Computationally efficient methods for fitting mixed models to electronic health records data

Motivated by two case studies using primary care records from the Clinical Practice Research Datalink, we describe statistical methods that facilitate the analysis of tall data, with very large numbers of observations. Our focus is on investigating the association between patient characteristics and an outcome of interest, while allowing for variation among general practices. We explore ways to fit mixed effects models to tall data, including predictors of interest and confounding factors as covariates, and including random intercepts to allow for heterogeneity in outcome among practices. We introduce: (1) weighted regression and (2) meta-analysis of estimated regression coefficients from each practice. Both methods reduce the size of the dataset, thus decreasing the time required for statistical analysis. We compare the methods to an existing subsampling approach. All methods give similar point estimates, and weighted regression and meta-analysis give similar standard errors for point estimates to analysis of the entire dataset, but the subsampling method gives larger standard errors. Where all data are discrete, weighted regression is equivalent to fitting the mixed model to the entire dataset. In the presence of a continuous covariate, meta-analysis is useful. Both methods are easy to implement in standard statistical softwareThe authors are grateful to the CPRD team at the University of Cambridge. In particular, we thank Carol Wilson and Anna Cassel for providing access to the case study datasets that they spent much time preparing for analysis. Kirsty Rhodes was funded by Medical Research Council Unit Programmes U105260558 and MC_UU_00002/5. Rebecca Turner and Ian White were funded by Medical Research Council Unit Programmes U105260558 and MC_UU_12023/2

Label-invariant models for the analysis of meta-epidemiological data.

Rich meta-epidemiological data sets have been collected to explore associations between intervention effect estimates and study-level characteristics. Welton et al proposed models for the analysis of meta-epidemiological data, but these models are restrictive because they force heterogeneity among studies with a particular characteristic to be at least as large as that among studies without the characteristic. In this paper we present alternative models that are invariant to the labels defining the 2 categories of studies. To exemplify the methods, we use a collection of meta-analyses in which the Cochrane Risk of Bias tool has been implemented. We first investigate the influence of small trial sample sizes (less than 100 participants), before investigating the influence of multiple methodological flaws (inadequate or unclear sequence generation, allocation concealment, and blinding). We fit both the Welton et al model and our proposed label-invariant model and compare the results. Estimates of mean bias associated with the trial characteristics and of between-trial variances are not very sensitive to the choice of model. Results from fitting a univariable model show that heterogeneity variance is, on average, 88% greater among trials with less than 100 participants. On the basis of a multivariable model, heterogeneity variance is, on average, 25% greater among trials with inadequate/unclear sequence generation, 51% greater among trials with inadequate/unclear blinding, and 23% lower among trials with inadequate/unclear allocation concealment, although the 95% intervals for these ratios are very wide. Our proposed label-invariant models for meta-epidemiological data analysis facilitate investigations of between-study heterogeneity attributable to certain study characteristics

The Epidemiology of Multimorbidity in Primary Care

Background: Multimorbidity places a substantial burden on patients and the healthcare system but few contemporary data are available. Aim: To describe the epidemiology of multimorbidity in adults in England and quantify associations between multimorbidity and health service utilisation. Design: Retrospective cohort study Setting: A random sample of 403,985 adult patients (≥18 years) in England who were registered with a general practice on 1 January 2012 and included in the Clinical Practice Research Datalink. Methods: We defined multimorbidity as having two or more of 36 long-term conditions recorded in patients’ medical records and quantified associations between multimorbidity and health service utilisation (GP consultations, prescriptions, and hospitalisations) over four years. Results: 27.2% of patients were multimorbid. The most prevalent conditions were hypertension (18.2%), depression or anxiety (10.3%), and chronic pain (10.1%). Prevalence of multimorbidity was higher in females than males (30% vs. 24.4% respectively) and among those with lower socioeconomic status (33.8% in the most deprived quintile vs. 24.2% in the least deprived quintile). Physical-mental comorbidity contributed a much greater proportion of overall morbidity in both younger patients and those patients with lower socioeconomic status. Multimorbidity was strongly associated with health service utilisation. Multimorbid patients accounted for 53% of GP consultations, 79% of prescriptions, and 56% percent of hospital admissions. Conclusion: Multimorbidity is common, socially patterned, and associated with increased health service utilisation. These findings support the need to improve the quality and efficiency of health services providing care to multimorbid patients at the practice and national-level.This study received no specific funding. Kirsty Rhodes was supported by the UK Medical Research Council (grant number: U105260558)

Adjusting trial results for biases in meta-analysis: combining data-based evidence on bias with detailed trial assessment.

Flaws in the conduct of randomized trials can lead to biased estimation of the intervention effect. Methods for adjustment of within-trial biases in meta-analysis include the use of empirical evidence from an external collection of meta-analyses, and the use of expert opinion informed by the assessment of detailed trial information. Our aim is to present methods to combine these two approaches to gain the advantages of both. We make use of the risk of bias information that is routinely available in Cochrane reviews, by obtaining empirical distributions for the bias associated with particular bias profiles (combinations of risk of bias judgements). We propose three methods: a formal combination of empirical evidence and opinion in a Bayesian analysis; asking experts to give an opinion on bias informed by both summary trial information and a bias distribution from the empirical evidence, either numerically or by selecting areas of the empirical distribution. The methods are demonstrated through application to two example binary outcome meta-analyses. Bias distributions based on opinion informed by trial information alone were most dispersed on average, and those based on opinions obtained by selecting areas of the empirical distribution were narrowest. Although the three methods for combining empirical evidence with opinion vary in ease and speed of implementation, they yielded similar results in the two examples

Association of comorbidity and health service usage among patients with dementia in the UK: a population-based study

$\textbf{BACKGROUND}$: The majority of people with dementia have other long-term diseases, the presence of which may affect the progression and management of dementia. This study aimed to identify subgroups with higher healthcare needs, by analysing how primary care consultations, number of prescriptions and hospital admissions by people with dementia varies with having additional long-term diseases (comorbidity). $\textbf{METHODS}$: A retrospective cohort study based on health data from the Clinical Practice Research Datalink (CPRD) was conducted. Incident cases of dementia diagnosed in the year starting 1/3/2008 were selected and followed for up to 5 years. The number of comorbidities was obtained from a set of 34 chronic health conditions. Service usage (primary care consultations, hospitalisations and prescriptions) and time-to-death were determined during follow-up. Multilevel negative binomial regression and Cox regression, adjusted for age and gender, were used to model differences in service usage and death between differing numbers of comorbidities. $\textbf{RESULTS}$: Data from 4999 people (14 866 person-years of follow-up) were analysed. Overall, 91.7% of people had 1 or more additional comorbidities. Compared with those with 2 or 3 comorbidities, people with ≥6 comorbidities had higher rates of primary care consultations (rate ratio (RR) 1.31, 95% CI 1.25 to 1.36), prescriptions (RR 1.68, 95% CI 1.57 to 1.81), and hospitalisation (RR 1.62, 95% CI 1.44 to 1.83), and higher risk of death (HR 1.56, 95% CI 1.37 to 1.78). $\textbf{DISCUSSION}$: In the UK, people with dementia with higher numbers of comorbidities die earlier and have considerably higher health service usage in terms of primary care consultations, hospital admissions and prescribing. This study provides strong evidence that comorbidity is a key factor that should be considered when allocating resources and planning care for people with dementia

Agreement was moderate between data-based and opinion-based assessments of biases affecting randomised trials within meta-analyses

BACKGROUND: Randomised trials included in meta-analyses are often affected by bias caused by methodological flaws or limitations, but the degree of bias is unknown. Two proposed methods adjust trial results for bias using: (1) empirical evidence from published meta-epidemiological studies; or (2) expert opinion. METHODS: We investigated agreement between data-based and opinion-based approaches to assessing bias in each of four domains: sequence generation, allocation concealment, blinding and incomplete outcome data. From each sampled meta-analysis, a pair of trials with the highest and lowest empirical model-based bias estimates was selected. Independent assessors were asked which trial within each pair was judged more biased on the basis of detailed trial design summaries. RESULTS: Assessors judged trials to be equally biased in 68% of pairs evaluated. When assessors judged one trial as more biased, the proportion of judgements agreeing with the model-based ranking was highest for allocation concealment (79%) and blinding (79%) and lower for sequence generation (59%) and incomplete outcome data (56%). CONCLUSIONS: Most trial pairs found to be discrepant empirically were judged to be equally biased by assessors. We found moderate agreement between opinion and data-based evidence in pairs where assessors ranked one trial as more biased

Adjusting trial results for biases in meta-analysis:combining data-based evidence on bias with detailed trial assessment

Flaws in the conduct of randomised trials can lead to biased estimation of the intervention effect. Methods for adjustment of within-trial biases in meta-analysis include use of empirical evidence from an external collection of meta-analyses, and use of expert opinion informed by assessment of detailed trial information. Our aim is to present methods to combine these two approaches in order to gain the advantages of both. We make use of the risk of bias information routinely available in Cochrane reviews, by obtaining empirical distributions for the bias associated with particular bias profiles (combinations of risk of bias judgements). We propose three methods: (i) formal combination of empirical evidence and opinion in a Bayesian analysis; (ii) asking experts to give an opinion on bias informed by both summary trial information and a bias distribution from the empirical evidence, either numerically or by (iii) selecting areas of the empirical distribution. The methods are demonstrated through application to two example binary outcome meta-analyses. Bias distributions based on opinion informed by trial information alone were most dispersed on average, and those based on opinions obtained by selecting areas of the empirical distribution were narrowest. Although the three different methods for combining empirical evidence with opinion vary in ease and speed of implementation, they yielded similar results in the two examples

Patient and public involvement in patient safety research: a workshop to review patient information, minimise psychological risk and inform research

Background Patient safety has attracted increasing attention in recent years. This paper explores patients’ contributions to informing patient safety research at an early stage, within a project on intravenous infusion errors. Currently, there is little or no guidance on how best to involve patients and the wider public in shaping patient safety research, and indeed, whether such efforts are worthwhile. Method We ran a 3-hour workshop involving nine patients with experience of intravenous therapy in the hospital setting. The first part explored patients’ experiences of intravenous therapy. We derived research questions from the resulting discussion through qualitative analysis. In the second part, patients were asked for feedback on patient information sheets considering both content and clarity, and on two potential approaches to framing our patient information: one that focused on research on safety and error, the other on quality improvement. Results The workshop led to a thorough review of how we should engage with patients. Importantly, there was a clear steer away from terms such as ‘error’ and ‘safety’ that could worry patients. The experiences that patients revealed were also richer than we had anticipated, revealing different conceptions of how patients related to their treatment and care, their role in safety and use of medical devices, the different levels of information they preferred, and broader factors impacting perceptions of their care. Conclusion Involving patients at an early stage in patient safety research can be of great value. Our workshop highlighted sensitivities around potentially worrying patients about risks that they might not have considered previously, and how to address these. Patient representatives also emphasised a need to expand the focus of patient safety research beyond clinicians and error, to include factors affecting perceptions of quality and safety for patients more broadly

Left gaze bias in humans, rhesus monkeys and domestic dogs

While viewing faces, human adults often demonstrate a natural gaze bias towards the left visual field, that is, the right side of the viewee’s face is often inspected first and for longer periods. Using a preferential looking paradigm, we demonstrate that this bias is neither uniquely human nor limited to primates, and provide evidence to help elucidate its biological function within a broader social cognitive framework. We observed that 6-month-old infants showed a wider tendency for left gaze preference towards objects and faces of different species and orientation, while in adults the bias appears only towards upright human faces. Rhesus monkeys showed a left gaze bias towards upright human and monkey faces, but not towards inverted faces. Domestic dogs, however, only demonstrated a left gaze bias towards human faces, but not towards monkey or dog faces, nor to inanimate object images. Our findings suggest that face- and species-sensitive gaze asymmetry is more widespread in the animal kingdom than previously recognised, is not constrained by attentional or scanning bias, and could be shaped by experience to develop adaptive behavioural significance

Guillain-Barré syndrome: a century of progress

In 1916, Guillain, Barré and Strohl reported on two cases of acute flaccid paralysis with high cerebrospinal fluid protein levels and normal cell counts — novel findings that identified the disease we now know as Guillain–Barré syndrome (GBS). 100 years on, we have made great progress with the clinical and pathological characterization of GBS. Early clinicopathological and animal studies indicated that GBS was an immune-mediated demyelinating disorder, and that severe GBS could result in secondary axonal injury; the current treatments of plasma exchange and intravenous immunoglobulin, which were developed in the 1980s, are based on this premise. Subsequent work has, however, shown that primary axonal injury can be the underlying disease. The association of Campylobacter jejuni strains has led to confirmation that anti-ganglioside antibodies are pathogenic and that axonal GBS involves an antibody and complement-mediated disruption of nodes of Ranvier, neuromuscular junctions and other neuronal and glial membranes. Now, ongoing clinical trials of the complement inhibitor eculizumab are the first targeted immunotherapy in GBS