Current development in clinical trials: issues old and new

A Practical Overview and Reporting Strategies for Statistical Analysis of Survival Studies

Survival (time-to-event) analysis is commonly used in clinical research. Key features of performing a survival analysis include checking proportional hazards assumptions, reporting CIs for hazards ratios and relative risks, graphically displaying the findings, and analyzing with consideration of competing risks. This article provides a brief overview of important statistical considerations for survival analysis. Censoring schemes, different methods of survival function estimation, and ways to compare survival curves are described. We also explain competing risk and how to model survival data in the presence of it. Different kinds of bias that influence survival estimation and avenues to model the data under these circumstances are also described. Several analysis techniques are accompanied by graphical representations illustrating proper reporting strategies. We provide a list of guiding statements for researchers and reviewers.

Essential statistical principles of clinical trials of pain treatments

This article presents an overview of fundamental statistical principles of clinical trials of pain treatments. Statistical considerations relevant to phase 2 proof of concept and phase 3 confirmatory randomized trials investigating efficacy and safety are discussed, including (1) research design; (2) endpoints and analyses; (3) sample size determination and statistical power; (4) missing data and trial estimands; (5) data monitoring and interim analyses; and (6) interpretation of results. Although clinical trials of pharmacologic treatments are emphasized, the key issues raised by these trials are also directly applicable to clinical trials of other types of treatments, including biologics, devices, nonpharmacologic therapies (eg, physical therapy and cognitive-behavior therapy), and complementary and integrative health interventions.

A two-stage phase II clinical trial design with nested criteria for early stopping and efficacy

We propose a two-stage design for a single arm clinical trial with an early stopping rule for futility. This design employs different endpoints to assess early stopping and efficacy. The early stopping rule is based on a criteria determined more quickly than that for efficacy. These separate criteria are also nested in the sense that efficacy is a special case of, but usually not identical to, the early stopping endpoint. The design readily allows for planning in terms of statistical significance, power, expected sample size, and expected duration. This method is illustrated with a phase II design comparing rates of disease progression in elderly patients treated for lung cancer to rates found using a historical control. In this example, the early stopping rule is based on the number of patients who exhibit progression-free survival (PFS) at 2 months post treatment follow-up. Efficacy is judged by the number of patients who have PFS at 6 months. We demonstrate our design has expected sample size and power comparable with the Simon two-stage design but exhibits shorter expected duration under a range of useful parameter values.

Biomarker-Guided Adaptive Trial Designs in Phase II and Phase III: A Methodological Review

BACKGROUND

Personalized medicine is a growing area of research which aims to tailor the treatment given to a patient according to one or more personal characteristics. These characteristics can be demographic such as age or gender, or biological such as a genetic or other biomarker. Prior to utilizing a patient's biomarker information in clinical practice, robust testing in terms of analytical validity, clinical validity and clinical utility is necessary. A number of clinical trial designs have been proposed for testing a biomarker's clinical utility, including Phase II and Phase III clinical trials which aim to test the effectiveness of a biomarker-guided approach to treatment; these designs can be broadly classified into adaptive and non-adaptive. While adaptive designs allow planned modifications based on accumulating information during a trial, non-adaptive designs are typically simpler but less flexible.

METHODS AND FINDINGS

We have undertaken a comprehensive review of biomarker-guided adaptive trial designs proposed in the past decade. We have identified eight distinct biomarker-guided adaptive designs and nine variations from 107 studies. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. We have graphically displayed the current biomarker-guided adaptive trial designs and summarised the characteristics of each design.

CONCLUSIONS

Our in-depth overview provides future researchers with clarity in definition, methodology and terminology for biomarker-guided adaptive trial designs.

Effect of telehealth on glycaemic control: analysis of patients with type 2 diabetes in the Whole Systems Demonstrator cluster randomised trial

BACKGROUND

The Whole Systems Demonstrator was a large, pragmatic, cluster randomised trial that compared telehealth with usual care among 3,230 patients with long-term conditions in three areas of England. Telehealth involved the regular transmission of physiological information such as blood glucose to health professionals working remotely. We examined whether telehealth led to changes in glycosylated haemoglobin (HbA1c) among the subset of patients with type 2 diabetes.

METHODS

The general practice electronic medical record was used as the source of information on HbA1c. Effects on HbA1c were assessed using a repeated measures model that included all HbA1c readings recorded during the 12-month trial period, and adjusted for differences in HbA1c readings recorded before recruitment. Secondary analysis averaged multiple HbA1c readings recorded for each individual during the trial period.

RESULTS

513 of the 3,230 participants were identified as having type 2 diabetes and thus were included in the study. Telehealth was associated with lower HbA1c than usual care during the trial period (difference 0.21% or 2.3 mmol/mol, 95% CI, 0.04% to 0.38%, p = 0.013). Among the 457 patients in the secondary analysis, mean HbA1c showed little change for controls following recruitment, but fell for intervention patients from 8.38% to 8.15% (68 to 66 mmol/mol). A higher proportion of intervention patients than controls had HbA1c below the 7.5% (58 mmol/mol) threshold that was targeted by general practices (30.4% vs. 38.0%). This difference, however, did not quite reach statistical significance (adjusted odds ratio 1.63, 95% CI, 0.99 to 2.68, p = 0.053).

CONCLUSIONS

Telehealth modestly improved glycaemic control in patients with type 2 diabetes over 12 months. The scale of the improvements is consistent with previous meta-analyses, but was relatively modest and seems unlikely to produce significant patient benefit.

TRIAL REGISTRATION NUMBER

International Standard Randomized Controlled Trial Number Register ISRCTN43002091.

In pursuit of an HIV vaccine: designing efficacy trials in the context of partially effective nonvaccine prevention modalities

The HIV prevention landscape is evolving rapidly, and future efficacy trials of candidate vaccines, which remain the best long-term option for stemming the HIV epidemic, will be conducted in the context of partially effective nonvaccine prevention modalities. It is essential that these trials provide for valid and efficient evaluation of vaccine efficacy and immune correlates. The availability of partially effective prevention modalities presents opportunities to study their interactions with vaccines to maximally reduce HIV incidence. This article proposes an approach for conducting future vaccine efficacy trials in the context of background use of partially effective nonvaccine prevention modalities, and for conducting future vaccine efficacy trials that provide nonvaccine prevention modalities in one or more of the randomized study groups. Strategies are discussed for responding to emerging evidence on nonvaccine prevention modalities during ongoing vaccine trials. Next-generation HIV vaccine efficacy trials will almost certainly be more complex in their design and implementation but may become more relevant to at-risk populations and better suited to the ultimate goal of reducing HIV incidence at the population level.