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Cambridge, United Kingdom

Strang J.,Kings College London | Bird S.M.,MRC Biostatistics Unit | Parmar M.K.B.,MRC Clinical Trials Unit
Journal of Urban Health

The naloxone investigation (N-ALIVE) randomized trial commenced in the UK in May 2012, with the preliminary phase involving 5,600 prisoners on release. The trial is investigating whether heroin overdose deaths post-prison release can be prevented by prior provision of a take-home emergency supply of naloxone. Heroin contributes disproportionately to drug deaths through opiate-induced respiratory depression. Take-home emergency naloxone is a novel preventive measure for which there have been encouraging preliminary reports from community schemes. Overdoses are usually witnessed, and drug users themselves and also family members are a vast intervention workforce who are willing to intervene, but whose responses are currently often inefficient or wrong. Approximately 10% of provided emergency naloxone is thought to be used in subsequent emergency resuscitation but, as yet, there have been no definitive studies. The period following release from prison is a time of extraordinarily high mortality, with heroin overdose deaths increased more than sevenfold in the first fortnight after release. Of prisoners with a previous history of heroin injecting who are released from prison, 1 in 200 will die of a heroin overdose within the first 4 weeks. There are major scientific and logistical challenges to assessing the impact of take-home naloxone. Even in recently released prisoners, heroin overdose death is a relatively rare event: hence, large numbers of prisoners need to enter the trial to assess whether take-home naloxone significantly reduces the overdose death rate. The commencement of pilot phase of the N-ALIVE trial is a significant step forward, with prisoners being randomly assigned either to treatment-as-usual or to treatment-as-usual plus a supply of take-home emergency naloxone. The subsequent full N-ALIVE trial (contingent on a successful pilot) will involve 56,000 prisoners on release, and will give a definitive conclusion on lives saved in real-world application. Advocates call for implementation, while naysayers raise concerns. The issue does not need more public debate; it needs good science. © 2013 The Author(s). Source

Heroin users/injectors' risk of drugs-related death by sex and current age is weakly estimated both in individual cohorts of under 1000 clients, 5000 person-years or 50 drugs-related deaths and when using cross-sectional data. A workshop in Cambridge analysed six cohorts who were recruited according to a common European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) protocol from drug treatment agencies in Barcelona, Denmark, Dublin, Lisbon, Rome and Vienna in the 1990s; and, as external reference, opiate-user arrestees in France and Hepatitis C diagnosed ever-injectors in Scotland in 19932001, both followed by database linkage to December 2001. EMCDDA cohorts recorded approximately equal numbers of drugs-related deaths (864) and deaths from other non-HIV causes (865) during 106,152 person-years of follow-up. External cohorts contributed 376 drugs-related deaths (Scotland 195, France 181) and 418 deaths from non-HIV causes (Scotland 221, France 197) during 86,417 person-years of follow-up (Scotland 22, 670, France 63, 747). EMCDDA cohorts reported 707 drugs-related deaths in 81,367 man-years (8.7 per 1000 person-years, 95 CI: 8.19.4) but only 157 in 24,785 person-years for females (6.3 per 1000 person-years, 95 CI: 5.47.4). Except in external cohorts, relative risks by current age group were not particularly strong, and more modest in Poisson regression than in cross-sectional analyses: relative risk was 1.2 (95 CI: 1.01.4) for 3544 year olds compared to 1524 year olds, but 1.4 for males (95 CI: 1.21.6), and dramatically lower at 0.44 after the first year of follow-up (95 CI: 0.370.52). © 2010 Informa UK Ltd All rights reserved: reproduction in whole or part not permitted. Source

Emsley R.,University of Manchester | Dunn G.,University of Manchester | White I.R.,MRC Biostatistics Unit
Statistical Methods in Medical Research

Complex intervention trials should be able to answer both pragmatic and explanatory questions in order to test the theories motivating the intervention and help understand the underlying nature of the clinical problem being tested. Key to this is the estimation of direct effects of treatment and indirect effects acting through intermediate variables which are measured post-randomisation. Using psychological treatment trials as an example of complex interventions, we review statistical methods which crucially evaluate both direct and indirect effects in the presence of hidden confounding between mediator and outcome. We review the historical literature on mediation and moderation of treatment effects. We introduce two methods from within the existing causal inference literature, principal stratification and structural mean models, and demonstrate how these can be applied in a mediation context before discussing approaches and assumptions necessary for attaining identifiability of key parameters of the basic causal model. Assuming that there is modification by baseline covariates of the effect of treatment (i.e. randomisation) on the mediator (i.e. covariate by treatment interactions), but no direct effect on the outcome of these treatment by covariate interactions leads to the use of instrumental variable methods. We describe how moderation can occur through post-randomisation variables, and extend the principal stratification approach to multiple group methods with explanatory models nested within the principal strata. We illustrate the new methodology with motivating examples of randomised trials from the mental health literature. © 2010 The Author(s). Source

Keogh R.,London School of Hygiene and Tropical Medicine | White I.,MRC Biostatistics Unit
Statistics in Medicine

Exposure measurement error is a problem in many epidemiological studies, including those using biomarkers and measures of dietary intake. Measurement error typically results in biased estimates of exposure-disease associations, the severity and nature of the bias depending on the form of the error. To correct for the effects of measurement error, information additional to the main study data is required. Ideally, this is a validation sample in which the true exposure is observed. However, in many situations, it is not feasible to observe the true exposure, but there may be available one or more repeated exposure measurements, for example, blood pressure or dietary intake recorded at two time points. The aim of this paper is to provide a toolkit for measurement error correction using repeated measurements. We bring together methods covering classical measurement error and several departures from classical error: systematic, heteroscedastic and differential error. The correction methods considered are regression calibration, which is already widely used in the classical error setting, and moment reconstruction and multiple imputation, which are newer approaches with the ability to handle differential error. We emphasize practical application of the methods in nutritional epidemiology and other fields. We primarily consider continuous exposures in the exposure-outcome model, but we also outline methods for use when continuous exposures are categorized. The methods are illustrated using the data from a study of the association between fibre intake and colorectal cancer, where fibre intake is measured using a diet diary and repeated measures are available for a subset. © 2014 The Authors. Source

Bowden J.,MRC Biostatistics Unit | Vansteelandt S.,Ghent University
Statistics in Medicine

'Instrumental Variable' (IV) methods provide a basis for estimating an exposure's causal effect on the risk of disease. In Mendelian randomization studies, where genetic information plays the role of the IV, IV analyses are routinely performed on case-control data, rather than prospectively collected observational data. Although it is a well-appreciated fact that ascertainment bias may invalidate such analyses, ad hoc assumptions and approximations are made to justify their use. In this paper we attempt to explain and clarify why they may fail and show how they can be adjusted for improved performance. In particular, we propose consistent estimators of the causal relative risk and odds ratio if a priori knowledge is available regarding either the population disease prevalence or the population distribution of the IV (e.g. population allele frequencies). We further show that if no such information is available, approximate estimators can be obtained under a rare disease assumption. We illustrate this with matched case-control data from the recently completed EPIC study, from which we attempt to assess the evidence for a causal relationship between C-reactive protein levels and the risk of Coronary Artery Disease. © 2010 John Wiley & Sons, Ltd. Source

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