Abstract:BackgroundMucosal antibodies against capsular polysaccharides offer protection against acquisition and carriage of encapsulated bacteria like Neisseria meningitidis serogroup C. Measurements of salivary antibodies as replacement for blood testing has important (cost-effective) advantages, particular in studies that assess the impact of large-scale vaccination or in populations in which blood sampling is difficult. This study aimed to estimate a threshold for meningococcal IgG salivary antibody levels to discri… Show more
“…For example, the most common stopping rule is to stop for safety—none of the studied combinations are deemed safe given the current data and the trial is terminated [ 13 ]. Furthermore, a design could stop once a given number of patients has been assigned to the same combination [ 14 ].…”
“…Therefore, we propose to perform a grid search using a subset of scenarios such that these would represent noticeably different (but still clinically plausible) combination-toxicity relationships. For example, one scenario with many doses having toxicity probabilities far below the MTC, and another where most doses have toxicity probabilities far above the MTC would reflect different extreme settings [ 14 , 15 ]. Additionally, some of the scenarios selected for the calibration should reflect various interaction mechanism between the two compounds.…”
There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial.
“…For example, the most common stopping rule is to stop for safety—none of the studied combinations are deemed safe given the current data and the trial is terminated [ 13 ]. Furthermore, a design could stop once a given number of patients has been assigned to the same combination [ 14 ].…”
“…Therefore, we propose to perform a grid search using a subset of scenarios such that these would represent noticeably different (but still clinically plausible) combination-toxicity relationships. For example, one scenario with many doses having toxicity probabilities far below the MTC, and another where most doses have toxicity probabilities far above the MTC would reflect different extreme settings [ 14 , 15 ]. Additionally, some of the scenarios selected for the calibration should reflect various interaction mechanism between the two compounds.…”
There is growing interest in Phase I dose-finding studies studying several doses of more than one agent simultaneously. A number of combination dose-finding designs were recently proposed to guide escalation/de-escalation decisions during the trials. The majority of these proposals are model-based: a parametric combination-toxicity relationship is fitted as data accumulates. Various parameter shapes were considered but the unifying theme for many of these is that typically between 4 and 6 parameters are to be estimated. While more parameters allow for more flexible modelling of the combination-toxicity relationship, this is a challenging estimation problem given the typically small sample size in Phase I trials of between 20 and 60 patients. These concerns gave raise to an ongoing debate whether including more parameters into combination-toxicity model leads to more accurate combination selection. In this work, we extensively study two variants of a 4-parameter logistic model with reduced number of parameters to investigate the effect of modelling assumptions. A framework to calibrate the prior distributions for a given parametric model is proposed to allow for fair comparisons. Via a comprehensive simulation study, we have found that the inclusion of the interaction parameter between two compounds does not provide any benefit in terms of the accuracy of selection, on average, but is found to result in fewer patients allocated to the target combination during the trial.
“…Meta-ethnography in its original form comprised seven steps: (a) getting started, (b) deciding what is relevant to the initial interest, (c) reading the studies, (d) determining how the studies are related, (e) translating studies into one another, (f) synthesizing translations, and (g) expressing the synthesis ( Atkins et al, 2008 ; Noblit and Hare, 1988 ). Through this process, and particularly that of translating studies into one another—where data across articles are compared in order to explain phenomena—“meta-ethnography is suited to producing a new interpretation, model or theory, which goes beyond the findings of the individual studies synthesised” ( France et al, 2019 , p. 2).…”
Section: Introductionmentioning
confidence: 99%
“…It therefore lends itself particularly well to advancing our understanding of how music is reported by participants to support mental well-being, an area that has so far received relatively little academic attention and that relies largely on qualitative, idiosyncratic data. Meta-ethnography offers a form of synthesis that brings together a body of qualitative research to make it stronger than the sum of its parts through a systematic process that generates new theoretical insight ( Brookfield et al, 2019 ; France et al, 2019 ). In this article, we report on a meta-ethnography designed to answer the following research question:…”
Participatory music engagement has the capacity to support well-being. Yet, there is little research that has scrutinized the processes through which music has an effect. In this meta-ethnography [PROSPERO CRD42019130164], we conducted a systematic search of 19 electronic databases and a critical appraisal to identify 46 qualitative studies reporting on participants’ subjective views of how participatory music engagement supports their mental well-being. Synthesis of first-order and second-order interpretations using thematic coding resulted in four third-order pathways that account for how participatory music engagement supports mental well-being: managing and expressing emotions, facilitating self-development, providing respite, and facilitating connections. Our interpretation suggests that people benefit from participatory music engagement by engaging with specific and multiple processes that meet their individual needs and circumstances. These findings inform research directions within the field of music and well-being, as well as guiding the development and delivery of future music interventions.
“…We analysed each domain (and their interactions) to consider how SUPPORT-HF2’s ‘standardised’ socio-technical intervention played out differently in different trial sites. The application of NASSS to a process evaluation is an example of what Mills et al call a ‘type 4 logic model’—that is, a systematic way of exploring interdependencies in dynamic systems [ 15 ]. Fig.…”
Background
The SUPPORT-HF2 randomised controlled trial compared telehealth technology alone with the same technology combined with centralised remote support, in which a clinician responds promptly to biomarker changes. The intervention was implemented differently in different sites; no overall impact was found on the primary endpoint (proportion of patients on optimum treatment). We sought to explain the trial’s findings in a qualitative evaluation.
Methods
Fifty-one people (25 patients, 3 carers, 18 clinicians, 4 additional researchers) were interviewed and observed in 7 UK trial sites in 2016–2018. We collected 110 pages of documents. The analysis was informed by the NASSS framework, a multi-level theoretical lens which considers non-adoption and abandonment of technologies by individuals and challenges to scale-up, spread and sustainability. In particular, we used NASSS to tease out why a ‘standardised’ socio-technical intervention played out differently in different sites.
Results
Patients’ experiences of the technology were largely positive, though influenced by the nature and severity of their illness. In each trial site, existing services, staffing levels, technical capacity and previous telehealth experiences influenced how the complex intervention of ‘telehealth technology plus centralised specialist remote support’ was interpreted and the extent to which it was adopted and used to its full potential. In some sites, the intervention was quickly mobilised to fill significant gaps in service provision. In others, it was seen as usefully extending the existing care model for selected patients. Elsewhere, the new model was actively resisted and the technology little used. In one site, centralised provision of specialist advice aligned awkwardly with an existing community-based heart failure support service.
Conclusions
Complex socio-technical interventions, even when implemented in a so-called standardised way with uniform inclusion and exclusion criteria, are inevitably implemented differently in different local settings because of how individual staff members interpret the technology and the trial protocol and because of the practical realities and path dependencies of local organisations. Site-specific iteration and embedding of a new technology-supported complex intervention may be required (in addition to co-design of the user interface) before such interventions are ready for testing in clinical trials.
Trial registration
BMC ISRCTN Registry 86212709. Retrospectively registered on 5 September 2014
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