Search

Although clinical trials are often designed with randomization and well‐controlled protocols, complications will inevitably arise in the presence of intercurrent events (ICEs) such as treatment discontinuation. These can lead to missing outcome data and possibly confounding causal inference when the missingness is a function of a latent stratification of patients defined by intermediate outcomes. The pharmaceutical industry has been focused on developing new methods that can yield pertinent causal inferences in trials with ICEs. However, it is difficult to compare the properties of different methods developed in this endeavor as real‐life clinical trial data cannot be easily shared to provide benchmark data sets. Furthermore, different methods consider distinct assumptions for the underlying data‐generating mechanisms, and simulation studies often are customized to specific situations or methods. We develop a novel, general simulation model and corresponding Shiny application in R for clinical trials with ICEs, aptly named the Clinical Trials with Intercurrent Events Simulator (CITIES). It is formulated under the Rubin Causal Model where the considered treatment effects account for ICEs in clinical trials with repeated measures. CITIES facilitates the effective generation of data that resemble real‐life clinical trials with respect to their reported summary statistics, without requiring the use of the original trial data. We illustrate the utility of CITIES via two case studies involving real‐life clinical trials that demonstrate how CITIES provides a comprehensive tool for practitioners in the pharmaceutical industry to compare methods for the analysis of clinical trials with ICEs on identical, benchmark settings that resemble real‐life trials. [ABSTRACT FROM AUTHOR]

Randomized controlled trials are considered the gold standard to evaluate the treatment effect (estimand) for efficacy and safety. According to the recent International Council on Harmonization (ICH)‐E9 addendum (R1), intercurrent events (ICEs) need to be considered when defining an estimand, and principal stratum is one of the five strategies to handle ICEs. Qu et al. (2020, Statistics in Biopharmaceutical Research 12:1–18) proposed estimators for the adherer average causal effect (AdACE) for estimating the treatment difference for those who adhere to one or both treatments based on the causal‐inference framework, and demonstrated the consistency of those estimators; however, this method requires complex custom programming related to high‐dimensional numeric integrations. In this article, we implemented the AdACE estimators using multiple imputation (MI) and constructed confidence intervals (CIs) through bootstrapping. A simulation study showed that the MI‐based estimators provided consistent estimators with the nominal coverage probabilities of CIs for the treatment difference for the adherent populations of interest. As an illustrative example, the new method was applied to data from a real clinical trial comparing two types of basal insulin for patients with type 1 diabetes. [ABSTRACT FROM AUTHOR]

Summary: Intercurrent events (ICEs) and missing values are inevitable in clinical trials of any size and duration, making it difficult to assess the treatment effect for all patients in randomized clinical trials. Defining the appropriate estimand that is relevant to the clinical research question is the first step in analyzing data. The tripartite estimands, which evaluate the treatment differences in the proportion of patients with ICEs due to adverse events, the proportion of patients with ICEs due to lack of efficacy, and the primary efficacy outcome for those who can adhere to study treatment under the causal inference framework, are of interest to many stakeholders in understanding the totality of treatment effects. In this manuscript, we discuss the details of how to estimate tripartite estimands based on a causal inference framework and how to interpret tripartite estimates through a phase 3 clinical study evaluating a basal insulin treatment for patients with type 1 diabetes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

Objective: To evaluate the efficacy and safety of basal insulin peglispro (BIL) versus insulin glargine in patients with type 2 diabetes (hemoglobin A1c [HbA1c] ≤9% [75 mmol/mol]) treated with basal insulin alone or with three or fewer oral antihyperglycemic medications.Research Design and Methods: This 52-week, open-label, treat-to-target study randomized patients (mean HbA1c 7.42% [57.6 mmol/mol]) to BIL (n = 307) or glargine (n = 159). The primary end point was change from baseline HbA1c to 26 weeks (0.4% [4.4 mmol/mol] noninferiority margin).Results: At 26 weeks, reduction in HbA1c was superior with BIL versus glargine (-0.82% [-8.9 mmol/mol] vs. -0.29% [-3.2 mmol/mol]; least squares mean difference -0.52%, 95% CI -0.67 to -0.38 [-5.7 mmol/mol, 95% CI -7.3 to -4.2; P < 0.001); greater reduction in HbA1c with BIL was maintained at 52 weeks. More BIL patients achieved HbA1c <7% (53 mmol/mol) at weeks 26 and 52 (P < 0.001). With BIL versus glargine, nocturnal hypoglycemia rate was 60% lower, more patients achieved HbA1c <7% (53 mmol/mol) without nocturnal hypoglycemia at 26 and 52 weeks (P < 0.001), and total hypoglycemia rates were lower at 52 weeks (P = 0.03). At weeks 26 and 52, glucose variability was lower (P < 0.01), basal insulin dose was higher (P < 0.001), and triglycerides and aminotransferases were higher with BIL versus glargine (P < 0.05). Liver fat content (LFC), assessed in a subset of patients (n = 162), increased from baseline with BIL versus glargine (P < 0.001), with stable levels between 26 and 52 weeks.Conclusions: BIL provided superior glycemic control versus glargine, with reduced nocturnal and total hypoglycemia, lower glucose variability, and increased triglycerides, aminotransferases, and LFC. [ABSTRACT FROM AUTHOR]