Population sampling in longitudinal suverys

The paper questions the need for observational studies to achieve representativeness for real populations, in particular for longitudinal studies. It draws upon recent debates and argues for the need to distinguish scientific inference from population inference

Doubly Robust Inference when Combining Probability and Non-probability Samples with High-dimensional Data

Non-probability samples become increasingly popular in survey statistics but may suffer from selection biases that limit the generalizability of results to the target population. We consider integrating a non-probability sample with a probability sample which provides high-dimensional representative covariate information of the target population. We propose a two-step approach for variable selection and finite population inference. In the first step, we use penalized estimating equations with folded-concave penalties to select important variables for the sampling score of selection into the non-probability sample and the outcome model. We show that the penalized estimating equation approach enjoys the selection consistency property for general probability samples. The major technical hurdle is due to the possible dependence of the sample under the finite population framework. To overcome this challenge, we construct martingales which enable us to apply Bernstein concentration inequality for martingales. In the second step, we focus on a doubly robust estimator of the finite population mean and re-estimate the nuisance model parameters by minimizing the asymptotic squared bias of the doubly robust estimator. This estimating strategy mitigates the possible first-step selection error and renders the doubly robust estimator root-n consistent if either the sampling probability or the outcome model is correctly specified

Population sampling in longitudinal suverys

In an opening paper Harvey Goldstein questions the need for observational studies to achieve representativeness for real populations, in particular for longitudinal studies. He draws upon recent debates and argues for the need to distinguish scientific inference from population inference. The points he raises are then debated in commentaries by Peter Lynn, Graciela Muniz-Terrera and Rebecca Hardy, Colm O'Muircheartaigh, Chris Skinner and Risto Lehtonen. These commentaries are followed by a response from Goldstein

Parallel Associations and the Structure of Autobiographical Knowledge

The self-memory system (SMS) model of autobiographical knowledge conceives that memories are structured thematically, organized both hierarchically and temporally. This model has been challenged on several fronts, including the absence of parallel linkages across pathways. Calendar survey interviewing shows the frequent and varied use of parallel associations in autobiographical recall. Parallel associations in these data are commonplace, and are driven more by respondents? generative retrieval than by interviewers? probing. Parallel associations represent a number of autobiographical knowledge themes that are interrelated across life domains. The content of parallel associations is nearly evenly split between general and transitional events, supporting the importance of transitions in autographical memory. Associations in respondents? memories (both parallel and sequential), demonstrate complex interactions with interviewer verbal behaviors during generative retrieval. In addition to discussing the implications of these results to the SMS model, implications are also drawn for transition theory and the basic-systems model

International Veterinary Epilepsy Task Force recommendations for systematic sampling and processing of brains from epileptic dogs and cats

Traditionally, histological investigations of the epileptic brain are required to identify epileptogenic brain lesions, to evaluate the impact of seizure activity, to search for mechanisms of drug-resistance and to look for comorbidities. For many instances, however, neuropathological studies fail to add substantial data on patients with complete clinical work-up. This may be due to sparse training in epilepsy pathology and or due to lack of neuropathological guidelines for companion animals. The protocols introduced herein shall facilitate systematic sampling and processing of epileptic brains and therefore increase the efficacy, reliability and reproducibility of morphological studies in animals suffering from seizures. Brain dissection protocols of two neuropathological centres with research focus in epilepsy have been optimised with regards to their diagnostic yield and accuracy, their practicability and their feasibility concerning clinical research requirements. The recommended guidelines allow for easy, standardised and ubiquitous collection of brain regions, relevant for seizure generation. Tissues harvested the prescribed way will increase the diagnostic efficacy and provide reliable material for scientific investigations

Age specific recruitment and retention to a large multicentre observational breast cancer trial in older women: the Age Gap Trial

Introduction Recruitment and retention are two of the most important factors in successfully running clinical trials. Many trials encounter problems with both, causing delays or preventing study progress. These issues are greater in older adults and patients with cancer. Materials and methods We assessed recruitment and retention in a large, multicentre, observational breast cancer study in older female patients (>70 years, N = 3440). Data collected by the Age Gap study were used to assess rates of, and reasons for, patients not being recruited or retained. Statistical analysis assessed the impact of age as a predictor of recruitment and retention. Results Between February 2013 and June 2018, 6876 patients were screened and 3456 were consented across 56 United Kingdom (UK) breast units. Reasons for non-recruitment included ineligibility, clinician issues, staffing resource issues, patients' lack of interest or time and trial burden. In comparison with the age demographics of patients with breast cancer in the UK, women aged 70–75 years were over-represented compared to older age groups. Logistic regression demonstrated that older age significantly reduced the odds of consent (OR = 0.96, CI: 0.938–0.982; p < 0.001). Multivariate analysis showed that age (p < 0.001), markers of poor functional ability (Eastern Cooperative Oncology Group Performance Status (p = 0.011)) and instrumental activities of daily living (p = 0.026) were significant predictors of withdrawal. Discussion This study has demonstrated that selection and attrition bias for age are apparent despite a range of ‘age friendly’ study design measures. Exploration of the underlying reasons for this and development of measures to address this should be the focus of further research

Myelination is associated with processing speed in early childhood:preliminary insights

Processing speed is an important contributor to working memory performance and fluid intelligence in young children. Myelinated white matter plays a central role in brain messaging, and likely mediates processing speed, but little is known about the relationship between myelination and processing speed in young children. In the present study, processing speed was measured through inspection times, and myelin volume fraction (VFM) was quantified using a multicomponent magnetic resonance imaging (MRI) approach in 2- to 5-years of age. Both inspection times and VFM were found to increase with age. Greater VFM in the right and left occipital lobes, the body of the corpus callosum, and the right cerebellum was significantly associated with shorter inspection times, after controlling for age. A hierarchical regression showed that VFM in the left occipital lobe predicted inspection times over and beyond the effects of age and the VFM in the other brain regions. These findings are consistent with the hypothesis that myelin supports processing speed in early childhood

Interpretable surface-based detection of focal cortical dysplasias:a Multi-centre Epilepsy Lesion Detection study

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy

High resolution and contrast 7 tesla MR brain imaging of the neonate

IntroductionUltra-high field MR imaging offers marked gains in signal-to-noise ratio, spatial resolution, and contrast which translate to improved pathological and anatomical sensitivity. These benefits are particularly relevant for the neonatal brain which is rapidly developing and sensitive to injury. However, experience of imaging neonates at 7T has been limited due to regulatory, safety, and practical considerations. We aimed to establish a program for safely acquiring high resolution and contrast brain images from neonates on a 7T system.MethodsImages were acquired from 35 neonates on 44 occasions (median age 39 + 6 postmenstrual weeks, range 33 + 4 to 52 + 6; median body weight 2.93 kg, range 1.57 to 5.3 kg) over a median time of 49 mins 30 s. Peripheral body temperature and physiological measures were recorded throughout scanning. Acquired sequences included T2 weighted (TSE), Actual Flip angle Imaging (AFI), functional MRI (BOLD EPI), susceptibility weighted imaging (SWI), and MR spectroscopy (STEAM).ResultsThere was no significant difference between temperature before and after scanning (p = 0.76) and image quality assessment compared favorably to state-of-the-art 3T acquisitions. Anatomical imaging demonstrated excellent sensitivity to structures which are typically hard to visualize at lower field strengths including the hippocampus, cerebellum, and vasculature. Images were also acquired with contrast mechanisms which are enhanced at ultra-high field including susceptibility weighted imaging, functional MRI, and MR spectroscopy.DiscussionWe demonstrate safety and feasibility of imaging vulnerable neonates at ultra-high field and highlight the untapped potential for providing important new insights into brain development and pathological processes during this critical phase of early life

Treatment effects may remain the same even when trial participants differed from the target population

Objective RCTs have been criticised for lacking external validity. We assessed whether a trial in people with type I diabetes mellitus (T1DM) mirrored the wider population, and applied sample-weighting methods to assess the impact of differences on our trial's findings. Study design and setting The REPOSE trial was nested within a large UK cohort capturing demographic, clinical and quality of life (QoL) data for people with T1DM undergoing structured diabetes-specific education. We firstly assessed whether our RCT participants were comparable to this cohort using propensity score modelling. Following this we re-weighted the trial population to better match the wider cohort and re-estimated the treatment effect. Results Trial participants differed from the cohort in regards to sex, weight, HbA1c and also QoL and satisfaction with current treatment. Nevertheless, the treatment effects derived from alternative model weightings were similar to that of the original RCT. Conclusions Our RCT participants differed in composition to the wider population but the original findings were unaffected by sampling adjustments. We encourage investigators take steps to address criticisms of generalisability, but doing so is problematic: external data, even if available, may contain limited information and analyses can be susceptible to model misspecification