The pursuit of balance: An overview of covariate-adaptive randomization techniques in clinical trials

Toward Precision in Critical Care Research: Methods for Observational and Interventional Studies

Critical care trials evaluate the effect of interventions in patients with diverse personal histories and causes of illness, often under the umbrella of heterogeneous clinical syndromes, such as sepsis or acute respiratory distress syndrome. Given this variation, it is reasonable to expect that the effect of treatment on outcomes may differ for individuals with variable characteristics. However, in randomized controlled trials, efficacy is typically assessed by the average treatment effect (ATE), which quantifies the average effect of the intervention on the outcome in the study population. Importantly, the ATE may hide variations of the treatment's effect on a clinical outcome across levels of patient characteristics, which may erroneously lead to the conclusion that an intervention does not work overall when it may in fact benefit certain patients. In this review, we describe methodological approaches for assessing heterogeneity of treatment effect (HTE), including expert-derived subgrouping, data-driven subgrouping, baseline risk modeling, treatment effect modeling, and individual treatment rule estimation. Next, we outline how insights from HTE analyses can be incorporated into the design of clinical trials. Finally, we propose a research agenda for advancing the field and bringing HTE approaches to the bedside.

Adaptive Randomization Method to Prevent Extreme Instances of Group Size and Covariate Imbalance in Stroke Trials

BACKGROUND

A recent review of randomization methods used in large multicenter clinical trials within the National Institutes of Health Stroke Trials Network identified preservation of treatment allocation randomness, achievement of the desired group size balance between treatment groups, achievement of baseline covariate balance, and ease of implementation in practice as critical properties required for optimal randomization designs. Common-scale minimal sufficient balance (CS-MSB) adaptive randomization effectively controls for covariate imbalance between treatment groups while preserving allocation randomness but does not balance group sizes. This study extends the CS-MSB adaptive randomization method to achieve both group size and covariate balance while preserving allocation randomness in hyperacute stroke trials.

METHODS

A full factorial in silico simulation study evaluated the performance of the proposed new CSSize-MSB adaptive randomization method in achieving group size balance, covariate balance, and allocation randomness compared with the original CS-MSB method. Data from 4 existing hyperacute stroke trials were used to investigate the performance of CSSize-MSB for a range of sample sizes and covariate numbers and types. A discrete-event simulation model created with AnyLogic was used to dynamically visualize the decision logic of the CSSize-MSB randomization process for communication with clinicians.

RESULTS

The proposed new CSSize-MSB algorithm uniformly outperformed the CS-MSB algorithm in controlling for group size imbalance while maintaining comparable levels of covariate balance and allocation randomness in hyperacute stroke trials. This improvement was consistent across a distribution of simulated trials with varying levels of imbalance but was increasingly pronounced for trials with extreme cases of imbalance. The results were consistent across a range of trial data sets of different sizes and covariate numbers and types.

CONCLUSIONS

The proposed adaptive CSSize-MSB algorithm successfully controls for group size imbalance in hyperacute stroke trials under various settings, and its logic can be readily explained to clinicians using dynamic visualization.

Effects of Silybum marianum, Pueraria lobate, combined with Salvia miltiorrhiza tablets on non-alcoholic fatty liver disease in adults: A triple-blind, randomized, placebo-controlled clinical trial

BACKGROUND & AIMS

Several medicinal plant extracts have demonstrated hepatoprotective effects. However, data are scarce regarding their combined effects on non-alcoholic fatty liver disease (NAFLD). This study aimed to investigate the effects of tablets containing Silybum marianum, Pueraria lobata, and Salvia miltiorrhiza (SPS) on NAFLD progression in Chinese adults.

METHODS

In this randomized, triple-blind, placebo-controlled clinical trial, 121 NAFLD patients (60 female and 61 male), diagnosed via magnetic resonance imaging (MRI) and aged 18-65 years, were enrolled. Participants were randomly allocated to receive SPS tablets (n = 60; three tablets per dose, twice daily) or placebo (n = 61) for 24 weeks. Each SPS tablet contained approximately 23.0 mg of silybin, 11.4 mg of puerarin, and 10.9 mg of salvianolic acid. There were no differences in appearance, taste and odour between the SPS tablets and placebo manufactured by BYHEALTH Co., LTD (Guangzhou, China). The primary endpoints were changes in the liver fat content (LFC) and steatosis grade from baseline to 24 weeks. Secondary outcomes included changes in biomarkers/scores of liver fibrosis and steatosis, oxidative stress, inflammatory cytokines, alcohol metabolism, and glucose metabolism.

RESULTS

A total of 112 participants completed the research. The intention-to-treat results showed a trend toward reduction in both absolute LFC (-0.52%) and percentage of LFC (-4.57%) in the SPS group compared to the placebo group after 24 weeks, but these changes didn't reach statistical significance (p > 0.05). The SPS intervention (vs. placebo) significantly decreased hypersensitive C-reactive protein level (-6.76%) and increased aldehyde dehydrogenase activity (+18.1%) at 24 weeks post-intervention (all p < 0.05). Per-protocol analysis further supported these effects. This trial is registered at Clinical Trials.gov (NCT05076058).

CONCLUSION

SPS supplementation may have potential benefits in improving NAFLD, but further larger-scale trials are necessary to confirm these findings.

Categorisation of continuous covariates for stratified randomisation: How should we adjust?

To obtain valid inference following stratified randomisation, treatment effects should be estimated with adjustment for stratification variables. Stratification sometimes requires categorisation of a continuous prognostic variable (eg, age), which raises the question: should adjustment be based on randomisation categories or underlying continuous values? In practice, adjustment for randomisation categories is more common. We reviewed trials published in general medical journals and found none of the 32 trials that stratified randomisation based on a continuous variable adjusted for continuous values in the primary analysis. Using data simulation, this article evaluates the performance of different adjustment strategies for continuous and binary outcomes where the covariate-outcome relationship (via the link function) was either linear or non-linear. Given the utility of covariate adjustment for addressing missing data, we also considered settings with complete or missing outcome data. Analysis methods included linear or logistic regression with no adjustment for the stratification variable, adjustment for randomisation categories, or adjustment for continuous values assuming a linear covariate-outcome relationship or allowing for non-linearity using fractional polynomials or restricted cubic splines. Unadjusted analysis performed poorly throughout. Adjustment approaches that misspecified the underlying covariate-outcome relationship were less powerful and, alarmingly, biased in settings where the stratification variable predicted missing outcome data. Adjustment for randomisation categories tends to involve the highest degree of misspecification, and so should be avoided in practice. To guard against misspecification, we recommend use of flexible approaches such as fractional polynomials and restricted cubic splines when adjusting for continuous stratification variables in randomised trials.

Mathematical programming tools for randomization purposes in small two-arm clinical trials: A case study with real data

Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate-adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.

Sequential covariate-adjusted randomization via hierarchically minimizing Mahalanobis distance and marginal imbalance

In comparative studies, covariate balance and sequential allocation schemes have attracted growing academic interest. Although many theoretically justified adaptive randomization methods achieve the covariate balance, they often allocate patients in pairs or groups. To better meet the practical requirements where the clinicians cannot wait for other participants to assign the current patient for some economic or ethical reasons, we propose a method that randomizes patients individually and sequentially. The proposed method conceptually separates the covariate imbalance, measured by the newly proposed modified Mahalanobis distance, and the marginal imbalance, that is the sample size difference between the 2 groups, and it minimizes them with an explicit priority order. Compared with the existing sequential randomization methods, the proposed method achieves the best possible covariate balance while maintaining the marginal balance directly, offering us more control of the randomization process. We demonstrate the superior performance of the proposed method through a wide range of simulation studies and real data analysis, and also establish theoretical guarantees for the proposed method in terms of both the convergence of the imbalance measure and the subsequent treatment effect estimation.

A Review on the Feasibility and Efficacy of Home-Based Cognitive Remediation in People with Multiple Sclerosis

Cognitive impairment affects 34-65% of People with Multiple Sclerosis (PwMS), significantly impacting their quality of life. Clinicians routinely address cognitive deficits with in-clinic neuro-behavioural interventions, but accessibility issues exist. Given these challenges, coupled with the lifelong need for continuous assistance in PwMS, researchers have underscored the advantageous role of telerehabilitation in addressing these requirements. Nonetheless, the feasibility and efficacy of home-based cognitive remediation remain to be firmly established. In this narrative review, we aimed to investigate the feasibility and efficacy of digital telerehabilitation for cognition in PwMS. Thirteen relevant studies were identified and carefully assessed. Regarding the feasibility of cognitive telerehabilitation, evidence shows adherence rates are generally good, although, surprisingly, not all studies reported measures of compliance with the cognitive training explored. Considering the efficacy of rehabilitative techniques on cognitive performance in PwMS, findings are generally inconsistent, with only one study reporting uniformly positive results. A range of methodological limitations are reported as potential factors contributing to the variable results. Future research must address these challenges, as more rigorous studies are required to draw definitive conclusions regarding the efficacy of home-based cognitive remediation in PwMS. Researchers must prioritise identifying optimal intervention approaches and exploring the long-term effects of telerehabilitation.

Protocol for fever control using external cooling in mechanically ventilated patients with septic shock: SEPSISCOOL II randomised controlled trial

INTRODUCTION

Fever treatment is commonly applied in patients with sepsis but its impact on survival remains undetermined. Patients with respiratory and haemodynamic failure are at the highest risk for not tolerating the metabolic cost of fever. However, fever can help to control infection. Treating fever with paracetamol has been shown to be less effective than cooling. In the SEPSISCOOL pilot study, active fever control by external cooling improved organ failure recovery and early survival. The main objective of this confirmatory trial is to assess whether fever control at normothermia can improve the evolution of organ failure and mortality at day 60 of febrile patients with septic shock. This study will compare two strategies within the first 48 hours of septic shock: treatment of fever with cooling or no treatment of fever.

METHODS AND ANALYSIS

SEPSISCOOL II is a pragmatic, investigator-initiated, adaptive, multicentre, open-label, randomised controlled, superiority trial in patients admitted to the intensive care unit with febrile septic shock. After stratification based on the acute respiratory distress syndrome status, patients will be randomised between two arms: (1) cooling and (2) no cooling. The primary endpoint is mortality at day 60 after randomisation. The secondary endpoints include the evolution of organ failure, early mortality and tolerance. The target sample size is 820 patients.

ETHICS AND DISSEMINATION

The study is funded by the French health ministry and was approved by the ethics committee CPP Nord Ouest II (Amiens, France). The results will be submitted for publication in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04494074.

Comparison of Pocock and Simon's covariate-adaptive randomization procedures in clinical trials

When multiple influential covariates need to be balanced during a clinical trial, stratified blocked randomization and covariate-adaptive randomization procedures are frequently used in trials to prevent bias and enhance the validity of data analysis results. The latter approach is increasingly used in practice for a study with multiple covariates and limited sample sizes. Among a group of these approaches, the covariate-adaptive procedures proposed by Pocock and Simon are straightforward to be utilized in practice. We aim to investigate the optimal design parameters for the patient treatment assignment probability of their developed three methods. In addition, we seek to answer the question related to the randomization performance when additional covariates are added to the existing randomization procedure. We conducted extensive simulation studies to address these practically important questions.

The specially designed nudging tableware promotes healthy food choices: Evidence from a randomized crossover trial in normal-weight young adults

PURPOSE

To evaluate the effects of the specially designed nudging tableware, including a plate and bowl, on individual food choices in normal-weight young adults and preliminarily explore its mechanisms. We hypothesized that the toolset could increase the choice of vegetables and decrease that of rice.

METHODS

A randomized, single-blind, two-period crossover trial was carried out among 40 normal-weight university students in China. All subjects completed two buffets separated by an interval of one week, wearing the eye tracker. Vegetable choice, evaluated through the proportion of vegetables, was the primary outcome, and the weight of vegetables and rice were the secondary outcomes. The mechanisms of the decision-making process were preliminarily explored through eye tracking.

RESULTS

The usage of the nudging tableware significantly increased the proportion of vegetables and decreased the amount of rice taken (P<0.05), while insignificantly increased the weight of vegetables (P = 0.079). Eye tracking shows that the nudging plate significantly prolonged the food-choosing process and fixation duration on vegetables (P<0.05), and the latter was positively correlated to the increased quantity of vegetables while using the nudging plate (r = 0.493, P<0.01).

CONCLUSION

The specially designed nudging tableware might be an effective and practical tool to promote the choice of less rice and more vegetables. Mechanisms behind this change might include automatic and unconscious processes with the inconspicuously smaller capacity of the bowl and larger portion size of the vegetable segment, and increased attention triggered by the vegetable patterns and larger green underpainting.

Accuracy and precision of fixed and random effects in meta-analyses of randomized control trials for continuous outcomes

Meta-analyses of treatment effects in randomized control trials are often faced with the problem of missing information required to calculate effect sizes and their sampling variances. Particularly, correlations between pre- and posttest scores are frequently not available. As an ad-hoc solution, researchers impute a constant value for the missing correlation. As an alternative, we propose adopting a multivariate meta-regression approach that models independent group effect sizes and accounts for the dependency structure using robust variance estimation or three-level modeling. A comprehensive simulation study mimicking realistic conditions of meta-analyses in clinical and educational psychology suggested that imputing a fixed correlation 0.8 or adopting a multivariate meta-regression with robust variance estimation work well for estimating the pooled effect but lead to slightly distorted between-study heterogeneity estimates. In contrast, three-level meta-regressions resulted in largely unbiased fixed effects but more inconsistent prediction intervals. Based on these results recommendations for meta-analytic practice and future meta-analytic developments are provided.

Network and covariate adjusted response-adaptive design for binary response

Randomization is a distinguishing feature of clinical trials for unbiased assessment of treatment efficacy. With a growing demand for more flexible and efficient randomization schemes and motivated by the idea of adaptive design, in this article we propose the network and covariate adjusted response-adaptive (NCARA) design that can concurrently manage three challenges: (1) maximizing benefits of a trial by assigning more patients to the superior treatment group randomly; (2) balancing social network ties across treatment arms to eliminate potential network interference; and (3) ensuring balance of important covariates, such as age, gender, and other potential confounders. We conduct simulation with different network structures and a variety of parameter settings. It is observed that the NCARA design outperforms four alternative randomization designs in solving the above-mentioned problems and has comparable power and type I error for detecting true difference between treatment groups. In addition, we conduct real data analysis to implement the new design in two clinical trials. Compared to equal randomization (the original design utilized in the trials), the NCARA design slightly increases power, largely increases the percentage of patients assigned to the better-performing group, and significantly improves network and covariate balances. It is also noted that the advantages of the NCARA design are augmented when the sample size is small and the level of network interference is high. In summary, the proposed NCARA design assists researchers in conducting clinical trials with high-quality and high-efficiency.

Injectiondesign: web service of plate design with optimized stratified block randomization for modern GC/LC-MS-based sample preparation

BACKGROUND

Plate design is a necessary and time-consuming operation for GC/LC-MS-based sample preparation. The implementation of the inter-batch balancing algorithm and the intra-batch randomization algorithm can have a significant impact on the final results. For researchers without programming skills, a stable and efficient online service for plate design is necessary.

RESULTS

Here we describe InjectionDesign, a free online plate design service focused on GC/LC-MS-based multi-omics experiment design. It offers the ability to separate the position design from the sequence design, making the output more compatible with the requirements of a modern mass spectrometer-based laboratory. In addition, it has implemented an optimized block randomization algorithm, which can be better applied to sample stratification with block randomization for an unbalanced distribution. It is easy to use, with built-in support for common instrument models and quick export to a worksheet.

CONCLUSIONS

InjectionDesign is an open-source project based on Java. Researchers can get the source code for the project from Github: https://github.com/CSi-Studio/InjectionDesign . A free web service is also provided: http://www.injection.design .

Bayesian adaptive design for covariate-adaptive historical control information borrowing

Interest in incorporating historical data in the clinical trial has increased with the rising cost of conducting clinical trials. The intervention arm for the current trial often requires prospective data to assess a novel treatment, and thus borrowing historical control data commensurate in distribution to current control data is motivated in order to increase the allocation ratio to the current intervention arm. Existing historical control borrowing adaptive designs adjust allocation ratios based on the commensurability assessed through study-level summary statistics of the response agnostic of the distributions of the trial subject characteristics in the current and historical trials. This can lead to distributional imbalance of the current trial subject characteristics across the treatment arms as well as between current control data and borrowed historical control data. Such covariate imbalance may threaten the internal validity of the current trial by introducing confounding factors that affect study endpoints. In this article, we propose a Bayesian design which borrows and updates the treatment allocation ratios both covariate-adaptively and commensurate to covariate dependently assessed similarity between the current and historical control data. We employ covariate-dependent discrepancy parameters which are allowed to grow with the sample size and propose a regularized local regression procedure for the estimation of the parameters. The proposed design also permits the current and the historical controls to be similar to varying degree, depending on the subject level characteristics. We evaluate the proposed design extensively under the settings derived from two placebo-controlled randomized trials on vertebral fracture risk in post-menopausal women.

Regression-based multiple treatment effect estimation under covariate-adaptive randomization

Covariate-adaptive randomization methods are widely used in clinical trials to balance baseline covariates. Recent studies have shown the validity of using regression-based estimators for treatment effects without imposing functional form requirements on the true data generation model. These studies have had limitations in certain scenarios; for example, in the case of multiple treatment groups, these studies did not consider additional covariates or assumed that the allocation ratios were the same across strata. To address these limitations, we develop a stratum-common estimator and a stratum-specific estimator under multiple treatments. We derive the asymptotic behaviors of these estimators and propose consistent nonparametric estimators for asymptotic variances. To determine their efficiency, we compare the estimators with the stratified difference-in-means estimator as the benchmark. We find that the stratum-specific estimator guarantees efficiency gains, regardless of whether the allocation ratios across strata are the same or different. Our conclusions were also validated by simulation studies and a real clinical trial example.

Accuracy of generative artificial intelligence models in differential diagnoses of familial Mediterranean fever and deficiency of Interleukin-1 receptor antagonist

With the increasing development of artificial intelligence, large language models (LLMs) have been utilized to solve problems in natural language processing tasks. More recently, LLMs have shown unique potential in numerous applications within medicine but have been particularly investigated for their ability in clinical reasoning. Although the diagnostic accuracy of LLMs in forming differential diagnoses has been reviewed in general internal medicine applications, much is unknown in autoinflammatory disorders. From the nature of autoinflammatory diseases, forming a differential diagnosis is challenging due to the overlapping symptoms between disorders and even more difficult without genetic screening. In this work, the diagnostic accuracy of the Generative Pre-Trained Transformer Model-4 (GPT-4), GPT-3.5, and Large Language Model Meta AI (LLaMa) were evaluated in clinical vignettes of Deficiency of Interleukin-1 Receptor Antagonist (DIRA) and Familial Mediterranean Fever (FMF). We then compared these models to a control group including one internal medicine physician. It was found that GPT-4 did not significantly differ in correctly identifying DIRA and FMF patients compared to the internist. However, the physician maintained a significantly higher accuracy than GPT-3.5 and LLaMa 2 for either disease. Overall, we explore and discuss the unique potential of LLMs in diagnostics for autoimmune diseases.

Improving the mixed model for repeated measures to robustly increase precision in randomized trials

In randomized trials, repeated measures of the outcome are routinely collected. The mixed model for repeated measures (MMRM) leverages the information from these repeated outcome measures, and is often used for the primary analysis to estimate the average treatment effect at the primary endpoint. MMRM, however, can suffer from bias and precision loss when it models intermediate outcomes incorrectly, and hence fails to use the post-randomization information harmlessly. This paper proposes an extension of the commonly used MMRM, called IMMRM, that improves the robustness and optimizes the precision gain from covariate adjustment, stratified randomization, and adjustment for intermediate outcome measures. Under regularity conditions and missing completely at random, we prove that the IMMRM estimator for the average treatment effect is robust to arbitrary model misspecification and is asymptotically equal or more precise than the analysis of covariance (ANCOVA) estimator and the MMRM estimator. Under missing at random, IMMRM is less likely to be misspecified than MMRM, and we demonstrate via simulation studies that IMMRM continues to have less bias and smaller variance. Our results are further supported by a re-analysis of a randomized trial for the treatment of diabetes.

Sequestration of imaging studies in MIDRC: stratified sampling to balance demographic characteristics of patients in a multi-institutional data commons

Purpose

The Medical Imaging and Data Resource Center (MIDRC) is a multi-institutional effort to accelerate medical imaging machine intelligence research and create a publicly available image repository/commons as well as a sequestered commons for performance evaluation and benchmarking of algorithms. After de-identification, approximately 80% of the medical images and associated metadata become part of the open commons and 20% are sequestered from the open commons. To ensure that both commons are representative of the population available, we introduced a stratified sampling method to balance the demographic characteristics across the two datasets.

Approach

Our method uses multi-dimensional stratified sampling where several demographic variables of interest are sequentially used to separate the data into individual strata, each representing a unique combination of variables. Within each resulting stratum, patients are assigned to the open or sequestered commons. This algorithm was used on an example dataset containing 5000 patients using the variables of race, age, sex at birth, ethnicity, COVID-19 status, and image modality and compared resulting demographic distributions to naïve random sampling of the dataset over 2000 independent trials.

Results

Resulting prevalence of each demographic variable matched the prevalence from the input dataset within one standard deviation. Mann-Whitney U test results supported the hypothesis that sequestration by stratified sampling provided more balanced subsets than naïve randomization, except for demographic subcategories with very low prevalence.

Conclusions

The developed multi-dimensional stratified sampling algorithm can partition a large dataset while maintaining balance across several variables, superior to the balance achieved from naïve randomization.

Comparing analytical strategies for balancing site-level characteristics in stepped-wedge cluster randomized trials: a simulation study

BACKGROUND

Stepped-wedge cluster randomized trials (SWCRTs) are a type of cluster-randomized trial in which clusters are randomized to cross-over to the active intervention sequentially at regular intervals during the study period. For SWCRTs, sequential imbalances of cluster-level characteristics across the random sequence of clusters may lead to biased estimation. Our study aims to examine the effects of balancing cluster-level characteristics in SWCRTs.

METHODS

To quantify the level of cluster-level imbalance, a novel imbalance index was developed based on the Spearman correlation and rank regression of the cluster-level characteristic with the cross-over timepoints. A simulation study was conducted to assess the impact of sequential cluster-level imbalances across different scenarios varying the: number of sites (clusters), sample size, number of cross-over timepoints, site-level intra-cluster correlation coefficient (ICC), and effect sizes. SWCRTs assumed either an immediate "constant" treatment effect, or a gradual "learning" treatment effect which increases over time after crossing over to the active intervention. Key performance metrics included the relative root mean square error (RRMSE) and relative mean bias.

RESULTS

Fully-balanced designs almost always had the highest efficiency, as measured by the RRMSE, regardless of the number of sites, ICC, effect size, or sample sizes at each time for SWCRTs with learning effect. A consistent decreasing trend of efficiency was observed by increasing RRMSE as imbalance increased. For example, for a 12-site study with 20 participants per site/timepoint and ICC of 0.10, between the most balanced and least balanced designs, the RRMSE efficiency loss ranged from 52.5% to 191.9%. In addition, the RRMSE was decreased for larger sample sizes, larger number of sites, smaller ICC, and larger effect sizes. The impact of pre-balancing diminished when there was no learning effect.

CONCLUSION

The impact of pre-balancing on preventing efficiency loss was easily observed when there was a learning effect. This suggests benefit of pre-balancing with respect to impacting factors of treatment effects.

Handling misclassified stratification variables in the analysis of randomised trials with continuous outcomes

Many trials use stratified randomisation, where participants are randomised within strata defined by one or more baseline covariates. While it is important to adjust for stratification variables in the analysis, the appropriate method of adjustment is unclear when stratification variables are affected by misclassification and hence some participants are randomised in the incorrect stratum. We conducted a simulation study to compare methods of adjusting for stratification variables affected by misclassification in the analysis of continuous outcomes when all or only some stratification errors are discovered, and when the treatment effect or treatment-by-covariate interaction effect is of interest. The data were analysed using linear regression with no adjustment, adjustment for the strata used to perform the randomisation (randomisation strata), adjustment for the strata if all errors are corrected (true strata), and adjustment for the strata after some errors are discovered and corrected (updated strata). The unadjusted model performed poorly in all settings. Adjusting for the true strata was optimal, while the relative performance of adjusting for the randomisation strata or the updated strata varied depending on the setting. As the true strata are unlikely to be known with certainty in practice, we recommend using the updated strata for adjustment and performing subgroup analyses, provided the discovery of errors is unlikely to depend on treatment group, as expected in blinded trials. Greater transparency is needed in the reporting of stratification errors and how they were addressed in the analysis.

An observational study on the effect of seasonal variation on peritoneal dialysis patients

Background: Seasonal variation has an impact on plants, wild animals, and also human beings. Data have shown seasonal variation has a significant impact on patients' fluid status, biochemistry results, and outcomes in hemodialysis populations. The relevant data on peritoneal dialysis is scant. Methods: This was a cross sectional study. All patients followed up in our center had a peritoneal equilibration test and PD adequacy test every 6 months. All the peritoneal equilibration test and PD adequacy test data were collected during December 2019 to November 2020. The monthly delivery information of the whole center was collected from 2015 to 2019. Results: There were 366 patients and 604 sets of peritoneal equilibration test and PD adequacy test results in the study. Plasma albumin and phosphate levels were higher in summer. The monthly average outdoor temperature was positively correlated with plasma albumin. There was no seasonal difference in peritoneal dialysis ultrafiltration or urine volume. The percentage of low glucose concentration (1.5%) usage was higher in summer and lower in winter. Conclusion: Plasma albumin and phosphate levels were higher in summer in PD patients. Weaker glucose peritoneal dialysis dialysate was more widely used in summer. Understanding the seasonal variation of peritoneal dialysis is helpful in individualized treatment.

Efficacy and safety of high-dose intramuscular vitamin D2 injection in type 2 diabetes mellitus with distal symmetric polyneuropathy combined with vitamin D insufficiency: study protocol for a multicenter, randomized, double-blinded, and placebo-controlled trial

Background

Distal symmetric polyneuropathy (DSPN) is the most common chronic complication of type 2 diabetes mellitus (T2DM). DSPN may lead to more serious complications, such as diabetic foot ulcer, amputation, and reduced life expectancy. Observational studies have suggested that vitamin D deficiency may be associated with the development of DSPN in T2DM. However, interventional studies have found that low-dose vitamin D supplementation does not significantly improve neuropathy in DSPN. This study aims to evaluate the efficacy and safety of intramuscular injection of high-dose vitamin D (HDVD) in T2DM with DSPN combined with vitamin D insufficiency.

Methods and analysis

We will conduct a multicenter, randomized, double-blinded, and placebo-controlled trial in four large hospitals. All eligible participants will be randomly assigned to either the vitamin D₂ supplement or placebo control group and injected intramuscularly monthly for 3 months. Additionally, anthropometric measurements and clinical data will be collected at baseline and 3 months. Adverse events will be collected at 1, 2, and 3 months. The primary outcome measure is the change in the mean Michigan Neuropathy Screening Instrument (MNSI) score at baseline and 3 months post-intervention. We will use the gold-standard liquid chromatography-tandem mass spectrometry method to distinguish between 25(OH)D₂ and 25(OH)D₃ levels. The MNSN score before the intervention will be used as a covariate to compare the changes between both groups before and after the intervention, and the analysis of covariance will be used to analyze the change in the MNSI score after HDVD supplementation.

Discussion

Glycemic control alone does not prevent the progression of DSPN in T2DM. Some studies have suggested that vitamin D may improve DSPN; however, the exact dose, method, and duration of vitamin D supplementation are unknown. Additionally, neuropathy repair requires HDVD supplementation to sustain adequate vitamin D levels. This once-a-month intramuscular method avoids daily medication; therefore, compliance is high. This study will be the first randomized controlled trial in China to analyze the efficacy and safety of HDVD supplementation for patients with T2DM and DSPN and will provide new ideas for pharmacological research and clinical treatment of diabetic neuropathy.

Clinical trial registration

https://www.chictr.org.cn/, identifier ChiCTR2200062266.

Automated detection of over- and under-dispersion in baseline tables in randomised controlled trials

Background: Papers describing the results of a randomised trial should include a baseline table that compares the characteristics of randomised groups. Researchers who fraudulently generate trials often unwittingly create baseline tables that are implausibly similar (under-dispersed) or have large differences between groups (over-dispersed). I aimed to create an automated algorithm to screen for under- and over-dispersion in the baseline tables of randomised trials. Methods: Using a cross-sectional study I examined 2,245 randomised controlled trials published in health and medical journals on PubMed Central. I estimated the probability that a trial's baseline summary statistics were under- or over-dispersed using a Bayesian model that examined the distribution of t-statistics for the between-group differences, and compared this with an expected distribution without dispersion. I used a simulation study to test the ability of the model to find under- or over-dispersion and compared its performance with an existing test of dispersion based on a uniform test of p-values. My model combined categorical and continuous summary statistics, whereas the uniform test used only continuous statistics. Results: The algorithm had a relatively good accuracy for extracting the data from baseline tables, matching well on the size of the tables and sample size. Using t-statistics in the Bayesian model out-performed the uniform test of p-values, which had many false positives for skewed, categorical and rounded data that were not under- or over-dispersed. For trials published on PubMed Central, some tables appeared under- or over-dispersed because they had an atypical presentation or had reporting errors. Some trials flagged as under-dispersed had groups with strikingly similar summary statistics. Conclusions: Automated screening for fraud of all submitted trials is challenging due to the widely varying presentation of baseline tables. The Bayesian model could be useful in targeted checks of suspected trials or authors.

Automated detection of over- and under-dispersion in baseline tables in randomised controlled trials

Background: Papers describing the results of a randomised trial should include a baseline table that compares the characteristics of randomised groups. Researchers who fraudulently generate trials often unwittingly create baseline tables that are implausibly similar (under-dispersed) or have large differences between groups (over-dispersed). I aimed to create an automated algorithm to screen for under- and over-dispersion in the baseline tables of randomised trials. Methods: Using a cross-sectional study I examined 2,245 randomised controlled trials published in health and medical journals on PubMed Central. I estimated the probability that a trial's baseline summary statistics were under- or over-dispersed using a Bayesian model that examined the distribution of t-statistics for the between-group differences, and compared this with an expected distribution without dispersion. I used a simulation study to test the ability of the model to find under- or over-dispersion and compared its performance with an existing test of dispersion based on a uniform test of p-values. My model combined categorical and continuous summary statistics, whereas the uniform test used only continuous statistics. Results: The algorithm had a relatively good accuracy for extracting the data from baseline tables, matching well on the size of the tables and sample size. Using t-statistics in the Bayesian model out-performed the uniform test of p-values, which had many false positives for skewed, categorical and rounded data that were not under- or over-dispersed. For trials published on PubMed Central, some tables appeared under- or over-dispersed because they had an atypical presentation or had reporting errors. Some trials flagged as under-dispersed had groups with strikingly similar summary statistics. Conclusions: Automated screening for fraud of all submitted trials is challenging due to the widely varying presentation of baseline tables. The Bayesian model could be useful in targeted checks of suspected trials or authors.

Precuneus magnetic stimulation for Alzheimer's disease: a randomized, sham-controlled trial

Repetitive transcranial magnetic stimulation (rTMS) is emerging as a non-invasive therapeutic strategy in the battle against Alzheimer's disease. Alzheimer's disease patients primarily show alterations of the default mode network for which the precuneus is a key node. Here, we hypothesized that targeting the precuneus with TMS represents a promising strategy to slow down cognitive and functional decline in Alzheimer's disease patients. We performed a randomized, double-blind, sham-controlled, phase 2, 24-week trial to determine the safety and efficacy of precuneus stimulation in patients with mild-to-moderate Alzheimer's disease. Fifty Alzheimer's disease patients were randomly assigned in a 1:1 ratio to either receive precuneus or sham rTMS (mean age 73.7 years; 52% female). The trial included a 24-week treatment, with a 2-week intensive course in which rTMS (or sham) was applied daily five times per week, followed by a 22-week maintenance phase in which stimulation was applied once weekly. The Clinical Dementia Rating Scale-Sum of Boxes was selected as the primary outcome measure, in which post-treatment scores were compared to baseline. Secondary outcomes included score changes in the Alzheimer's Disease Assessment Scale-Cognitive Subscale, Mini-Mental State Examination and Alzheimer's Disease Cooperative Study-Activities of Daily Living scale. Moreover, single-pulse TMS in combination with EEG was used to assess neurophysiological changes in precuneus cortical excitability and oscillatory activity. Our findings show that patients that received precuneus repetitive magnetic stimulation presented a stable performance of the Clinical Dementia Rating Scale-Sum of Boxes score, whereas patients treated with sham showed a worsening of their score. Compared with the sham stimulation, patients in the precuneus stimulation group also showed also significantly better performances for the secondary outcome measures, including the Alzheimer's Disease Assessment Scale-Cognitive Subscale, Mini-Mental State Examination and Alzheimer's Disease Cooperative Study-Activities of Daily Living scale. Neurophysiological results showed that precuneus cortical excitability remained unchanged after 24 weeks in the precuneus stimulation group, whereas it was significantly reduced in the sham group. Finally, we found an enhancement of local gamma oscillations in the group treated with precuneus stimulation but not in patients treated with sham. We conclude that 24 weeks of precuneus rTMS may slow down cognitive and functional decline in Alzheimer's disease. Repetitive TMS targeting the default mode network could represent a novel therapeutic approach in Alzheimer's disease patients.

The benefits of covariate adjustment for adaptive multi-arm designs

Covariate adjustment via a regression approach is known to increase the precision of statistical inference when fixed trial designs are employed in randomized controlled studies. When an adaptive multi-arm design is employed with the ability to select treatments, it is unclear how covariate adjustment affects various aspects of the study. Consider the design framework that relies on pre-specified treatment selection rule(s) and a combination test approach for hypothesis testing. It is our primary goal to evaluate the impact of covariate adjustment on adaptive multi-arm designs with treatment selection. Our secondary goal is to show how the Uniformly Minimum Variance Conditionally Unbiased Estimator can be extended to account for covariate adjustment analytically. We find that adjustment with different sets of covariates can lead to different treatment selection outcomes and hence probabilities of rejecting hypotheses. Nevertheless, we do not see any negative impact on the control of the familywise error rate when covariates are included in the analysis model. When adjusting for covariates that are moderately or highly correlated with the outcome, we see various benefits to the analysis of the design. Conversely, there is negligible impact when including covariates that are uncorrelated with the outcome. Overall, pre-specification of covariate adjustment is recommended for the analysis of adaptive multi-arm design with treatment selection. Having the statistical analysis plan in place prior to the interim and final analyses is crucial, especially when a non-collapsible measure of treatment effect is considered in the trial.

Regression analysis for covariate-adaptive randomization: A robust and efficient inference perspective

Linear regression is arguably the most fundamental statistical model; however, the validity of its use in randomized clinical trials, despite being common practice, has never been crystal clear, particularly when stratified or covariate-adaptive randomization is used. In this article, we investigate several of the most intuitive and commonly used regression models for estimating and inferring the treatment effect in randomized clinical trials. By allowing the regression model to be arbitrarily misspecified, we demonstrate that all these regression-based estimators robustly estimate the treatment effect, albeit with possibly different efficiency. We also propose consistent non-parametric variance estimators and compare their performances to those of the model-based variance estimators that are readily available in standard statistical software. Based on the results and taking into account both theoretical efficiency and practical feasibility, we make recommendations for the effective use of regression under various scenarios. For equal allocation, it suffices to use the regression adjustment for the stratum covariates and additional baseline covariates, if available, with the usual ordinary-least-squares variance estimator. For unequal allocation, regression with treatment-by-covariate interactions should be used, together with our proposed variance estimators. These recommendations apply to simple and stratified randomization, and minimization, among others. We hope this work helps to clarify and promote the usage of regression in randomized clinical trials.

Effects of a Gamified, Behavior Change Technique-Based Mobile App on Increasing Physical Activity and Reducing Anxiety in Adults With Autism Spectrum Disorder: Feasibility Randomized Controlled Trial

BACKGROUND

Physical activity (PA) has an impact on physical and mental health in neurotypical populations, and addressing these variables may improve the prevalent burden of anxiety in adults with autism spectrum disorder (ASD). Gamified mobile apps using behavior change techniques present a promising way of increasing PA and reducing sedentary time, thus reducing anxiety in adults with ASD.

OBJECTIVE

This study aimed to compare the effectiveness of a gamified and behavior change technique-based mobile app, PuzzleWalk, versus a commercially available app, Google Fit, on increasing PA and reducing sedentary time as an adjunct anxiety treatment for this population.

METHODS

A total of 24 adults with ASD were assigned to either the PuzzleWalk or Google Fit group for 5 weeks using a covariate-adaptive randomization design. PA and anxiety were assessed over 7 days at 3 different data collection periods (ie, baseline, intervention start, and intervention end) using triaxial accelerometers and the Beck Anxiety Inventory. Group differences in outcome variables were assessed using repeated-measures analysis of covariance, adjusting for age, sex, and BMI.

RESULTS

The findings indicated that the PuzzleWalk group spent a significantly longer amount of time on app use compared with the Google Fit group (F2,38=5.07; P=.01; partial η2=0.21), whereas anxiety was unfavorably associated with increases in light PA and decreases in sedentary time after intervention (all P<.05).

CONCLUSIONS

Further research is needed to clarify the determinants of physical and mental health and their interrelationship in adults with ASD to identify the factors that facilitate the use and adoption of mobile health technologies in these individuals. Despite these mixed results, the small changes in PA or anxiety may be clinically significant for adults with ASD.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05466617; https://clinicaltrials.gov/show/NCT05466617.

Multi-arm covariate-adaptive randomization

Simultaneously investigating multiple treatments in a single study achieves considerable efficiency in contrast to the traditional two-arm trials. Balancing treatment allocation for influential covariates has become increasingly important in today's clinical trials. The multi-arm covariate-adaptive randomized clinical trial is one of the most powerful tools to incorporate covariate information and multiple treatments in a single study. Pocock and Simon's procedure has been extended to the multi-arm case. However, the theoretical properties of multi-arm covariate-adaptive randomization have remained largely elusive for decades. In this paper, we propose a general framework for multi-arm covariate-adaptive designs which also includes the two-arm case, and establish the corresponding theory under widely satisfied conditions. The theoretical results provide new insights into the balance properties of covariate-adaptive randomization procedures and make foundations for most existing statistical inferences under two-arm covariate-adaptive randomization. Furthermore, these open a door to study the theoretical properties of statistical inferences for clinical trials based on multi-arm covariate-adaptive randomization procedures.

Lasso-adjusted treatment effect estimation under covariate-adaptive randomization

We consider the problem of estimating and inferring treatment effects in randomized experiments. In practice, stratified randomization, or more generally, covariate-adaptive randomization, is routinely used in the design stage to balance treatment allocations with respect to a few variables that are most relevant to the outcomes. Then, regression is performed in the analysis stage to adjust the remaining imbalances to yield more efficient treatment effect estimators. Building upon and unifying the recent results obtained for ordinary least squares adjusted estimators under covariate-adaptive randomization, this paper presents a general theory of regression adjustment that allows for model misspecification and the presence of a large number of baseline covariates. We exemplify the theory on two lasso-adjusted treatment effect estimators, both of which are optimal in their respective classes. In addition, nonparametric consistent variance estimators are proposed to facilitate valid inferences, which work irrespective of the specific randomization methods used. The robustness and improved efficiency of the proposed estimators are demonstrated through numerical studies.

Trial Designs for Integrating Novel Therapeutics into the Management of Intermediate-Stage Hepatocellular Carcinoma

Intermediate-stage hepatocellular carcinoma (HCC) consists of heterogeneous groups of patients in terms of tumor burden and organ function reserves. Although liver-directed therapy (LDT), including trans-catheter arterial chemoembolization, radiofrequency ablation or even surgical resection, is the recommended frontline treatment modality, intrahepatic and distant failures are common. The recent advances in systemic treatment, notably the introduction of immune checkpoint inhibitor (ICI)-based therapy, have significantly improved the objective tumor response rate, quality of response and overall survival in patients with recurrent and advanced HCC. Whether the combination of systemic treatment and LDT can further improve the outcome of patients with intermediate-stage HCC is currently being extensively evaluated. In this article, the recent clinical trials incorporating different ICI-based combinations with different LDT for intermediate-stage HCC were reviewed focusing on trial design issues, including patient selection, endpoint definition, and biomarker development. The strength and caveats of different combination strategies and novel biomarker development were discussed.

Internal Validity in Resistance Training Research: A Systematic Review

Ensuring internal validity is the key procedure when planning the study design. Numerous systematic reviews have demonstrated that considerations for internal validity do not receive adequate attention in the primary research in sport sciences. Therefore, the purpose of this study was to review methodological procedures in current literature where the effects of resistance training on strength, speed, and endurance performance in athletes were analyzed. A computer-based literature searches of SPORTDiscus, Scopus, Medline, and Web of Science was conducted. The internal validity of individual studies was assessed using the PEDro scale. Peer-reviewed studies were accepted only if they met all the following eligibility criteria: (a) healthy male and female athletes between the ages of 18-65 years; (b) training program based on resistance exercises; (c) training program lasted for at least 4 weeks or 12 training sessions, with at least two sessions per week; (d) the study reported maximum strength, speed, or endurance outcomes; and (e) systematic reviews, cohort studies, case-control studies, cross-sectional studies were excluded. Of the 6,516 articles identified, 133 studies were selected for rating by the PEDro scale. Sixty-eight percent of the included studies used random allocation to groups, but only one reported concealed allocation. Baseline data are presented in almost 69% of the studies. Thirty-eight percent of studies demonstrated adequate follow-up of participants. The plan to follow the intention-to-treat or stating that all participants received training intervention or control conditions as allocated were reported in only 1.5% of studies. The procedure of blinding of assessors was also satisfied in only 1.5% of the studies. The current study highlights the gaps in designing and reporting research in the field of strength and conditioning. Randomization, blinding of assessors, reporting of attrition, and intention-to-treat analysis should be more fully addressed to reduce threats to internal validity in primary research.

Study protocol of a randomised controlled trial for the effectiveness of a functional partial body weight support treadmill training (FPBWSTT) on motor and functional skills of children with ataxia

INTRODUCTION

A great heterogeneity characterises the paediatric population with ataxia, which has been studied poorly. The lack of postural control and coordination, in addition with features of the 'ataxic' gait are linked with functional limitations. Studies on physiotherapy interventions for children with ataxia are highly needed for identifying optimal training strategies for improving motor and functional related skills.

METHODS AND ANALYSIS

A stratified randomised control clinical trial of a 4-week functional partial body weight support treadmill training, (5 days/week 45 min/day) and 2-month follow-up period will be applied in children with ataxia, aged 8-18 years old with Gross Motor Function Classification System II-IV. Participants will be allocated to experimental group (intervention and usual care) or control group (usual care), using stratified randomisation process into two strata (progressive and non-progressive ataxia). Participants will be assessed at baseline, by the end of the 4-week period and by the end of a 2-month period as a follow-up measurement. Motor and functional skills will be assessed using the Gross Motor Function Measure-D and E, the Pediatric Balance Scale, the 10-meter walk test, the 6-minute walk test, the Scale for Assessment and Rating Ataxia, the timed up and go test and children's spatiotemporal gait features will be assessed through GaitSens software recording over a 2 min low treadmill gait speed, while three-dimensional gait analysis will be performed for kinetic and kinematic analysis of the lower limbs in all three levels of movement. Two-way mixed Analysis of Variance (ANOVA) with factors 'intervention' (between group) and 'time' (within group) will be used for the analysis of all parameters. Analysis of Covariance (ANCOVA) will be used in case of imbalance of baseline measurements. Statistical significance will be set at p<0.05 using the statistical package SPSS V.21.00.

ETHICS AND DISSEMINATION

University of West Attica (study's protocol: 14η/26-04-2021) and 'ATTIKON' General University Hospital of Athens (study's protocol: Γ ΠΑΙΔ, ΕΒΔ 149/20-3-2020). Trial results of the main trial will be submitted for publication in a peer-reviewed journal and/or international conference.

TRAIL REGISTRATION NUMBER

ISRCTN54463720.

Common scale minimal sufficient balance: An improved method for covariate-adaptive randomization based on the Wilcoxon-Mann-Whitney odds ratio statistic

Minimal sufficient balance (MSB) is a recently suggested method for adaptively controlling covariate imbalance in randomized controlled trials in a manner which reduces the impact on randomness of allocation over other approaches by only intervening when the imbalance is sufficiently significant. Despite its improvements, the approach is unable to consider the relative clinical importance or magnitude of imbalance in each covariate weight, and ignores any imbalance which is not statistically significant, even when these imbalances may collectively justify intervention. We propose the common scale MSB (CS‐MSB) method which addresses these limitations, and present simulation studies comparing our proposed method to MSB. We demonstrate that CS‐MSB requires less intervention than MSB to achieve the same level of covariate balance, and does not adversely impact either statistical power or Type‐I error.

Acupuncture combined with opioids for cancer pain: a pilot pragmatic randomized controlled trial

OBJECTIVE

Given the existing evidence for the analgesic effect of acupuncture, the current study aimed to assess whether acupuncture could be feasible and manageable as an adjunctive therapy for cancer pain in a real-world hospital setting.

METHODS

Thirty patients in an Oncology department with moderate or severe pain were recruited and randomized to an adjunctive acupuncture group or control group, who received pharmacotherapy for pain management without acupuncture. The duration of the treatment course was 1 week with a 2-week follow-up. In total, four acupuncture sessions were administered, on days 1/2/4/6 of the trial. Pain intensity was measured using a numerical rating scale (NRS) and the daily opioid dose was recorded.

RESULTS

The overall trends favored acupuncture for both pain intensity and daily opioid consumption. The proportion of participants experiencing at least a 2-point reduction in the NRS at the end of the treatment was 93% (n = 14/15) for the acupuncture group and 57% (n = 8/14) for the control group (risk difference (RD) 36.1%, 95% confidence interval (CI) [7.4%-65.0%]; relative risk (RR) 1.63, 95% CI [1.02-2.62]; p = 0.04). There were no serious adverse events and no dropouts during the treatment.

CONCLUSION

This pilot study showed that adding acupuncture to routine analgesia for patients with cancer pain was feasible and acceptable to patients. The clinical effects of adding acupuncture as an adjunctive therapy need to be further evaluated.

CLINICAL TRIAL REGISTRATION NUMBER

ChiCTR1800017023 (Chinese Clinical Trial Registry).

Do Norms Matter? Examining Norm-Based Messages in HPV Vaccination Promotion

This research examines the influence of norm-based messages (positively worded descriptive vs. negatively worded descriptive vs. injunctive vs. basic information) on intentions and risk perceptions about receiving human papillomavirus (HPV) vaccination among young adults aged 18-29. Results from an experiment show that compared to the basic information condition, additional exposure to injunctive normative messages increased intention to seek further information about HPV vaccination, which in turn enhanced intention to get the vaccine. Negatively worded descriptive normative messaging should be used with caution since it resulted in the highest risk perceptions of the HPV vaccine, compared to positively worded descriptive norms and basic information messages. Theoretical and practical implications are further discussed.

Feasibility of a Responsibility-Based Leadership Training Program for Novice Physical Activity Instructors

Most coaches and instructors would like to teach more than just sport skills to their athletes and children. However, to promote athletes’ or children’s holistic development and teach them to take responsibility and lead, requires the coaches and instructors to first master the skills themselves. Therefore, feasible, high quality leadership training programs where coaches and physical activity instructors are taught to teach and share leadership are needed. The aim of the current study was to evaluate the feasibility of a leadership training program to optimize it and to determine whether to proceed with its evaluation. In the leadership training program, eight Finnish novice physical activity instructors, aged 18 to 22, were taught to promote positive youth development, personal and social responsibility, and shared leadership in a physical activity context. The participants had minimal to no leadership training or experience. The training program consisted of seven meetings totaling 20 h. Helllison’s teaching personal and social responsibility (TPSR) model was the theoretical and practical framework of the training program. Feasibility of the leadership training program was evaluated across four domains of an evidence-based framework: demand, practicality, acceptability, and implementation fidelity. Data of the current complex intervention were collected with application videos, questionnaires, researcher’s log, lesson plans, video recordings, and a semi-structured focus group interview. The quantitative data were analyzed using descriptive statistics and the qualitative data using deductive and inductive content analysis. There was a demand for the leadership training program. The training program was perceived as practical and highly acceptable by the novice instructors and the trainers, and implemented with fidelity, indicating high overall feasibility. No implementation issues were found. Consequently, the current leadership training program has a high probability of efficacy and can be accepted for further evaluation.

Daily apple consumption reduces plasma and peripheral blood mononuclear cell-secreted inflammatory biomarkers in adults with overweight and obesity: a 6-week randomized, controlled, parallel-arm trial

BACKGROUND

Obesity-associated low-grade inflammation contributes to the development of cardiovascular disease (CVD). Apples are rich in anti-inflammatory bioactives including polyphenols and fiber.

OBJECTIVES

We aimed to determine the effects of regular apple consumption on fasting plasma biomarkers of inflammation (primary outcome), endotoxemia, carbohydrate and lipid metabolism (glucose, insulin, triacylglycerol; secondary outcomes), and peripheral blood mononuclear cell (PBMC)-secreted cytokines (secondary outcome) in individuals with overweight and obesity.

METHODS

A randomized, controlled, parallel-arm trial was conducted with n = 46 participants. After avoiding foods and beverages rich in polyphenols and fiber for 2 wk, participants consumed 3 whole Gala apples (∼200 g edible parts)/d as part of their habitual diet (n = 23) or avoided apples (control, n = 23) for 6 wk. All participants limited consumption of polyphenols and fiber during the 6-wk trial. Fasting blood samples were collected before and after 6 wk for analysis of plasma biomarkers and isolation of PBMCs, which were cultured for 24 h unstimulated or stimulated with LPS (10 ng/mL).

RESULTS

Forty-four participants completed the trial (30 female, 14 male; mean ± SEM age: 45.4 ± 2.2 y; BMI: 33.4 ± 0.9 kg/m2). After ANCOVA and correcting for multiple comparisons, apples decreased fasting plasma C-reactive protein by 17.0% (range: 14.3%-19.6%, P = 0.005), IL-6 by 12.4% (range: 6.7%-17.5%, P < 0.001), and LPS-binding protein by 20.7% (range: 14.1%-26.4%, P < 0.001) compared with control. Apples also decreased PBMC-secreted IL-6 by 28.3% (range: 22.4%-33.5%, P < 0.001) and IL-17 by 11.0% (range 5.8-15.6%, P = 0.003) in the unstimulated condition compared with control. Exploratory analysis showed apples also increased plasma total antioxidant capacity by 9.6% (range: 1.7-18.9%, P = 0.002) compared with control. However, apples had no effect on anthropometric or other CVD risk markers.

CONCLUSIONS

Six-week daily whole Gala apple consumption may be an effective dietary strategy to mitigate the obesity-associated inflammation that exacerbates CVD risk, without weight loss. This trial was registered at clinicaltrials.gov as NCT03523403.

When randomisation is not good enough: Matching groups in intervention studies

Randomised assignment of individuals to treatment and controls groups is often considered the gold standard to draw valid conclusions about the efficacy of an intervention. In practice, randomisation can lead to accidental differences due to chance. Researchers have offered alternatives to reduce such differences, but these methods are not used frequently due to the requirement of advanced statistical methods. Here, we recommend a simple assignment procedure based on variance minimisation (VM), which assigns incoming participants automatically to the condition that minimises differences between groups in relevant measures. As an example of its application in the research context, we simulated an intervention study whereby a researcher used the VM procedure on a covariate to assign participants to a control and intervention group rather than controlling for the covariate at the analysis stage. Among other features of the simulated study, such as effect size and sample size, we manipulated the correlation between the matching covariate and the outcome variable and the presence of imbalance between groups in the covariate. Our results highlighted the advantages of VM over prevalent random assignment procedure in terms of reducing the Type I error rate and providing accurate estimates of the effect of the group on the outcome variable. The VM procedure is valuable in situations whereby the intervention to an individual begins before the recruitment of the entire sample size is completed. We provide an Excel spreadsheet, as well as scripts in R, MATLAB, and Python to ease and foster the implementation of the VM procedure.

Testing for treatment effect in covariate-adaptive randomized trials with generalized linear models and omitted covariates

Concerns have been expressed over the validity of statistical inference under covariate-adaptive randomization despite the extensive use in clinical trials. In the literature, the inferential properties under covariate-adaptive randomization have been mainly studied for continuous responses; in particular, it is well known that the usual two-sample t-test for treatment effect is typically conservative. This phenomenon of invalid tests has also been found for generalized linear models without adjusting for the covariates and are sometimes more worrisome due to inflated Type I error. The purpose of this study is to examine the unadjusted test for treatment effect under generalized linear models and covariate-adaptive randomization. For a large class of covariate-adaptive randomization methods, we obtain the asymptotic distribution of the test statistic under the null hypothesis and derive the conditions under which the test is conservative, valid, or anti-conservative. Several commonly used generalized linear models, such as logistic regression and Poisson regression, are discussed in detail. An adjustment method is also proposed to achieve a valid size based on the asymptotic results. Numerical studies confirm the theoretical findings and demonstrate the effectiveness of the proposed adjustment method.

Clinical Trial Designs for Neuromodulation in Chronic Spinal Cord Injury Using Epidural Stimulation

STUDY DESIGN

This is a narrative review focused on specific challenges related to adequate controls that arise in neuromodulation clinical trials involving perceptible stimulation and physiological effects of stimulation activation.

OBJECTIVES

1) To present the strengths and limitations of available clinical trial research designs for the testing of epidural stimulation to improve recovery after spinal cord injury. 2) To describe how studies can control for the placebo effects that arise due to surgical implantation, the physical presence of the battery, generator, control interfaces, and rehabilitative activity aimed to promote use-dependent plasticity. 3) To mitigate Hawthorne effects that may occur in clinical trials with intensive supervised participation, including rehabilitation.

MATERIALS AND METHODS

Focused literature review of neuromodulation clinical trials with integration to the specific context of epidural stimulation for persons with chronic spinal cord injury.

CONCLUSIONS

Standard of care control groups fail to control for the multiple effects of knowledge of having undergone surgical procedures, having implanted stimulation systems, and being observed in a clinical trial. The irreducible effects that have been identified as "placebo" require sham controls or comparison groups in which both are implanted with potentially active devices and undergo similar rehabilitative training.

Evaluating performance of covariate-constrained randomization (CCR) techniques under misspecification of cluster-level variables in cluster-randomized trials

Covariate constrained randomization (CCR) is a method of controlling imbalance in important baseline covariates in cluster-randomized trials (CRT). We use simulated CRTs to investigate the performance (control of imbalance) of CCR relative to simple randomization (SR) under conditions of misspecification of the cluster-level variable used in the CCR algorithm. We use data from a Patient-Centered Outcomes Research Institute (PCORI)-funded CRT evaluating the Mothers and Babies (MB) intervention (AD-1507-31,473). CCR methodology was used in the MB study to control imbalance in, among other baseline variables, the percent minority (i.e., non-White) participants at each study site. Simulation schemes explored variation in degree of misspecification in the baseline covariate of interest, and include correct report, observed misspecification, and a range of simulated misspecification for intervals within and beyond that observed in the MB study. We also consider three within-site sample size scenarios: that observed in the MB study, small (mean 10) and large (mean 50). Simulations at every level of baseline covariate misspecification suggest that use of the CCR strategy provides between-arm imbalance that is simultaneously lower and less variable, on average, than that produced from the SR strategy. We find that the gains to using CCR over SR are nearly twice as high with accurate reporting (Δ = -5.33) compared to the observed study-level misspecification (Δ = -3.03). Although CCR still outperforms SR as the level of misspecification increases, the gains to using CCR over SR decrease; thus, every effort should still be made to obtain high-quality baseline data.

Machine Learning Methods to Predict Acute Respiratory Failure and Acute Respiratory Distress Syndrome

Acute respiratory failure (ARF) is a common problem in medicine that utilizes significant healthcare resources and is associated with high morbidity and mortality. Classification of acute respiratory failure is complicated, and it is often determined by the level of mechanical support that is required, or the discrepancy between oxygen supply and uptake. These phenotypes make acute respiratory failure a continuum of syndromes, rather than one homogenous disease process. Early recognition of the risk factors for new or worsening acute respiratory failure may prevent that process from occurring. Predictive analytical methods using machine learning leverage clinical data to provide an early warning for impending acute respiratory failure or its sequelae. The aims of this review are to summarize the current literature on ARF prediction, to describe accepted procedures and common machine learning tools for predictive tasks through the lens of ARF prediction, and to demonstrate the challenges and potential solutions for ARF prediction that can improve patient outcomes.

Postdischarge Intervention for Stroke Caregivers: Protocol for a Randomized Controlled Trial

BACKGROUND

The majority of stroke survivors return to their homes and need assistance from family caregivers to perform activities of daily living. These increased demands coupled with the lack of preparedness for their new roles lead to a high risk for caregivers developing depressive symptoms and other negative outcomes. Follow-up home support and problem-solving interventions with caregivers are crucial for maintaining stroke survivors in their homes. Problem-solving interventions are effective but are underused in practice because they require large amounts of staff time to implement and are difficult for caregivers logistically.

OBJECTIVE

The aim of this study is to test a problem-solving intervention for stroke caregivers that can be delivered over the telephone during the patient's transitional care period (time when the stroke survivor is discharged to home) followed by 8 asynchronous online sessions.

METHODS

The design is a two-arm parallel randomized clinical trial with repeated measures. We will enroll 240 caregivers from eight Veterans Affairs (VA) medical centers. Participants randomized into the intervention arm receive a modified problem-solving intervention that uses telephone and web-based support and training with interactive modules, fact sheets, and tools on the previously developed and nationally available Resources and Education for Stroke Caregivers' Understanding and Empowerment Caregiver website. In the usual care group, no changes are made in the information, discharge planning, or care the patients who have had a stroke normally receive, and caregivers have access to existing VA resources (eg, caregiver support line, self-help materials). The primary outcome is a change in caregiver depressive symptoms at 11 and 19 weeks after baseline data collection. Secondary outcomes include changes in stroke caregivers' burden, knowledge, positive aspects of caregiving, self-efficacy, perceived stress, health-related quality of life, and satisfaction with care and changes in stroke survivors' functional abilities and health care use. The team will also determine the budgetary impact, facilitators, barriers, and best practices for implementing the intervention. Throughout all phases of the study, we will collaborate with members of an advisory panel.

RESULTS

Study enrollment began in June 2015 and is ongoing. The first results are expected to be submitted for publication in 2021.

CONCLUSIONS

This is the first known study to test a transitional care and messaging center intervention combined with technology to decrease caregiver depressive symptoms and to improve the recovery of stroke survivors. If successful, findings will support an evidence-based model that can be transported into clinical practice to improve the quality of caregiving post stroke.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01600131; https://www.clinicaltrials.gov/ct2/show/NCT01600131.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/21799.

Resveratrol Improves Heart Function by Moderating Inflammatory Processes in Patients with Systolic Heart Failure

The effects of resveratrol (RES) in heart failure have already been evaluated in animal models; however, in human clinical trials, they have not been confirmed yet. The aim of this study was to assess the effects of resveratrol treatment in systolic heart failure patients (heart failure with reduced ejection fraction or HFrEF). In this human clinical trial, 60 outpatients with NYHA (New York Heart Association) class II-III HFrEF were enrolled and randomized into two groups: receiving either 100-mg resveratrol daily or placebo for three months. At the beginning and at the end of the study echocardiography, a six-minute walk test, spirometry, quality of life questionnaire, lab test and RNA profile analysis were performed. The systolic and diastolic left ventricular function, as well as the global longitudinal strain, were improved significantly in the resveratrol-treated group (RES). Exercise capacity, ventilation parameters and quality of life also improved significantly in the RES group. In parallel, the cardiac biomarker levels (N-terminal prohormone of brain natriuretic peptide (NT-proBNP) and galectin-3) decreased in the treated group. The level of inflammatory cytokines decreased significantly after RES supplementation, as a consequence of the decreased expression level of leucocyte electron transport chain proteins. The main findings of our trial are that RES treatment added to the standard heart failure therapy improved heart function and the clinical condition by moderating the inflammatory processes in patients with HFrEF.