Data-driven design of targeted gene panels for estimating immunotherapy biomarkers

We introduce a novel data-driven framework for the design of targeted gene panels for estimating exome-wide biomarkers in cancer immunotherapy. Our first goal is to develop a generative model for the profile of mutation across the exome, which allows for gene- and variant type-dependent mutation rates. Based on this model, we then propose a new procedure for estimating biomarkers such as Tumour Mutation Burden and Tumour Indel Burden. Our approach allows the practitioner to select a targeted gene panel of a prespecified size, and then construct an estimator that only depends on the selected genes. Alternatively, the practitioner may apply our method to make predictions based on an existing gene panel, or to augment a gene panel to a given size. We demonstrate the excellent performance of our proposal using an annotated mutation dataset from 1144 Non-Small Cell Lung Cancer patients.Comment: 21 pages, 10 figure

The new Checklist of British Columbia Lepidoptera and how it came to be

Volume: 110Start Page: 3End Page:

Changes in the Status and Distribution of the Yellow-faced Bumble Bee

Bombus vosnesenskii, the distinctively-patterned Yellow-faced Bumble Bee, has undergone a significant and rapid range extension in British Columbia. Known initially from a single record of a few specimens at Osoyoos in 1951, it was put forward in 1996 as a species that warranted a threatened or endangered status because of its severely restricted range in the province. However, since 2000, the species has expanded north in the Okanagan Valley, west to the Similkameen Valley and, especially, has become firmly established in south coastal regions of the province, including Vancouver Island. Population increases in B. vosnesenskii to the south of BC have also been reported. The reasons for the rapid expansion of B. vosnesenskii in BC are unclear. Particularly in lowland southwestern BC, the range expansion might have been enhanced through escapes from colonies kept as pollinators of agricultural crops. The spread of B. vosnesenskii has coincided with the decline of B. occidentalis, so the former may have been introduced or naturally expanded its range at the same time as a niche was becoming vacant

Lessons learned from using linked administrative data to evaluate the Family Nurse Partnership in England and Scotland

Introduction “Big data” – including linked administrative data – can be exploited to evaluate interventions for maternal and child health, providing time- and cost-effective alternatives to randomised controlled trials. However, using these data to evaluate population-level interventions can be challenging. Objectives We aimed to inform future evaluations of complex interventions by describing sources of bias, lessons learned, and suggestions for improvements, based on two observational studies using linked administrative data from health, education and social care sectors to evaluate the Family Nurse Partnership (FNP) in England and Scotland. Methods We first considered how different sources of potential bias within the administrative data could affect results of the evaluations. We explored how each study design addressed these sources of bias using maternal confounders captured in the data. We then determined what additional information could be captured at each step of the complex intervention to enable analysts to minimise bias and maximise comparability between intervention and usual care groups, so that any observed differences can be attributed to the intervention. Results Lessons learned include the need for i) detailed data on intervention activity (dates/geography) and usual care; ii) improved information on data linkage quality to accurately characterise control groups; iii) more efficient provision of linked data to ensure timeliness of results; iv) better measurement of confounding characteristics affecting who is eligible, approached and enrolled. Conclusions Linked administrative data are a valuable resource for evaluations of the FNP national programme and other complex population-level interventions. However, information on local programme delivery and usual care are required to account for biases that characterise those who receive the intervention, and to inform understanding of mechanisms of effect. National, ongoing, robust evaluations of complex public health evaluations would be more achievable if programme implementation was integrated with improved national and local data collection, and robust quasi-experimental designs

Linkage of people experiencing homeless using two consent models

Objectives Administrative data linkage is relatively under-utilised as a way of generating evidence to guide homelessness policy and service delivery in the UK. Our objective is to contribute insight into the ethical, legal, and practical challenges of using data linkage with data from people experiencing homelessness (PEH). Approach We outline the data collection and linkage methodologies for two UK-based studies related to PEH. The first design aimed to explore the acceptability and feasibility of consented linkage of trial data (‘Moving On’ trial) to NHS Digital records in a cohort of recruited PEH in two English local authorities (n=50). The second design used administrative data originating from a local authority homelessness service in Wales (n=17,000 cases) to explore educational outcomes of children in homeless households. The resultant data linkage rates are contrasted and discussed in relation to the mechanisms for obtaining and linking personal data. Results The Moving On trial demonstrated high rates of consent for data linkage and the ability to collect sufficient personal identifiable data to increase the chance of successful matching. Aggregate match rates will be discussed. Of the roughly 17,000 cases included in the local authority administrative data, 75% could be linked to unique individuals using probabilistic matching and were therefor ‘useable’ in linkage research. The proportion of useable cases rapidly decreased as the cut-off for matching quality was increased, to roughly 50% of cases being useable when a 99% match probability cut-off was used. Matching rates were higher amongst priority need homeless cases, possibly reflecting business need to identify and work closely with these people. Conclusion Where homelessness administrative data systems are not designed to enable data linkage, low matching rates can result, reducing study sample sizes and potentially leading to bias towards more extreme cases of homelessness if missed-matches are not random. Consented linkage within large-scale trials offers one possibility for generating long-term evidence