Improving the measurement of QALYs in dementia: developing patient- and carer-reported health state classification systems using Rasch analysis

Objectives: Cost-utility analysis is increasingly used to inform resource allocation. This requires a means of valuing health states before and after intervention. Although generic measures are typically used to generate values, these do not perform well with people with dementia. We report the development of a health state classification system amenable to valuation for use in studies of dementia, derived from the DEMQOL system, a measure of health-related quality of life in dementia by patient self-report (DEMQOL) and carer proxy-report (DEMQOL-Proxy). Methods: Factor analysis was used to determine the dimensional structure of DEMQOL and DEMQOL-Proxy. Rasch analysis was subsequently used to investigate item performance across factors in terms of item-level ordering, functioning across subgroups, model fit and severity-range coverage. This enabled the selection of one item from each factor for the classification system. A sample of people with a diagnosis of mild/moderate dementia (n=644) and a sample of carers of those with mild/moderate dementia (n=683) were used. Results: Factor analysis found different 5-factor solutions for DEMQOL and DEMQOL-Proxy. Following item reduction and selection using Rasch analysis, a 5-dimension classification for DEMQOL and a 4-dimension classification for DEMQOL-Proxy were developed. Each item contained 4 health state levels. Conclusion: Combining Rasch and classical psychometric analysis is a valid method of selecting items for dementia health state classifications from both the patient and carer perspectives. The next stage is to obtain preference weights so that the measure can be used in the economic evaluation of treatment, care and support arrangements for dementia.quality adjusted life years; health related quality of life; Rasch analysis; preference-based measures of health; health states; dementia

Improving the measurement of QALYs in dementia: developing patient- and carer-reported health state classification systems using Rasch analysis

Objectives: Cost-utility analysis is increasingly used to inform resource allocation. This requires a means of valuing health states before and after intervention. Although generic measures are typically used to generate values, these do not perform well with people with dementia. We report the development of a health state classification system amenable to valuation for use in studies of dementia, derived from the DEMQOL system, a measure of health-related quality of life in dementia by patient self-report (DEMQOL) and carer proxy-report (DEMQOL-Proxy). Methods: Factor analysis was used to determine the dimensional structure of DEMQOL and DEMQOL-Proxy. Rasch analysis was subsequently used to investigate item performance across factors in terms of item-level ordering, functioning across subgroups, model fit and severity-range coverage. This enabled the selection of one item from each factor for the classification system. A sample of people with a diagnosis of mild/moderate dementia (n=644) and a sample of carers of those with mild/moderate dementia (n=683) were used. Results: Factor analysis found different 5-factor solutions for DEMQOL and DEMQOL-Proxy. Following item reduction and selection using Rasch analysis, a 5-dimension classification for DEMQOL and a 4-dimension classification for DEMQOL-Proxy were developed. Each item contained 4 health state levels. Conclusion: Combining Rasch and classical psychometric analysis is a valid method of selecting items for dementia health state classifications from both the patient and carer perspectives. The next stage is to obtain preference weights so that the measure can be used in the economic evaluation of treatment, care and support arrangements for dementia

A Genome-Wide Association Study on Obesity and Obesity-Related Traits

Large-scale genome-wide association studies (GWAS) have identified many loci associated with body mass index (BMI), but few studies focused on obesity as a binary trait. Here we report the results of a GWAS and candidate SNP genotyping study of obesity, including extremely obese cases and never overweight controls as well as families segregating extreme obesity and thinness. We first performed a GWAS on 520 cases (BMI>35 kg/m2) and 540 control subjects (BMI<25 kg/m2), on measures of obesity and obesity-related traits. We subsequently followed up obesity-associated signals by genotyping the top ∼500 SNPs from GWAS in the combined sample of cases, controls and family members totaling 2,256 individuals. For the binary trait of obesity, we found 16 genome-wide significant signals within the FTO gene (strongest signal at rs17817449, P = 2.5×10−12). We next examined obesity-related quantitative traits (such as total body weight, waist circumference and waist to hip ratio), and detected genome-wide significant signals between waist to hip ratio and NRXN3 (rs11624704, P = 2.67×10−9), previously associated with body weight and fat distribution. Our study demonstrated how a relatively small sample ascertained through extreme phenotypes can detect genuine associations in a GWAS

Developing a utility index for the Aberrant Behavior Checklist (ABC-C) for fragile X syndrome

Purpose This study aimed to develop a utility index (the ABC-UI) from the Aberrant Behavior Checklist-Community (ABC-C), for use in quantifying the benefit of emerging treatments for fragile X syndrome (FXS). Methods The ABC-C is a proxy-completed assessment of behaviour and is a widely used measure in FXS. A subset of ABC-C items across seven dimensions was identified to include in health state descriptions. This item reduction process was based on item performance, factor analysis and Rasch analysis performed on an observational study dataset, and consultation with five clinical experts and a methodological expert. Dimensions were combined into health states using an orthogonal design and valued using time trade-off (TTO), with lead-time TTO methods used where TTO indicated a state valued as worse than dead. Preference weights were estimated using mean, individual level, ordinary least squares and random-effects maximum likelihood estimation [RE (MLE)] regression models. Results A representative sample of the UK general public (n = 349; mean age 35.8 years, 58.2 % female) each valued 12 health states. Mean observed values ranged from 0.92 to 0.16 for best to worst health states. The RE (MLE) model performed best based on number of significant coefficients and mean absolute error of 0.018. Mean utilities predicted by the model covered a similar range to that observed. Conclusions The ABC-UI estimates a wide range of utilities from patient-level FXS ABC-C data, allowing estimation of FXS health-related quality of life impact for economic evaluation from an established FXS clinical trial instrument

International Regulations and Recommendations for Utility Data for Health Technology Assessment

Recommendations and guidelines for the collection, generation, source and usage of utility data for health technology assessment (HTA) vary across different countries, with no international consensus. Many international agencies generate their own guidelines providing details on their preferred methods for HTA submissions, and there is variability in both what they recommend and the clarity and amount of detail provided in their guidelines. This article provides an overview of international regulations and recommendations for utility data in HTA for a selection of key HTA countries: Australia, Canada, France, Germany, the Netherlands, Spain (Catalonia), Sweden and the UK (England/Wales and Scotland). International guidelines are typically clear and detailed for the selection of countries assessed regarding the source description of health states (e.g. generic preference-based measure) and who should provide preference weights for these health states (e.g. general population for own country). Many guidelines specify the use of off-the-shelf generic preference-based measures, and some further specify a measure, such as EQ-5D. However, international guidelines are either unclear or lack detailed guidance regarding the collection (e.g. patients report own health), source (e.g. clinical trial) and usage (e.g. adjusting for comorbidities) of utility values. It is argued that there is a need for transparent and detailed international guidelines on utility data recommendations to provide decision makers with the best possible evidence. Where this is not possible it is recommended that best practice should be used to inform the collection, source and usage of utility values in HTA

Biomarkers in T cell therapy clinical trials

T cell therapy represents an emerging and promising modality for the treatment of both infectious disease and cancer. Data from recent clinical trials have highlighted the potential for this therapeutic modality to effect potent anti-tumor activity. Biomarkers, operationally defined as biological parameters measured from patients that provide information about treatment impact, play a central role in the development of novel therapeutic agents. In the absence of information about primary clinical endpoints, biomarkers can provide critical insights that allow investigators to guide the clinical development of the candidate product. In the context of cell therapy trials, the definition of biomarkers can be extended to include a description of parameters of the cell product that are important for product bioactivity

Evaluating the effectiveness and cost-effectiveness of Dementia Care Mapping™ to enable person-centred care for people with dementia and their carers (DCM-EPIC) in care homes: study protocol for a randomised controlled trial

Background Up to 90 % of people living with dementia in care homes experience one or more behaviours that staff may describe as challenging to support (BSC). Of these agitation is the most common and difficult to manage. The presence of agitation is associated with fewer visits from relatives, poorer quality of life and social isolation. It is recommended that agitation is treated through psychosocial interventions. Dementia Care Mapping™ (DCM™) is an established, widely used observational tool and practice development cycle, for ensuring a systematic approach to providing person-centred care. There is a body of practice-based literature and experience to suggests that DCM™ is potentially effective but limited robust evidence for its effectiveness, and no examination of its cost-effectiveness, as a UK health care intervention. Therefore, a definitive randomised controlled trial (RCT) of DCM™ in the UK is urgently needed. Methods/design A pragmatic, multi-centre, cluster-randomised controlled trial of Dementia Care Mapping (DCM™) plus Usual Care (UC) versus UC alone, where UC is the normal care delivered within the care home following a minimum level of dementia awareness training. The trial will take place in residential, nursing and dementia-specialist care homes across West Yorkshire, Oxfordshire and London, with residents with dementia. A random sample of 50 care homes will be selected within which a minimum of 750 residents will be registered. Care homes will be randomised in an allocation ratio of 3:2 to receive either intervention or control. Outcome measures will be obtained at 6 and 16 months following randomisation. The primary outcome is agitation as measured by the Cohen-Mansfield Agitation Inventory, at 16 months post randomisation. Key secondary outcomes are other BSC and quality of life. There will be an integral cost-effectiveness analysis and a process evaluation. Discussion The protocol was refined following a pilot of trial procedures. Changes include replacement of a questionnaire, whose wording caused some residents distress, to an adapted version specifically designed for use in care homes, a change to the randomisation stratification factors, adaption in how the staff measures are collected to encourage greater compliance, and additional reminders to intervention homes of when mapping cycles are due, via text message. Trial registration Current Controlled Trials ISRCTN82288852. Registered on 16 January 2014. Full protocol version and date: v7.1: 18 December 2015

Altered mRNA expression of genes related to nerve cell activity in the fracture callus of older rats: A randomized, controlled, microarray study

BACKGROUND: The time required for radiographic union following femoral fracture increases with age in both humans and rats for unknown reasons. Since abnormalities in fracture innervation will slow skeletal healing, we explored whether abnormal mRNA expression of genes related to nerve cell activity in the older rats was associated with the slowing of skeletal repair. METHODS: Simple, transverse, mid-shaft, femoral fractures with intramedullary rod fixation were induced in anaesthetized female Sprague-Dawley rats at 6, 26, and 52 weeks of age. At 0, 0.4, 1, 2, 4, and 6 weeks after fracture, a bony segment, one-third the length of the femur, centered on the fracture site, including the external callus, cortical bone, and marrow elements, was harvested. cRNA was prepared and hybridized to 54 Affymetrix U34A microarrays (3/age/time point). RESULTS: The mRNA levels of 62 genes related to neural function were affected by fracture. Of the total, 38 genes were altered by fracture to a similar extent at the three ages. In contrast, eight neural genes showed prolonged down-regulation in the older rats compared to the more rapid return to pre-fracture levels in younger rats. Seven genes were up-regulated by fracture more in the younger rats than in the older rats, while nine genes were up-regulated more in the older rats than in the younger. CONCLUSIONS: mRNA of 24 nerve-related genes responded differently to fracture in older rats compared to young rats. This differential expression may reflect altered cell function at the fracture site that may be causally related to the slowing of fracture healing with age or may be an effect of the delayed healing

Genetic regulation of Nrnx1 expression: an integrative cross-species analysis of schizophrenia candidate genes

Neurexin 1 (NRXN1) is a large presynaptic transmembrane protein that has complex and variable patterns of expression in the brain. Sequence variants in NRXN1 are associated with differences in cognition, and with schizophrenia and autism. The murine Nrxn1 gene is also highly polymorphic and is associated with significant variation in expression that is under strong genetic control. Here, we use co-expression analysis, high coverage genomic sequence, and expression quantitative trait locus (eQTL) mapping to study the regulation of this gene in the brain. We profiled a family of 72 isogenic progeny strains of a cross between C57BL/6J and DBA/2J (the BXD family) using exon arrays and massively parallel RNA sequencing. Expression of most Nrxn1 exons have high genetic correlation (r>0.6) because of the segregation of a common trans eQTL on chromosome (Chr) 8 and a common cis eQTL on Chr 17. These two loci are also linked to murine phenotypes relevant to schizophrenia and to a novel human schizophrenia candidate gene with high neuronal expression (Pleckstrin and Sec7 domain containing 3). In both human and mice, NRXN1 is co-expressed with numerous synaptic and cell signaling genes, and known schizophrenia candidates. Cross-species co-expression and protein interaction network analyses identified glycogen synthase kinase 3 beta (GSK3B) as one of the most consistent and conserved covariates of NRXN1. By using the Molecular Genetics of Schizophrenia data set, we were able to test and confirm that markers in NRXN1 and GSK3B have epistatic interactions in human populations that can jointly modulate risk of schizophrenia