes was not associated with increased risk for major CV events in the studies analyzed.


Assuntos
Glicemia/efeitos dos fármacos , Diabetes Mellitus Tipo 1/tratamento farmacológico , Diabetes Mellitus Tipo 2/tratamento farmacológico , Hipoglicemiantes/efeitos adversos , Insulina/efeitos adversos , Infarto do Miocárdio/epidemiologia , Adulto , Feminino , Humanos , Hipoglicemiantes/uso terapêutico , Insulina/metabolismo , Masculino , Pessoa de Meia-Idade , Infarto do Miocárdio/induzido quimicamente , Ensaios Clínicos Controlados Aleatórios como Assunto , Medição de Risco , Fatores de Risco , Acidente Vascular Cerebral/induzido quimicamente , Acidente Vascular Cerebral/epidemiologia
9.
J Diabetes ; 8(5): 610-8, 2016 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-27100270

RESUMO

Crossover design has been widely used in late-phase clinical studies, as well as in pharmacokinetic and pharmacodynamic, bioequivalence, and medical device studies; however, its interpretability and applicability continue to be debated. Herein we provide discussions around a crossover design's scientific benefit, applicability, and how it can be implemented in late-phase diabetes studies by properly handling key issues: carryover effect, washout period, and baseline selection. Specifically, detailed considerations are provided about the validity and situations of having appropriate length of study duration to deal with carryover effects so that a washout period may not be needed. A simulation study and data mining results on 12 crossover late-phase insulin clinical trials are presented to examine the discussion points and proposals.


Assuntos
Glicemia/metabolismo , Diabetes Mellitus/tratamento farmacológico , Insulina/uso terapêutico , Projetos de Pesquisa/normas , Pesquisa Biomédica/métodos , Pesquisa Biomédica/normas , Ensaios Clínicos como Assunto/métodos , Ensaios Clínicos como Assunto/normas , Estudos Cross-Over , Diabetes Mellitus/sangue , Diabetes Mellitus/classificação , Humanos , Hipoglicemiantes/uso terapêutico , Reprodutibilidade dos Testes
10.
J Biopharm Stat ; 26(2): 280-98, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-25437847

RESUMO

Diabetes affects an estimated 25.8 million people in the United States and is one of the leading causes of death. A major safety concern in treating diabetes is the occurrence of hypoglycemic events. Despite this concern, the current methods of analyzing hypoglycemic events, including the Wilcoxon rank sum test and negative binomial regression, are not satisfactory. The aim of this article is to propose a new model to analyze hypoglycemic events with the goal of making this model a standard method in industry. Our method is based on a gamma frailty recurrent event model. To make this method broadly accessible to practitioners, this article provides many details of how this method works and discusses practical issues with supporting theoretical proofs. In particular, we make efforts to translate conditions and theorems from abstract counting process and martingale theories to intuitive and clinical meaningful explanations. For example, we provide a simple proof and illustration of the coarsening at random condition so that the practitioner can easily verify this condition. Connections and differences with traditional methods are discussed, and we demonstrate that under certain scenarios the widely used Wilcoxon rank sum test and negative binomial regression cannot control type 1 error rates while our proposed method is robust in all these situations. The usefulness of our method is demonstrated through a diabetes dataset which provides new clinical insights on the hypoglycemic data.


Assuntos
Simulação por Computador , Hipoglicemia/induzido quimicamente , Hipoglicemiantes/efeitos adversos , Modelos Estatísticos , Ensaios Clínicos Controlados Aleatórios como Assunto/estatística & dados numéricos , Interpretação Estatística de Dados , Humanos , Hipoglicemia/epidemiologia , Hipoglicemiantes/administração & dosagem , Hipoglicemiantes/uso terapêutico , Funções Verossimilhança , Recidiva
12.
Pharm Stat ; 14(1): 56-62, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-25406099

RESUMO

Generalized linear models are commonly used to analyze categorical data such as binary, count, and ordinal outcomes. Adjusting for important prognostic factors or baseline covariates in generalized linear models may improve the estimation efficiency. The model-based mean for a treatment group produced by most software packages estimates the response at the mean covariate, not the mean response for this treatment group for the studied population. Although this is not an issue for linear models, the model-based group mean estimates in generalized linear models could be seriously biased for the true group means. We propose a new method to estimate the group mean consistently with the corresponding variance estimation. Simulation showed the proposed method produces an unbiased estimator for the group means and provided the correct coverage probability. The proposed method was applied to analyze hypoglycemia data from clinical trials in diabetes.


Assuntos
Ensaios Clínicos como Assunto/estatística & dados numéricos , Modelos Lineares , Humanos , Funções Verossimilhança , Análise de Regressão
13.
Diabetes Technol Ther ; 16(8): 499-505, 2014 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-24825416

RESUMO

BACKGROUND: Because insulin dosing requires optimization of glycemic control, it is important to use a single metric of benefit and risk to determine best insulin dosing practices. We retrospectively applied a multiplicative clinical utility index (CUI) to a study of LY2605541 (Eli Lilly and Company, Indianapolis, IN), a novel, long-acting basal insulin. MATERIALS AND METHODS: A CUI was developed to transform the multidimensional problem of assessing benefit/risk of multiple dosing algorithms into a single decision-making metric to evaluate two LY2605541 dosing algorithms relative to the insulin glargine (GL) dosing algorithm. The CUI was applied to data in a 12-week, open-label, Phase 2 trial in patients with type 2 diabetes mellitus who were randomized to one of two dosing algorithms for LY2605541 (LY1 or LY2) or GL (algorithm similar to LY1). The CUI was created (via expert input) by weighing the relative benefit/risk of four components (glycosylated hemoglobin [HbA1c], percentage of patients with HbA1c ≤ 7%, hypoglycemia rate, and time to steady-state dose); individual utility values were multiplied to compute CUI values for LY1 and LY2 relative to GL, and bootstrap samples were used to determine variability. RESULTS: The mean CUI was 0.82 for LY1 and 1.35 for LY2. Based on 3,000 bootstrap samples, there was a 48% probability of LY2 performing better than LY1 and a 28% probability of LY1 performing better than LY2. CONCLUSIONS: CUI methodology, and in particular this CUI, is a useful tool for comparing dosing algorithms. Based on this CUI, LY2 is likely to be a better dosing algorithm than LY1.


Assuntos
Glicemia/efeitos dos fármacos , Diabetes Mellitus Tipo 2/tratamento farmacológico , Hipoglicemia/prevenção & controle , Hipoglicemiantes/administração & dosagem , Insulina Lispro/administração & dosagem , Insulina de Ação Prolongada/administração & dosagem , Polietilenoglicóis/administração & dosagem , Algoritmos , Glicemia/metabolismo , Ensaios Clínicos Fase II como Assunto , Tomada de Decisões , Diabetes Mellitus Tipo 2/sangue , Esquema de Medicação , Feminino , Humanos , Hipoglicemia/induzido quimicamente , Hipoglicemiantes/efeitos adversos , Insulina Glargina , Insulina Lispro/efeitos adversos , Masculino , Pessoa de Meia-Idade , Polietilenoglicóis/efeitos adversos , Estudos Retrospectivos , Medição de Risco , Resultado do Tratamento
14.
Diabetes Care ; 37(3): 659-65, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-24198302

RESUMO

OBJECTIVE: To use continuous glucose monitoring (CGM) to evaluate the impact of the novel, long-acting basal insulin analog LY2605541 on hypoglycemia and glycemic variability in patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: Hypoglycemia and glucose variability were assessed with CGM of interstitial glucose (IG) in a subset of patients with type 2 diabetes from a phase II, randomized, open-label, parallel study of LY2605541 (n = 51) or insulin glargine (GL) (n = 25). CGM was conducted on 3 consecutive days (72-84 h) during the week before week 0, 6, and 12 study visits. RESULTS: Measured by CGM for 3 days prior to the 12-week visit, fewer LY2605541-treated patients experienced hypoglycemic events overall (50.0 vs. 78.3%, P = 0.036) and nocturnally (20.5 vs. 47.8%, P = 0.027) and spent less time with IG ≤70 mg/dL than GL-treated patients during the 24-h (25 ± 6 vs. 83 ± 16 min, P = 0.012) and nocturnal periods (11 ± 5 vs. 38 ± 13 min, P = 0.024). These observations were detected without associated differences in the average duration of individual hypoglycemic episodes (LY2605541 compared with GL 57.2 ± 5.4 vs. 69.9 ± 10.2 min per episode, P = NS). Additionally, LY2605541-treated patients had lower within-day glucose SD for both 24-h and nocturnal periods. CONCLUSIONS: By CGM, LY2605541 treatment compared with GL resulted in fewer patients with hypoglycemic events and less time in the hypoglycemic range and was not associated with protracted hypoglycemia.


Assuntos
Diabetes Mellitus Tipo 2/tratamento farmacológico , Hipoglicemia/induzido quimicamente , Hipoglicemiantes/administração & dosagem , Insulina Lispro/administração & dosagem , Insulina de Ação Prolongada/administração & dosagem , Polietilenoglicóis/administração & dosagem , Glicemia/metabolismo , Automonitorização da Glicemia , Preparações de Ação Retardada , Feminino , Hemoglobina A Glicada/metabolismo , Humanos , Hipoglicemiantes/efeitos adversos , Insulina Glargina , Insulina Lispro/efeitos adversos , Insulina de Ação Prolongada/efeitos adversos , Masculino , Pessoa de Meia-Idade , Polietilenoglicóis/efeitos adversos , Resultado do Tratamento
15.
Clin Trials ; 10(5): 693-5, 2013 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-23988465

RESUMO

Identification of surrogate markers for a marketed drug is important in monitoring the efficacy or safety after a patient uses the drug. In this article, we clarify the statistical definitions of the surrogate endpoint and surrogate marker and introduce the concept of the validity and efficiency of a surrogate marker. We also review some existing methods and suggest the proportion of information gain is appropriate to be used to evaluate the validity of a surrogate marker.


Assuntos
Biomarcadores , Ensaios Clínicos como Assunto/métodos , Interpretação Estatística de Dados , Humanos , Reprodutibilidade dos Testes , Sensibilidade e Especificidade
16.
Diabetes Technol Ther ; 15(8): 654-61, 2013 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-23883405

RESUMO

BACKGROUND: Baseline hypoglycemia rates are generally not collected or included as a covariate in statistical models used for analyzing hypoglycemia data. The objective of the present study was to examine the effect of adjusting for baseline hypoglycemia on estimation efficiency and statistical power. SUBJECTS AND METHODS: A post hoc analysis of data from 15 insulin trials, including patients with type 1 diabetes mellitus (T1DM) (n=210), previously insulin-treated type 2 diabetes mellitus (T2DM) (n=1,511), or T2DM and previously insulin-naive (n=1,075). Hypoglycemic episodes were analyzed with a negative binomial regression model. RESULTS: Baseline nocturnal hypoglycemia rate was significantly correlated with post-baseline nocturnal hypoglycemia rate in previously insulin-treated patients with T1DM and T2DM (correlation range, 0.37-0.63; P<0.001). Adjusting for baseline hypoglycemia resulted in a reduction in the SE for negative binomial regression for previously insulin-treated patients with T1DM and T2DM (range, 2.2-11.8%) and increased statistical power. Modeling of lengthening the lead-in period increases the correlation between baseline and post-baseline hypoglycemia event rate and statistical power. CONCLUSIONS: Baseline hypoglycemia rate is significantly correlated with post-baseline hypoglycemia rate for patients with diabetes treated with insulin prior to randomization. The length of the lead-in period can impact correlations between baseline and post-baseline data, and adjustment for baseline hypoglycemia may improve the estimation efficiency for hypoglycemia data analyses in clinical trials.


Assuntos
Diabetes Mellitus Tipo 1/sangue , Diabetes Mellitus Tipo 2/sangue , Hipoglicemia/induzido quimicamente , Hipoglicemiantes/efeitos adversos , Insulina/efeitos adversos , Modelos Estatísticos , Adulto , Idoso , Ritmo Circadiano , Diabetes Mellitus Tipo 1/tratamento farmacológico , Diabetes Mellitus Tipo 2/tratamento farmacológico , Feminino , Humanos , Hipoglicemia/prevenção & controle , Hipoglicemiantes/uso terapêutico , Insulina/uso terapêutico , Masculino , Pessoa de Meia-Idade , Ensaios Clínicos Controlados Aleatórios como Assunto , Projetos de Pesquisa , Sono , Fatores de Tempo
17.
Pharm Stat ; 12(4): 233-42, 2013.
Artigo em Inglês | MEDLINE | ID: mdl-23776062

RESUMO

Negative binomial regression is a standard model to analyze hypoglycemic events in diabetes clinical trials. Adjusting for baseline covariates could potentially increase the estimation efficiency of negative binomial regression. However, adjusting for covariates raises concerns about model misspecification, in which the negative binomial regression is not robust because of its requirement for strong model assumptions. In some literature, it was suggested to correct the standard error of the maximum likelihood estimator through introducing overdispersion, which can be estimated by the Deviance or Pearson Chi-square. We proposed to conduct the negative binomial regression using Sandwich estimation to calculate the covariance matrix of the parameter estimates together with Pearson overdispersion correction (denoted by NBSP). In this research, we compared several commonly used negative binomial model options with our proposed NBSP. Simulations and real data analyses showed that NBSP is the most robust to model misspecification, and the estimation efficiency will be improved by adjusting for baseline hypoglycemia.


Assuntos
Ensaios Clínicos como Assunto/métodos , Hipoglicemia/induzido quimicamente , Hipoglicemiantes/toxicidade , Modelos Estatísticos , Distribuição Binomial , Distribuição de Qui-Quadrado , Simulação por Computador , Interpretação Estatística de Dados , Diabetes Mellitus/tratamento farmacológico , Humanos , Funções Verossimilhança , Análise de Regressão
19.
Stat Med ; 32(21): 3636-45, 2013 Sep 20.
Artigo em Inglês | MEDLINE | ID: mdl-23589168

RESUMO

In clinical research and practice, biomarkers help in understanding disease progression and are useful when monitoring patients and making treatment decisions. Correct quantification of biomarkers in predicting the clinical outcome is important for better treatment of individual patients. Despite the rich literature in statistical validation, application of these validation methods in real data is not well studied. Specifically, the question is whether the change in a biomarker or the actual assessed value of the biomarker should be used. Contrary to most published papers in which the actual value of the biomarker is used, we showed through theory, simulation, and an example that it is more appropriate to use the change or the actual value in the marker with adjustment for the baseline value in evaluating a marker.


Assuntos
Biomarcadores/análise , Pesquisa Biomédica/métodos , Interpretação Estatística de Dados , Resultado do Tratamento , Simulação por Computador , Feminino , Fraturas Ósseas/prevenção & controle , Humanos , Vértebras Lombares/efeitos dos fármacos , Cloridrato de Raloxifeno/farmacologia , Moduladores Seletivos de Receptor Estrogênico/farmacologia
20.
Diabetes Care ; 36(3): 522-8, 2013 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-23193209

RESUMO

OBJECTIVE To compare effects of LY2605541 versus insulin glargine on daily mean blood glucose as part of a basal-bolus regimen for type 1 diabetes. RESEARCH DESIGN AND METHODS In this randomized, Phase 2, open-label, 2 × 2 crossover study, 137 patients received once-daily basal insulin (LY2605541 or glargine) plus mealtime insulin for 8 weeks, followed by crossover treatment for 8 weeks. Daily mean blood glucose was obtained from 8-point self-monitored blood glucose profiles. The noninferiority margin was 10.8 mg/dL. RESULTS LY2605541 met noninferiority and superiority criteria compared with insulin glargine in daily mean blood glucose (144.2 vs. 151.7 mg/dL, least squares mean difference = -9.9 mg/dL [90% CI -14.6 to -5.2], P < 0.001). Fasting blood glucose variability and A1C were reduced with LY2605541 compared with insulin glargine (both P < 0.001). Mealtime insulin dose decreased with LY2605541 and increased with insulin glargine. Mean weight decreased 1.2 kg with LY2605541 and increased 0.7 kg with insulin glargine (P < 0.001). The total hypoglycemia rate was higher for LY2605541 (P = 0.04) and the nocturnal hypoglycemia rate was lower (P = 0.01), compared with insulin glargine. Adverse events (including severe hypoglycemia) were similar, although more gastrointestinal-related events occurred with LY2605541 (15% vs. 4%, P < 0.001). Mean changes (all within normal range) were higher for alanine aminotransferase, aspartate aminotransferase, triglycerides, and LDL-cholesterol and lower for HDL-cholesterol with LY2605541 compared with insulin glargine (all P < 0.02). CONCLUSIONS In type 1 diabetes, compared with insulin glargine, LY2605541, a novel, long-acting basal insulin, demonstrated greater improvements in glycemic control, increased total hypoglycemia, and reduced nocturnal hypoglycemia, as well as reduced weight and lowered mealtime insulin doses.


Assuntos
Diabetes Mellitus Tipo 1/tratamento farmacológico , Insulina de Ação Prolongada/uso terapêutico , Adulto , Estudos Cross-Over , Feminino , Humanos , Hipoglicemiantes/administração & dosagem , Hipoglicemiantes/uso terapêutico , Insulina Glargina , Insulina de Ação Prolongada/administração & dosagem , Masculino , Resultado do Tratamento
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