Missing at random, likelihood ignorability and model completeness

This paper provides further insight into the key concept of missing at random (MAR) in incomplete data analysis. Following the usual selection modelling approach we envisage two models with separable parameters: a model for the response of interest and a model for the missing data mechanism (MDM). If the response model is given by a complete density family, then frequentist inference from the likelihood function ignoring the MDM is valid if and only if the MDM is MAR. This necessary and sufficient condition also holds more generally for models for coarse data, such as censoring. Examples are given to show the necessity of the completeness of the underlying model for this equivalence to hold

The radial plot in meta-analysis : approximations and applications

Fixed effects meta-analysis can be thought of as least squares analysis of the radial plot, the plot of standardized treatment effect against precision (reciprocal of the standard deviation) for the studies in a systematic review. For example, the least squares slope through the origin estimates the treatment effect, and a widely used test for publication bias is equivalent to testing the significance of the regression intercept. However, the usual theory assumes that the within-study variances are known, whereas in practice they are estimated. This leads to extra variability in the points of the radial plot which can lead to a marked distortion in inferences that are derived from these regression calculations. This is illustrated by a clinical trials example from the Cochrane database. We derive approximations to the sampling properties of the radial plot and suggest bias corrections to some of the commonly used methods of meta-analysis. A simulation study suggests that these bias corrections are effective in controlling levels of significance of tests and coverage of confidence intervals

Confidence intervals and P-valves for meta analysis with publication bias

We study publication bias in meta analysis by supposing there is a population (y, σ) of studies which give treatment effect estimates y ~ N(θ, σ2). A selection function describes the probability that each study is selected for review. The overall estimate of θ depends on the studies selected, and hence on the (unknown) selection function. Our previous paper, Copas and Jackson (2004, A bound for publication bias based on the fraction of unpublished studies, Biometrics 60, 146-153), studied the maximum bias over all possible selection functions which satisfy the weak condition that large studies (small σ) are as likely, or more likely, to be selected than small studies (large σ). This led to a worstcase sensitivity analysis, controlling for the overall fraction of studies selected. However, no account was taken of the effect of selection on the uncertainty in estimation. This paper extends the previous work by finding corresponding confidence intervals and P-values, and hence a new sensitivity analysis for publication bias. Two examples are discussed

Examining the potential public health benefit of offering STI testing to men in amateur football clubs: evidence from cross-sectional surveys

Background: In Britain, young people continue to bear the burden of sexually transmitted infections (STIs) so efforts are required, especially among men, to encourage STI testing. The SPORTSMART study trialled an intervention that sought to achieve this by offering chlamydia and gonorrhoea test-kits to men attending amateur football clubs between October and December 2012. With football the highest participation team sport among men in England, this paper examines the potential public health benefit of offering STI testing to men in this setting by assessing their sociodemographic characteristics, sexual behaviours, and healthcare behaviour and comparing them to men in the general population. Methods: Data were collected from 192 (male) members of 6 football clubs in London, United Kingdom, aged 18–44 years via a 20-item pen-and-paper self-completion questionnaire administered 2 weeks after the intervention. These were compared to data collected from 409 men of a similar age who were resident in London when interviewed during 2010–2012 for the third National Survey of Sexual Attitudes and Lifestyles (Natsal-3), a national probability survey that used computer-assisted-personal-interviewing with computer-assisted-self-interview. Age standardisation and multivariable regression were used to account for sociodemographic differences between the surveys. Results: Relative to men in the general population, SPORTSMART men were younger (32.8 % vs. 21.7 % aged under 25 y), and more likely to report (all past year) at least 2 sexual partners (adjusted odds ratio, AOR: 3.25, 95 % CI: 2.15–4.92), concurrent partners (AOR: 2.05, 95 % CI: 1.39–3.02), and non-use of condoms (AOR: 2.17, 95 % CI: 1.39–3.41). No difference was observed in STI/HIV risk perception (AOR for reporting “not at all at risk” of STIs: 1.25, 95 % CI: 0.76–2.04; of HIV: AOR: 1.54, 95 % CI: 0.93–2.55), nor in reporting STI testing in the past year (AOR: 0.83, 95 % CI: 0.44–1.54), which was reported by only one in six men. Conclusions: Relative to young men in the general population, football club members who completed the SPORTSMART survey reported greater sexual risk behaviour but similar STI/HIV risk perception and STI testing history. Offering STI testing in amateur football clubs may therefore widen access to STI testing and health promotion messages for men at higher STI risk, which, given the minority currently testing and the popularity of football in England, should yield both individual and public health benefit

Natural T cell–mediated protection against seasonal and pandemic Influenza: results of the Flu Watch cohort study

Rationale: A high proportion of influenza infections are asymptomatic. Animal and human challenge studies and observational studies suggest T cells protect against disease among those infected, but the impact of T-cell immunity at the population level is unknown. Objectives: To investigate whether naturally preexisting T-cell responses targeting highly conserved internal influenza proteins could provide cross-protective immunity against pandemic and seasonal influenza. Methods: We quantified influenza A(H3N2) virus–specific T cells in a population cohort during seasonal and pandemic periods between 2006 and 2010. Follow-up included paired serology, symptom reporting, and polymerase chain reaction (PCR) investigation of symptomatic cases. Measurements and Main Results: A total of 1,414 unvaccinated individuals had baseline T-cell measurements (1,703 participant observation sets). T-cell responses to A(H3N2) virus nucleoprotein (NP) dominated and strongly cross-reacted with A(H1N1)pdm09 NP (P < 0.001) in participants lacking antibody to A(H1N1)pdm09. Comparison of paired preseason and post-season sera (1,431 sets) showed 205 (14%) had evidence of infection based on fourfold influenza antibody titer rises. The presence of NP-specific T cells before exposure to virus correlated with less symptomatic, PCR-positive influenza A (overall adjusted odds ratio, 0.27; 95% confidence interval, 0.11–0.68; P = 0.005, during pandemic [P = 0.047] and seasonal [P = 0.049] periods). Protection was independent of baseline antibodies. Influenza-specific T-cell responses were detected in 43%, indicating a substantial population impact. Conclusions: Naturally occurring cross-protective T-cell immunity protects against symptomatic PCR-confirmed disease in those with evidence of infection and helps to explain why many infections do not cause symptoms. Vaccines stimulating T cells may provide important cross-protective immunity

Cohort Profile: The Flu Watch Study

Influenza is a common, highly contagious respiratory virus which infects all age groups, causing a range of outcomes from asymptomatic infection and mild respiratory disease to severe respiratory disease and death.1 If infected, the adaptive immune system produces a humoral (antibody) and cell-mediated (T cell) immune response to fight the infection.2 Influenza viruses continually evolve through antigenic drift, resulting in slightly different ‘seasonal’ influenza strains circulating each year. Population-level antibody immunity to these seasonal viruses builds up over time, so in any given season only a proportion of the population is susceptible to the circulating strains. Occasionally, influenza A viruses evolve rapidly through antigenic shift by swapping genes with influenza viruses usually circulating in animals. This process creates an immunologically distinct virus to which the population may have little to no antibody immunity. The virus can result in a pandemic if a large portion of the population is susceptible and the virus is easily spread

Comparative community burden and severity of seasonal and pandemic influenza: results of the Flu Watch cohort study

BACKGROUND: Assessment of the effect of influenza on populations, including risk of infection, illness if infected, illness severity, and consultation rates, is essential to inform future control and prevention. We aimed to compare the community burden and severity of seasonal and pandemic influenza across different age groups and study years and gain insight into the extent to which traditional surveillance underestimates this burden. METHODS: Using preseason and postseason serology, weekly illness reporting, and RT-PCR identification of influenza from nasal swabs, we tracked the course of seasonal and pandemic influenza over five successive cohorts (England 2006-11; 5448 person-seasons' follow-up). We compared burden and severity of seasonal and pandemic strains. We weighted analyses to the age and regional structure of England to give nationally representative estimates. We compared symptom profiles over the first week of illness for different strains of PCR-confirmed influenza and non-influenza viruses using ordinal logistic regression with symptom severity grade as the outcome variable. FINDINGS: Based on four-fold titre rises in strain-specific serology, on average influenza infected 18% (95% CI 16-22) of unvaccinated people each winter. Of those infected there were 69 respiratory illnesses per 100 person-influenza-seasons compared with 44 per 100 in those not infected with influenza. The age-adjusted attributable rate of illness if infected was 23 illnesses per 100 person-seasons (13-34), suggesting most influenza infections are asymptomatic. 25% (18-35) of all people with serologically confirmed infections had PCR-confirmed disease. 17% (10-26) of people with PCR-confirmed influenza had medically attended illness. These figures did not differ significantly when comparing pandemic with seasonal influenza. Of PCR-confirmed cases, people infected with the 2009 pandemic strain had markedly less severe symptoms than those infected with seasonal H3N2. INTERPRETATION: Seasonal influenza and the 2009 pandemic strain were characterised by similar high rates of mainly asymptomatic infection with most symptomatic cases self-managing without medical consultation. In the community the 2009 pandemic strain caused milder symptoms than seasonal H3N2

Publishing data to support the fight against human vector-borne diseases

Vector-borne diseases are responsible for more than 17% of human cases of infectious diseases. In most situations, effective control of debilitating and deadly vector-bone diseases (VBDs), such as malaria, dengue, chikungunya, yellow fever, Zika and Chagas requires up-to-date, robust and comprehensive information on the presence, diversity, ecology, bionomics and geographic spread of the organisms that carry and transmit the infectious agents. Huge gaps exist in the information related to these vectors, creating an essential need for campaigns to mobilise and share data. The publication of data papers is an effective tool for overcoming this challenge. These peer-reviewed articles provide scholarly credit for researchers whose vital work of assembling and publishing well-described, properly-formatted datasets often fails to receive appropriate recognition. To address this, GigaScience 's sister journal GigaByte partnered with the Global Biodiversity Information Facility (GBIF) to publish a series of data papers, with support from the Special Programme for Research and Training in Tropical Diseases (TDR), hosted by the World Health Organisation (WHO). Here we outline the initial results of this targeted approach to sharing data and describe its importance for controlling VBDs and improving public health

Effect of remote ischaemic conditioning on clinical outcomes in patients with acute myocardial infarction (CONDI-2/ERIC-PPCI): a single-blind randomised controlled trial.

BACKGROUND: Remote ischaemic conditioning with transient ischaemia and reperfusion applied to the arm has been shown to reduce myocardial infarct size in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI). We investigated whether remote ischaemic conditioning could reduce the incidence of cardiac death and hospitalisation for heart failure at 12 months. METHODS: We did an international investigator-initiated, prospective, single-blind, randomised controlled trial (CONDI-2/ERIC-PPCI) at 33 centres across the UK, Denmark, Spain, and Serbia. Patients (age >18 years) with suspected STEMI and who were eligible for PPCI were randomly allocated (1:1, stratified by centre with a permuted block method) to receive standard treatment (including a sham simulated remote ischaemic conditioning intervention at UK sites only) or remote ischaemic conditioning treatment (intermittent ischaemia and reperfusion applied to the arm through four cycles of 5-min inflation and 5-min deflation of an automated cuff device) before PPCI. Investigators responsible for data collection and outcome assessment were masked to treatment allocation. The primary combined endpoint was cardiac death or hospitalisation for heart failure at 12 months in the intention-to-treat population. This trial is registered with ClinicalTrials.gov (NCT02342522) and is completed. FINDINGS: Between Nov 6, 2013, and March 31, 2018, 5401 patients were randomly allocated to either the control group (n=2701) or the remote ischaemic conditioning group (n=2700). After exclusion of patients upon hospital arrival or loss to follow-up, 2569 patients in the control group and 2546 in the intervention group were included in the intention-to-treat analysis. At 12 months post-PPCI, the Kaplan-Meier-estimated frequencies of cardiac death or hospitalisation for heart failure (the primary endpoint) were 220 (8·6%) patients in the control group and 239 (9·4%) in the remote ischaemic conditioning group (hazard ratio 1·10 [95% CI 0·91-1·32], p=0·32 for intervention versus control). No important unexpected adverse events or side effects of remote ischaemic conditioning were observed. INTERPRETATION: Remote ischaemic conditioning does not improve clinical outcomes (cardiac death or hospitalisation for heart failure) at 12 months in patients with STEMI undergoing PPCI. FUNDING: British Heart Foundation, University College London Hospitals/University College London Biomedical Research Centre, Danish Innovation Foundation, Novo Nordisk Foundation, TrygFonden