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  • 1
    Online Resource
    Online Resource
    Institute of Mathematical Statistics ; 2019
    In:  Statistical Science Vol. 34, No. 3 ( 2019-8-1)
    In: Statistical Science, Institute of Mathematical Statistics, Vol. 34, No. 3 ( 2019-8-1)
    Type of Medium: Online Resource
    ISSN: 0883-4237
    Language: Unknown
    Publisher: Institute of Mathematical Statistics
    Publication Date: 2019
    detail.hit.zdb_id: 2009740-2
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  • 2
    In: Statistical Communications in Infectious Diseases, Walter de Gruyter GmbH, Vol. 13, No. 1 ( 2021-01-27)
    Abstract: The causal impact method (CIM) was recently introduced for evaluation of binary interventions using observational time-series data. The CIM is appealing for practical use as it can adjust for temporal trends and account for the potential of unobserved confounding. However, the method was initially developed for applications involving large datasets and hence its potential in small epidemiological studies is still unclear. Further, the effects that measurement error can have on the performance of the CIM have not been studied yet. The objective of this work is to investigate both of these open problems. Methods Motivated by an existing dataset of HCV surveillance in the UK, we perform simulation experiments to investigate the effect of several characteristics of the data on the performance of the CIM. Further, we quantify the effects of measurement error on the performance of the CIM and extend the method to deal with this problem. Results We identify multiple characteristics of the data that affect the ability of the CIM to detect an intervention effect including the length of time-series, the variability of the outcome and the degree of correlation between the outcome of the treated unit and the outcomes of controls. We show that measurement error can introduce biases in the estimated intervention effects and heavily reduce the power of the CIM. Using an extended CIM, some of these adverse effects can be mitigated. Conclusions The CIM can provide satisfactory power in public health interventions. The method may provide misleading results in the presence of measurement error.
    Type of Medium: Online Resource
    ISSN: 2194-6310 , 1948-4690
    Language: English
    Publisher: Walter de Gruyter GmbH
    Publication Date: 2021
    detail.hit.zdb_id: 2535330-5
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  • 3
    In: BMJ Open, BMJ, Vol. 11, No. 4 ( 2021-04), p. e050346-
    Abstract: To measure the effects of the tier system on the COVID-19 pandemic in the UK between the first and second national lockdowns, before the emergence of the B.1.1.7 variant of concern. Design This is a modelling study combining estimates of real-time reproduction number R t (derived from UK case, death and serological survey data) with publicly available data on regional non-pharmaceutical interventions. We fit a Bayesian hierarchical model with latent factors using these quantities to account for broader national trends in addition to subnational effects from tiers. Setting The UK at lower tier local authority (LTLA) level. 310 LTLAs were included in the analysis. Primary and secondary outcome measures Reduction in real-time reproduction number R t . Results Nationally, transmission increased between July and late September, regional differences notwithstanding. Immediately prior to the introduction of the tier system, R t averaged 1.3 (0.9–1.6) across LTLAs, but declined to an average of 1.1 (0.86–1.42) 2 weeks later. Decline in transmission was not solely attributable to tiers. Tier 1 had negligible effects. Tiers 2 and 3, respectively, reduced transmission by 6% (5%–7%) and 23% (21%–25%). 288 LTLAs (93%) would have begun to suppress their epidemics if every LTLA had gone into tier 3 by the second national lockdown, whereas only 90 (29%) did so in reality. Conclusions The relatively small effect sizes found in this analysis demonstrate that interventions at least as stringent as tier 3 are required to suppress transmission, especially considering more transmissible variants, at least until effective vaccination is widespread or much greater population immunity has amassed.
    Type of Medium: Online Resource
    ISSN: 2044-6055 , 2044-6055
    Language: English
    Publisher: BMJ
    Publication Date: 2021
    detail.hit.zdb_id: 2599832-8
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  • 4
    Online Resource
    Online Resource
    Wiley ; 2022
    In:  Journal of Viral Hepatitis Vol. 29, No. 1 ( 2022-01), p. 43-51
    In: Journal of Viral Hepatitis, Wiley, Vol. 29, No. 1 ( 2022-01), p. 43-51
    Abstract: Many people with chronic hepatitis C infection don't engage in treatment. To eliminate hepatitis C and avoid health inequalities therapy must be provided to everyone. In other diseases peers with lived experience of the condition have improved care but, for hepatitis C, studies have not shown unequivocal benefit. We completed a retrospective analysis of the English National Health Service treatment registry comparing treatment networks with and without peers using Bayesian Poisson (for count outcomes) or Bayesian Binomial (for proportion outcomes) mixed effects models with time fixed effects. For each outcome, we estimated relative ratio (RR‐Poisson model) or odds ratio (Odds Ratio (OR)‐Binomial model) between peer and non‐peer networks. We analysed 30,729 patients within 20 operational delivery networks. In networks with peers there was an increase in the number of people initiating therapy (RR 1.12 95%, credible interval 1.02–1.21) and an increase in the proportion completing therapy (OR 2.45 95%, credible interval 1.49–3.84). However, we saw no change in proportions of people using drugs who initiated therapy nor any significant change in virological response (OR 1.14 95% credible interval 0.979–1.36). We repeated the analysis looking at the impact of peers two months after they had been introduced, when they had established networks of contacts, and saw an increase in the proportion of people treated in addiction services. In treating patients with chronic hepatitis C infection the inclusion of peer supporters may increase the number of people who initiate and complete antiviral therapy.
    Type of Medium: Online Resource
    ISSN: 1352-0504 , 1365-2893
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 2007924-2
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  • 5
    Online Resource
    Online Resource
    Institute of Mathematical Statistics ; 2017
    In:  Statistical Science Vol. 32, No. 4 ( 2017-11-1)
    In: Statistical Science, Institute of Mathematical Statistics, Vol. 32, No. 4 ( 2017-11-1)
    Type of Medium: Online Resource
    ISSN: 0883-4237
    Language: Unknown
    Publisher: Institute of Mathematical Statistics
    Publication Date: 2017
    detail.hit.zdb_id: 2009740-2
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  • 6
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2019
    In:  Journal of the Royal Statistical Society Series C: Applied Statistics Vol. 68, No. 1 ( 2019-01-01), p. 217-234
    In: Journal of the Royal Statistical Society Series C: Applied Statistics, Oxford University Press (OUP), Vol. 68, No. 1 ( 2019-01-01), p. 217-234
    Abstract: Working memory (WM) was one of the first cognitive processes studied with functional magnetic resonance imaging. With now over 20 years of studies on WM, each study with tiny sample sizes, there is a need for meta-analysis to identify the brain regions that are consistently activated by WM tasks, and to understand the interstudy variation in those activations. However, current methods in the field cannot fully account for the spatial nature of neuroimaging meta-analysis data or the heterogeneity observed among WM studies. In this work, we propose a fully Bayesian random-effects metaregression model based on log-Gaussian Cox processes, which can be used for meta-analysis of neuroimaging studies. An efficient Markov chain Monte Carlo scheme for posterior simulations is presented which makes use of some recent advances in parallel computing using graphics processing units. Application of the proposed model to a real data set provides valuable insights regarding the function of the WM.
    Type of Medium: Online Resource
    ISSN: 0035-9254 , 1467-9876
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 204797-4
    detail.hit.zdb_id: 1482300-7
    detail.hit.zdb_id: 1476894-X
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  • 7
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2022
    In:  Journal of the Royal Statistical Society Series C: Applied Statistics Vol. 71, No. 5 ( 2022-11-01), p. 1266-1281
    In: Journal of the Royal Statistical Society Series C: Applied Statistics, Oxford University Press (OUP), Vol. 71, No. 5 ( 2022-11-01), p. 1266-1281
    Abstract: Understanding the trajectory of the daily number of COVID-19 deaths is essential to decisions on how to respond to the pandemic, but estimating this trajectory is complicated by the delay between deaths occurring and being reported. In England the delay is typically several days, but it can be weeks. This causes considerable uncertainty about how many deaths occurred in recent days. Here we estimate the deaths per day in five age strata within seven English regions, using a Bayesian model that accounts for reporting-day effects and longer-term changes in the delay distribution. We show how the model can be computationally efficiently fitted when the delay distribution is the same in multiple strata, for example, over a wide range of ages.
    Type of Medium: Online Resource
    ISSN: 0035-9254 , 1467-9876
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 204797-4
    detail.hit.zdb_id: 1482300-7
    detail.hit.zdb_id: 1476894-X
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  • 8
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2020
    In:  Journal of the Royal Statistical Society Series A: Statistics in Society Vol. 183, No. 4 ( 2020-10-01), p. 1437-1459
    In: Journal of the Royal Statistical Society Series A: Statistics in Society, Oxford University Press (OUP), Vol. 183, No. 4 ( 2020-10-01), p. 1437-1459
    Abstract: A problem that is frequently encountered in many areas of scientific research is that of estimating the effect of a non-randomized binary intervention on an outcome of interest by using time series data on units that received the intervention (‘treated’) and units that did not (‘controls’). One popular estimation method in this setting is based on the factor analysis (FA) model. The FA model is fitted to the preintervention outcome data on treated units and all the outcome data on control units, and the counterfactual treatment-free post-intervention outcomes of the former are predicted from the fitted model. Intervention effects are estimated as the observed outcomes minus these predicted counterfactual outcomes. We propose a model that extends the FA model for estimating intervention effects by jointly modelling the multiple outcomes to exploit shared variability, and assuming an auto-regressive structure on factors to account for temporal correlations in the outcome. Using simulation studies, we show that the method proposed can improve the precision of the intervention effect estimates and achieve better control of the type I error rate (compared with the FA model), especially when either the number of preintervention measurements or the number of control units is small. We apply our method to estimate the effect of stricter alcohol licensing policies on alcohol-related harms.
    Type of Medium: Online Resource
    ISSN: 0964-1998 , 1467-985X
    RVK:
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 204794-9
    detail.hit.zdb_id: 1490715-X
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  • 9
    In: Biostatistics, Oxford University Press (OUP), ( 2023-12-06)
    Abstract: Assessing the impact of an intervention by using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. Here, we propose a novel Bayesian multivariate factor analysis model for estimating intervention effects in such settings and develop an efficient Markov chain Monte Carlo algorithm to sample from the high-dimensional and nontractable posterior of interest. The proposed method is one of the few that can simultaneously deal with outcomes of mixed type (continuous, binomial, count), increase efficiency in the estimates of the causal effects by jointly modeling multiple outcomes affected by the intervention, and easily provide uncertainty quantification for all causal estimands of interest. Using the proposed approach, we evaluate the impact that Local Tracing Partnerships had on the effectiveness of England’s Test and Trace programme for COVID-19.
    Type of Medium: Online Resource
    ISSN: 1465-4644 , 1468-4357
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 2020601-X
    SSG: 12
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  • 10
    In: BMJ Open, BMJ, Vol. 9, No. 9 ( 2019-09), p. e029538-
    Abstract: Hepatitis C virus (HCV) is the second largest contributor to liver disease in the UK, with injecting drug use as the main risk factor among the estimated 200 000 people currently infected. Despite effective prevention interventions, chronic HCV prevalence remains around 40% among people who inject drugs (PWID). New direct-acting antiviral (DAA) HCV therapies combine high cure rates ( 〉 90%) and short treatment duration (8 to 12 weeks). Theoretical mathematical modelling evidence suggests HCV treatment scale-up can prevent transmission and substantially reduce HCV prevalence/incidence among PWID. Our primary aim is to generate empirical evidence on the effectiveness of HCV ‘Treatment as Prevention’ (TasP) in PWID. Methods and analysis We plan to establish a natural experiment with Tayside, Scotland, as a single intervention site where HCV care pathways are being expanded (including specialist drug treatment clinics, needle and syringe programmes (NSPs), pharmacies and prison) and HCV treatment for PWID is being rapidly scaled-up. Other sites in Scotland and England will act as potential controls. Over 2 years from 2017/2018, at least 500 PWID will be treated in Tayside, which simulation studies project will reduce chronic HCV prevalence among PWID by 62% (from 26% to 10%) and HCV incidence will fall by approximately 2/3 (from 4.2 per 100 person-years (p100py) to 1.4 p100py). Treatment response and re-infection rates will be monitored. We will conduct focus groups and interviews with service providers and patients that accept and decline treatment to identify barriers and facilitators in implementing TasP. We will conduct longitudinal interviews with up to 40 PWID to assess whether successful HCV treatment alters their perspectives on and engagement with drug treatment and recovery. Trained peer researchers will be involved in data collection and dissemination. The primary outcome – chronic HCV prevalence in PWID – is measured using information from the Needle Exchange Surveillance Initiative survey in Scotland and the Unlinked Anonymous Monitoring Programme in England, conducted at least four times before and three times during and after the intervention. We will adapt Bayesian synthetic control methods (specifically the Causal Impact Method) to generate the cumulative impact of the intervention on chronic HCV prevalence and incidence. We will use a dynamic HCV transmission and economic model to evaluate the cost-effectiveness of the HCV TasP intervention, and to estimate the contribution of the scale-up in HCV treatment to observe changes in HCV prevalence. Through the qualitative data we will systematically explore key mechanisms of TasP real world implementation from provider and patient perspectives to develop a manual for scaling up HCV treatment in other settings. We will compare qualitative accounts of drug treatment and recovery with a ‘virtual cohort’ of PWID linking information on HCV treatment with Scottish Drug treatment databases to test whether DAA treatment improves drug treatment outcomes. Ethics and dissemination Extending HCV community care pathways is covered by ethics (ERADICATE C, ISRCTN27564683 , Super DOT C Trial clinicaltrials.gov: NCT02706223 ). Ethical approval for extra data collection from patients including health utilities and qualitative interviews has been granted (REC ref: 18/ES/0128) and ISCRCTN registration has been completed ( ISRCTN72038467 ). Our findings will have direct National Health Service and patient relevance; informing prioritisation given to early HCV treatment for PWID. We will present findings to practitioners and policymakers, and support design of an evaluation of HCV TasP in England.
    Type of Medium: Online Resource
    ISSN: 2044-6055 , 2044-6055
    Language: English
    Publisher: BMJ
    Publication Date: 2019
    detail.hit.zdb_id: 2599832-8
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