Exploring mechanisms of action in clinical trials of complex surgical interventions using mediation analysis.

BACKGROUND: Surgical interventions allow for tailoring of treatment to individual patients and implementation may vary with surgeon and healthcare provider. In addition, in clinical trials assessing two competing surgical interventions, the treatments may be accompanied by co-interventions. AIMS: This study explores the use of causal mediation analysis to (1) delineate the treatment effect that results directly from the surgical intervention under study and the indirect effect acting through a co-intervention and (2) to evaluate the benefit of the surgical intervention if either everybody in the trial population received the co-intervention or nobody received it. METHODS: Within a counterfactual framework, relevant direct and indirect effects of a surgical intervention are estimated and adjusted for confounding via parametric regression models, for the situation where both mediator and outcome are binary, with baseline stratification factors included as fixed effects and surgeons as random intercepts. The causal difference in probability of a successful outcome (estimand of interest) is calculated using Monte Carlo simulation with bootstrapping for confidence intervals. Packages for estimation within standard statistical software are reviewed briefly. A step by step application of methods is illustrated using the Amaze randomised trial of ablation as an adjunct to cardiac surgery in patients with irregular heart rhythm, with a co-intervention (removal of the left atrial appendage) administered to a subset of participants at the surgeon's discretion. The primary outcome was return to normal heart rhythm at one year post surgery. RESULTS: In Amaze, 17% (95% confidence interval: 6%, 28%) more patients in the active arm had a successful outcome, but there was a large difference between active and control arms in the proportion of patients who received the co-intervention (55% and 30%, respectively). Causal mediation analysis suggested that around 1% of the treatment effect was attributable to the co-intervention (16% natural direct effect). The controlled direct effect ranged from 18% (6%, 30%) if the co-intervention were mandated, to 14% (2%, 25%) if it were prohibited. Including age as a moderator of the mediation effects showed that the natural direct effect of ablation appeared to decrease with age. CONCLUSIONS: Causal mediation analysis is a useful quantitative tool to explore mediating effects of co-interventions in surgical trials. In Amaze, investigators could be reassured that the effect of the active treatment, not explainable by differential use of the co-intervention, was significant across analyses

Bounds for the adiabatic approximation with applications to quantum computation

We present straightforward proofs of estimates used in the adiabatic approximation. The gap dependence is analyzed explicitly. We apply the result to interpolating Hamiltonians of interest in quantum computing.Comment: 15 pages, one figure. Two comments added in Secs. 2 and

Methoden für die Unterscheidung von ökologisch und konventionell erzeugten Lebensmitteln

Bioprodukte werden heute immer stärker international und über komplexe Wertschöpfungsketten produziert und gehandelt. Dadurch werden Kontrollen, welche die Authentizität der Bio-Produkte zuverlässig gewähren, immer schwieriger. Höhere Preise für Bioprodukte waren in der Vergangenheit zudem Motivation hinter einzelnen Betrugsfällen in der Biobranche. Aus diesem Grund braucht es weitere Methoden, welche die Kontrollen der Zertifizierungsstellen unterstützen und ergänzen. Außer dem analytischen Nachweis von chemisch-synthetischen Pestizidrückständen, der bereits seit geraumer Zeit in der Praxis etabliert ist, gibt es weitere analytische und ganzheitliche Methoden, welche biologische und konventionelle Lebensmittel differenzieren können. Vor diesem Hintergrund war es das Ziel der Studie, die bestehenden oder in der Entwicklung befindlichen differenzierenden Methoden zur Unterscheidung biologisch von herkömmlich erzeugten Nahrungsmitteln zu beschreiben und ihre Praxistauglichkeit zu bewerten. Hierfür wurden eine umfangreiche Literaturstudie sowie ergänzende Expertengespräche durchgeführt

Borrowing strength from clinical trials in analysing longitudinal data from a treated cohort: investigating the effectiveness of acetylcholinesterase inhibitors in the management of dementia

Abstract Background Health care professionals seek information about effectiveness of treatments in patients who would be offered them in routine clinical practice. Electronic medical records (EMRs) and randomized controlled trials (RCTs) can both provide data on treatment effects; however, each data source has limitations when considered in isolation. Methods A novel modelling methodology which incorporates RCT estimates in the analysis of EMR data via informative prior distributions is proposed. A Bayesian mixed modelling approach is used to model outcome trajectories among patients in the EMR dataset receiving the treatment of interest. This model incorporates an estimate of treatment effect based on a meta-analysis of RCTs as an informative prior distribution. This provides a combined estimate of treatment effect based on both data sources. Results The superior performance of the novel combined estimator is demonstrated via a simulation study. The new approach is applied to estimate the effectiveness at 12 months after treatment initiation of acetylcholinesterase inhibitors in the management of the cognitive symptoms of dementia in terms of Mini-Mental State Examination scores. This demonstrated that estimates based on either trials data only (1.10, SE = 0.316) or cohort data only (1.56, SE = 0.240) overestimated this compared with the estimate using data from both sources (0.86, SE = 0.327). Conclusions It is possible to combine data from EMRs and RCTs in order to provide better estimates of treatment effectiveness. </jats:sec

How does cognitive behaviour therapy for dissociative seizures work? A mediation analysis of the CODES Trial

Background We compared dissociative seizure specific cognitive behaviour therapy (DS-CBT) plus standardised medical care (SMC) to SMC alone in a randomised controlled trial. DS-CBT resulted in better outcomes on several secondary trial outcome measures at the 12-month follow-up point. The purpose of this paper is to evaluate putative treatment mechanisms. Methods We carried out a secondary mediation analysis of the CODES trial. 368 participants were recruited from the National Health Service in secondary / tertiary care in England, Scotland and Wales. Sixteen mediation hypotheses corresponding to combinations of important trial outcomes and putative mediators were assessed. Twelve-month trial outcomes considered were final-month seizure frequency, Work and Social Adjustment Scale (WSAS), and the SF-12v2, a quality-of-life measure providing physical (PCS) and mental component summary (MCS) scores. Mediators chosen for analysis at six months (broadly corresponding to completion of DS-CBT) included: a) beliefs about emotions, b) a measure of avoidance behaviour, c) anxiety and d) depression. Results All putative mediator variables except beliefs about emotions were found to be improved by DS-CBT. We found evidence for DS-CBT effect mediation for the outcome variables dissociative seizures, WSAS and SF-12v2 MCS scores by improvements in target variables avoidance behaviour, anxiety and depression. The only variable to mediate the DS-CBT effect on the SF-12v2 PCS score was avoidance behaviour. Conclusions Our findings largely confirmed the logic model underlying the development of CBT for patients with dissociative seizures. Interventions could be additionally developed to specifically address beliefs about emotions to assess whether it improves outcomes. <br/

A scoping review of the problems and solutions associated with contamination in trials of complex interventions in mental health.

BACKGROUND: In a randomised controlled trial, contamination is defined as the receipt of active intervention amongst participants in the control arm. This review assessed the processes leading to contamination, its typical quantity, methods used to mitigate it, and impact of use of cluster randomisation to prevent it on study findings in trials of complex interventions in mental health. METHODS: This is a scoping review of trial design approaches and methods of study conduct to address contamination. Studies included were randomised controlled trials of complex interventions in mental health that described the process leading to, amount of, or solution used to counter contamination. The Medline, Embase, and PsycInfo databases were searched for trials published between 2000 and 2015. Risk of bias was assessed using the Jadad score and domains recommended by Cochrane plus some relevant to cluster randomised trials. RESULTS: Two hundred and thirty-four articles were included in the review. The main processes that led to contamination were health professionals delivering both active and comparator treatments and communication among clinicians and participants from the different trial arms. Twenty-three trials (10%) measured binary treatment receipt in the control arm with median 13% of participants found to be contaminated (IQR 5-33%). The most common design approach for dealing with contamination was the use of cluster randomisation (n = 93). In addition, many researchers used simple trial conduct methods to minimise contamination due to suspected contamination processes, such as organising for each clinician to provide only one treatment and separating trial arms spatially or temporally. There was little evidence for a relationship between cluster randomisation to avoid contamination and size of treatment effect estimate. CONCLUSION: There was some evidence of modest levels of treatment contamination with a large range, although a minority of studies reported the amount of contamination. A limitation was that many trials described the problem in little detail. Overall there is a need for greater measurement and reporting of treatment receipt in the control arm of trials. Researchers should be aware of trial conduct methods that can be used to minimise contamination without resorting to cluster randomisation