Sample Size Calculations for Variant Surveillance in the Presence of Biological and Systematic Biases

As demonstrated during the SARS-CoV-2 pandemic, detecting and tracking the emergence and spread of pathogen variants is an important component of monitoring infectious disease outbreaks. Pathogen genome sequencing has emerged as the primary tool for variant characterization, so it is important to consider the number of sequences needed when designing surveillance programs or studies, both to ensure accurate conclusions and to optimize use of limited resources. However, current approaches to calculating sample size for variant monitoring often do not account for the biological and logistical processes that can bias which infections are detected and which samples are ultimately selected for sequencing. In this manuscript, we introduce a framework that models the full process from infection detection to variant characterization and demonstrate how to use this framework to calculate appropriate sample sizes for sequencing-based surveillance studies. We consider both cross-sectional and continuous sampling, and we have implemented our method in a publicly available tool that allows users to estimate necessary sample sizes given a specific aim (e.g., variant detection or measuring variant prevalence) and sampling method. Our framework is designed to be easy to use, while also flexible enough to be adapted to other pathogens and surveillance scenarios.

Longitudinal assessment of blood-borne musculoskeletal disease biomarkers in the DE50-MD dog model of Duchenne muscular dystrophy

Background: Duchenne muscular dystrophy (DMD) is a fatal muscle wasting disease caused by mutations in the dystrophin gene. Due to their phenotypic similarity to human patients, large animal models are invaluable tools for pre-clinical trials. The DE50-MD dog is a relatively new model of DMD, and carries a therapeutically-tractable mutation lying within the hotspot for human patients, making it especially valuable. Prior to conducting therapeutic trials using this novel animal model, it is essential to establish a panel of viable biomarkers. Methods: We evaluated a panel of blood-borne biomarkers of musculoskeletal disease in the DE50-MD dog. Venous blood samples were obtained monthly throughout an 18-month study period in DE50-MD (N=18) and wild-type (WT) control (N=14) dogs. A panel of potential plasma/serum biomarkers of DMD was measured and their theoretical utility in future clinical trials determined using sample size calculations. Results: Compared to WT dogs, DE50-MD dogs had substantially higher circulating creatine kinase (CK) activities, myomesin-3 (MYOM3), and the dystromiRs miR-1, miR-133a and miR-206, but significantly lower serum myostatin concentrations. An age-associated pattern, similar to that observed in DMD patients, was seen for CK and MYOM3. Sample size calculations suggested that low cohort sizes (N≤3) could be used to detect up to a 50% improvement in DE50-MD results towards WT levels for each biomarker or a combination thereof (via principal component analysis); as few as N=3 animals should enable detection of a 25% improvement using a combined biomarker approach (alpha 0.05, power 0.8). Conclusions: We have established a panel of blood-borne biomarkers that could be used to monitor musculoskeletal disease or response to a therapeutic intervention in the DE50-MD dog using low numbers of animals. The blood biomarker profile closely mimics that of DMD patients, supporting the hypothesis that this DMD model would be suitable for use in pre-clinical trials.

Methodological challenges in pragmatic trials in Alzheimer’s disease and related dementias: Opportunities for improvement

Background and Aims We need more pragmatic trials of interventions to improve care and outcomes for people living with Alzheimer’s disease and related dementias. However, these trials present unique methodological challenges in their design, analysis, and reporting—often, due to the presence of one or more sources of clustering. Failure to account for clustering in the design and analysis can lead to increased risks of Type I and Type II errors. We conducted a review to describe key methodological characteristics and obtain a “baseline assessment” of methodological quality of pragmatic trials in dementia research, with a view to developing new methods and practical guidance to support investigators and methodologists conducting pragmatic trials in this field. Methods We used a published search filter in MEDLINE to identify trials more likely to be pragmatic and identified a subset that focused on people living with Alzheimer’s disease or other dementias or included them as a defined subgroup. Pairs of reviewers extracted descriptive information and key methodological quality indicators from each trial. Results We identified N = 62 eligible primary trial reports published across 36 different journals. There were 15 (24%) individually randomized, 38 (61%) cluster randomized, and 9 (15%) individually randomized group treatment designs; 54 (87%) trials used repeated measures on the same individual and/or cluster over time and 17 (27%) had a multivariate primary outcome (e.g. due to measuring an outcome on both the patient and their caregiver). Of the 38 cluster randomized trials, 16 (42%) did not report sample size calculations accounting for the intracluster correlation and 13 (34%) did not account for intracluster correlation in the analysis. Of the 9 individually randomized group treatment trials, 6 (67%) did not report sample size calculations accounting for intracluster correlation and 8 (89%) did not account for it in the analysis. Of the 54 trials with repeated measurements, 45 (83%) did not report sample size calculations accounting for repeated measurements and 19 (35%) did not utilize at least some of the repeated measures in the analysis. No trials accounted for the multivariate nature of their primary outcomes in sample size calculation; only one did so in the analysis. Conclusion There is a need and opportunity to improve the design, analysis, and reporting of pragmatic trials in dementia research. Investigators should pay attention to the potential presence of one or more sources of clustering. While methods for longitudinal and cluster randomized trials are well developed, accessible resources and new methods for dealing with multiple sources of clustering are required. Involvement of a statistician with expertise in longitudinal and clustered designs is recommended.

Colon capsule endoscopy in colorectal cancer screening: Interim analyses of randomized controlled trial CareForColon2015

Background and study aims The Danish CareForColon2015 trial, launched in 2020 as part of the Danish Colorectal Cancer Screening program, is the largest randomized controlled trial to date on colon capsule endoscopy (CCE). This paper presents the interim analysis with the objective of ensuring the safety of patients in the intervention group and evaluating the clinical performance of the trial’s predefined clinical parameters. Patients and methods We evaluated the initial 234 CCEs according to quality, safety, and completion. The participation rates and preference distribution of all individuals invited were analyzed and sample size calculations were adjusted. Results Fecal immunochemical test and diagnostic participation rates were 62.1 % and 91.1 %, respectively. The completion rate for CCEs was 67.9 % and the rate of conclusive investigations was 80.3 %. The polyp detection rate (PDR) was high (73.5 %), only two (0.85 %) technical failures in 234 videos were observed, and six suspected cancers were identified (2.6 %). No major adverse events were recorded. The required number of invitations had been underestimated due to inaccurate assumptions in sample size calculations. Conclusions The trial was efficient and safe in terms of CCE quality and time to diagnostic investigation. Participation rates and PDRs were high. The proportion of suspected cancers was lower than expected and will be followed. The completion rate for CCEs was acceptable but lower than expected and the CCE procedure was reviewed for potential improvements and Resolor was added to the regime. The number of invitations for the intervention group of the trial has been adjusted from 62,107 to 185,153.

A Feasibility and Pilot Randomised Dismantling Trial of the Efficacy of Self-As-Context During Acceptance and Commitment Therapy

The comparative clinical utility of the components of the psychological flexibility model of acceptance and commitment therapy (ACT) have not been equally evaluated. This study therefore conducted a feasibility and pilot two-arm dismantling trial by quarantining the self-as-context component. Sixteen participants were randomised to either 8 sessions of protocol-based ACT (Full-ACT) or 8 sessions of protocol-based ACT minus self-as-context (ACT-SAC). Process measures (flexibility and decentring) were taken at start of treatment, end of treatment, and at 6-week follow-up. Clinical outcome measures (functioning, anxiety, and depression) were collected on a session-by-session basis. Randomisation was well tolerated, all measures were completed, both interventions were competently delivered, and one adverse effect occurred in the full-ACT arm. Ten participants attended all 8 sessions creating a dropout rate of 37.50%. Clinical change appeared linear in both treatments and that treatment gains were maintained. Findings suggest that a full trial is possible and sample size calculations and methodological improvements are provided for this.

Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command

Trials of interventions that aim to slow disease progression may analyze a continuous outcome by comparing its change over time—its slope—between the treated and the untreated group using a linear mixed model. To perform a sample-size calculation for such a trial, one must have estimates of the parameters that govern the between- and within-subject variability in the outcome, which are often unknown. The algebra needed for the sample-size calculation can also be complex for such trial designs. We have written a new user-friendly command, slopepower, that performs sample-size or power calculations for trials that compare slope outcomes. The package is based on linear mixed-model methodology, described for this setting by Frost, Kenward, and Fox (2008, Statistics in Medicine 27: 3717–3731). In the first stage of this approach, slopepower obtains estimates of mean slopes together with variances and covariances from a linear mixed model fit to previously collected user-supplied data. In the second stage, these estimates are combined with user input about the target effectiveness of the treatment and design of the future trial to give an estimate of either a sample size or a statistical power. In this article, we present the slopepower command, briefly explain the methodology behind it, and demonstrate how it can be used to help plan a trial and compare the sample sizes needed for different trial designs.