Sample Size Considerations Research Articles

Regional consistency and sample size considerations in a multiregional equivalence trial.

The main objective of a confirmatory multiregional clinical trial (MRCT) is to demonstrate the overall efficacy of test drugs in all participating regions as well as to evaluate the possibility of extrapolating the overall results to each region. With the emergence of the demands of biosimilar drugs development, some guidelines recommended using equivalence design to demonstrate the comparability of efficacy between biosimilar and reference drugs. Previous discussions about assessing regional consistency in MRCT are mainly focused on superiority or non-inferiority designs, while the extensions to equivalence designs were limited. In this work, we proposed a flexible regional consistency criterion for the MRCT with equivalence design. Based on this criterion, sample size determination and sample allocation were also discussed.

Design and sample size considerations for valuation studies of multi-attribute utility instruments.

The EQ-5D, a widely used multiattribute utility instrument, is commonly used in health economic evaluations where the goal is to decide on which treatments to reimburse. Like other instruments, value sets of the EQ-5D are constructed using valuation studies typically valuing a subset of the health states and using predicted values from a regression model for the unvalued health states. In current practice the prediction errors associated with the value sets are substantial. The goal of this work is 2-fold. First, derive a formula of the mean squared error (MSE) of a value set assuming that the value set is estimated using a linear mixed model with either an independent or a Gaussian spatial correlation on the model misspecification error. Second, explore the effect of the number of health states directly valued, the number of participants and the correlation structure on the MSE. Keeping the total number of participants and the total number of valuations fixed, valuing all 242 health states of the EQ-5D-3L was found to reduce the MSE considerably compared with the common practice of valuing only 42 health states. Furthermore, an independent correlation structure with 3773 participants valuing 42 health states produced the MSE that can be achieved with less than 600 participants valuing all 242 health states under a Gaussian spatial correlation structure. Based on the comparison of the MSE values of some of the well-known designs our suggestion is to value more health states and to use a model with spatially correlated misspecification errors.

The stratified micro-randomized trial design: Sample size considerations for testing nested causal effects of time-varying treatments

Technological advancements in the field of mobile devices and wearable sensors have helped overcome obstacles in the delivery of care, making it possible to deliver behavioral treatments anytime and anywhere. Here, we discuss our work on the design of a mobile health smoking cessation intervention study with the goal of assessing whether reminders, delivered at times of stress, result in a reduction/prevention of stress in the near-term, and whether this effect changes with time in study. Multiple statistical challenges arose in this effort, leading to the development of the stratified micro-randomized trial design. In these designs, each individual is randomized to treatment repeatedly at times determined by predictions of risk. These risk times may be impacted by prior treatment. We describe the statistical challenges and detail how they can be met.

Optimizing sample size in relative bioavailability trials using a Bayesian decision-theoretic framework

Bioequivalence (BE) trials are sometimes preceded by a pilot relative bioavailability (BA) trial to investigate whether the test formulation is sufficiently similar to the reference. The geometric mean ratio and its confidence bounds provide guidance as to how the BE trial can be appropriately sized to attain sufficient power. The aim of this work is to optimize the sample size of a pilot BA trial in order to minimize the overall sample size for the combination of pilot and pivotal trials. This is done through specification of a gain function associated with any of two possible outcomes of the trial; that is, abandon further development of the test formulation or proceed to a pivotal BE trial. The gain functions will be constructed on the basis of sample size considerations only, because subject numbers are indicative of both the cost and the feasibility of a clinical trial. Using simulations, it is demonstrated that for drugs with high intrasubject variability, the BA trial should be sufficiently sized to avoid erroneous decision making and to control the overall cost. In contrast, when the intrasubject variability of the pharmacokinetic (PK) parameters is low, not conducting the BA trial should be considered. It is concluded that the rather typical practice of conducting small pilot trials is unlikely to be a cost-effective approach.

COMPARATIVE STUDY BETWEEN TWO DIFFERENT TYPES OF ATTACHMENTS RETAINED IMPLANT MANDIBULAR OVER DENTUREON ON THE ANTERIOR MAXILLARY BONE RESORPTION

This study was conducted to evaluate and compare the effect of using two different types of attachments (Locator and ball); to retain implants mandibular over denture on the anterior maxillary bone resorption. Material and methods: Fourteen completely edentulous male patients with problematic existing mandibular dentures , received two implants in the canine region of the mandible. New maxillary complete dentures and mandibular overdentures were fabricated for all patients, following conventional clinical and laboratory techniques. Patients were divided randomly by closed envelops into two groups; Group I: Patients received implant retained overdenture by locator attachment, while Group II : patients received implant retained overdenture by ball attachment, four months after implant placement. Traced rotational tomograms were used for measurements of maxillary alveolar bone loss. The proportional value between bone areas and reference areas was expressed as a ratio R. Change in R for all patients were evaluated immediately before insertion overdenture (T0), and after two years (T2) later, and calculated for anterior maxillary area. Results: During the two years; the follow up period showed a significant decreasing change in R in the anterior maxillary area. group II (ball attachment) showed a significant change in R more anterior maxillary ridge resorption than group I (locator attachment) (P = 0.011). Conclusion : Within the limitation of this study and consideration of the small sample size, it could be concluded that; Ball attachments for implant retained mandibular overdentures are associated with more maxillary ridge resorption than locator attachments.

Noninferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs).

Adaptive interventions (AIs) are increasingly popular in the behavioral sciences. An AI is a sequence of decision rules that specify for whom and under what conditions different intervention options should be offered, in order to address the changing needs of individuals as they progress over time. The sequential, multiple assignment, randomized trial (SMART) is a novel trial design that was developed to aid in empirically constructing effective AIs. The sequential randomizations in a SMART often yield multiple AIs that are embedded in the trial by design. Many SMARTs are motivated by scientific questions pertaining to the comparison of such embedded AIs. Existing data analytic methods and sample size planning resources for SMARTs are suitable only for superiority testing, namely for testing whether one embedded AI yields better primary outcomes on average than another. This calls for noninferiority/equivalence testing methods, because AIs are often motivated by the need to deliver support/care in a less costly or less burdensome manner, while still yielding benefits that are equivalent or noninferior to those produced by a more costly/burdensome standard of care. Here, we develop data-analytic methods and sample-size formulas for SMARTs testing the noninferiority or equivalence of one AI over another. Sample size and power considerations are discussed with supporting simulations, and online resources for sample size planning are provided. A simulated data analysis shows how to test noninferiority and equivalence hypotheses with SMART data. For illustration, we use an example from a SMART in the area of health psychology aiming to develop an AI for promoting weight loss among overweight/obese adults. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Q-Learning: Theory and Applications

Q-learning, originally an incremental algorithm for estimating an optimal decision strategy in an infinite-horizon decision problem, now refers to a general class of reinforcement learning methods widely used in statistics and artificial intelligence. In the context of personalized medicine, finite-horizon Q-learning is the workhorse for estimating optimal treatment strategies, known as treatment regimes. Infinite-horizon Q-learning is also increasingly relevant in the growing field of mobile health. In computer science, Q-learning methods have achieved remarkable performance in domains such as game-playing and robotics. In this article, we ( a) review the history of Q-learning in computer science and statistics, ( b) formalize finite-horizon Q-learning within the potential outcomes framework and discuss the inferential difficulties for which it is infamous, and ( c) review variants of infinite-horizon Q-learning and the exploration-exploitation problem, which arises in decision problems with a long time horizon. We close by discussing issues arising with the use of Q-learning in practice, including arguments for combining Q-learning with direct-search methods; sample size considerations for sequential, multiple assignment randomized trials; and possibilities for combining Q-learning with model-based methods.

Experimental design and sample size considerations in longitudinal magnetic resonance imaging-based biomarker detection for multiple sclerosis.

Several modeling approaches have been developed to quantify differences in multiple sclerosis lesion evolution on magnetic resonance imaging to identify the effect of treatment on disease progression. These studies have limited clinical applicability due to onerous scan frequency and lengthy study duration. Efficient methods are needed to reduce the required sample size, study duration, and sampling frequency in longitudinal magnetic resonance imaging studies. We develop a data-driven approach to identify parameters of study design for evaluation of longitudinal magnetic resonance imaging biomarkers of multiple sclerosis lesion evolution. Our design strategies are considerably shorter than those described in previous studies, thus having the potential to lower costs of clinical trials. From a dataset of 36 multiple sclerosis patients with at least six monthly magnetic resonance imagings, we extracted new lesions and performed principal component analysis to estimate a biomarker that recapitulated lesion recovery. We tested the effect of multiple sclerosis disease modifying therapy on the lesion evolution index in three experimental designs and calculated sample sizes needed to appropriately power studies. Our proposed methods can be used to calculate required sample size and scan frequency in observational studies of multiple sclerosis disease progression as well as in designing clinical trials to find effects of treatment on multiple sclerosis lesion evolution.

Sample Size Considerations: Basics for Preparing Clinical or Basic Research

Background: Sample size estimation is an important and integral part of a research protocol. While "how large a sample?" is a simple question, the answer is only meaningful within the context of the research question. Methods: Clear definitions of the variable of interest and target population parameters are key to estimating sample size. In turn the sample must be sized such that it can accurately detect the 'effect' of interest, adequately represent the target population and maintain maximum design efficiency. Four basic pieces of information are utilized in most sample estimation across all clinical research: significance level, power, magnitude of effect and variability of the variable of interest. Results/Discussion: Preliminary determination of these will greatly facilitate work with a biostatistician or a computer application to create a sample size estimation. While applications can support relatively simple sample size calculations consultation with a biostatistician is recommended.

A tale of two islands: evidence for impaired stress response and altered immune functions in an insular pit viper following ecological disturbance.

The frequency and intensity of ecological perturbations affecting wild animal populations is expected to increase in the future with animals facing numerous global threats. Seahorse Key is a continental island off mainland Florida that has historically been a major rookery for several species of waterbirds. As a result of an unknown disturbance, the entire rookery abandoned Seahorse Key in April 2015 and shifted nesting activities to nearby Snake Key, resulting in an influx of food resources in the form of fish carrion to resident Florida cottonmouth snakes (Agkistrodon conanti), while snakes on Seahorse Key experienced a drastic reduction in food resources. Our objective was to assess plasma corticosterone concentrations, corticosterone negative feedback using dexamethasone, blood glucose, body condition, packed cell volume, natural antibody agglutination, white blood cell counts and ratios and erythrocyte sedimentation rate to characterize the long-term effects of differential resource availability in these two snake populations 3years after this major ecological disturbance. We collected blood samples at three time points from cottonmouths on Seahorse Key (n = 6 individuals) and Snake Key (n = 13 individuals) in fall 2018. In due consideration of the small sample size, our study shows evidence that 3years after the shift in waterbird nesting Seahorse Key cottonmouths exhibit a dampened acute stress response and presumptive impaired innate immune functions relative to cottonmouths on Snake Key. These results highlight the context-dependent nature of biomarkers and implicate the significant decrease in food resources on Seahorse Key in altering hormonal stress responses and innate immune functions, possibly leading to unknown long-term downstream effects. This study assessed the response of a wild population of pit viper to ecological disturbance in situ with the aim to improve our understanding of how animals cope with such perturbations and improve our capacity to make informed decisions for conservation.

A fast FFT method for 3D pore-scale rock-typing of heterogeneous rock samples via Minkowski functionals and hydraulic attributes

The integration of numerical simulation and physical measurements, e.g. digital and conventional core analysis, requires the consideration of significant sample sizes when heterogeneous core samples are considered. In such case a hierarchical upscaling of properties may be achieved through a workflow of partitioning the sample into homogeneous regions followed by characterization of these homogeneous regions and upscaling of properties. Examples of such heterogeneities are e.g. fine laminations in core samples or different micro-porosity types as consequence of source rock components and diagenesis. In this work we utilize regional measures based on the Minkowski functionals as well as local saturation information derived through a morphological capillary drainage transform as a basis for such a classification/partitioning. An important consideration is the size of the measurement elements utilized, which could be considerable in the case of larger heterogeneities; in such case the calculation of the regional measures can be computationally very expensive. Here we introduce an FFT approach to calculate these measures locally, utilizing their additivity. The algorithms are compared against direct summation techniques and shift-overlap approaches for a selection of different averaging supports to illustrate their speed and practical applicability. We consider a range of artificial Boolean models to illustrate the effect of including hydraulic information on the resulting classifications scheme. This allows the determination of bias, since for these model systems local classes are known ab-initio. The classification framework is tested by comparing to the known initial micro-structure distribution and relative bias quantified in terms of choice of averaging elements (size and shape). Importantly, depending on the actual morphological transition between micro-type partitions, partitions including hydraulic attributes differ from pure morphological partitions with applications to electrofacies and hydraulic unit definitions.

A proposed standardized approach to studying attrition in pediatric weight management

Pediatric obesity is a major public health issue. Lifestyle and behavioral interventions are the foundation of pediatric weight management; however, intervention effectiveness is compromised when families (children, youth, and/or parent[s]) discontinue care prematurely. Intervention attrition minimizes the potential health benefits derived from interventions, results in inefficient use of health services resources, and can magnify health disparities. Most attrition research in pediatric weight management has been descriptive, highlighting the need to advance the field, both academically and clinically. Herein, we propose a standard approach to studying attrition in pediatric weight management interventions to enhance our understanding, elevate the quality of research, enable study-to-study comparisons, and inform strategies designed to mitigate its impact. We focus on three issues. First, “Conceptualization and operationalization,” whereby the processes underlying attrition from interventions should be decomposed into clinically important phases that are defined based on intervention characteristics. Relatedly, theoretically relevant variables should be identified with different mechanisms driving attrition in each phase. We propose a matrix of attrition, a tool designed to delineate the relevant stages of attrition and associated variables of analytical value. Second, “Pre-study” underscores the value of developing a plan to study attrition a priori rather than post hoc, including variable and sample size considerations, which broadens the range and quality of analysis. Finally, “Post-study” emphasizes comprehensive reporting of attrition, outlines typical comparisons of analytical interest, and statistical techniques used to handle missing data. Implications for clinical practice in pediatric weight management are discussed.Clinical Trial Registration: Not applicable.

Effectiveness of Daratumumab in Combination with Lenalidomide and Dexamethasone (DRd) Vs. Common Standard-of-Care Regimens in Patients with Non-Transplant Newly Diagnosed Multiple Myeloma (NDMM)

Introduction: Daratumumab, a CD38-directed monoclonal antibody, in combination with lenalidomide and dexamethasone (D-Rd), recently received FDA approval for the treatment of patients with newly diagnosed multiple myeloma (NDMM) ineligible for autologous stem cell transplantation (ASCT). FDA approval was based on results of the prespecified interim analysis of a pivotal phase III trial (MAIA) in which D-Rd was compared to Rd among patients with NDMM who were ineligible for ASCT. In the MAIA trial, D-Rd significantly reduced the risk of progression or death compared to Rd. However, no trials have compared D-Rd to other standard-of-care (SOC) regimens. The present study compared progression-free survival (PFS) of D-Rd from the MAIA trial database to common SOC regimens from the Flatiron Health EHR Database, a de-identified nationwide US database of records primarily from community-based oncology practices. Methods: Patient-level data were obtained from the MAIA trial and the Flatiron database. Patients in the MAIA trial were enrolled between March 2015 and January 2017. To be included in MAIA, patients had to be deemed ineligible for high-dose chemotherapy with ASCT due to either age (≥65 years) or presence of comorbidities, have Eastern Cooperative Oncology Group performance status (ECOG PS) ≤2, creatinine clearance ≥30mL/min, and adequate bone marrow reserve. In the Flatiron Database, patients had to be diagnosed with MM between January 2011 and April 2019, age ≥65 years at initiation of first line treatment (LOT1), could not have received stem cell transplant as part of LOT1, and met MAIA inclusion/exclusion criteria. Regimens received by fewer than 10% of patients in Flatiron were excluded due to sample size and statistical power considerations. Bortezomib-lenalidomide-dexamethasone (VRd), bortezomib-dexamethasone (Vd), and Rd were the most commonly used regimens in non-transplant NDMM patients, representing 67% of regimens used in this setting. Multiple imputation by chained equations was used to address missing data in Flatiron. Eligible Flatiron patients treated with VRd, Vd, or Rd as LOT1 were reweighted to resemble the MAIA trial population on measured baseline characteristics using propensity-score (PS) weighting. An anchored adjusted indirect treatment comparison (ITC) was performed to compare D-Rd to VRd and D-Rd to Vd, using a stratified Cox proportional hazards regression model with Rd as the common anchor across both data sources. The model calculates hazard ratios (HRs) for direct comparisons within MAIA (D-Rd vs Rd) and within Flatiron (VRd vs. Rd, Vd vs. Rd) to estimate the HRs indirectly for D-Rd vs. VRd and D-Rd vs. Vd. Results: After application of MAIA inclusion criteria and PS-reweighting, the Flatiron VRd, Rd, and Vd patients resembled the MAIA trial population on baseline patient demographics and clinical characteristics (Table 1). D-Rd statistically significantly reduced the risk of progression or death compared to Rd (HR=0.55; 95% CI, 0.41-0.72) based on the direct comparison with D-Rd using the MAIA trial data; D-Rd statistically significantly reduced the risk of progression or death compared to VRd (HR=0.66; 95% CI, 0.46-0.95) and Vd (HR=0.47; 95% CI, 0.33-0.67) based on the anchored ITC (Figure 1). Sensitivity analyses will be performed and updated analysis using longer follow-up data from the MAIA study will be presented at the meeting. Conclusion: This analysis demonstrated that D-Rd reduced the risk of progression or death compared to other SOCs VRd and Vd for patients with NDMM who are transplant-ineligible. In the absence of head-to-head trials, this type of patient-level analysis provides important information that can help optimize treatment for patients with NDMM who are transplant-ineligible. Disclosures Durie: Amgen, Celgene, Johnson & Johnson, and Takeda: Consultancy. Kumar:Takeda: Research Funding; Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding. Usmani:Amgen, Celgene, Janssen, Sanofi, Takeda: Speakers Bureau; Amgen Array Biopharma, Bristol-Myers Squibb, Celgene, Janssen, Merck, Pharmacyclics, Sanofi, Takeda: Other: Research Grant; Amgen, Bristol-Myers Squibb, Celgene, Janssen, Merck, SkylineDX, Takeda: Other: Consultant/Advisor. Ammann:Janssen: Employment, Equity Ownership. Lam:Janssen: Employment, Equity Ownership. Kobos:Janssen: Employment. Maiese:Janssen: Employment, Equity Ownership. Facon:Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Janssen: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees.

A systematic review of prediction models used in tuberculosis contact tracing

Abstract Background Contact tracing forms a key part of tuberculosis (TB) control in high-income, low-incidence settings. It aims to reduce morbidity, mortality and onward transmission of TB. Contact tracing is a complex and resource intensive intervention. Risk assessment of contacts is needed to ensure appropriate allocation of resources and greatest possible impact. Current prioritisation procedures are based on expert opinion and consensus. Prognostic prediction models offer a way to synthesise evidence about this decision. Methods We searched Medline, Embase, BNI, CINAHL, HMIC, and the Cochrane Library for peer reviewed publications in English about TB contact tracing prediction models. Studies were included if there was statistical combination of predictors. No date, age or other restrictions were applied. Study selection was carried out by two independent reviewers. Data were extracted using the CHARMS checklist and studies evaluated for risk of bias using PROBAST. Results Five reports were selected from a total of 16,585 non-identical returns. Each study was carried out in demographically distinct settings (Peru, USA, France, Taiwan). The choice and definition of outcomes and predictors varied. All the models included external validation and some included internal validation. Calibration and discrimination measures were variably reported. The models were at high risk of bias due to challenges in defining TB disease and due to statistical approaches taken: there was poor reporting of sample size considerations, universal use of univariable analysis to select predictors, and dichotomisation of data. There were some concerns about applicability due to differing populations and diagnostic approaches. None of the models included social risk factors. Conclusions The use of existing models is problematic. There are constraints upon resources which means that contact tracing needs to be carried out efficiently. A robust prediction model is urgently needed to achieve this. Key messages Contact tracing for tuberculosis would benefit from more robust prioritisation tools to save resources and increase impact. Existing prognostic prediction models are at high risk of bias and there are concerns about applicability in high-income, low-incidence settings.

How the public, and scientists, perceive advancement of knowledge from conflicting study results

AbstractScience often advances through disagreement among scientists and the studies they produce. For members of the public, however, conflicting results from scientific studies may trigger a sense of uncertainty that in turn leads to a feeling that nothing new has been learned from those studies. In several scenario studies, participants read about pairs of highly similar scientific studies with results that either agreed or disagreed, and were asked, “When we take the results of these two studies together, do we now know more, less, or the same as we did before about (the study topic)?” We find that over half of participants do not feel that “we know more” as the result of the two new studies when the second study fails to replicate the first. When the two study results strongly conflict (e.g., one finds a positive and the other a negative association between two variables), a non-trivial proportion of participants actually say that “we know less” than we did before. Such a sentiment arguably violates normative principles of statistical and scientific inference positing that new study findings can never reduce our level of knowledge (and that only completely uninformative studies can leave our level of knowledge unchanged). Drawing attention to possible moderating variables, or to sample size considerations, did not influence people’s perceptions of knowledge advancement. Scientist members of the American Academy of Arts and Sciences, when presented with the same scenarios, were less inclined to say that nothing new is learned from conflicting study results.

Assembly of the LongSHOT cohort: public record linkage on a grand scale

BackgroundVirtually all existing evidence linking access to firearms to elevated risks of mortality and morbidity comes from ecological and case–control studies. To improve understanding of the health risks and benefits...

Commentary on “To replicate or not to replicate, or when did we start to ignore the concept of statistical power?” by Meyners, Carr and Hasted

Studies should be planned with due attention to power and sample size considerations. Abandoning power computations, including analyses of the utility of replications, is a sure way to reduce the likelihood that an experiment yields insights. This commentary describes how screening for effects with low power leads to biased effect estimates and detrimentally high rates of false findings, and why aiming for, say, 90% power is not enough to ensure that power is on average 90%. It is also suggested that scenario-based evaluations of operating characteristics can expose the consequences of power policies. These discussions show that we are easily deceived by low powered studies and that power considerations are crucial for meaningful scientific experimentation.

Sample size considerations for comparing dynamic treatment regimens in a sequential multiple-assignment randomized trial with a continuous longitudinal outcome

Clinicians and researchers alike are increasingly interested in how best to personalize interventions. A dynamic treatment regimen is a sequence of prespecified decision rules which can be used to guide the delivery of a sequence of treatments or interventions that is tailored to the changing needs of the individual. The sequential multiple-assignment randomized trial is a research tool which allows for the construction of effective dynamic treatment regimens. We derive easy-to-use formulae for computing the total sample size for three common two-stage sequential multiple-assignment randomized trial designs in which the primary aim is to compare mean end-of-study outcomes for two embedded dynamic treatment regimens which recommend different first-stage treatments. The formulae are derived in the context of a regression model which leverages information from a longitudinal outcome collected over the entire study. We show that the sample size formula for a sequential multiple-assignment randomized trial can be written as the product of the sample size formula for a standard two-arm randomized trial, a deflation factor that accounts for the increased statistical efficiency resulting from a longitudinal analysis, and an inflation factor that accounts for the design of a sequential multiple-assignment randomized trial. The sequential multiple-assignment randomized trial design inflation factor is typically a function of the anticipated probability of response to first-stage treatment. We review modeling and estimation for dynamic treatment regimen effect analyses using a longitudinal outcome from a sequential multiple-assignment randomized trial, as well as the estimation of standard errors. We also present estimators for the covariance matrix for a variety of common working correlation structures. Methods are motivated using the ENGAGE study, a sequential multiple-assignment randomized trial aimed at developing a dynamic treatment regimen for increasing motivation to attend treatments among alcohol- and cocaine-dependent patients.

Sample Size Considerations for a Phase III Clinical Trial with Diluted Treatment Effect

In clinical development of new drugs, investment increases over time—Phase III investment can be up to hundreds of millions of dollars. However, some drugs failed because the treatment effect in Phase III is diluted from Phase II. When this is the case, a few strategies can be contemplated by the project team develops this new drug. One is to increase sample size based on the uniform and truncated normal prior distribution. Another is to design the Phase III trials using the average success probability (ASP) approach. Finally, the numerical simulations are carried out to calculate the sample size for the Phase III clinical trial with diluted treatment effect.

Repeatability of Quantitative Brown Adipose Tissue Imaging Metrics on Positron Emission Tomography with 18F-Fluorodeoxyglucose in Humans

Brown adipose tissue (BAT) is a promising target for anti-obesity interventions. This prospective test-retest study assessed the repeatability of several important quantitative BAT metrics. After cold activation, 24 subjects underwent positron emission tomography (PET)/computed tomography (CT) and PET/magnetic resonance imaging (MRI), utilizing 18F-fluorodeoxyglucose. Repeat imaging occurred within 14days per an identical protocol. BAT volumes were strongly correlated between sessions for PET/CT (intraclass correlation coefficient [ICC], 0.85) and PET/MRI (ICC, 0.82). BAT maximum lean-body-mass-adjusted standardized uptake values (SULmax) were also strongly correlated between sessions for both PET/CT (ICC, 0.74) and PET/MRI (ICC, 0.83). Much longitudinal variability in BAT metrics was likely due to biological factors intrinsic toBAT, whole-body metabolic fluctuations, or temporal differences in cold-activation efficacy, rather than imaging factors. Future studies utilizing these imaging metrics to track the response BATto interventions should incorporate this variation into sample-size considerations and response criteria.