Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements

Average treatment effects on binary outcomes with stochastic covariates

When evaluating the effect of psychological treatments on a dichotomous outcome variable in a randomized controlled trial (RCT), covariate adjustment using logistic regression models is often applied. In the presence of covariates, average marginal effects (AMEs) are often preferred over odds ratios, as AMEs yield a clearer substantive and causal interpretation. However, standard error computation of AMEs neglects sampling-based uncertainty (i.e., covariate values are assumed to be fixed over repeated sampling), which leads to underestimation of AME standard errors in other generalized linear models (e.g., Poisson regression). In this paper, we present and compare approaches allowing for stochastic (i.e., randomly sampled) covariates in models for binary outcomes. In a simulation study, we investigated the quality of the AME and stochastic-covariate approaches focusing on statistical inference in finite samples. Our results indicate that the fixed-covariate approach provides reliable results only if there is no heterogeneity in interindividual treatment effects (i.e., presence of treatment-covariate interactions), while the stochastic-covariate approaches are preferable in all other simulated conditions. We provide an illustrative example from clinical psychology investigating the effect of a cognitive bias modification training on post-traumatic stress disorder while accounting for patients' anxiety using an RCT.

Impact of Individual Budgets on Work and Financial Well-Being of Supported Employment Recipients With Serious Mental Illness

OBJECTIVE

The authors sought to determine whether providing recipients of supported employment with individual budgets from which they could purchase employment-related goods and services would improve employment and financial outcomes.

METHODS

Sixty study participants were recruited from an individual placement and support (IPS) program and randomly assigned (1:1) to receive IPS services only (N=32) or IPS services with a 12-month $950 flexible fund called a career account (N=28). Participants receiving IPS and a career account met with staff who helped them identify employment goals and create a budget for purchases directly tied to these goals. The primary outcome was competitive employment; secondary outcomes included job tenure, days worked, total earnings, and financial well-being. Outcomes were analyzed by using adjusted generalized linear models (GLMs) with binary logistic, negative binomial, and linear distributions.

RESULTS

The proportion of participants who achieved competitive employment was largely similar for those in the career account+IPS group (54%) and in the IPS-only group (47%). However, the GLM analysis revealed that career account+IPS participants had significantly longer job tenure, more total days of employment, and higher total earnings than IPS-only participants. Feelings of financial well-being increased significantly among career account participants, whereas financial well-being declined among control participants. The amount of career account dollars participants spent was positively and significantly associated with longer job tenure, more days employed, and higher total earnings.

CONCLUSIONS

Combining flexible funds with IPS-supported employment achieved some superior outcomes compared with IPS only. Further research is needed to assess the longer-term effects of this practice and its cost-effectiveness.

Impacts of ocean warming on fish size reductions on the world's hottest coral reefs

The impact of ocean warming on fish and fisheries is vigorously debated. Leading theories project limited adaptive capacity of tropical fishes and 14-39% size reductions by 2050 due to mass-scaling limitations of oxygen supply in larger individuals. Using the world's hottest coral reefs in the Persian/Arabian Gulf as a natural laboratory for ocean warming - where species have survived >35.0 °C summer temperatures for over 6000 years and are 14-40% smaller at maximum size compared to cooler locations - we identified two adaptive pathways that enhance survival at elevated temperatures across 10 metabolic and swimming performance metrics. Comparing Lutjanus ehrenbergii and Scolopsis ghanam from reefs both inside and outside the Persian/Arabian Gulf across temperatures of 27.0 °C, 31.5 °C and 35.5 °C, we reveal that these species show a lower-than-expected rise in basal metabolic demands and a right-shifted thermal window, which aids in maintaining oxygen supply and aerobic performance to 35.5 °C. Importantly, our findings challenge traditional oxygen-limitation theories, suggesting a mismatch in energy acquisition and demand as the primary driver of size reductions. Our data support a modified resource-acquisition theory to explain how ocean warming leads to species-specific size reductions and why smaller individuals are evolutionarily favored under elevated temperatures.

Covariate adjustment in Bayesian adaptive randomized controlled trials

In conventional randomized controlled trials, adjustment for baseline values of covariates known to be at least moderately associated with the outcome increases the power of the trial. Recent work has shown a particular benefit for more flexible frequentist designs, such as information adaptive and adaptive multi-arm designs. However, covariate adjustment has not been characterized within the more flexible Bayesian adaptive designs, despite their growing popularity. We focus on a subclass of these which allow for early stopping at an interim analysis given evidence of treatment superiority. We consider both collapsible and non-collapsible estimands and show how to obtain posterior samples of marginal estimands from adjusted analyses. We describe several estimands for three common outcome types. We perform a simulation study to assess the impact of covariate adjustment using a variety of adjustment models in several different scenarios. This is followed by a real-world application of the compared approaches to a COVID-19 trial with a binary endpoint. For all scenarios, it is shown that covariate adjustment increases power and the probability of stopping the trials early, and decreases the expected sample sizes as compared to unadjusted analyses.

ADSS: A Composite Score to Detect Disease Progression in Alzheimer's Disease

Background

Composite scores have been increasingly used in trials for Alzheimer's disease (AD) to detect disease progression, such as the AD Composite Score (ADCOMS) in the lecanemab trial.

Objective

To develop a new composite score to improve the prediction of outcome change.

Methods

We proposed to develop a new composite score based on the statistical model in the ADCOMS, by removing duplicated sub-scales and adding the model selection in the partial least squares (PLS) regression.

Results

The new AD composite Score with variable Selection (ADSS) includes 7 cognitive sub-scales. ADSS can increase the sensitivity to detect disease progression as compared to the existing total scores, which leads to smaller sample sizes using the ADSS in trial designs.

Conclusions

ADSS can be utilized in AD trials to improve the success rate of drug development with a high sensitivity to detect disease progression in early stages.

Use of conditional estimates of effect in cancer epidemiology: An application to lung cancer treatment

BACKGROUND

In oncology clinical trials, there is the assumption that randomization sufficiently balances confounding covariates and therefore average treatment effects are usually reported. This paper explores the wider benefits provided by conditioning on covariates for reasons other than mitigation of confounding.

METHODS

We reanalyzed the data from primary randomized controlled trials listed in two meta-analyses to explore the significance of conditioning on smoking status in terms of the effect magnitude of treatment on progression free survival in non-small cell lung cancer.

RESULTS

The reanalysis revealed that conditioning on smoking status using sub-group analyses provided the closest empiric estimate of individual treatment effect based on smoking status and significantly reduced the heterogeneity of treatment effect observed across studies. In addition, smoking status was determined to be a modifier of the effect of treatment.

CONCLUSION

Conditioning on prognostic covariates in randomized trials in oncology helps generate the closest empiric estimates of individual treatment benefit, addresses heterogeneity due to varying covariate distributions across trials and facilitates future decision making as well as evidence synthesis. Conditioning using sub-group analyses also allows examination for effect modification in meta-analysis.

Comparison of different statistical models for the analysis of fracture events: findings from the Prevention of Falls Injury Trial (PreFIT)

BACKGROUND

Fractures are rare events and can occur because of a fall. Fracture counts are distinct from other count data in that these data are positively skewed, inflated by excess zero counts, and events can recur over time. Analytical methods used to assess fracture data and account for these characteristics are limited in the literature.

METHODS

Commonly used models for count data include Poisson regression, negative binomial regression, hurdle regression, and zero-inflated regression models. In this paper, we compare four alternative statistical models to fit fracture counts using data from a large UK based clinical trial evaluating the clinical and cost-effectiveness of alternative falls prevention interventions in older people (Prevention of Falls Injury Trial; PreFIT).

RESULTS

The values of Akaike information criterion and Bayesian information criterion, the goodness-of-fit statistics, were the lowest for negative binomial model. The likelihood ratio test of no dispersion in the data showed strong evidence of dispersion (chi-square = 225.68, p-value < 0.001). This indicates that the negative binomial model fits the data better compared to the Poisson regression model. We also compared the standard negative binomial regression and mixed effects negative binomial models. The LR test showed no gain in fitting the data using mixed effects negative binomial model (chi-square = 1.67, p-value = 0.098) compared to standard negative binomial model.

CONCLUSIONS

The negative binomial regression model was the most appropriate and optimal fit model for fracture count analyses.

TRIAL REGISTRATION

The PreFIT trial was registered as ISRCTN71002650.

Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet.

OBJECTIVES

To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS.

SEARCH METHODS

We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies.

SELECTION CRITERIA

We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome.

DATA COLLECTION AND ANALYSIS

Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression.

MAIN RESULTS

We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal.

AUTHORS' CONCLUSIONS

The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.

Intravenous vs. intraosseous vascular access during out-of-hospital cardiac arrest - protocol for a randomised clinical trial

Objective

During cardiac arrest, current guidelines recommend attempting intravenous access first and to consider intraosseous access if intravenous access is unsuccessful or impossible. However, these recommendations are only based on very low-certainty evidence. Therefore, the "Intravenous vs Intraosseous Vascular Access During Out-of-Hospital Cardiac Arrest" (IVIO) trial aims to determine whether there is a difference in patient outcomes depending on the type of vascular access attempted during out-of-hospital cardiac arrest. This current article describes the clinical IVIO trial.

Methods

The IVIO trial is an investigator-initiated, randomised trial of intravenous vs. intraosseous vascular access during adult non-traumatic out-of-hospital cardiac arrest in Denmark. The intervention will consist of minimum two attempts (if unsuccessful on the first attempt) to successfully establish intravenous or intraosseous vascular access during cardiac arrest. The intraosseous group will be further randomised to the humeral or tibial site. The primary outcome is sustained return of spontaneous circulation and key secondary outcomes include survival and survival with a favourable neurological outcome at 30 days. A total of 1,470 patients will be included.

Results

The trial started in March 2022 and the last patient is anticipated to be included in the spring of 2024. The primary results will be reported after 90-day follow-up and are anticipated in mid-2024.

Conclusion

The current article describes the design of the Danish IVIO trial. The findings of this trial will help inform future guidelines for selecting the optimal vascular access route during out-of-hospital cardiac arrest.

A protocol for a proof-of-concept randomized control trial testing increased protein quantity and quality in ready-to-use therapeutic food in improving linear growth among 6-23-month-old children with severe wasting in Malawi

INTRODUCTION

Ready-to-use therapeutic foods (RUTFs) have successfully promoted recovery from severe wasting and increased treatment coverage. However, RUTFs do not sufficiently improve linear growth, leaving many survivors of severe wasting at risk of persistent stunting, which is associated with high mortality risk, poor child development and non-communicable diseases in adulthood. High protein quantity and quality can stimulate linear growth.

AIM

The trial aims to assess whether higher-protein-RUTF leads to higher concentrations of markers of linear growth compared to standard RUTF among 6-23 months old children with severe wasting.

METHODS

We designed a higher protein quantity and quality RUTF for a proof-of-concept (PoC) double-blind randomized controlled trial.

OUTCOMES

The primary outcome is a change in insulin-like growth factor-1 (IGF-1), a hormone positively associated with linear growth after four weeks of treatment. Secondary outcomes include changes in ponderal and linear growth and in body composition from baseline to eight weeks later; plasma amino acid profile at four weeks; acceptability and safety.

IMPLICATIONS

These findings will help in informing the potential impact of increased protein in RUTF on linear growth when treating severe wasting towards conducting a larger clinical trial.

TRIAL REGISTRATION

The trial has been registered on clinicaltrial.gov (NCT05737472).

Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization

Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.

Use of days alive without life support and similar count outcomes in randomised clinical trials - an overview and comparison of methodological choices and analysis methods

BACKGROUND

Days alive without life support (DAWOLS) and similar outcomes that seek to summarise mortality and non-mortality experiences are increasingly used in critical care research. The use of these outcomes is challenged by different definitions and non-normal outcome distributions that complicate statistical analysis decisions.

METHODS

We scrutinized the central methodological considerations when using DAWOLS and similar outcomes and provide a description and overview of the pros and cons of various statistical methods for analysis supplemented with a comparison of these methods using data from the COVID STEROID 2 randomised clinical trial. We focused on readily available regression models of increasing complexity (linear, hurdle-negative binomial, zero-one-inflated beta, and cumulative logistic regression models) that allow comparison of multiple treatment arms, adjustment for covariates and interaction terms to assess treatment effect heterogeneity.

RESULTS

In general, the simpler models adequately estimated group means despite not fitting the data well enough to mimic the input data. The more complex models better fitted and thus better replicated the input data, although this came with increased complexity and uncertainty of estimates. While the more complex models can model separate components of the outcome distributions (i.e., the probability of having zero DAWOLS), this complexity means that the specification of interpretable priors in a Bayesian setting is difficult. Finally, we present multiple examples of how these outcomes may be visualised to aid assessment and interpretation.

CONCLUSIONS

This summary of central methodological considerations when using, defining, and analysing DAWOLS and similar outcomes may help researchers choose the definition and analysis method that best fits their planned studies.

TRIAL REGISTRATION

COVID STEROID 2 trial, ClinicalTrials.gov: NCT04509973, ctri.nic.in: CTRI/2020/10/028731.

More efficient and inclusive time-to-event trials with covariate adjustment: a simulation study

Adjustment for prognostic covariates increases the statistical power of randomized trials. The factors influencing the increase of power are well-known for trials with continuous outcomes. Here, we study which factors influence power and sample size requirements in time-to-event trials. We consider both parametric simulations and simulations derived from the Cancer Genome Atlas (TCGA) cohort of hepatocellular carcinoma (HCC) patients to assess how sample size requirements are reduced with covariate adjustment. Simulations demonstrate that the benefit of covariate adjustment increases with the prognostic performance of the adjustment covariate (C-index) and with the cumulative incidence of the event in the trial. For a covariate that has an intermediate prognostic performance (C-index=0.65), the reduction of sample size varies from 3.1% when cumulative incidence is of 10% to 29.1% when the cumulative incidence is of 90%. Broadening eligibility criteria usually reduces statistical power while our simulations show that it can be maintained with adequate covariate adjustment. In a simulation of adjuvant trials in HCC, we find that the number of patients screened for eligibility can be divided by 2.4 when broadening eligibility criteria. Last, we find that the Cox-Snell [Formula: see text] is a conservative estimation of the reduction in sample size requirements provided by covariate adjustment. Overall, more systematic adjustment for prognostic covariates leads to more efficient and inclusive clinical trials especially when cumulative incidence is large as in metastatic and advanced cancers. Code and results are available at https://github.com/owkin/CovadjustSim .

The effects of a sensory stimulation intervention on psychosocial and clinical outcomes of critically ill patients and their families: A randomised controlled trial

OBJECTIVES

To explore the effectiveness of a sensory stimulation intervention on intensive care unit patients' psychosocial, clinical, and family outcomes.

DESIGN

A prospective, assessor-blind, parallel-group randomised controlled trial.

SETTING

A surgical intensive care unit of one tertiary hospital in Guangzhou, mainland China.

INTERVENTION

Participants in the intervention group received a daily 30-minute auditory and visual stimulation session starting from recruitment and for a maximum of seven days while in the intensive care unit.

MEASUREMENT AND MAIN RESULTS

One hundred fifty-two patients and family caregiver dyads were recruited. Patients in the intervention group showed lower total scores of post-traumatic stress disorder (21.92 ± 6.34 vs 27.62 ± 10.35,p = 0.001), depressive symptoms (3.76 ± 3.99 vs 6.78 ± 4.75,p = 0.001) and delusional memories (0.47 ± 0.92 vs 0.82 ± 1.23,p = 0.001) collected immediately post-intervention than those in the control group, while not on depressive symptoms at one-month post-intervention (3.32 ± 4.03 vs 3.28 ± 3.77,p = 0.800). Sensory stimulation did not significantly impact patients' unit length of stay and 30-day mortality (allp > 0.05). For family outcomes, family caregivers in the intervention group had greater satisfaction with care (127.12 ± 14.14 vs 114.38 ± 21.97,p = 0.001) and a lower level of anxiety (28.49 ± 6.48 vs 34.64 ± 7.68,p = 0.001) than family caregivers in the control group.

CONCLUSIONS

Sensory stimulation may benefit patients' and family caregivers' psychological well-being, and further well-designed multi-centre clustered randomized controlled trials could be considered to strengthen the evidence.

Vitamin D and Risk for Type 2 Diabetes in People With Prediabetes : A Systematic Review and Meta-analysis of Individual Participant Data From 3 Randomized Clinical Trials

BACKGROUND

The role of vitamin D in people who are at risk for type 2 diabetes remains unclear.

PURPOSE

To evaluate whether administration of vitamin D decreases risk for diabetes among people with prediabetes.

DATA SOURCES

PubMed, Embase, and ClinicalTrials.gov from database inception through 9 December 2022.

STUDY SELECTION

Eligible trials that were specifically designed and conducted to test the effects of oral vitamin D versus placebo on new-onset diabetes in adults with prediabetes.

DATA EXTRACTION

The primary outcome was time to event for new-onset diabetes. Secondary outcomes were regression to normal glucose regulation and adverse events. Prespecified analyses (both unadjusted and adjusted for key baseline variables) were conducted according to the intention-to-treat principle.

DATA SYNTHESIS

Three randomized trials were included, which tested cholecalciferol, 20 000 IU (500 mcg) weekly; cholecalciferol, 4000 IU (100 mcg) daily; or eldecalcitol, 0.75 mcg daily, versus matching placebos. Trials were at low risk of bias. Vitamin D reduced risk for diabetes by 15% (hazard ratio, 0.85 [95% CI, 0.75 to 0.96]) in adjusted analyses, with a 3-year absolute risk reduction of 3.3% (CI, 0.6% to 6.0%). The effect of vitamin D did not differ in prespecified subgroups. Among participants assigned to the vitamin D group who maintained an intratrial mean serum 25-hydroxyvitamin D level of at least 125 nmol/L (≥50 ng/mL) compared with 50 to 74 nmol/L (20 to 29 ng/mL) during follow-up, cholecalciferol reduced risk for diabetes by 76% (hazard ratio, 0.24 [CI, 0.16 to 0.36]), with a 3-year absolute risk reduction of 18.1% (CI, 11.7% to 24.6%). Vitamin D increased the likelihood of regression to normal glucose regulation by 30% (rate ratio, 1.30 [CI, 1.16 to 1.46]). There was no evidence of difference in the rate ratios for adverse events (kidney stones: 1.17 [CI, 0.69 to 1.99]; hypercalcemia: 2.34 [CI, 0.83 to 6.66]; hypercalciuria: 1.65 [CI, 0.83 to 3.28]; death: 0.85 [CI, 0.31 to 2.36]).

LIMITATIONS

Studies of people with prediabetes do not apply to the general population. Trials may not have been powered for safety outcomes.

CONCLUSION

In adults with prediabetes, vitamin D was effective in decreasing risk for diabetes.

PRIMARY FUNDING SOURCE

None. (PROSPERO: CRD42020163522).

On the relevance of prognostic information for clinical trials: A theoretical quantification

The question of how individual patient data from cohort studies or historical clinical trials can be leveraged for designing more powerful, or smaller yet equally powerful, clinical trials becomes increasingly important in the era of digitalization. Today, the traditional statistical analyses approaches may seem questionable to practitioners in light of ubiquitous historical prognostic information. Several methodological developments aim at incorporating historical information in the design and analysis of future clinical trials, most importantly Bayesian information borrowing, propensity score methods, stratification, and covariate adjustment. Adjusting the analysis with respect to a prognostic score, which was obtained from some model applied to historical data, received renewed interest from a machine learning perspective, and we study the potential of this approach for randomized clinical trials. In an idealized situation of a normal outcome in a two-arm trial with 1:1 allocation, we derive a simple sample size reduction formula as a function of two criteria characterizing the prognostic score: (1) the coefficient of determination R² on historical data and (2) the correlation ρ between the estimated and the true unknown prognostic scores. While maintaining the same power, the original total sample size n planned for the unadjusted analysis reduces to ( 1 - R 2 ρ 2 ) × n $(1 - R^2 \rho ^2) \times n$ in an adjusted analysis. Robustness in less ideal situations was assessed empirically. We conclude that there is potential for substantially more powerful or smaller trials, but only when prognostic scores can be accurately estimated.

A Review of the Ring Trial Design for Evaluating Ring Interventions for Infectious Diseases

In trials of infectious disease interventions, rare outcomes and unpredictable spatiotemporal variation can introduce bias, reduce statistical power, and prevent conclusive inferences. Spillover effects can complicate inference if individual randomization is used to gain efficiency. Ring trials are a type of cluster-randomized trial that may increase efficiency and minimize bias, particularly in emergency and elimination settings with strong clustering of infection. They can be used to evaluate ring interventions, which are delivered to individuals in proximity to or contact with index cases. We conducted a systematic review of ring trials, compare them with other trial designs for evaluating ring interventions, and describe strengths and weaknesses of each design. Of 849 articles and 322 protocols screened, we identified 26 ring trials, 15 cluster-randomized trials, 5 trials that randomized households or individuals within rings, and 1 individually randomized trial. The most common interventions were postexposure prophylaxis (n = 23) and focal mass drug administration and screening and treatment (n = 7). Ring trials require robust surveillance systems and contact tracing for directly transmitted diseases. For rare diseases with strong spatiotemporal clustering, they may have higher efficiency and internal validity than cluster-randomized designs, in part because they ensure that no clusters are excluded from analysis due to zero cluster incidence. Though more research is needed to compare them with other types of trials, ring trials hold promise as a design that can increase trial speed and efficiency while reducing bias.

Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium

INTRODUCTION

Our aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR.

METHODS

We combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction.

RESULTS

Our sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %).

IMPLICATIONS

Our findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.

Symptom burden according to dialysis day of the week in three times a week haemodialysis patients

Background

Haemodialysis patients experience significant symptom burden and effects on health-related quality of life. Studies have shown increases in fluid overload, hospitalization and mortality immediately after the long interdialytic interval in thrice weekly in-centre haemodialysis patients, however the relationship between the dialytic interval and patient reported outcome measures (PROMs) has not been quantified and the extent to which dialysis day of PROM completion needs to be standardised is unknown.

Methods

Three times a week haemodialysis patients participating in a stepped wedge trial to increase patient participation in haemodialysis tasks completed PROMs (POS-S Renal symptom score and EQ-5D-5L) at recruitment, six, 12 and 18 months. Time from the long interdialytic interval, HD day of the week, and HD days vs non-HD days were included in mixed effects Linear Regression, estimating severity (none to overwhelming treated as 0 to 4) of 17 symptoms and EQ-5D-5L, adjusting for age, sex, time on HD, control versus intervention and Charlson Comorbidity Score.

Results

517 patients completed 1659 YHS questionnaires that could be assigned HD day (510 on Mon/Tue/Sun, 549 on Wed/Thu/Tue, 308 on Fri/Sat/Thu and 269 on non-HD days). With the exception of restless legs and skin changes, there was no statistically significant change in symptom severity or EQ-5D-5L with increasing time from the long interdialytic interval. Patients who responded on non-HD days had higher severity of poor appetite, constipation, difficulty sleeping, poor mobility and depression (approximately 0.2 severity level), and lower EQ-5D-5L (-0.06, CI -0.09 to -0.03) compared to HD days.

Conclusions

Measuring symptom severity and EQ-5D-5L in haemodialysis populations does not need to account for dialysis schedule, but completion either on HD or non-HD days could introduce bias that may impact evaluation of interventions. Researchers should ensure completion of these instruments are standardized on either dialysis or non-dialysis days.

Using individual participant data to improve network meta-analysis projects

A network meta-analysis combines the evidence from existing randomised trials about the comparative efficacy of multiple treatments. It allows direct and indirect evidence about each comparison to be included in the same analysis, and provides a coherent framework to compare and rank treatments. A traditional network meta-analysis uses aggregate data (eg, treatment effect estimates and standard errors) obtained from publications or trial investigators. An alternative approach is to obtain, check, harmonise and meta-analyse the individual participant data (IPD) from each trial. In this article, we describe potential advantages of IPD for network meta-analysis projects, emphasising five key benefits: (1) improving the quality and scope of information available for inclusion in the meta-analysis, (2) examining and plotting distributions of covariates across trials (eg, for potential effect modifiers), (3) standardising and improving the analysis of each trial, (4) adjusting for prognostic factors to allow a network meta-analysis of conditional treatment effects and (5) including treatment-covariate interactions (effect modifiers) to allow relative treatment effects to vary by participant-level covariate values (eg, age, baseline depression score). A running theme of all these benefits is that they help examine and reduce heterogeneity (differences in the true treatment effect between trials) and inconsistency (differences in the true treatment effect between direct and indirect evidence) in the network. As a consequence, an IPD network meta-analysis has the potential for more precise, reliable and informative results for clinical practice and even allows treatment comparisons to be made for individual patients and targeted populations conditional on their particular characteristics.

PREPARE trial: a protocol for a multicentre randomised trial of frailty-focused preoperative exercise to decrease postoperative complication rates and disability scores

INTRODUCTION

Frailty is a strong predictor of adverse postoperative outcomes. Prehabilitation may improve outcomes after surgery for older people with frailty by addressing physical and physiologic deficits. The objective of this trial is to evaluate the efficacy of home-based multimodal prehabilitation in decreasing patient-reported disability and postoperative complications in older people with frailty having major surgery.

METHODS AND ANALYSIS

We will conduct a multicentre, randomised controlled trial of home-based prehabilitation versus standard care among consenting patients >60 years with frailty (Clinical Frailty Scale>4) having elective inpatient major non-cardiac, non-neurologic or non-orthopaedic surgery. Patients will be partially blinded; clinicians and outcome assessors will be fully blinded. The intervention consists of >3 weeks of prehabilitation (exercise (strength, aerobic and stretching) and nutrition (advice and protein supplementation)). The study has two primary outcomes: in-hospital complications and patient-reported disability 30 days after surgery. Secondary outcomes include survival, lower limb function, quality of life and resource utilisation. A sample size of 750 participants (375 per arm) provides >90% power to detect a minimally important absolute difference of 8 on the 100-point patient-reported disability scale and a 25% relative risk reduction in complications, using a two-sided alpha value of 0.025 to account for the two primary outcomes. Analyses will follow intention to treat principles for all randomised participants. All participants will be followed to either death or up to 1 year.

ETHICS AND DISSEMINATION

Ethical approval has been granted by Clinical Trials Ontario (Project ID: 1785) and our ethics review board (Protocol Approval #20190409-01T). Results will be disseminated through presentation at scientific conferences, through peer-reviewed publication, stakeholder organisations and engagement of social and traditional media.

TRIAL REGISTRATION NUMBER

NCT04221295.

Are we leaving money on the table in infertility RCTs? Trialists should statistically adjust for prespecified, prognostic covariates to increase power

Infertility randomized controlled trials (RCTs) are often too small to detect realistic treatment effects. Large observational studies have been proposed as a solution. However, this strategy threatens to weaken the evidence base further, because non-random assignment to treatments makes it impossible to distinguish effects of treatment from confounding factors. Alternative solutions are required. Power in an RCT can be increased by adjusting for prespecified, prognostic covariates when performing statistical analysis, and if stratified randomization or minimization has been used, it is essential to adjust in order to get the correct answer. We present data showing that this simple, free and frequently necessary strategy for increasing power is seldom employed, even in trials appearing in leading journals. We use this article to motivate a pedagogical discussion and provide a worked example. While covariate adjustment cannot solve the problem of underpowered trials outright, there is an imperative to use sound methodology to maximize the information each trial yields.

Covariate Adjustment in Cardiovascular Randomized Controlled Trials: Its Value, Current Practice, and Need for Improvement

In randomized controlled trials, patient characteristics are expected to be well balanced between treatment groups; however, adjustment for characteristics that are prognostic can still be beneficial with a modest gain in statistical power. Nevertheless, previous reviews show that many trials use unadjusted analyses. In this article, we review current practice regarding covariate adjustment in cardiovascular trials among all 84 randomized controlled trials relating to cardiovascular disease published in the New England Journal of Medicine, The Lancet, and the Journal of the American Medical Association during 2019. We identify trials in which use of covariate adjustment led to a change in the trial conclusions. By using these trials as case studies, along with data from the CHARM trial and simulation studies, we demonstrate some of the potential benefits and pitfalls of covariate adjustment. We discuss some of the complexities of using covariate adjustment, including how many covariates to choose, how covariates should be modeled, how to handle missing data for baseline covariates, and how adjusted analyses are viewed by regulators. We conclude that contemporary cardiovascular trials do not make best use of covariate adjustment and that more frequent use could lead to improvements in the efficiency of future trials.

Praxisorientierte Forschung in der Psychotherapie

Zusammenfassung. In den letzten Jahrzehnten hat sich durch randomisiert-kontrollierte Studien (RCTs) eine breite Evidenzbasis von Psychotherapie mit mittleren bis großen Effekten für verschiedene psychische Störungen gebildet. Neben der Bestimmung dieser Wirksamkeit („Efficacy“) ebneten Studien zur Wirksamkeit unter alltäglichen Routinebedingungen („Effectiveness“) historisch den Weg zur Entwicklung eines praxisorientierten Forschungsparadigmas. Im Beitrag wird argumentiert, dass im Rahmen dieses Paradigmas praxisbasierte Studien eine wertvolle Ergänzung zu RCTs darstellen, da sie existierende Probleme in der Psychotherapieforschung adressieren können. In der gegenwärtigen praxisorientierten Forschung liefern dabei neue Ansätze aus der personalisierten Medizin und Methoden aus der ‚Computational Psychiatry‘ wichtige Anhaltspunkte zur Optimierung von Effekten in der Psychotherapie. Im Kontext der Personalisierung werden bspw. klinische multivariable Prädiktionsmodelle entwickelt, welche durch Rückmeldeschleifen an Praktiker_innen kurzfristig ein evidenzbasiertes Outcome-Monitoring ermöglicht und langfristig das Praxis-Forschungsnetzwerk in Deutschland stärkt. Am Ende des Beitrags werden zukünftige Richtungen für die praxisorientierte Forschung im Sinne des ‘Precision Mental Health Care’ -Paradigmas abgeleitet und diskutiert.

Rationale and design of a cluster-randomized pragmatic trial aimed at improving use of guideline directed medical therapy in outpatients with heart failure: PRagmatic trial of messaging to providers about treatment of heart failure (PROMPT-HF)

Heart failure with reduced ejection fraction (HFrEF) is one of the most common chronic illnesses in the United States and carries significant risk of morbidity and mortality. Use of guideline-directed medical therapy (GDMT) for patients with HFrEF has been shown to dramatically improve outcomes, but adoption of these treatments remains generally low. Possible explanations for poor GDMT uptake include lack of knowledge about recommended management strategies and provider reluctance due to uncertainties regarding application of said guidelines to real-world practice. One way to overcome these barriers is by harnessing the electronic health record (EHR) to create patient-centered "best practice alerts" (BPAs) that can guide clinicians to prescribe appropriate medical therapies. If found to be effective, these low-cost interventions can be rapidly applied across large integrated healthcare systems. The PRagmatic Trial Of Messaging to Providers about Treatment of Heart Failure (PROMPT-HF) trial is a pragmatic, cluster randomized controlled trial designed to test the hypothesis that tailored and timely alerting of recommended GDMT in heart failure (HF) will result in greater adherence to guidelines when compared with usual care. PROMPT-HF has completed enrollment of 1,310 ambulatory patients with HFrEF cared for by 100 providers who were randomized to receive a BPA vs usual care. The BPA alerted providers to GDMT recommended for their patients and displayed current left ventricular ejection fraction (LVEF) along with the most recent blood pressure, heart rate, serum potassium and creatinine levels, and estimated glomerular filtration rate. It also linked to an order set customized to the patient that suggests medications within each GDMT class not already prescribed. Our goal is to examine whether tailored EHR-based alerting for outpatients with HFrEF will lead to higher rates of GDMT at 30 days post randomization when compared with usual care. Additionally, we are assessing clinical outcomes such as hospital readmissions and death between the alert versus usual care group. Trial Registration: Clinicaltrials.gov NCT04514458.

Parent-focused prevention of adolescent health risk behavior: Study protocol for a multisite cluster-randomized trial implemented in pediatric primary care

Evidence-based parenting interventions play a crucial role in the sustained reduction of adolescent behavioral health concerns. Guiding Good Choices (GGC) is a 5-session universal anticipatory guidance curriculum for parents of early adolescents that has been shown to reduce substance use, depression symptoms, and delinquent behavior. Although prior research has demonstrated the effectiveness of evidence-based parenting interventions at achieving sustained reductions in adolescent behavioral health concerns, public health impact has been limited by low rates of uptake in community and agency settings. Pediatric primary care is an ideal setting for implementing and scaling parent-focused prevention programs as these settings have a broad reach, and prevention programs implemented within them have the potential to achieve population-level impact. The current investigation, Guiding Good Choices for Health (GGC4H), tests the feasibility and effectiveness of implementing GGC in 3 geographically and socioeconomically diverse large integrated healthcare systems. This pragmatic, cluster randomized clinical trial will compare GGC parenting intervention to usual pediatric primary care practice, and will include approximately 3750 adolescents; n = 1875 GGC intervention and n = 1875 usual care. The study team hypothesizes that adolescents whose parents are randomized into the GGC intervention arm will show reductions in substance use initiation, the study's primary outcomes, and other secondary (e.g., depression symptoms, substance use prevalence) and exploratory outcomes (e.g., health services utilization, anxiety symptoms). The investigative team anticipates that the implementation of GGC within pediatric primary care clinics will successfully fill an unmet need for effective preventive parenting interventions. Trial registration: Clinicaltrials.govNCT04040153.

Impact of minimal sufficient balance, minimization, and stratified permuted blocks on bias and power in the estimation of treatment effect in sequential clinical trials with a binary endpoint

Minimization is among the most common methods for controlling baseline covariate imbalance at the randomization phase of clinical trials. Previous studies have found that minimization does not preserve allocation randomness as well as other methods, such as minimal sufficient balance, making it more vulnerable to allocation predictability and selection bias. Additionally, minimization has been shown in simulation studies to inadequately control serious covariate imbalances when modest biased coin probabilities (≤0.65) are used. This current study extends the investigation of randomization methods to the analysis phase, comparing the impact of treatment allocation methods on power and bias in estimating treatment effects on a binary outcome using logistic regression. Power and bias in the estimation of treatment effect was found to be comparable across complete randomization, minimization, and minimal sufficient balance in unadjusted analyses. Further, minimal sufficient balance was found to have the most modest impact on power and the least bias in covariate-adjusted analyses. The minimal sufficient balance method is recommended for use in clinical trials as an alternative to minimization when covariate-adaptive subject randomization takes place.

Allowing for stratification in sample size planning of two-arm trials with continuous or binary outcome: Overview and tutorial

More often than not, clinical trials and even nonclinical medical experiments have to be run with observational units sampled from populations to be assumed heterogeneous with respect to covariates associated with the outcome. Relevant covariates which are known prior to randomization are usually categorical in type, and the corresponding subpopulations are called strata. In contrast to randomization which in most cases is performed in a way ensuring approximately constant sample size ratios across the strata, sample size planning is rarely done taking stratification into account. This holds true although the statistical literature provides a reasonably rich repertoire of testing procedures for stratified comparisons between two treatments in a parallel group design. For all of them, at least approximate methods of power calculation are available from which algorithms or even closed-form formulae for required sample sizes can be derived. The objective of this tutorial is to give a systematic review of the most frequently applicable of these methods and to compare them in terms of their efficiency under standard settings. Based on the results, recommendations for the sample size planning of stratified two-arm trials are given.

scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies

Single cell RNA-seq has revolutionized transcriptomics by providing cell type resolution for differential gene expression and expression quantitative trait loci (eQTL) analyses. However, efficient power analysis methods for single cell data and inter-individual comparisons are lacking. Here, we present scPower; a statistical framework for the design and power analysis of multi-sample single cell transcriptomic experiments. We modelled the relationship between sample size, the number of cells per individual, sequencing depth, and the power of detecting differentially expressed genes within cell types. We systematically evaluated these optimal parameter combinations for several single cell profiling platforms, and generated broad recommendations. In general, shallow sequencing of high numbers of cells leads to higher overall power than deep sequencing of fewer cells. The model, including priors, is implemented as an R package and is accessible as a web tool. scPower is a highly customizable tool that experimentalists can use to quickly compare a multitude of experimental designs and optimize for a limited budget.

ESCP Safe Anastomosis ProGramme in CoLorectal SurgEry (EAGLE): Study protocol for an international cluster randomised trial of a quality improvement intervention to reduce anastomotic leak following right colectomy

AIM

Cohort data suggest that anastomotic leak occurs after 8% of right colectomies causing significant morbidity and mortality. Patient selection, intra-operative factors, and technical variation all contribute to risk of leak. The EAGLE study will assess whether implementation of the European Society of Coloproctology (ESCP) Safe Anastomosis Intervention reduces anastomotic leak following right colectomy.

METHODS

An international, multi-centre, cluster randomised trial will be undertaken with hospitals as clusters. Hospitals will be recruited in a number of distinct phases, with each phase following the same research plan, in which clusters are randomised to one of three, staggered (dog-leg) schedules for implementation of the Safe Anastomosis Intervention.

RESULTS

Results from different phases will be meta-analysed. The intervention is a three-component behavioural change programme for surgeons, anaesthetists and operating room staff, supported by an online learning environment. All colorectal surgical units around the world will be eligible. Adults undergoing elective or emergency right colectomy or ileocaecal resection, by any approach and for any indication will be included. The primary outcome is 30-day anastomotic leak rate, defined as clinical or radiologically-detected leak or intra-abdominal or pelvic collection. Assuming hospitals provide data for an average of 10 patients per two month recruitment period, 333 clusters (4440 patients in total) will allow for detection of an absolute risk reduction of anastomotic leak from 8.1% to 5.6% (relative risk reduction 30%). This protocol adheres to Standard Protocol Items: Recommendations for Intervention Trials (SPIRIT).

DISCUSSION

The protocol describes the methods for an evaluation of a hospital-level, education-based quality improvement intervention targeted to reduce the life-threatening surgical complication of anastomotic leak.

Prognostic Research in Traumatic Brain Injury: Markers, Modeling, and Methodological Principles

Prognostic assessment in traumatic brain injury (TBI) is embedded deeply in clinical care. Considering the limitations of current prognostic indicators, there is increasing interest in understanding the role of new biomarkers, and in finding other prognostic indicators of long-term outcomes following TBI. New prognostic indicators may result in the development of more accurate prediction models that could be useful for both risk stratification and clinical decision making. We aimed to review methodological issues and provide tentative guidelines for prognostic research in TBI. Prognostic factor research focuses on the role of a specific patient or disease-related characteristic in relation to outcome. Typically, univariable relations of the prognostic factor are studied, followed by analyses adjusting for other variables related to the outcome. Following existing guidelines, we emphasize the importance of transparent reporting of patient and specimen characteristics, study design, clinical end-points, and statistical analysis. Prognostic model research considers combinations of predictors, with challenges for model specification, estimation, evaluation, validation, and presentation. We highlight modern approaches and opportunities related to missing values, exploration of non-linear effects, and assessing between-study heterogeneity. Prognostic research in TBI can be improved if key methodological principles are adhered to and when research is performed in collaboration among multiple centers to ensure generalizability.

Simple compared to covariate-constrained randomization methods in balancing baseline characteristics: a case study of randomly allocating 72 hemodialysis centers in a cluster trial

Background and aim

Some parallel-group cluster-randomized trials use covariate-constrained rather than simple randomization. This is done to increase the chance of balancing the groups on cluster- and patient-level baseline characteristics. This study assessed how well two covariate-constrained randomization methods balanced baseline characteristics compared with simple randomization.

Methods

We conducted a mock 3-year cluster-randomized trial, with no active intervention, that started April 1, 2014, and ended March 31, 2017. We included a total of 11,832 patients from 72 hemodialysis centers (clusters) in Ontario, Canada. We randomly allocated the 72 clusters into two groups in a 1:1 ratio on a single date using individual- and cluster-level data available until April 1, 2013. Initially, we generated 1000 allocation schemes using simple randomization. Then, as an alternative, we performed covariate-constrained randomization based on historical data from these centers. In one analysis, we restricted on a set of 11 individual-level prognostic variables; in the other, we restricted on principal components generated using 29 baseline historical variables.

We created 300,000 different allocations for the covariate-constrained randomizations, and we restricted our analysis to the 30,000 best allocations based on the smallest sum of the penalized standardized differences. We then randomly sampled 1000 schemes from the 30,000 best allocations. We summarized our results with each randomization approach as the median (25th and 75th percentile) number of balanced baseline characteristics. There were 156 baseline characteristics, and a variable was balanced when the between-group standardized difference was ≤ 10%.

Results

The three randomization techniques had at least 125 of 156 balanced baseline characteristics in 90% of sampled allocations. The median number of balanced baseline characteristics using simple randomization was 147 (142, 150). The corresponding value for covariate-constrained randomization using 11 prognostic characteristics was 149 (146, 151), while for principal components, the value was 150 (147, 151).

Conclusion

In this setting with 72 clusters, constraining the randomization using historical information achieved better balance on baseline characteristics compared with simple randomization; however, the magnitude of benefit was modest.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-021-05590-1.

Multiple imputation for handling missing outcome data in randomized trials involving a mixture of independent and paired data

Randomized trials involving independent and paired observations occur in many areas of health research, for example in paediatrics, where studies can include infants from both single and twin births. Multiple imputation (MI) is often used to address missing outcome data in randomized trials, yet its performance in trials with independent and paired observations, where design effects can be less than or greater than one, remains to be explored. Using simulated data and through application to a trial dataset, we investigated the performance of different methods of MI for a continuous or binary outcome when followed by analysis using generalized estimating equations to account for clustering due to the pairs. We found that imputing data separately for independent and paired data, with paired data imputed in wide format, was the best performing MI method, producing unbiased point and standard error estimates for the treatment effect throughout. Ignoring clustering in the imputation model performed well in settings where the design effect due to the inclusion of paired data was close to one, but otherwise led to moderately biased variance estimates. Including a random cluster effect in the imputation model led to slightly biased point estimates for binary outcome data and variance estimates that were too small in some settings. Based on these results, we recommend researchers impute independent and paired data separately where feasible to do so. The exception is if the design effect due to the inclusion of paired data is close to one, where ignoring clustering may be appropriate.

The implications of outcome truncation in reproductive medicine RCTs: a simulation platform for trialists and simulation study

BACKGROUND

Randomised controlled trials in reproductive medicine are often subject to outcome truncation, where the study outcomes are only defined in a subset of the randomised cohort. Examples include birthweight (measurable only in the subgroup of participants who give birth) and miscarriage (which can only occur in participants who become pregnant). These outcomes are typically analysed by making a comparison between treatment arms within the subgroup (for example, comparing birthweights in the subgroup who gave birth or miscarriages in the subgroup who became pregnant). However, this approach does not represent a randomised comparison when treatment influences the probability of being observed (i.e. survival). The practical implications of this for the design and interpretation of reproductive trials are unclear however.

METHODS

We developed a simulation platform to investigate the implications of outcome truncation for reproductive medicine trials. We used this to perform a simulation study, in which we considered the bias, type 1 error, coverage, and precision of standard statistical analyses for truncated continuous and binary outcomes. Simulation settings were informed by published assisted reproduction trials.

RESULTS

Increasing treatment effect on the intermediate variable, strength of confounding between the intermediate and outcome variables, and the presence of an interaction between treatment and confounder were found to adversely affect performance. However, within parameter ranges we would consider to be more realistic, the adverse effects were generally not drastic. For binary outcomes, the study highlighted that outcome truncation could cause separation in smaller studies, where none or all of the participants in a study arm experience the outcome event. This was found to have severe consequences for inferences.

CONCLUSION

We have provided a simulation platform that can be used by researchers in the design and interpretation of reproductive medicine trials subject to outcome truncation and have used this to conduct a simulation study. The study highlights several key factors which trialists in the field should consider carefully to protect against erroneous inferences. Standard analyses of truncated binary outcomes in small studies may be highly biassed, and it remains to identify suitable approaches for analysing data in this context.

Recommendations for Statistical Reporting in Cardiovascular Medicine: A Special Report From the American Heart Association

Statistical analyses are a crucial component of the biomedical research process and are necessary to draw inferences from biomedical research data. The application of sound statistical methodology is a prerequisite for publication in the American Heart Association (AHA) journal portfolio. The objective of this document is to summarize key aspects of statistical reporting that might be most relevant to the authors, reviewers, and readership of AHA journals. The AHA Scientific Publication Committee convened a task force to inventory existing statistical standards for publication in biomedical journals and to identify approaches suitable for the AHA journal portfolio. The experts on the task force were selected by the AHA Scientific Publication Committee, who identified 12 key topics that serve as the section headers for this document. For each topic, the members of the writing group identified relevant references and evaluated them as a resource to make the standards summarized herein. Each section was independently reviewed by an expert reviewer who was not part of the task force. Expert reviewers were also permitted to comment on other sections if they chose. Differences of opinion were adjudicated by consensus. The standards presented in this report are intended to serve as a guide for high-quality reporting of statistical analyses methods and results.

Covariate adjustment in subgroup analyses of randomized clinical trials: A propensity score approach

BACKGROUND

Subgroup analyses are frequently conducted in randomized clinical trials to assess evidence of heterogeneous treatment effect across patient subpopulations. Although randomization balances covariates within subgroups in expectation, chance imbalance may be amplified in small subgroups and adversely impact the precision of subgroup analyses. Covariate adjustment in overall analysis of randomized clinical trial is often conducted, via either analysis of covariance or propensity score weighting, but covariate adjustment for subgroup analysis has been rarely discussed. In this article, we develop propensity score weighting methodology for covariate adjustment to improve the precision and power of subgroup analyses in randomized clinical trials.

METHODS

We extend the propensity score weighting methodology to subgroup analyses by fitting a logistic regression propensity model with pre-specified covariate-subgroup interactions. We show that, by construction, overlap weighting exactly balances the covariates with interaction terms in each subgroup. Extensive simulations were performed to compare the operating characteristics of unadjusted estimator, different propensity score weighting estimators and the analysis of covariance estimator. We apply these methods to the Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training trial to evaluate the effect of exercise training on 6-min walk test in several pre-specified subgroups.

RESULTS

Standard errors of the adjusted estimators are smaller than those of the unadjusted estimator. The propensity score weighting estimator is as efficient as analysis of covariance, and is often more efficient when subgroup sample size is small (e.g. <125), and/or when outcome model is misspecified. The weighting estimators with full-interaction propensity model consistently outperform the standard main-effect propensity model.

CONCLUSION

Propensity score weighting is a transparent and objective method to adjust chance imbalance of important covariates in subgroup analyses of randomized clinical trials. It is crucial to include the full covariate-subgroup interactions in the propensity score model.

Methods for population adjustment with limited access to individual patient data: A review and simulation study

Population-adjusted indirect comparisons estimate treatment effects when access to individual patient data is limited and there are cross-trial differences in effect modifiers. Popular methods include matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC). There is limited formal evaluation of these methods and whether they can be used to accurately compare treatments. Thus, we undertake a comprehensive simulation study to compare standard unadjusted indirect comparisons, MAIC and STC across 162 scenarios. This simulation study assumes that the trials are investigating survival outcomes and measure continuous covariates, with the log hazard ratio as the measure of effect. MAIC yields unbiased treatment effect estimates under no failures of assumptions. The typical usage of STC produces bias because it targets a conditional treatment effect where the target estimand should be a marginal treatment effect. The incompatibility of estimates in the indirect comparison leads to bias as the measure of effect is non-collapsible. Standard indirect comparisons are systematically biased, particularly under stronger covariate imbalance and interaction effects. Standard errors and coverage rates are often valid in MAIC but the robust sandwich variance estimator underestimates variability where effective sample sizes are small. Interval estimates for the standard indirect comparison are too narrow and STC suffers from bias-induced undercoverage. MAIC provides the most accurate estimates and, with lower degrees of covariate overlap, its bias reduction outweighs the loss in precision under no failures of assumptions. An important future objective is the development of an alternative formulation to STC that targets a marginal treatment effect.

Effects of Soy Isoflavones on Glycemic Control and Lipid Profile in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

The aim of the report was to investigate the impact of soy protein and isoflavones on glucose homeostasis and lipid profile in type 2 diabetes. The studies used in this report were identified by searching through the MEDLINE and EMBASE databases (up to 2020). Meta-regression and subgroup analyses were performed to explore the influence of covariates on net glycemic control and lipid changes. Weighted mean differences and 95% confidence intervals (CI) were calculated by using random-effect models. Changes in the lipid profile showed statistically significant decreases in total cholesterol and LDL-C concentrations: ‒0.21 mmol/L; 95% CI, ‒0.33 to ‒0.09; p = 0.0008 and ‒0.20 mmol/L; 95% CI, ‒0.28 to ‒0.12; p < 0.0001, respectively, as well as in HDL-C (−0.02 mmol/L; 95% CI, −0.05 to 0.01; p = 0.2008 and triacylglycerols (−0.19 mmol/L; 95% CI, −0.48 to 0.09; p = 0.1884). At the same time, a meta-analysis of the included studies revealed statistically insignificant reduction in fasting glucose, insulin, HbA1c, and HOMA-IR (changes in glucose metabolism) after consumption of soy isoflavones. The observed ability of both extracted isoflavone and soy protein with isoflavones to modulate the lipid profile suggests benefits in preventing cardiovascular events in diabetic subjects. Further multicenter studies based on larger and longer duration studies are necessary to determine their beneficial effect on glucose and lipid metabolism.

Effects of Soy Isoflavones on Biochemical Markers of Bone Metabolism in Postmenopausal Women: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

This systematic review and meta-analysis of randomized controlled trials was performed to more completely assess potential changes in bone turnover marker levels in postmenopausal women during the intake of soy isoflavones. PubMed (Medline) and EMBASE were searched for relevant studies, and their quality was evaluated according to Cochrane criteria. The levels of markers were evaluated in a total of 1114 women who ingested mean daily doses of 98.2 mg (30.9 to 300) of soy isoflavones for 3 to 24 months, in comparison to those of 1081 subjects who used a placebo. Ten, eighteen, eight, and fourteen comparison studies were finally selected for an estimation of the effects on osteocalcin (OC), bone alkaline phosphatase (BAP), pyridinoline (PYD), and deoxypyridinoline (DPD), respectively. A summary of the results of intervention was as follows: 4.16%, 95% CI: -7.72-16.04, p = 0.49 for OC; 5.50%, 95% CI: -3.81-14.82, p = 0.25 for BAP; -12.09%, 95% CI: -25.37-1.20, p = 0.07 for PYD; and -7.48%, 95% CI: -15.37-0.41, p = 0.06 for DPD. The meta-analysis of the included studies revealed some statistically insignificant observations that soy isoflavones intake is associated with a trend in increased levels of OC and BAP, as well as a trend in reduced levels of PYD and DPD. Soy isoflavones may have a beneficial effect on bone formation markers, but this requires extensive multi-center research.

The effectiveness of COVID-related message framing on public beliefs and behaviors related to plant-based diets

Shifting the public towards plant-based diets is critical for achieving environmental and public health outcomes. Increasingly news articles and organizations have begun using the saliency of the COVID-19 crisis to highlight the link between animal agriculture, pandemic risks, and other widespread public health threats. Yet, little is known about the effectiveness of this messaging strategy for motivating dietary change. We conducted a randomized trial with an online sample to examine the impact of: (1) a message that uses the saliency of the COVID-19 pandemic to highlight the risk of disease transmission from factory farms, and (2) a message that uses the saliency of the COVID-19 pandemic to highlight the threat to worker's health created by factory farms. We examine whether these messages are more effective at changing beliefs about and behavioral intentions towards plant-based eating, as compared to more traditional messages that highlight the environmental, personal health, or animal welfare implications of factory farmed meat consumption. We find that all messages differentially influenced beliefs about the various negative consequences of meat consumption. However, these altered beliefs did not differentially motivate changes in respondents' intentions to reduce meat consumption and choose plant-based alternatives. This was possibly due to the numerous other barriers to behavior change identified in qualitative survey responses, such as cost, taste, and social factors. We did find that messages that highlight the personal health benefits of reduced meat consumption were more effective at increasing public trust in the message deliverer. Our results suggest that highlighting personal health benefits in messaging and addressing the additional identified barriers to behavior change may be critical for building trust and shifting the public towards plant-based diets.

Effect of Early High-Dose Vitamin D3 Repletion on Cognitive Outcomes in Critically Ill Adults

BACKGROUND

Long-term cognitive impairment frequently occurs after critical illness; no treatments are known to improve long-term cognition.

RESEARCH QUESTION

Does a single high-dose (540,000 International Units) enteral treatment of vitamin D3 given shortly after hospital admission in critically ill patients who are vitamin D deficient improve long-term global cognition or executive function?

STUDY DESIGN AND METHODS

This study evaluated long-term cognitive outcomes among patients enrolled in a multicenter, blinded, randomized clinical trial comparing vitamin D3 treatment vs placebo in critically ill adults with vitamin D deficiency. Global cognition was measured by the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Executive function was measured with a composite score derived from three Delis-Kaplan Executive Function System subscales. Outcomes were assessed at a median of 443 days (interquartile range, 390-482 days) after randomization and were compared using multivariate proportional odds regression. Adjusted ORs of > 1.0 would indicate better outcomes in the vitamin D3 group compared with the placebo group.

RESULTS

Ninety-five patients were enrolled, including 47 patients randomized to vitamin D3 treatment and 48 patients randomized to placebo. The adjusted median RBANS score at follow-up was 79.6 (95% CI, 73.0-84.0) in the vitamin D3 group and 82.1 (95% CI, 74.7-84.6) in the placebo group (adjusted OR, 0.83; 95% CI, 0.50-1.38). The adjusted median executive function composite scores were 8.1 (95% CI, 6.8-9.0) and 8.7 (95% CI, 7.4-9.3), respectively (adjusted OR, 0.72; 95% CI, 0.36-1.42).

INTERPRETATION

In vitamin D-deficient, critically-ill adults, a large dose of enteral vitamin D3 did not improve long-term global cognition or executive function.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT03733418; URL: www.clinicaltrials.gov.

Vasopressin and methylprednisolone for in-hospital cardiac arrest - Protocol for a randomized, double-blind, placebo-controlled trial

OBJECTIVE

To describe the clinical trial "Vasopressin and Methylprednisolone for In-Hospital Cardiac Arrest" (VAM-IHCA).

METHODS

The VAM-IHCA trial is an investigator-initiated, multicenter, randomized, placebo-controlled, parallel group, double-blind, superiority trial of vasopressin and methylprednisolone during adult in-hospital cardiac arrest. The study drugs consist of 40 mg methylprednisolone and 20 IU of vasopressin given as soon as possible after the first dose of adrenaline. Additional doses of vasopressin (20 IU) will be administered after each adrenaline dose for a maximum of four doses (80 IU).The primary outcome is return of spontaneous circulation and key secondary outcomes include survival and survival with a favorable neurological outcome at 30 days. 492 patients will be enrolled. The trial was registered at the EU Clinical Trials Register (EudraCT Number: 2017-004773-13) on Jan. 25, 2018 and ClinicalTrials.gov (Identifier: NCT03640949) on Aug. 21, 2018.

RESULTS

The trial started in October 2018 and the last patient is anticipated to be included in January 2021. The primary results will be reported after 3-months follow-up and are, therefore, anticipated in mid-2021.

CONCLUSION

The current article describes the design of the VAM-IHCA trial. The results from this trial will help clarify whether the combination of vasopressin and methylprednisolone when administered during in-hospital cardiac arrest improves outcomes.

Propensity score weighting for covariate adjustment in randomized clinical trials

Chance imbalance in baseline characteristics is common in randomized clinical trials. Regression adjustment such as the analysis of covariance (ANCOVA) is often used to account for imbalance and increase precision of the treatment effect estimate. An objective alternative is through inverse probability weighting (IPW) of the propensity scores. Although IPW and ANCOVA are asymptotically equivalent, the former may demonstrate inferior performance in finite samples. In this article, we point out that IPW is a special case of the general class of balancing weights, and advocate to use overlap weighting (OW) for covariate adjustment. The OW method has a unique advantage of completely removing chance imbalance when the propensity score is estimated by logistic regression. We show that the OW estimator attains the same semiparametric variance lower bound as the most efficient ANCOVA estimator and the IPW estimator for a continuous outcome, and derive closed-form variance estimators for OW when estimating additive and ratio estimands. Through extensive simulations, we demonstrate OW consistently outperforms IPW in finite samples and improves the efficiency over ANCOVA and augmented IPW when the degree of treatment effect heterogeneity is moderate or when the outcome model is incorrectly specified. We apply the proposed OW estimator to the Best Apnea Interventions for Research (BestAIR) randomized trial to evaluate the effect of continuous positive airway pressure on patient health outcomes. All the discussed propensity score weighting methods are implemented in the R package PSweight.

Patient reported outcome measures in clinical trials should be initially analyzed as continuous outcomes for statistical significance and responder analyses should be reserved as secondary analyses

OBJECTIVE

To evaluate the power of responder analyses in a randomized controlled trial.

STUDY DESIGN AND SETTING

Simulations were based on the Chronic Kidney Disease Antidepressant Sertraline Trial (CAST), which compared sertraline to placebo for the treatment of depression in kidney disease. Baseline disease severity, placebo response, effect size, and the proportion of responders were varied across 72 scenarios. Power was assessed using a t-test for change scores, and the chi-square test for dichotomized outcomes of the minimal important difference (MID), improvement and remission in 10,000 datasets with a fixed sample size of 193.

RESULTS

The t-test had >80% power except for scenarios with the lowest sertraline effect size. The chi-square test using the MID had <7% power in all scenarios while improvement and remission of achieved >80% power only at higher effect sizes and/or when the proportion of responders was highest at 0.5. The chi-square test for improvement had marginal power increases compared to the t-test (4/72 scenarios = 5.6%) and that for remission did not outperform the t-test in any scenario.

CONCLUSIONS

The t-test outperforms the chi-square test for dichotomized outcomes regardless of baseline disease severity, placebo response, effect size and the proportion of responders to the intervention.

A randomised controlled trial comparing palate surgery at 6 months versus 12 months of age (the TOPS trial): a statistical analysis plan

BACKGROUND

Cleft palate is among the most common birth abnormalities. The success of primary surgery in the early months of life is crucial for successful feeding, hearing, dental development, and facial growth. Over recent decades, age at palatal surgery in infancy has reduced. The Timing Of Primary Surgery for cleft palate (TOPS) trial aims to determine whether, in infants with cleft palate, it is better to perform primary surgery at age 6 or 12 months (corrected for gestational age).

METHODS/DESIGN

The TOPS trial is an international, two-arm, parallel group, randomised controlled trial. The primary outcome is insufficient velopharyngeal function at 5 years of age. Secondary outcomes, measured at 12 months, 3 years, and 5 years of age, include measures of speech development, safety of the procedure, hearing level, middle ear function, dentofacial development, and growth. The analysis approaches for primary and secondary outcomes are described here, as are the descriptive statistics which will be reported. The TOPS protocol has been published previously.

DISCUSSION

This paper provides details of the planned statistical analyses for the TOPS trial and will reduce the risk of outcome reporting bias and data-driven results.

TRIAL REGISTRATION

ClinicalTrials.gov NCT00993551 . Registered on 9 October 2009.

Essential statistical principles of clinical trials of pain treatments

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