Four alternative methodologies for simulated treatment comparison: How could the use of simulation be re-invigorated?

Simulated treatment comparison (STC) is an established method for performing population adjustment for the indirect comparison of two treatments, where individual patient data (IPD) are available for one trial but only aggregate level information is available for the other. The most commonly used method is what we call 'standard STC'. Here we fit an outcome model using data from the trial with IPD, and then substitute mean covariate values from the trial where only aggregate level data are available, to predict what the first of these trial's outcomes would have been if its population had been the same as the second. However, this type of STC methodology does not involve simulation and can result in bias when the link function used in the outcome model is non-linear. An alternative approach is to use the fitted outcome model to simulate patient profiles in the trial for which IPD are available, but in the other trial's population. This stochastic alternative presents additional challenges. We examine the history of STC and propose two new simulation-based methods that resolve many of the difficulties associated with the current stochastic approach. A virtue of the simulation-based STC methods is that the marginal estimands are then clearly targeted. We illustrate all methods using a numerical example and explore their use in a simulation study.

First Contact Physiotherapy: An evaluation of clinical effectiveness and costs

BACKGROUND

First Contact Physiotherapy Practitioners (FCPPs) are embedded within general practice, providing expert assessment, diagnosis and management plans for patients with musculoskeletal disorders (MSKDs), without the prior need for GP consultation.

AIM

To determine the clinical effectiveness and costs of FCPP-led compared to GP-led models of care.

DESIGN AND SETTING

Multiple site case study design. UK GP practices.

METHOD

General Practice sites were recruited representing three models: 1. GP-led care; 2. FCPPs who could not prescribe/inject (Standard (St)); 3. FCPPs who could prescribe/inject (Additional Qualifications (AQ)). Patient participants from each site completed clinical outcome data at baseline, 3 and 6 months. The primary outcome was the SF-36v.2 Physical Component Score (PCS). Healthcare usage was collected for 6 months.

RESULTS

N=426 adults were recruited from 46 practices across the UK. Non-inferiority analysis showed no significant difference in physical function (SF36-PCS) across all three arms at 6 months (p=0.999). At 3 months a significant difference in numbers improving was seen between arms: 54.7% GP consultees; 72.4% FCPP-St, 66.4% FCPP-AQ; (p=0.037). No safety issues were identified. Following initial consultation, a greater proportion of patients received medication (including opioids) in the GP-led arm (44.7%) compared with FCPP-St (17.5%) and FCPP-AQ (22.8%); (p<0.001). NHS costs (initial consultation and over 6 months follow up) were significantly higher in the GP-led model (median £105.50) vs FCPP-St (£41) and FCPP-AQ (£44); (p<0.001).

CONCLUSION

FCPP led models provide safe, clinically effective and cost-beneficial management for patients with MSKDs in general practice and reduced opioid use in this cohort.

Multistep estimators of the between-study covariance matrix under the multivariate random-effects model for meta-analysis

A wide variety of methods are available to estimate the between-study variance under the univariate random-effects model for meta-analysis. Some, but not all, of these estimators have been extended so that they can be used in the multivariate setting. We begin by extending the univariate generalised method of moments, which immediately provides a wider class of multivariate methods than was previously available. However, our main proposal is to use this new type of estimator to derive multivariate multistep estimators of the between-study covariance matrix. We then use the connection between the univariate multistep and Paule-Mandel estimators to motivate taking the limit, where the number of steps tends toward infinity. We illustrate our methodology using two contrasting examples and investigate its properties in a simulation study. We conclude that the proposed methodology is a fully viable alternative to existing estimation methods, is well suited to sensitivity analyses that explore the use of alternative estimators, and should be used instead of the existing DerSimonian and Laird-type moments based estimator in application areas where data are expected to be heterogeneous. However, multistep estimators do not seem to outperform the existing estimators when the data are more homogeneous. Advantages of the new multivariate multistep estimator include its semi-parametric nature and that it is computationally feasible in high dimensions. Our proposed estimation methods are also applicable for multivariate random-effects meta-regression, where study-level covariates are included in the model.

Bayesian meta-analysis for evaluating treatment effectiveness in biomarker subgroups using trials of mixed patient populations

During drug development, evidence can emerge to suggest a treatment is more effective in a specific patient subgroup. Whilst early trials may be conducted in biomarker-mixed populations, later trials are more likely to enroll biomarker-positive patients alone, thus leading to trials of the same treatment investigated in different populations. When conducting a meta-analysis, a conservative approach would be to combine only trials conducted in the biomarker-positive subgroup. However, this discards potentially useful information on treatment effects in the biomarker-positive subgroup concealed within observed treatment effects in biomarker-mixed populations. We extend standard random-effects meta-analysis to combine treatment effects obtained from trials with different populations to estimate pooled treatment effects in a biomarker subgroup of interest. The model assumes a systematic difference in treatment effects between biomarker-positive and biomarker-negative subgroups, which is estimated from trials which report either or both treatment effects. The systematic difference and proportion of biomarker-negative patients in biomarker-mixed studies are used to interpolate treatment effects in the biomarker-positive subgroup from observed treatment effects in the biomarker-mixed population. The developed methods are applied to an illustrative example in metastatic colorectal cancer and evaluated in a simulation study. In the example, the developed method improved precision of the pooled treatment effect estimate compared with standard random-effects meta-analysis of trials investigating only biomarker-positive patients. The simulation study confirmed that when the systematic difference in treatment effects between biomarker subgroups is not very large, the developed method can improve precision of estimation of pooled treatment effects while maintaining low bias.

Identification of Neighborhood Hotspots via the Cumulative Hazard Index: Results From a Community-Partnered Low-Cost Sensor Deployment

The Strathcona neighborhood in Vancouver is particularly vulnerable to environmental injustice due to its close proximity to the Port of Vancouver, and a high proportion of Indigenous and low-income households. Furthermore, local sources of air pollutants (e.g., roadways) can contribute to small-scale variations within communities. The aim of this study was to assess hyperlocal air quality patterns (intra-neighborhood variability) and compare them to average Vancouver concentrations (inter-neighborhood variability) to identify possible disparities in air pollution exposure for the Strathcona community. Between April and August 2022, 11 low-cost sensors (LCS) were deployed within the neighborhood to measure PM2.5, NO2, and O3 concentrations. The collected 15-min concentrations were down-averaged to daily concentrations and compared to greater Vancouver region concentrations to quantify the exposures faced by the community relative to the rest of the region. Concentrations were also estimated at every 25 m grid within the neighborhood to quantify the distribution of air pollution within the community. Using population information from census data, cumulative hazard indices (CHIs) were computed for every dissemination block. We found that although PM2.5 concentrations in the neighborhood were lower than regional Vancouver averages, daily NO2 concentrations and summer O3 concentrations were consistently higher. Additionally, although CHIs varied daily, we found that CHIs were consistently higher in areas with high commercial activity. As such, estimating CHI for dissemination blocks was useful in identifying hotspots and potential areas of concern within the neighborhood. This information can collectively assist the community in their advocacy efforts.

Associations between the EQ-5D-5L and exacerbations of chronic obstructive pulmonary disease in the ETHOS trial

PURPOSE

Exacerbations of chronic obstructive pulmonary disease (COPD) are associated with deteriorating health and health-related quality of life (HRQoL) among people with COPD during and after events. HRQoL data are key to evaluating treatment cost-effectiveness and informing reimbursement decisions in COPD. EuroQoL 5-dimension 5-level (EQ-5D-5L) utility scores, based on various HRQoL measures, are used in economic evaluations of pharmacotherapy. These analyses estimated associations between EQ-5D-5L utility scores and exacerbations (new and previous) in patients with moderate-to-very severe COPD.

METHODS

Longitudinal mixed models for repeated measures (MMRM), adjusted for time and treatment, were conducted using data from the ETHOS study (NCT02465567); models regressed EQ-5D-5L on current and past exacerbations that occurred during the study, adjusting for other patient reported outcomes and clinical factors.

RESULTS

Based on the simplest covariate adjusted model (adjusted for current exacerbations and number of previous exacerbations during the study), a current moderate exacerbation was associated with an EQ-5D-5L disutility of 0.055 (95% confidence interval: 0.048, 0.062) with an additional disutility of 0.035 (0.014, 0.055) if the exacerbation was severe. After resolving, each prior exacerbation was associated with a disutility that persisted for the remainder of the study (moderate exacerbation, 0.014 [0.011, 0.016]; further disutility for severe exacerbation, 0.011 [0.003, 0.018]).

CONCLUSION

An EQ-5D-5L disutility of 0.090 was associated with a current severe exacerbation in ETHOS. Our findings suggest incorporating the effects of current, recently resolved, and cumulative exacerbations into economic models when estimating benefits and costs of COPD pharmacotherapy, as exacerbations have both acute and persistent effects.

Generalizing some key results from "alternative weighting schemes when performing matching-adjusted indirect comparisons"

A recent paper proposed an alternative weighting scheme when performing matching-adjusted indirect comparisons. This alternative approach follows the conventional one in matching the covariate means across two studies but differs in that it maximizes the effective sample size when doing so. The appendix of this paper showed, assuming there is one covariate and negative weights are permitted, that the resulting weights are linear in the covariates. This explains how the alternative method achieves a larger effective sample size and results in a metric that quantifies the difficulty of matching on particular covariates. We explain how these key results generalize to the case where there are multiple covariates, giving rise to a new metric that can be used to quantify the impact of matching on multiple covariates.

Mortality risk reduction with budesonide/glycopyrrolate/formoterol fumarate versus fluticasone furoate/umeclidinium/vilanterol in COPD: a matching-adjusted indirect comparison based on ETHOS and IMPACT

OBJECTIVE

Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide. While two approved fixed-dose inhaled corticosteroid/long-acting muscarinic antagonist (LAMA)/long-acting β2-agonist (LABA) triple therapies reduce all-cause mortality (ACM) versus dual LAMA/LABA therapy in patients with COPD, head-to-head studies have not compared the effects of these therapies on ACM. We compared ACM in adults with moderate-to-very severe COPD receiving budesonide/glycopyrrolate/formoterol fumarate (BGF) in ETHOS versus fluticasone furoate/umeclidinium/vilanterol (FF/UMEC/VI) in IMPACT using a matching-adjusted indirect comparison (MAIC).

METHODS

A systematic literature review identified two studies (ETHOS [NCT02465567]; IMPACT [NCT02164513]) of ≥52 weeks reporting ACM as an efficacy endpoint in patients receiving triple therapy. As ETHOS and IMPACT lack a common comparator, an unanchored MAIC compared ACM between licensed doses of BGF (320/18/9.6 μg) from ETHOS and FF/UMEC/VI (100/62.5/25 μg) from IMPACT in patients with moderate-to-very severe COPD. Using on- and off-treatment data from the final retrieved datasets of the intention-to-treat populations, BGF data were adjusted according to aggregate FF/UMEC/VI data using 11 baseline covariates; a supplementary unadjusted indirect treatment comparison was also conducted. P-values for these post-hoc analyses are not adjusted for Type I error.

RESULTS

ACM over 52 weeks was statistically significantly reduced by 39% for BGF versus FF/UMEC/VI in the MAIC (hazard ratio [HR] [95% CI]: 0.61 [0.38, 0.95], p = 0.030) and unadjusted analysis (HR [95% CI]: 0.61 [0.41, 0.92], p = 0.019).

CONCLUSION

In this MAIC, which adjusted for population heterogeneity between ETHOS and IMPACT, ACM was significantly reduced with BGF versus FF/UMEC/VI in patients with moderate-to-very severe COPD.

Monitoring environmental microbiomes: Alignment of microbiology and computational biology competencies within a culturally integrated curriculum and research framework

We have developed a flexible undergraduate curriculum that leverages the place-based research of environmental microbiomes to increase the number of Indigenous researchers in microbiology, data science and scientific computing. Monitoring Environmental Microbiomes (MEM) provides a curriculum and research framework designed to integrate an Indigenous approach when conducting authentic scientific research and to build interest and confidence at the undergraduate level. MEM has been successfully implemented as a short summer workshop to introduce computing practices in microbiome analysis. Based on self-assessed student knowledge of topics and skills, increased scientific confidence and interest in genomics careers were observed. We propose MEM be incorporated in a scalable course-based research experience for undergraduate institutions, including tribal colleges and universities, community colleges and other minority serving institutions. This coupled curricular and research framework explicitly considers cultural perspectives, access and equity to train a diverse future workforce that is more informed to engage in microbiome research and to translate microbiome science to benefit community and environmental health.

Survival Extrapolation Incorporating General Population Mortality Using Excess Hazard and Cure Models: A Tutorial

BACKGROUND

Different parametric survival models can lead to widely discordant extrapolations and decision uncertainty in cost-effectiveness analyses. The use of excess hazard (EH) methods, which incorporate general population mortality data, has the potential to reduce model uncertainty. This review highlights key practical considerations of EH methods for estimating long-term survival.

METHODS

Demonstration of methods used a case study of 686 patients from the German Breast Cancer Study Group, followed for a maximum of 7.3 y and divided into low (1/2) and high (3) grade cancers. Seven standard parametric survival models were fit to each group separately. The same 7 distributions were then used in an EH framework, which incorporated general population mortality rates, and fitted both with and without a cure parameter. Survival extrapolations, restricted mean survival time (RMST), and difference in RMST between high and low grades were compared up to 30 years along with Akaike information criterion goodness-of-fit and cure fraction estimates. The sensitivity of the EH models to lifetable misspecification was investigated.

RESULTS

In our case study, variability in survival extrapolations was extensive across the standard models, with 30-y RMST ranging from 7.5 to 14.3 y. Incorporation of general population mortality rates using EH cure methods substantially reduced model uncertainty, whereas EH models without cure had less of an effect. Long-term treatment effects approached the null for most models but at varying rates. Lifetable misspecification had minimal effect on RMST differences.

CONCLUSIONS

EH methods may be useful for survival extrapolation, and in cancer, EHs may decrease over time and be easier to extrapolate than all-cause hazards. EH cure models may be helpful when cure is plausible and likely to result in less extrapolation variability.

HIGHLIGHTS

In health economic modeling, to help anchor long-term survival extrapolation, it has been recommended that survival models incorporate background mortality rates using excess hazard (EH) methods.We present a thorough description of EH methods with and without the assumption of cure and demonstrate user-friendly software to aid researchers wishing to use these methods.EH models are applied to a case study, and we demonstrate that EHs are easier to extrapolate and that the use of the EH cure model, when cure is plausible, can reduce extrapolation variability.EH methods are relatively robust to lifetable misspecification.

Identification of Fatigue and Sleepiness in Immune and Neurodegenerative Disorders from Measures of Real-World Gait Variability

Current assessments of fatigue and sleepiness rely on patient reported outcomes (PROs), which are subjective and prone to recall bias. The current study investigated the use of gait variability in the "real world" to identify patient fatigue and daytime sleepiness. Inertial measurement units were worn on the lower backs of 159 participants (117 with six different immune and neurodegenerative disorders and 42 healthy controls) for up to 20 days, whom completed regular PROs. To address walking bouts that were short and sparse, four feature groups were considered: sequence-independent variability (SIV), sequence-dependant variability (SDV), padded SDV (PSDV), and typical gait variability (TGV) measures. These gait variability measures were extracted from step, stride, stance, and swing time, step length, and step velocity. These different approaches were compared using correlations and four machine learning classifiers to separate low/high fatigue and sleepiness.Most balanced accuracies were above 50%, the highest was 57.04% from TGV measures. The strongest correlation was 0.262 from an SDV feature against sleepiness. Overall, TGV measures had lower correlations and classification accuracies.Identifying fatigue or sleepiness from gait variability is extremely complex and requires more investigation with a larger data set, but these measures have shown performances that could contribute to a larger feature set.Clinical relevance- Gait variability has been repeatedly used to assess fatigue in the lab. The current study, however, explores gait variability for fatigue and daytime sleepiness in real-world scenarios with multiple gait-impacted disorders.

A protocol for the evaluation of a wearable device for monitoring of symptoms, and cueing for the management of drooling, in people with Parkinson's disease

Drooling is a common symptom of Parkinson's Disease (PD) experienced in up to 70% of people with PD (PwP). Drooling can be a major problem in PwP leading to adverse physical and psychosocial issues. Current medical treatments decrease the production of saliva, whereas the problem is due to decreased swallowing frequency, not over production of saliva. Such treatments are problematic as saliva is essential for good oral health. Therefore, non-invasive treatments options such as behavioural cueing methods are recommended. A wrist-worn device delivering haptic cueing has been demonstrated to be an effective treatment method to increase swallowing frequency and a socially acceptable solution for PwP. However, the device had limited functionality and was tested on a small sample size over a short period of usage. Further work is required to understand the real-world behaviours and usage of the intervention to understand the longer-term effects with a larger sample size. This research will deploy CueBand, a discrete and comfortable wrist-worn device designed to work with a smartphone application to support the real-world evaluation of haptic cueing for the management of drooling. We will recruit 3,000 PwP to wear the device day and night for the intervention period to gain a greater understanding of the effectiveness and acceptability of the technology within real-world use. Additionally, 300 PwP who self-identity as having an issue with drooling will be recruited into an intervention study to evaluate the effectiveness of the wrist-worn CueBand to deliver haptic cueing (3-weeks) compared with smartphone cueing methods (3-weeks). PwP will use our smartphone application to self-assess their drooling frequency, severity, and duration using visual analogue scales and through the completion of daily diaries. Semi-structured interviews to gain feedback about utility of CueBand will be conducted following participants completion of the intervention.

Can wrist-worn devices and a smartphone application influence arm activity in children with unilateral cerebral palsy? A proof-of-concept study

Aim

To determine whether a wrist-worn triaxial accelerometer-based device and software (including smartphone application), incorporating feedback, is feasible, acceptable, and can lead to increased affected upper limb use during everyday activities in children with unilateral cerebral palsy (UCP).

Methods

Study design: Mixed methods proof of concept study. Participants: Children aged 8-18 years with UCP; age-matched typically developing controls ("Buddies"), therapists. Intervention: Baseline (2 weeks): devices recorded arm activity. Active feedback (6 weeks): devices also gave vibratory prompts if affected arm activity fell below pre-set personalised thresholds (UCP group only; control group continued as per Baseline). Final 2 weeks: as baseline. Both groups accessed a smartphone application providing feedback on relative arm motion throughout the study. Assessment and analysis: ABILHAND-Kids questionnaires and MACS classifications captured baseline participant characteristics (UCP group). Accelerometer data was used to calculate relative arm activity (signal vector magnitude) corrected for time worn/day, and trends in relative arm activity examined using single case experimental design (both groups). In-depth interviews with families, "Buddies" and therapists assessed feasibility and acceptability of implementation. A framework approach was used for qualitative data analysis.

Results

We recruited 19 participants with UCP; 19 buddies; and 7 therapists. Five participants (two with UCP) did not complete the study. Baseline mean (stdev) ABILHAND-Kids score of children with UCP who completed the study was 65.7 (16.2); modal MACS score was II.Qualitative analysis demonstrated acceptability and feasibility of the approach. Active therapist input for this group was minimal. Therapists appreciated the potential for summary patient data to inform management. Arm activity in children with UCP increased in the hour following a prompt (mean effect size z = 0.261) for the non-dominant hand, and the dominant hand (z = 0.247). However, a significant increase in affected arm activity between baseline and intervention periods was not demonstrated.

Discussion

Children with UCP were prepared to wear the wristband devices for prolonged periods. Whilst arm activity increased bilaterally in the hour following a prompt, increases were not sustained. Delivery of the study during the COVID-19 pandemic may have negatively influenced findings. Technological challenges occurred but could be overcome. Future testing should incorporate structured therapy input.

Wrist-Worn devices to encourage affected upper limb movement in unilateral cerebral palsy: Participatory design workshops

Background

Unilateral (Hemiplegic) cerebral palsy (UCP) causes weakness and stiffness affecting one sided of the body, often impacting activities of daily living. Upper limb therapy at effective intensity is not accessible to most.

Aim

To determine stakeholder views on design of an approach using wrist-worn devices and a smartphone application to encourage use of the affected upper limb for children with hemiplegia.

Method

Four participatory design workshops and one young people's advisory group workshop incorporating views of five young people with hemiplegia, 13 typically developing peers aged 8-18 years, four parents, three occupational therapists, one teacher and two paediatricians. Two special educational needs co-ordinators were consulted separately. Peers were included to explore a study design whereby each child with hemiplegia would have a participating "buddy". Topics included views on an acceptable wrist-worn device and smartphone application, participant age range, involvement of a buddy, and barriers to using the technology in a school setting. Ethical/welfare considerations included data security, and potential risks around providing smartphones to young children.

Results

Children wanted a comfortable, conventional-appearing wristband incorporating a watch face and a secure, well-fitting strap. They were prepared to wear a band on each wrist. They wanted support with explaining the study to schoolteachers. Most schools restricted smartphone use during the school day: the study design accommodated this. Children agreed with a game as reward but had different views on an acceptable game; direct access to feedback data was preferred by some. Parents commented on the lack of access to upper limb therapy for children with UCP; therapists concurred. The proposed participant age range was widened based on feedback. Typically developing children were prepared to be buddies to help a friend with CP. Stakeholders were reassured by data security explanations and plans to provide internet safety information to participants.

Conclusion

The participatory design process informed plans for the proof-of-concept stage of the study, hopefully leading to an approach that will be fun, easy to integrate into everyday life, and have the capacity to increase use of the affected arm and hand.

353 Patients with N2 Non-Small Cell Primary Lung Cancer, Does Surgery Offer an Advantage?

Background

Non-small cell lung cancer (NSCLC) is one of two histological types of lung cancers. Evidence-based guidelines recommend radical resection in early-stage lung cancer, N2 disease is when there is moderately advanced lymph node spread of the cancer, recommendations for surgery at this stage are more contested.

Aim

This was a service evaluation of the management of patients with NSCLC N2 disease. We aimed to evaluate the management and outcomes of these patients to see whether surgery offers an advantage over other modalities.

Method

This was a retrospective cohort study reviewing patients treated at University Hospital Southampton (UHS) who were diagnosed with N2 NSCLC before the start of treatment from 2016–2018 and comparing survival of surgically managed patients with those treated in other ways.

Results

50 N2 patients were included, 32 had radical treatment and 11 of these underwent surgery. Kaplan-Meier survival analysis showed no significant statistical difference (Log rank = 2.342 p = 0.679) between surgery and other treatments, the survival plots showed a slightly better initial survival for chemoradiotherapy. Men were statistically more likely to have squamous cell carcinomas than women who had more adenocarcinomas. PS was shown to have significant effect on survival with higher PS having a worse survival.

Conclusions

We didn't find a statistically significant difference in the survival between patients managed surgically and other modalities. PS's effect on survival reiterates its importance when deciding on treatments to offer patients.

Multivariate meta-analysis of multiple outcomes: characteristics and predictors of borrowing of strength from Cochrane reviews

OBJECTIVES

Multivariate meta-analysis allows the joint synthesis of multiple outcomes accounting for their correlation. This enables borrowing of strength (BoS) across outcomes, which may lead to greater efficiency and even different conclusions compared to separate univariate meta-analyses. However, multivariate meta-analysis is complex to apply, so guidance is needed to flag (in advance of analysis) when the approach is most useful.

STUDY DESIGN AND SETTING

We use 43 Cochrane intervention reviews to empirically investigate the characteristics of meta-analysis datasets that are associated with a larger BoS statistic (from 0 to 100%) when applying a bivariate meta-analysis of binary outcomes.

RESULTS

Four characteristics were identified as strongly associated with BoS: the total number of studies, the number of studies with the outcome of interest, the percentage of studies missing the outcome of interest, and the largest absolute within-study correlation. Using these characteristics, we then develop a model for predicting BoS in a new dataset, which is shown to have good performance (an adjusted R² of 50%). Applied examples are used to illustrate the use of the BoS prediction model.

CONCLUSIONS

Cochrane reviewers mainly use univariate meta-analysis methods, but the identified characteristics associated with BoS and our subsequent prediction model for BoS help to flag when a multivariate meta-analysis may also be beneficial in Cochrane reviews with multiple binary outcomes. Extension to non-Cochrane reviews and other outcome types is still required.

Comment on 'statistical consideration and challenges in bridging study of personalized medicine': a modified variance for sensitivity analysis

A pivotal clinical trial is often necessary to assess drug efficacy in the intended to use (IU) population. Ideally, patients should be enrolled based on a positive test result from a well-characterized companion diagnostic (CDx). However, the central challenge is that patients are instead recruited on the basis of a clinical trial assay (CTA) result. This challenge arises because, CTA is available at all local labs; the time delay to enable enrollment based on CDx could result in a significant proportion of patients being unable to participate, adversely affecting precision and/or bias. The difficulty is therefore that patients are recruited on the basis that their CTA result is positive (CTA+) but the goal is to assess the drug efficacy in patients positive by the companion diagnostic (CDx+). In this commentary, we will examine an apparent weakness of a variance formula that is proposed in the context of a sensitivity analysis. We will develop an alternative formula, and argue that this should be used instead.

A Cost-Utility Analysis of Trabecular Bypass Devices Versus Usual Care for Patients With Open-Angle Glaucoma

OBJECTIVE

The aim of this study was to determine whether insertion of a trabecular bypass device (TBD) is a cost-effective intervention for the treatment of open-angle glaucoma (OAG) with mild to moderate vision loss in the Australian setting.

METHODS

We performed a cost-utility analysis of TBD implantation in conjunction with cataract surgery or as a standalone procedure in patients with OAG. The model used a Monte Carlo simulation to follow individual patients through a glaucoma treatment algorithm that included TBD and compared the costs and outcomes with those of patients simulated through an algorithm without TBD (usual care). The model tracked the intraocular pressure (IOP) of individual patients and then, based on this IOP, tracked the progression of the patient's glaucoma. Utility values were assigned dependent on severity of glaucoma. The analysis took the perspective of the Australian health care system. The main outcome was incremental cost per quality-adjusted life-year (QALY) of TBD versus usual care for the treatment of OAG.

RESULTS

In the cataract surgery population, TBD surgery was associated with incremental healthcare costs of A$177 and 0.0726 QALYs per patient, resulting in an incremental cost per QALY gained of A$2430. In the standalone population, the overall incremental cost of TBD surgery versus usual care was A$2234. With QALYs gained of 0.1526 per patient, this equated to an incremental cost per QALY gained ratio of A$14,644.

CONCLUSION

The incremental cost per QALY estimates for TBD were below thresholds generally accepted by Australian healthcare payers, suggesting that TBD is a cost-effective intervention for patients with primary OAG in the Australian setting.

Quantitative CT Characteristics of Cluster Phenotypes in the Severe Asthma Research Program Cohorts

Background Clustering key clinical characteristics of participants in the Severe Asthma Research Program (SARP), a large, multicenter prospective observational study of patients with asthma and healthy controls, has led to the identification of novel asthma phenotypes. Purpose To determine whether quantitative CT (qCT) could help distinguish between clinical asthma phenotypes. Materials and Methods A retrospective cross-sectional analysis was conducted with the use of qCT images (maximal bronchodilation at total lung capacity [TLC], or inspiration, and functional residual capacity [FRC], or expiration) from the cluster phenotypes of SARP participants (cluster 1: minimal disease; cluster 2: mild, reversible; cluster 3: obese asthma; cluster 4: severe, reversible; cluster 5: severe, irreversible) enrolled between September 2001 and December 2015. Airway morphometry was performed along standard paths (RB1, RB4, RB10, LB1, and LB10). Corresponding voxels from TLC and FRC images were mapped with use of deformable image registration to characterize disease probability maps (DPMs) of functional small airway disease (fSAD), voxel-level volume changes (Jacobian), and isotropy (anisotropic deformation index [ADI]). The association between cluster assignment and qCT measures was evaluated using linear mixed models. Results A total of 455 participants were evaluated with cluster assignments and CT (mean age ± SD, 42.1 years ± 14.7; 270 women). Airway morphometry had limited ability to help discern between clusters. DPM fSAD was highest in cluster 5 (cluster 1 in SARP III: 19.0% ± 20.6; cluster 2: 18.9% ± 13.3; cluster 3: 24.9% ± 13.1; cluster 4: 24.1% ± 8.4; cluster 5: 38.8% ± 14.4; P < .001). Lower whole-lung Jacobian and ADI values were associated with greater cluster severity. Compared to cluster 1, cluster 5 lung expansion was 31% smaller (Jacobian in SARP III cohort: 2.31 ± 0.6 vs 1.61 ± 0.3, respectively, P < .001) and 34% more isotropic (ADI in SARP III cohort: 0.40 ± 0.1 vs 0.61 ± 0.2, P < .001). Within-lung Jacobian and ADI SDs decreased as severity worsened (Jacobian SD in SARP III cohort: 0.90 ± 0.4 for cluster 1; 0.79 ± 0.3 for cluster 2; 0.62 ± 0.2 for cluster 3; 0.63 ± 0.2 for cluster 4; and 0.41 ± 0.2 for cluster 5; P < .001). Conclusion Quantitative CT assessments of the degree and intraindividual regional variability of lung expansion distinguished between well-established clinical phenotypes among participants with asthma from the Severe Asthma Research Program study. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Verschakelen in this issue.

Legal, mental health and psychosocial outcomes of the RePresent Games: a quasi-experimental study

This study evaluated a pair of video games called the RePresent games that taught users how to represent themselves in civil court. A quasi-experimental study was conducted that compared 69 RePresent game users and 78 non-game users with civil legal issues across four U.S. states on legal, mental health and psychosocial outcomes over 3 months. The results revealed that RePresent game users reported greater legal knowledge, better mental health and higher quality of life than non-game users across time, and a greater rate of improvement in legal knowledge than non-game users over time. These findings suggest that gamifying education about legal procedures for the general public holds great potential in helping individuals obtain self-help legal assistance although some formal mental health treatment may be needed for many seeking legal aid.

Treatments for Chronic Kidney Disease: A Systematic Literature Review of Randomized Controlled Trials

Delaying disease progression and reducing the risk of mortality are key goals in the treatment of chronic kidney disease (CKD). New drug classes to augment renin-angiotensin-aldosterone system (RAAS) inhibitors as the standard of care have scarcely met their primary endpoints until recently. This systematic literature review explored treatments evaluated in patients with CKD since 1990 to understand what contemporary data add to the treatment landscape. Eighty-nine clinical trials were identified that had enrolled patients with estimated glomerular filtration rate 13.9-102.8 mL/min/1.73 m2 and urinary albumin-to-creatinine ratio (UACR) 29.9-2911.0 mg/g, with (75.5%) and without (20.6%) type 2 diabetes (T2D). Clinically objective outcomes of kidney failure and all-cause mortality (ACM) were reported in 32 and 64 trials, respectively. Significant reductions (P < 0.05) in the risk of kidney failure were observed in seven trials: five small trials published before 2008 had evaluated the RAAS inhibitors losartan, benazepril, or ramipril in patients with (n = 751) or without (n = 84-436) T2D; two larger trials (n = 2152-2202) published onwards of 2019 had evaluated the sodium-glucose co-transporter 2 (SGLT2) inhibitors canagliflozin (in patients with T2D and UACR > 300-5000 mg/g) and dapagliflozin (in patients with or without T2D and UACR 200-5000 mg/g) added to a background of RAAS inhibition. Significant reductions in ACM were observed with dapagliflozin in the DAPA-CKD trial. Contemporary data therefore suggest that augmenting RAAS inhibitors with new drug classes has the potential to improve clinical outcomes in a broad range of patients with CKD.

Models of cardiovascular surgery biobanking to facilitate translational research and precision medicine

Biobanking in health care has evolved over the last few decades from simple biological sample repositories to complex and dynamic units with multi‐organizational infrastructure networks and has become an essential tool for modern medical research. Cardiovascular tissue biobanking provides a unique opportunity to utilize cardiac and vascular samples for translational research into heart failure and other related pathologies. Current techniques for diagnosis, classification, and treatment monitoring of cardiac disease relies primarily on interpretation of clinical signs, imaging, and blood biomarkers. Further research at the disease source (i.e. myocardium and blood vessels) has been limited by a relative lack of access to quality human cardiac tissue and the inherent shortcomings of most animal models of heart disease. In this review, we describe a model for cardiovascular tissue biobanking and databasing, and its potential to facilitate basic and translational research. We share techniques to procure endocardial samples from patients with hypertrophic cardiomyopathy, heart failure with reduced ejection fraction, and heart failure with preserved ejection fraction, in addition to aortic disease samples. We discuss some of the issues with respect to data collection, privacy, biobank consent, and the governance of tissue biobanking. The development of tissue biobanks as described here has significant scope to improve and facilitate translational research in multi‐omic fields such as genomics, transcriptomics, proteomics, and metabolomics. This research heralds an era of precision medicine, in which patients with cardiovascular pathology can be provided with optimized and personalized medical care for the treatment of their individual phenotype.

Glutaredoxins regulate maize inflorescence meristem development via redox control of TGA transcriptional activity

Glutaredoxins (GRXs) are small oxidoreductases that can modify target protein activities through control of the redox (reduction/oxidation) state by reducing or glutathionylating disulfide bridges. Although CC-type GRXs are plant specific and play important roles in many processes, the mechanisms by which they modulate the activity of target proteins in vivo are unknown. In this study, we show that a maize CC-type GRX, MALE STERILE CONVERTED ANTHER1 (MSCA1), acts redundantly with two paralogues, ZmGRX2 and ZmGRX5, to modify the redox state and the activity of its putative target, the TGA transcription factor FASCIATED EAR4 (FEA4) that acts as a negative regulator of inflorescence meristem development. We used CRISPR-Cas9 to create a GRX triple knockout, resulting in severe suppression of meristem, ear and tassel growth and reduced plant height. We further show that GRXs regulate the redox state, DNA accessibility and transcriptional activities of FEA4, which acts downstream of MSCA1 and its paralogues to control inflorescence development. Our findings reveal the function of GRXs in meristem development, and also provide direct evidence for GRX-mediated redox modification of target proteins in plants.

Optimizing the discovery and assessment of therapeutic targets in heart failure with preserved ejection fraction

There is an urgent need for models that faithfully replicate heart failure with preserved ejection fraction (HFpEF), now recognized as the most common form of heart failure in the world. In vitro approaches have several shortcomings, most notably the immature nature of stem cell‐derived human cardiomyocytes [induced pluripotent stem cells (iPSC)] and the relatively short lifespan of primary cardiomyocytes. Three‐dimensional ‘organoids’ incorporating mature iPSCs with other cell types such as endothelial cells and fibroblasts are a significant advance, but lack the complexity of true myocardium. Animal models can replicate many features of human HFpEF, and rodent models are the most common, and recent attempts to incorporate haemodynamic, metabolic, and ageing contributions are encouraging. Differences relating to species, physiology, heart rate, and heart size are major limitations for rodent models. Porcine models mitigate many of these shortcomings and approximate human physiology more closely, but cost and time considerations limit their potential for widespread use. Ex vivo analysis of failing hearts from animal models offer intriguing possibilities regarding cardiac substrate utilisation, but are ultimately subject to the same constrains as the animal models from which the hearts are obtained. Ex vivo approaches using human myocardial biopsies can uncover new insights into pathobiology leveraging myocardial energetics, substrate turnover, molecular changes, and systolic/diastolic function. In collaboration with a skilled cardiothoracic surgeon, left ventricular endomyocardial biopsies can be obtained at the time of valvular surgery in HFpEF patients. Critically, these tissues maintain their disease phenotype, preserving inter‐relationship of myocardial cells and extracellular matrix. This review highlights a novel approach, where ultra‐thin myocardial tissue slices from human HFpEF hearts can be used to assess changes in myocardial structure and function. We discuss current approaches to modelling HFpEF, describe in detail the novel tissue slice model, expand on exciting opportunities this model provides, and outline ways to improve this model further.

Correlation Between Early Endpoints and Overall Survival in Non-Small-Cell Lung Cancer: A Trial-Level Meta-Analysis

Early endpoints, such as progression-free survival (PFS), are increasingly used as surrogates for overall survival (OS) to accelerate approval of novel oncology agents. Compiling trial-level data from randomized controlled trials (RCTs) could help to develop a predictive framework to ascertain correlation trends between treatment effects for early and late endpoints. Through trial-level correlation and random-effects meta-regression analysis, we assessed the relationship between hazard ratio (HR) OS and (1) HR PFS and (2) odds ratio (OR) PFS at 4 and 6 months, stratified according to the mechanism of action of the investigational product. Using multiple source databases, we compiled a data set including 81 phase II-IV RCTs (35 drugs and 156 observations) of patients with non-small-cell lung cancer. Low-to-moderate correlations were generally observed between treatment effects for early endpoints (based on PFS) and HR OS across trials of agents with different mechanisms of action. Moderate correlations were seen between treatment effects for HR PFS and HR OS across all trials, and in the programmed cell death-1/programmed cell death ligand-1 and epidermal growth factor receptor trial subsets. Although these results constitute an important step, caution is advised, as there are some limitations to our evaluation, and an additional patient-level analysis would be needed to establish true surrogacy.

Study specific prediction intervals for random-effects meta-analysis: A tutorial: Prediction intervals in meta-analysis

The pooled estimate of the average effect is of primary interest when fitting the random-effects model for meta-analysis. However estimates of study specific effects, for example those displayed on forest plots, are also often of interest. In this tutorial, we present the case, with the accompanying statistical theory, for estimating the study specific true effects using so called 'empirical Bayes estimates' or 'Best Unbiased Linear Predictions' under the random-effects model. These estimates can be accompanied by prediction intervals that indicate a plausible range of study specific true effects. We coalesce and elucidate the available literature and illustrate the methodology using two published meta-analyses as examples. We also perform a simulation study that reveals that coverage probability of study specific prediction intervals are substantially too low if the between-study variance is small but not negligible. Researchers need to be aware of this defect when interpreting prediction intervals. We also show how empirical Bayes estimates, accompanied with study specific prediction intervals, can embellish forest plots. We hope that this tutorial will serve to provide a clear theoretical underpinning for this methodology and encourage its widespread adoption.

Frequentist rules for regulatory approval of subgroups in phase III trials: A fresh look at an old problem

Background

The number of Phase III trials that include a biomarker in design and analysis has increased due to interest in personalised medicine. For genetic mutations and other predictive biomarkers, the trial sample comprises two subgroups, one of which, say B+ is known or suspected to achieve a larger treatment effect than the other B−. Despite treatment effect heterogeneity, trials often draw patients from both subgroups, since the lower responding B− subgroup may also gain benefit from the intervention. In this case, regulators/commissioners must decide what constitutes sufficient evidence to approve the drug in the B− population.

Methods and Results

Assuming trial analysis can be completed using generalised linear models, we define and evaluate three frequentist decision rules for approval. For rule one, the significance of the average treatment effect in B− should exceed a pre-defined minimum value, say ZB−>L. For rule two, the data from the low-responding group B− should increase statistical significance. For rule three, the subgroup-treatment interaction should be non-significant, using type I error chosen to ensure that estimated difference between the two subgroup effects is acceptable. Rules are evaluated based on conditional power, given that there is an overall significant treatment effect. We show how different rules perform according to the distribution of patients across the two subgroups and when analyses include additional (stratification) covariates in the analysis, thereby conferring correlation between subgroup effects.

Conclusions

When additional conditions are required for approval of a new treatment in a lower response subgroup, easily applied rules based on minimum effect sizes and relaxed interaction tests are available. Choice of rule is influenced by the proportion of patients sampled from the two subgroups but less so by the correlation between subgroup effects.

Pre-existing influenza-specific nasal IgA or nasal viral infection does not affect live attenuated influenza vaccine immunogenicity in children

The United Kingdom has a national immunization programme which includes annual influenza vaccination in school-aged children, using live attenuated influenza vaccine (LAIV). LAIV is given annually, and it is unclear whether repeat administration can affect immunogenicity. Because LAIV is delivered intranasally, pre-existing local antibody might be important. In this study, we analysed banked samples from a study performed during the 2017/18 influenza season to investigate the role of pre-existing influenza-specific nasal immunoglobulin (Ig)A in children aged 6-14 years. Nasopharyngeal swabs were collected prior to LAIV immunization to measure pre-existing IgA levels and test for concurrent upper respiratory tract viral infections (URTI). Oral fluid samples were taken at baseline and 21-28 days after LAIV to measure IgG as a surrogate of immunogenicity. Antibody levels at baseline were compared with a pre-existing data set of LAIV shedding from the same individuals, measured by reverse transcription-polymerase chain reaction. There was detectable nasal IgA specific to all four strains in the vaccine at baseline. However, baseline nasal IgA did not correlate with the fold change in IgG response to the vaccine. Baseline nasal IgA also did not have an impact upon whether vaccine virus RNA was detectable after immunization. There was no difference in fold change of antibody between individuals with and without an URTI at the time of immunization. Overall, we observed no effect of pre-existing influenza-specific nasal antibody levels on immunogenicity, supporting annual immunization with LAIV in children.

Alternative weighting schemes when performing matching-adjusted indirect comparisons

Methods for indirect comparisons and network meta-analysis use aggregate level data from multiple studies. A very common, and closely related, scenario is where a company has individual patient data (IPD) from its own trial, but only has published aggregate data from a competitor's trial, and an indirect comparison of the treatments evaluated in these two trials is required. Matching-Adjusted Indirect Comparison (MAIC) has been developed for this situation, where we use the available IPD to adjust for between-trial imbalances in the distributions of observed baseline covariates between the two trials. We extend the current MAIC methodology, where we compute the weights that satisfy the conventional method of moments and result in the largest possible effective sample size (ESS). We show that the approach proposed by Zubizarreta in a previous study can be used for this purpose. We derive a new analytical result that shows why this alternative approach provides a larger ESS than a conventional MAIC. We also derive a new formula for the maximum ESS that can be achieved, even when permitting negative weights, when adjusting for one covariate. This can be used as an easily computed new metric that quantifies the difficulty in adjusting for covariates. What is already known: MAIC is an established way to perform population adjustment in the situation where IPD is available from one trial but only aggregate level data is available from another trial, and an indirect comparison is required. However the effective sample size (ESS) can be small after making the adjustment. What is new: We show that an alternative method can result in a larger ESS. We provide new analytical results showing why this is the case. We derive a new descriptive statistic that is based on maximising the ESS that quantifies the difficulties in adjusting for particular covariates. Potential impact for RSM readers outside the authors' field: Reweighting methods for population adjustment are becoming more commonly used and their implications for research synthesis methodology is now considerable. This paper provides important new links between the theoretical literature, and the more applied research synthesis methodology literature, relating to this topic.

Psychosocial and Mental Health Characteristics of RePresent Game Users

The RePresent games are online video games that are publicly available and designed to educate people about legal self-representation in civil court. This study was part of a project to examine use of the RePresent games in Connecticut, Massachusetts, Maine, and New Hampshire from January 2018 to May 2018. Data on game use across the four states were analyzed, and an online survey was conducted to examine characteristics of RePresent game users and nonusers seeking civil legal aid (n = 277). The RePresent games were accessed more than 7,000 times in five months. The most common legal problems reported were related to debt, family, and housing. Compared with nonusers, RePresent game users were significantly more likely to be nonwhite, to have an incarceration history, to have more legal problems, and to screen positive for alcohol use problems. In the total sample, 83 percent screened positive for depression, 81 percent for generalized anxiety disorder, and 45 percent for drug problems. Only 34 percent reported use of mental health services, and 17 percent reported substance abuse treatment in the past year. These findings demonstrate that products like the RePresent games can be widely accessible to adults from disadvantaged backgrounds. In addition, civil legal settings may be a new area for mental health screening and intervention.

One-stage individual participant data meta-analysis models for continuous and binary outcomes: Comparison of treatment coding options and estimation methods

A one-stage individual participant data (IPD) meta-analysis synthesizes IPD from multiple studies using a general or generalized linear mixed model. This produces summary results (eg, about treatment effect) in a single step, whilst accounting for clustering of participants within studies (via a stratified study intercept, or random study intercepts) and between-study heterogeneity (via random treatment effects). We use simulation to evaluate the performance of restricted maximum likelihood (REML) and maximum likelihood (ML) estimation of one-stage IPD meta-analysis models for synthesizing randomized trials with continuous or binary outcomes. Three key findings are identified. First, for ML or REML estimation of stratified intercept or random intercepts models, a t-distribution based approach generally improves coverage of confidence intervals for the summary treatment effect, compared with a z-based approach. Second, when using ML estimation of a one-stage model with a stratified intercept, the treatment variable should be coded using "study-specific centering" (ie, 1/0 minus the study-specific proportion of participants in the treatment group), as this reduces the bias in the between-study variance estimate (compared with 1/0 and other coding options). Third, REML estimation reduces downward bias in between-study variance estimates compared with ML estimation, and does not depend on the treatment variable coding; for binary outcomes, this requires REML estimation of the pseudo-likelihood, although this may not be stable in some situations (eg, when data are sparse). Two applied examples are used to illustrate the findings.

Bivariate network meta-analysis for surrogate endpoint evaluation

Surrogate endpoints are very important in regulatory decision making in healthcare, in particular if they can be measured early compared to the long-term final clinical outcome and act as good predictors of clinical benefit. Bivariate meta-analysis methods can be used to evaluate surrogate endpoints and to predict the treatment effect on the final outcome from the treatment effect measured on a surrogate endpoint. However, candidate surrogate endpoints are often imperfect, and the level of association between the treatment effects on the surrogate and final outcomes may vary between treatments. This imposes a limitation on methods which do not differentiate between the treatments. We develop bivariate network meta-analysis (bvNMA) methods, which combine data on treatment effects on the surrogate and final outcomes, from trials investigating multiple treatment contrasts. The bvNMA methods estimate the effects on both outcomes for all treatment contrasts individually in a single analysis. At the same time, they allow us to model the trial-level surrogacy patterns within each treatment contrast and treatment-level surrogacy, thus enabling predictions of the treatment effect on the final outcome either for a new study in a new population or for a new treatment. Modelling assumptions about the between-studies heterogeneity and the network consistency, and their impact on predictions, are investigated using an illustrative example in advanced colorectal cancer and in a simulation study. When the strength of the surrogate relationships varies across treatment contrasts, bvNMA has the advantage of identifying treatment comparisons for which surrogacy holds, thus leading to better predictions.

A new justification of the Hartung-Knapp method for random-effects meta-analysis based on weighted least squares regression

The Hartung-Knapp method for random-effects meta-analysis, that was also independently proposed by Sidik and Jonkman, is becoming advocated for general use. This method has previously been justified by taking all estimated variances as known and using a different pivotal quantity to the more conventional one when making inferences about the average effect. We provide a new conceptual framework for, and justification of, the Hartung-Knapp method. Specifically, we show that inferences from fitted random-effects models, using both the conventional and the Hartung-Knapp method, are equivalent to those from closely related intercept only weighted least squares regression models. This observation provides a new link between Hartung and Knapp's methodology for meta-analysis and standard linear models, where it can be seen that the Hartung-Knapp method can be justified by a linear model that makes a slightly weaker assumption than taking all variances as known. This provides intuition for why the Hartung-Knapp method has been found to perform better than the conventional one in simulation studies. Furthermore, our new findings give more credence to ad hoc adjustments of confidence intervals from the Hartung-Knapp method that ensure these are at least as wide as more conventional confidence intervals. The conceptual basis for the Hartung-Knapp method that we present here should replace the established one because it more clearly illustrates the potential benefit of using it.

Empirical comparison of univariate and multivariate meta-analyses in Cochrane Pregnancy and Childbirth reviews with multiple binary outcomes

BACKGROUND

Multivariate meta-analysis (MVMA) jointly synthesizes effects for multiple correlated outcomes. The MVMA model is potentially more difficult and time-consuming to apply than univariate models, so if its use makes little difference to parameter estimates, it could be argued that it is redundant.

METHODS

We assessed the applicability and impact of MVMA in Cochrane Pregnancy and Childbirth (CPCB) systematic reviews. We applied MVMA to CPCB reviews published between 2011 and 2013 with two or more binary outcomes with at least three studies and compared findings with results of univariate meta-analyses. Univariate random effects meta-analysis models were fitted using restricted maximum likelihood estimation (REML).

RESULTS

Eighty CPCB reviews were published. MVMA could not be applied in 70 of these reviews. MVMA was not feasible in three of the remaining 10 reviews because the appropriate models failed to converge. Estimates from MVMA agreed with those of univariate analyses in most of the other seven reviews. Statistical significance changed in two reviews: In one, this was due to a very small change in P value; in the other, the MVMA result for one outcome suggested that previous univariate results may be vulnerable to small-study effects and that the certainty of clinical conclusions needs consideration.

CONCLUSIONS

MVMA methods can be applied only in a minority of reviews of interventions in pregnancy and childbirth and can be difficult to apply because of missing correlations or lack of convergence. Nevertheless, clinical and/or statistical conclusions from MVMA may occasionally differ from those from univariate analyses.

Are Gypsy Roma Traveller communities indigenous and would identification as such better address their public health needs?

INTRODUCTION

Across Europe, large numbers of Gypsy Roma Traveller communities experience significant health inequities such as higher morbidity, mortality and infant mortality. This health inequity is perpetuated by wider determinants such as a lower social status, lower educational attainment and substandard accommodation. This is not dissimilar to other indigenous peoples, even though many Gypsy Roma Traveller communities are not identified as indigenous.

METHODS

This article presents contemporary literature and research alongside the internationally agreed principles of indigenous peoples, examining similarities between Gypsy Roma Traveller communities and other indigenous peoples.

RESULTS

We argue that Gypsy Roma Traveller communities could be recognised as indigenous in terms of the internationally agreed principles of indigeneity and shared experiences of health inequity, colonisation and cultural genocide. Doing so would enable a more robust public health strategy and development of public health guidelines that take into account their cultural views and practices.

CONCLUSION

Recognising Gypsy Roma Traveller communities in this way is important, especially concerning public health, as formal recognition of indigeneity provides certain rights and protection that can be used to develop appropriate public health strategies. Included within this are more nuanced approaches to promoting health, which focus on strengths and assets rather than deficit constructs that can perpetuate problematising of these communities.

Behavioural intervention for weight loss maintenance versus standard weight advice in adults with obesity: A randomised controlled trial in the UK (NULevel Trial)

BACKGROUND

Scalable weight loss maintenance (WLM) interventions for adults with obesity are lacking but vital for the health and economic benefits of weight loss to be fully realised. We examined the effectiveness and cost-effectiveness of a low-intensity technology-mediated behavioural intervention to support WLM in adults with obesity after clinically significant weight loss (≥5%) compared to standard lifestyle advice.

METHODS AND FINDINGS

The NULevel trial was an open-label randomised controlled superiority trial in 288 adults recruited April 2014 to May 2015 with weight loss of ≥5% within the previous 12 months, from a pre-weight loss BMI of ≥30 kg/m2. Participants were self-selected, and the majority self-certified previous weight loss. We used a web-based randomisation system to assign participants to either standard lifestyle advice via newsletter (control arm) or a technology-mediated low-intensity behavioural WLM programme (intervention arm). The intervention comprised a single face-to-face goal-setting meeting, self-monitoring, and remote feedback on weight, diet, and physical activity via links embedded in short message service (SMS). All participants were provided with wirelessly connected weighing scales, but only participants in the intervention arm were instructed to weigh themselves daily and told that they would receive feedback on their weight. After 12 months, we measured the primary outcome, weight (kilograms), as well as frequency of self-weighing, objective physical activity (via accelerometry), psychological variables, and cost-effectiveness. The study was powered to detect a between-group weight difference of ±2.5 kg at follow-up. Overall, 264 participants (92%) completed the trial. Mean weight gain from baseline to 12 months was 1.8 kg (95% CI 0.5-3.1) in the intervention group (n = 131) and 1.8 kg (95% CI 0.6-3.0) in the control group (n = 133). There was no evidence of an effect on weight at 12 months (difference in adjusted mean weight change from baseline: -0.07 [95% CI 1.7 to -1.9], p = 0.9). Intervention participants weighed themselves more frequently than control participants and were more physically active. Intervention participants reported greater satisfaction with weight outcomes, more planning for dietary and physical activity goals and for managing lapses, and greater confidence for healthy eating, weight loss, and WLM. Potential limitations, such as the use of connected weighing study in both trial arms, the absence of a measurement of energy intake, and the recruitment from one region of the United Kingdom, are discussed.

CONCLUSIONS

There was no difference in the WLM of participants who received the NULevel intervention compared to participants who received standard lifestyle advice via newsletter. The intervention affected some, but not all, process-related secondary outcomes of the trial.

TRIAL REGISTRATION

This trial is registered with the ISRCTN registry (ISRCTN 14657176; registration date 20 March 2014).

Two new approaches for the visualisation of models for network meta-analysis

Background

Meta-analysis is a useful tool for combining evidence from multiple studies to estimate a pooled treatment effect. An extension of meta-analysis, network meta-analysis, is becoming more commonly used as a way to simultaneously compare multiple treatments in a single analysis. Despite the variety of approaches available for presenting fitted models, ascertaining an intuitive understanding of these models is often difficult. This is especially challenging in large networks with many different treatments. Here we propose two visualisation methods, so that network meta-analysis models can be more easily interpreted.

Methods

Our methods can be used irrespective of the statistical model or the estimation method used and are grounded in network analysis. We define three types of distance measures between the treatments that contribute to the network. These three distance measures are based on 1) the estimated treatment effects, 2) their standard errors and 3) the corresponding p-values. Then, by using a suitable threshold, we categorise some treatment pairs as being “close” (short distances). Treatments that are close are regarded as “connected” in the network analysis theory. Finally, we group the treatments into communities using standard methods for network analysis. We are then able to identify which parts of the network are estimated to have similar (or different) treatment efficacy and which parts of the network are better identified. We also propose a second method using parametric bootstrapping, where a heat map is used in the visualisation. We use the software R and provide the code used.

Results

We illustrate our new methods using a challenging dataset containing 22 treatments, and a previously fitted model for this data. Two communities of treatments that appear to have similar efficacy are identified. Furthermore using our methods we can identify parts of the network that are better (and less well) identified.

Conclusions

Our new visualisation approaches may be used by network meta-analysts to gain an intuitive understanding of the implications of their fitted models. Our visualisation methods may be used informally, to identify the most salient features of the fitted models that can then be reported, or more formally by presenting the new visualisation devices within published reports.

Electronic supplementary material

The online version of this article (10.1186/s12874-019-0689-9) contains supplementary material, which is available to authorized users.

Lung nodules are reliably detectable on ultra-low-dose CT utilising model-based iterative reconstruction with radiation equivalent to plain radiography

AIM

To determine if ultra-low-dose (ULD) computed tomography (CT) utilising model-based iterative reconstruction (MBIR) with radiation equivalent to plain radiography allows the detection of lung nodules.

MATERIALS AND METHODS

Ninety-nine individuals undergoing surveillance of solid pulmonary nodules undertook a low-dose (LD) and ULD CT during the same sitting. Image pairs were read blinded, in random order, and independently by two experienced thoracic radiologists. With LD-CT as the reference standard, the number, size, and location of nodules was compared, and inter-rater agreement was established.

RESULTS

There was very good inter-rater agreement with regards nodules ≥4mm for both the LD- (k=0.931) and ULD-CT (k=0.869). One hundred and ninety-nine nodules were reported on the LD-CT by both radiologists and 196 reported on the ULD-CT, with no nodules reported only on the ULD-CT. This gives a sensitivity of 98.5% and specificity of 100% for ULD-CT with MBIR. The effective dose of radiation was significantly different between the two scans (p<0.0001), 1.67 mSv for the LD-CT and 0.13 mSv for the ULD-CT.

CONCLUSION

ULD-CT utilising MBIR and delivering radiation equivalent to plain radiography, allows detection of lung nodules with high sensitivity. The attendant 10-fold reduction in radiation may allow for dramatic reductions in cumulative radiation exposure.

Feasibility of parent communication training with remote coaching using smartphone apps

BACKGROUND

Communication training for parents of young children with neurodisability is often delivered in groups and includes video coaching. Group teaching is problematic when there is wide variation in the characteristics and needs amongst participants.

AIMS

To assess the potential feasibility and acceptability of delivering one-to-one parent training supported by remote coaching using smartphone apps and of conducting further trials of the intervention.

METHODS & PROCEDURES

We aimed to recruit eight children aged 12-48 months with motor disorders and communication difficulties and to provide families with individual parent training in six weekly home visits supplemented by remote coaching via smartphone apps. For outcome measurement, parents recorded their interaction with their child thrice weekly during baseline (3 weeks), intervention, post-intervention (3 weeks) and follow-up (1 week). Measures comprised parent responsiveness and counts of children's communication and vocalization. Research design feasibility was measured through rates of recruitment, attrition, outcome measure completion and agreement between raters on outcome measurement. Intervention feasibility was assessed through the proportion of therapy sessions received, the number of videos and text messages shared using the apps in remote coaching, and message content. Parents were interviewed about the acceptability of the intervention and trial design. Interviews were transcribed and analyzed using inductive thematic analysis.

OUTCOMES & RESULTS

Nine children were recruited over 16 weeks. All fitted the inclusion criteria. Four families withdrew from the study. Five families completed the intervention. No family submitted the target number of video recordings for outcome measurement. Interrater agreement was moderate for child communication (K = 0.46) and vocalization (K = 0.60) and high for The Responsive Augmentative and Alternative Communication Style scale (RAACS) (r_s = 0.96). Parents who completed the intervention reported positive experiences of the programme and remote coaching via the apps. Therapist messages via the app contained comments on parent and child behaviour and requests for parental reflection/action; parental messages contained reflections on children's communication.

CONCLUSIONS & IMPLICATIONS

The intervention and study design demanded high levels of parental involvement and was not suitable for all families. Recording shorter periods of interaction via mobile phones or using alternative methods of data collection may increase feasibility of outcome measurement.

A Cross-Sectional Investigation of Cognition and Ultrasound-Based Vascular Strain Indices

OBJECTIVE

We examine the relationship between variability in the plaque strain distribution estimated using ultrasound with multiple cognitive domains including executive, language, visuospatial reasoning, and memory function.

METHOD

Asymptomatic (n = 42) and symptomatic (n = 34) patients with significant (>60%) carotid artery stenosis were studied for plaque instability using ultrasound strain imaging and multiple cognitive domains including executive, language, visuospatial reasoning, and memory function. Correlation and ROC analyses were performed between ultrasound strain indices and cognitive function. Strain indices and cognition scores were also compared between symptomatic and asymptomatic patients to determine whether there are significant group differences.

RESULTS

Association of high-strain distributions with dysexecutive function was observed in both asymptomatic and symptomatic patients. For memory, visuospatial, and language functions, the correlations between strain and cognition were weaker for the asymptomatic compared to symptomatic group.

CONCLUSIONS

Both asymptomatic and symptomatic patients demonstrate a relationship between vessel strain indices and executive function indicating that silent strokes and micro-emboli could initially contribute to a decline in executive function, whereas strokes and transient ischemic attacks may cause the further decline in other cognitive functions.