Application of Bayesian approaches in drug development: starting a virtuous cycle.

The pharmaceutical industry and its global regulators have routinely used frequentist statistical methods, such as null hypothesis significance testing and p values, for evaluation and approval of new treatments. The clinical drug development process, however, with its accumulation of data over time, can be well suited for the use of Bayesian statistical approaches that explicitly incorporate existing data into clinical trial design, analysis and decision-making. Such approaches, if used appropriately, have the potential to substantially reduce the time and cost of bringing innovative medicines to patients, as well as to reduce the exposure of patients in clinical trials to ineffective or unsafe treatment regimens.

Accurate Collection of Reasons for Treatment Discontinuation to Better Define Estimands in Clinical Trials.

Background: Reasons for treatment discontinuation are important not only to understand the benefit and risk profile of experimental treatments, but also to help choose appropriate strategies to handle intercurrent events in defining estimands. The current case report form (CRF) commonly in use mixes the underlying reasons for treatment discontinuation and who makes the decision for treatment discontinuation, often resulting in an inaccurate collection of reasons for treatment discontinuation.

Methods And Results: We systematically reviewed and analyzed treatment discontinuation data from nine phase 2 and phase 3 studies for insulin peglispro.

Estimating the treatment effect for adherers using multiple imputation.

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.

Assessing and communicating heterogeneity of treatment effects for patient subpopulations: The hardest problem there is.

Heterogeneity is an enormously complex problem because there are so many dimensions and variables that can be considered when assessing which ones may influence an efficacy or safety outcome for an individual patient. This is difficult in randomized controlled trials and even more so in observational settings. An alternative approach is presented in which the individual patient becomes the "subgroup," and similar patients are identified in the clinical trial database or electronic medical record that can be used to predict how that individual patient may respond to treatment.

Implementation of tripartite estimands using adherence causal estimators under the causal inference framework.

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.

Détente: A Practical Understanding of P values and Bayesian Posterior Probabilities.

Null hypothesis significance testing (NHST) with its benchmark P value < 0.05 has long been a stalwart of scientific reporting and such statistically significant findings have been used to imply scientifically or clinically significant findings. Challenges to this approach have arisen over the past 6 decades, but they have largely been unheeded.

Efficiency of two sample tests via the restricted mean survival time for analyzing event time observations.

In comparing two treatments with the event time observations, the hazard ratio (HR) estimate is routinely used to quantify the treatment difference. However, this model dependent estimate may be difficult to interpret clinically especially when the proportional hazards (PH) assumption is violated. An alternative estimation procedure for treatment efficacy based on the restricted means survival time or t-year mean survival time (t-MST) has been discussed extensively in the statistical and clinical literature.

Considerations for Evaluating Treatment Effects From Randomized Clinical Trials.

This article focuses on the choice of treatment effect measures in randomized clinical trials (RCTs). Traditionally, an intention-to-treat (ITT) analysis is conducted with an implicit understanding that a treatment-policy effect is of greatest interest. In this article we contend that this approach may not always provide accurate information about clinically meaningful treatment effects, and we present an argument that for any RCT it is desirable to require an explicit definition of what treatment effect is of primary interest, known as the "estimand.

Making what's advanced today routine tomorrow.

Statistical principles and ongoing proliferation of novel statistical methodologies have dramatically improved the clinical drug development process. This journey over the last seven decades reshaped the pharmaceutical industry and regulatory agencies, highlighted the importance of statistical thinking in drug development and decision-making, and, most importantly, improved the lives of countless patients around the world. Some significant highlights in the history of this journey are recounted here as well as some exciting opportunities of what the future may hold for the science and profession of statistics.

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.

Subgroup identification from randomized clinical trial data.

We consider the problem of identifying a subgroup of patients who may have an enhanced treatment effect in a randomized clinical trial, and it is desirable that the subgroup be defined by a limited number of covariates. For this problem, the development of a standard, pre-determined strategy may help to avoid the well-known dangers of subgroup analysis. We present a method developed to find subgroups of enhanced treatment effect.

Early improvement in pain predicts pain response at endpoint in patients with fibromyalgia.

Unlabelled: An unanswered, but clinically important question is whether there are early indicators that a patient might respond to duloxetine treatment for fibromyalgia pain. To address this question, pooled data from 4 double-blind, placebo-controlled trials in duloxetine-treated patients (N = 797) with primary fibromyalgia as defined by the American College for Rheumatology were analyzed. Classification and Regression Tree (CART) analysis was used to determine what level of early pain improvement as measured by the 24-hour average pain severity question on the Brief Pain Inventory (BPI) best predicted later response.

Identification of early changes in specific symptoms that predict longer-term response to atypical antipsychotics in the treatment of patients with schizophrenia.

Background: To identify a simple decision tree using early symptom change to predict response to atypical antipsychotic therapy in patients with (Diagnostic and Statistical Manual, Fourth Edition, Text Revised) chronic schizophrenia.

Methods: Data were pooled from moderately to severely ill patients (n = 1494) from 6 randomized, double-blind trials (N = 2543). Response was defined as a ≥ 30% reduction in Positive and Negative Syndrome Scale (PANSS) Total score by Week 8 of treatment.

The mean does not mean as much anymore: finding sub-groups for tailored therapeutics.

Background: The genomics revolution is still in its infancy, and there is much to learn about how to transform biological knowledge into useful medicines to further public health. At the bedside, we are asking how and why individual patients respond to different drug treatments in different ways. In addition to genetic mechanisms, there are many clinical markers (e.

Post hoc analysis: early changes in ADHD-RS items predict longer term response to atomoxetine in pediatric patients.

Data from 5 atomoxetine trials in pediatric outpatients with attention-deficit/hyperactivity disorder (ADHD) were divided into training and validation data sets to develop models predicting atomoxetine treatment response, using changes in individual ADHD Rating Scale (ADHD-RS) items early in treatment. Treatment response was predicted after 1 week by a > or =1-point score decrease in ADHD-RS item 15 ("easily distracted;" positive predictive values [PPVs]: 84.9%, 74.