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Background It is widely accepted that the risk of hepatitis C virus (HCV) vertical transmission (VT) is 5%–6% in monoinfected women, and that 25%–40% of HCV infection clears spontaneously within 5 years. However, there is no consensus on how VT rates should be estimated, and there is a lack of information on VT rates “net” of clearance. Methods We reanalyzed data on 1749 children in 3 prospective cohorts to obtain coherent estimates of overall VT rate and VT rates net of clearance at different ages. Clearance rates were used to impute the proportion of uninfected children who had been infected and then cleared before testing negative. The proportion of transmission early in utero, late in utero, and at delivery was estimated from data on the proportion of HCV RNA positive within 3 days of birth, and differences between elective cesarean and nonelective cesarean deliveries. Results Overall VT rates were 7.2% (95% credible interval [CrI], 5.6%–8.9%) in mothers who were human immunodeficiency virus (HIV) negative and 12.1% (95% CrI, 8.6%–16.8%) in HIV-coinfected women. The corresponding rates net of clearance at 5 years were 2.4% (95% CrI, 1.1%–4.1%), and 4.1% (95% CrI, 1.7%–7.3%). We estimated that 24.8% (95% CrI, 12.1%–40.8%) of infections occur early in utero, 66.0% (95% CrI, 42.5%–83.3%) later in utero, and 9.3% (95% CrI, 0.5%–30.6%) during delivery. Conclusions Overall VT rates are about 24% higher than previously assumed, but the risk of infection persisting beyond age 5 years is about 38% lower. The results can inform design of trials of interventions to prevent or treat pediatric HCV infection, and strategies to manage children exposed in utero.

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Summary Background Prospective studies of Zika virus in pregnancy have reported rates of congenital Zika syndrome and other adverse outcomes by trimester. However, Zika virus can infect and damage the fetus early in utero, but clear before delivery. The true vertical transmission rate is therefore unknown. We aimed to provide the first estimates of underlying vertical transmission rates and adverse outcomes due to congenital infection with Zika virus by trimester of exposure. Methods This was a Bayesian latent class analysis of data from seven prospective studies of Zika virus in pregnancy. We estimated vertical transmission rates, rates of Zika-virus-related and non-Zika-virus-related adverse outcomes, and the diagnostic sensitivity of markers of congenital infection. We allowed for variation between studies in these parameters and used information from women in comparison groups with no PCR-confirmed infection, where available. Findings The estimated mean risk of vertical transmission was 47% (95% credible interval 26 to 76) following maternal infection in the first trimester, 28% (15 to 46) in the second, and 25% (13 to 47) in the third. 9% (4 to 17) of deliveries following infections in the first trimester had symptoms consistent with congenital Zika syndrome, 3% (1 to 7) in the second, and 1% (0 to 3) in the third. We estimated that in infections during the first, second, and third trimester, respectively, 13% (2 to 27), 3% (−5 to 14), and 0% (−7 to 11) of pregnancies had adverse outcomes attributable to Zika virus infection. Diagnostic sensitivity of markers of congenital infection was lowest in the first trimester (42% [18 to 72]), but increased to 85% (51 to 99) in trimester two, and 80% (42 to 99) in trimester three. There was substantial between-study variation in the risks of vertical transmission and congenital Zika syndrome. Interpretation This preliminary analysis recovers the causal effects of Zika virus from disparate study designs. Higher transmission in the first trimester is unusual with congenital infections but accords with laboratory evidence of decreasing susceptibility of placental cells to infection during pregnancy. Funding European Union Horizon 2020 programme.

Standard network meta-analysis and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any factors that interact with treatment effects (effect modifiers) are balanced across populations. Population adjustment methods such as multilevel network meta-regression (ML-NMR), matching-adjusted indirect comparison (MAIC), and simulated treatment comparison (STC) relax this assumption using individual patient data from one or more studies, and are becoming increasingly prevalent in health technology appraisals and the applied literature.Motivated by an applied example and two recent reviews of applications, we undertook an extensive simulation study to assess the performance of these methods in a range of scenarios under various failures of assumptions. We investigated the impact of varying sample size, missing effect modifiers, strength of effect modification and validity of the shared effect modifier assumption, validity of extrapolation and varying between-study overlap, and different covariate distributions and correlations.ML-NMR and STC performed similarly, eliminating bias when the requisite assumptions were met. Serious concerns are raised for MAIC, which performed poorly in nearly all simulation scenarios and may even increase bias compared to standard indirect comparisons. All methods incur bias when an effect modifier is missing, highlighting the necessity of careful selection of potential effect modifiers prior to analysis. When all effect modifiers are included, ML-NMR and STC are robust techniques for population adjustment. ML-NMR offers additional advantages over MAIC and STC, including extending to larger treatment networks and producing estimates in any target population, making this an attractive choice in a variety of scenarios.

Background and Aims Indirect estimation methods are required for estimating the size of populations where only a portion of individuals are observed directly, such as problem drug users (PDUs). Capture-recapture and multiplier methods are widely used but have been criticised as subject to bias. We propose a new approach to estimating prevalence of PDU from numbers of fatal drug-related poisonings (fDRPs) using linked databases, addressing the key limitations of simplistic ‘mortality multipliers’. Methods Our approach requires linkage of data on a large cohort of known PDUs to mortality registers, and summary information about additional fDRPs observed outside of this cohort. We model fDRP rates among the cohort and assume that rates in unobserved PDUs are equal to rates in the cohort during periods out of treatment. Prevalence is estimated in a Bayesian statistical framework, in which we simultaneously fit regression models to fDRP rates and prevalence, allowing both to vary by demographic factors and the former also by treatment status. Findings We report a case study analysis, estimating the prevalence of opioid dependence in England in 2008/09, by gender, age group and geographical region. Overall prevalence was estimated as 0.82% (95% credible interval 0.74-0.94%) of 15-64 year olds, which is similar to a published estimate based on capture-recapture analysis. Conclusions Our modelling approach estimates prevalence from drug-related mortality data, while addressing the main limitations of simplistic multipliers. This offers an alternative approach for the common situation where available data sources do not meet the strong assumptions required for valid capture-recapture estimation. In a case study analysis, prevalence estimates based on our approach were surprisingly similar to existing capture-recapture estimates but, we argue, are based on a much more objective and justifiable modelling approach.

Indirect comparisons are used to obtain estimates of relative effectiveness between two treatments that have not been compared in the same randomised controlled trial, but have instead been compared against a common comparator in separate trials. Standard indirect comparisons use only aggregate data, under the assumption that there are no differences in effect-modifying variables between the trial populations. Population-adjusted indirect comparisons aim to relax this assumption by using individual patient data (IPD) from one trial to adjust for differences in effect modifiers between populations. At present, the most commonly used approach is matching-adjusted indirect comparison (MAIC), where weights are estimated that match the covariate distributions of the reweighted IPD to the aggregate trial.MAIC was originally proposed using the method of moments to estimate the weights, but more recently entropy balancing has been proposed as an alternative. Entropy balancing has an additional "optimality" property ensuring that the weights are as uniform as possible, reducing the standard error of the estimates.In this brief method note, we show that MAIC weights are mathematically identical whether estimated using entropy balancing or the method of moments. Importantly, this means that the standard MAIC (based on the method of moments) also enjoys the "optimality" property. Moreover, the additional flexibility of entropy balancing suggests several interesting avenues for further research, such as combining population adjustment via MAIC with adjustments for treatment switching or non-parametric covariate adjustment.

BackgroundDespite a growing number of prescriptions for antidepressants (over 70 million in 2018), there is uncertainty about when people with depression might benefit from antidepressant medication and concern that antidepressants are prescribed unnecessarily.ObjectivesThe main objective of the PANDA (What are the indications for Prescribing ANtiDepressAnts that will lead to a clinical benefit?) research programme was to provide more guidance about when antidepressants are likely to benefit people with depression. We aimed to estimate the minimal clinically important difference for commonly used self-administered scales for depression and anxiety, and to understand more about how patients respond to such assessments. We carried out an observational study of patients with depressive symptoms and a placebo-controlled randomised controlled trial of sertraline versus placebo to estimate the treatment effect in UK primary care. The hypothesis was that the severity and duration of symptoms were related to treatment response.DesignThe programme consisted of three phases. The first phase relied on the secondary analysis of existing data extracted from published trials. The second phase was the PANDA cohort study of patients with depressive symptoms who presented to primary care and were followed up 2, 4 and 6 weeks after a baseline assessment. Both quantitative and qualitative methods were used in the analysis. The third phase was a multicentre randomised placebo-controlled double-blind trial of sertraline versus placebo in patients presenting to primary care with depressive symptoms.SettingUK primary care in Bristol, London, Liverpool and York.ParticipantsPatients aged 18–74 years who were experiencing depressive symptoms in primary care. Eligibility for the PANDA randomised controlled trial included that there was uncertainty about the benefits about treatment with an antidepressant.InterventionsIn the PANDA randomised controlled trial, patients were individually randomised to 100 mg daily of sertraline or an identical placebo. The PANDA cohort study was an observational study.Main outcome measuresDepressive symptoms measured using the Patient Health Questionnaire were the primary outcome for the randomised controlled trial. Other outcomes included anxiety symptoms using the Generalised Anxiety Disorder-7; depressive symptoms using the Beck Depression Inventory, version 2; health-related quality of life; self-reported improvement; and cost-effectiveness.ResultsThe secondary analysis of existing randomised controlled trials [GENetic and clinical Predictors Of treatment response in Depression (GenPod), TREAting Depression with physical activity (TREAD) and Clinical effectiveness and cost-effectiveness of cognitive Behavioural Therapy as an adjunct to pharmacotherapy for treatment-resistant depression in primary care (CoBalT)] found evidence that the minimal clinically important difference increased as the initial severity of depressive symptoms rose. Our estimates of minimal clinically important difference were a 17% and 18% reduction in Beck Depression Inventory scores for GenPod and TREAD, respectively. In CoBalT, a 32% reduction corresponded to the minimal clinically important difference but the participants in this study had depression that had not responded to antidepressants. In the PANDA study cohort, and from our analyses in existing data, we found that the minimal clinically important difference varies considerably with the initial severity of depressive and anxiety symptoms. Expressing the minimal clinically important difference as a percentage reduction reduces this variation at higher scores, but at low scores the percentage reduction increased substantially. The results from the qualitative studies pointed out many limitations of the Patient Health Questionnaire-9 items in assessing change and recovery from depression. In the PANDA randomised controlled trial, there was no evidence that sertraline resulted in a reduction in depressive symptoms within 6 weeks of randomisation, but there was some evidence of a reduction by 12 weeks. However, sertraline led to a reduction in anxiety symptoms, an improvement of mental health-related quality of life and an increased likelihood of reporting improvement. The mean Patient Health Questionnaire-9 items score at 6 weeks was 7.98 (standard deviation 5.63) in the sertraline group and 8.76 (standard deviation 5.86) in the placebo group (5% relative reduction, 95% confidence interval –7% to 15%;p = 0.41). Of the secondary outcomes, there was strong evidence that sertraline reduced anxiety symptoms (Generalised Anxiety Disorder-7 score reduced by 17% (95% confidence interval 9% to 25%;p = 0.00005). Sertraline had a high probability (> 90%) of being cost-effective at 12 weeks. The PANDA randomised controlled trial found no evidence that treatment response or cost-effectiveness was related to severity or duration of depressive symptoms. The minimal clinically important difference estimates suggested that sertraline’s effect on anxiety, but not on depression, was likely to be clinically important.LimitationsThe results from the randomised controlled trial and the estimates of minimal clinically important difference were not sufficiently precise to provide specific clinical guidance for individuals. We had low power in testing whether or not initial severity and duration of depressive symptoms are related to treatment response.ConclusionsThe results of the trial support the use of sertraline and probably other selective serotonin reuptake inhibitors because of their action in reducing anxiety symptoms and the likelihood of longer-term benefit on depressive symptoms. Sertraline could be prescribed for anxiety symptoms that commonly occur with depression and many patients will experience a clinical benefit. The Patient Health Questionnaire-9 items and similar self-administered scales should not be used on their own to assess clinical outcome, but should be supplemented with further clinical assessment.Future workWe need to examine the longer-term effects of antidepressant treatment. We need more precise estimates of the treatment effects and minimal clinically important difference at different severities to provide more specific guidance for individuals. However, the methods we have developed provide an approach towards providing such detailed guidance.Trial registrationCurrent Controlled Trials ISRCTN84544741 and EudraCT number 2013-003440-22.FundingThis project was funded by the National Institute for Health Research (NIHR) Programme Grants for Applied Research programme and will be published in full inProgramme Grants for Applied Research; Vol. 7, No. 10. See the NIHR Journals Library website for further project information.

Randomised controlled trials of cancer treatments typically report progression free survival (PFS) and overall survival (OS) outcomes. Existing methods to synthesise evidence on PFS and OS either rely on the proportional hazards assumption or make parametric assumptions which may not capture the diverse survival curve shapes across studies and treatments. Furthermore, PFS and OS are not independent: OS is the sum of PFS and post-progression survival (PPS). Our aim was to develop a non-parametric approach for jointly synthesising evidence from published Kaplan-Meier survival curves of PFS and OS without assumingproportional hazards. Restricted mean survival times (RMST) are estimated by the area under the survival curves (AUCs) up to a restricted follow-up time. The correlation between AUCs due to the constraint that OS>PFS is estimated using bootstrap re-sampling. Network meta-analysis models are given for RMST for PFS and PPS and ensure that OS=PFS + PPS. Both additive and multiplicative network meta-analysis models are presented to obtain relative treatment effects as either differences or ratios of RMST. The methods are illustrated with a networkmeta-analysis of treatments for Stage IIIA-N2 Non-Small Cell Lung Cancer.The approach has implications for health economic models of cancer treatments which require estimates of the mean time spent in the PFS and PPS health-states. The methods can be applied to a single time-to-event outcome, and so have wide applicability in any field where time-to-event outcomes are reported, the proportional hazards assumption is in doubt, and survival curve shapes differ across studies and interventions.

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills., Author summary The primary scientific literature catalogs the results from publicly funded scientific research about gene function in human-readable format. Information captured from those studies in a widely adopted, machine-readable standard format comes in the form of Gene Ontology (GO) annotations about gene functions from all domains of life. Manual annotations based on inferences directly from the scientific literature, including the evidence used to make such inferences, represent the best return on investment by improving data accessibility across the biological sciences and allowing novel insights between evolutionarily related organisms. To supplement professional curation, our Community Assessment of Community Annotation with Ontologies (CACAO) project enabled annotation of the scientific literature by community annotators, in this case undergraduates, which resulted in the contribution of thousands of unique, validated entries to public resources. Importantly, the annotations described here initiated by nonexperts often deal with topics not typically covered by the experts. These annotations are now being used by scientists worldwide in their research efforts.

BackgroundCurrent guidelines recommend that infants born to women with hepatitis C (HCV) viremia are screened for HCV antibody at age 18 months, and if positive, referred for RNA testing at 3 years to confirm chronic infection. This policy is based in part on analyses suggesting 25%-40% of vertically acquired HCV infections clear spontaneously within 4-5 years.MethodsData on 179 infants with RNA and/or anti-HCV evidence of vertically acquired viraemia (single PCR+) or confirmed infection (2 PCR+ or anti-HCV beyond 18 months) in three prospective European cohorts were investigated. Ages at clearance of viremia and confirmed infection were estimated taking account of interval censoring and delayed entry. We also investigated clearance in infants in whom RNA was not detectable until after 6 weeks.ResultsClearance rates decline rapidly over the first 6 months. An estimated 90.6% (95%CrI: 83.5-95.9) of viremia cleared by 5 years, most within 3 months, and 65.9% (50.1-81.6) of confirmed infection cleared by 5 years, at a median 12.4 (7.1-18.9) months. If treatment began at age 6 months, 18 months or 3 years, at least 59.0% (42.0-76.9), 39.7 (17.9-65.9), and 20.9 (4.6-44.8) of those treated would clear without treatment. In seven (6.6%) confirmed infections, RNA was not detectable until after 6 weeks, and in 2 (1.9%) not until after 6 months. However, all such cases subsequently cleared.ConclusionsMost viraemia clears within 3 months, and most confirmed infection by 3 years. Delaying treatment avoids but does not eliminate over-treatment and should be balanced against loss to follow-up.Key pointsBased on a re-analysis of the largest purely prospective dataset assembled so far, 66% (50-82) of confirmed vertically acquired HCV clears spontaneously by age 5 years, rather than the 25-40% assumed in guidelines.

BackgroundIt is widely accepted that the risk of HCV vertical transmission (VT) is 5-6% in mono-infected women, and that 25-40% of HCV infection clears spontaneously within 5 years. However, VT and clearance rates have not been estimated from the same datasets, and there is a lack of information on VT rates “net” of clearance.MethodsWe re-analysed data on 1749 children in 3 prospective cohorts to obtain coherent estimates of overall VT rate and VT rates “net” of clearance at different ages. Clearance rates were used to impute the proportion of uninfected children who had been infected and then cleared before testing negative. The proportion of transmission early in utero, late in utero and at delivery was estimated from data on the proportion of RNA positives in samples tested within three days of birth, and differences between elective caesarean and non-elective caesarean deliveries.FindingsOverall VT rates were 7.2% (95% credible interval 5.6-8.9) in mothers who were HIV negative and 12.1% (8.6-16.8) in HIV-co-infected women. The corresponding rates net of clearance at 5 years were 2.4% (1.1-4.1) and 4.1% (1.7-7.3). We estimated that 24.8% (12.1-40.8) of infections occur early in utero, 66.0% (42.5-83.3) later in utero, and 9.3% (0.5-30.6) during delivery.ConclusionOverall VT rates are about 24% higher than previously assumed, but the risk of infection persisting beyond age 5 years is about 38% lower. The results can inform design of trials of to prevent or treat pediatric HCV infection, and strategies to manage children exposed in utero.Key pointsTaking account of infections that would have cleared spontaneously before detection, the rate of HCV vertical transmission is 7.2% (95%CrI 5.6-8.9) in mono-infected women, but transmission “net” of clearance is 3.1% (1.8-4.4) at 3 years, and 2.4% (1.1-4.1) at 5.

BackgroundSARS-CoV-2 antibody tests are used for population surveillance and might have a future role in individual risk assessment. Lateral flow immunoassays (LFIAs) can deliver results rapidly and at scale, but have widely varying accuracy.MethodsIn a laboratory setting, we performed head-to-head comparisons of four LFIAs: the Rapid Test Consortium’s AbC-19™Rapid Test, OrientGene COVID IgG/IgM Rapid Test Cassette, SureScreen COVID-19 Rapid Test Cassette, and Biomerica COVID-19 IgG/IgM Rapid Test. We analysed blood samples from 2,847 key workers and 1,995 pre-pandemic blood donors with all four devices.FindingsWe observed a clear trade-off between sensitivity and specificity: the IgG band of the SureScreen device and the AbC-19™device had higher specificities but OrientGene and Biomerica higher sensitivities. Based on analysis of pre-pandemic samples, SureScreen IgG band had the highest specificity (98.9%, 95% confidence interval 98.3 to 99.3%), which translated to the highest positive predictive value across any pre-test probability: for example, 95.1% (95%CI 92.6, 96.8%) at 20% pre-test probability. All four devices showed higher sensitivity at higher antibody concentrations (“spectrum effects”), but the extent of this varied by device.InterpretationThe estimates of sensitivity and specificity can be used to adjust for test error rates when using these devices to estimate the prevalence of antibody. If tests were used to determine whether an individual has SARS-CoV-2 antibodies, in an example scenario in which 20% of individuals have antibodies we estimate around 5% of positive results on the most specific device would be false positives.FundingPublic Health England.Research in contextEvidence before this studyWe searched for evidence on the accuracy of the four devices compared in this study: OrientGene COVID IgG/IgM Rapid Test Cassette, SureScreen COVID-19™ Rapid Test Cassette, Biomerica COVID-19 IgG/IgM Rapid Test and the UK Rapid Test Consortium’s AbC-19™ Rapid Test. We searched Ovid MEDLINE (In-Process & Other Non-Indexed Citations and Daily), PubMed, MedRxiv/BioRxiv and Google Scholar from January 2020 to 16thJanuary 2021. Search terms included device names AND ((SARS-CoV-2) OR (covid)). Of 303 records assessed, data were extracted from 24 studies: 18 reporting on the accuracy of the OrientGene device, 7 SureScreen, 2 AbC-19™ and 1 Biomerica. Only three studies compared the accuracy of two or more of the four devices. With the exception of our previous report on the accuracy of the AbC-19™ device, which the current manuscript builds upon, sample size ranged from 7 to 684. For details, see Supplementary Materials.The largest study compared OrientGene, SureScreen and Biomerica. SureScreen was estimated to have the highest specificity (99.8%, 95% CI 98.9 to 100%) and OrientGene the highest sensitivity (92.6%), but with uncertainty about the latter result due to small sample sizes. The other two comparative studies were small (n = 65, n = 67) and therefore provide very uncertain results.We previously observed spectrum effects for the AbC-19™ device, such that sensitivity is upwardly biased if estimated only from PCR-confirmed cases. The vast majority of previous studies estimated sensitivity in this way.Added value of this studyWe performed a large scale (n = 4,842), head-to-head laboratory-based evaluation and comparison of four lateral flow devices, which were selected for evaluation by the UK Department of Health and Social Care’s New Tests Advisory Group, on the basis of a survey of test and performance data available. We evaluated the performance of diagnosis based on both IgG and IgM bands, and the IgG band alone. We found a clear trade-off between sensitivity and specificity across devices, with the SureScreen and AbC-19™ devices being more specific and OrientGene and Biomerica more sensitive. Based on analysis of 1,995 pre-pandemic blood samples, we are 99% confident that SureScreen (IgG band reading) has the highest specificity of the four devices (98.9%, 95% CI 98.3, 99.3%).We found evidence that all four devices have reduced sensitivity at lower antibody indices, i.e. spectrum effects. However, the extent of this varies by device and appears to be less for other devices than for AbC-19.Our estimates of sensitivity and specificity are likely to be higher than would be observed in real use of these devices, as they were based on majority readings of three trained laboratory personnel.Implications of all the available evidenceWhen used in epidemiological studies of antibody prevalence, the estimates of sensitivity and specificity provided in this study can be used to adjust for test errors. Increased precision in error rates will translate to increased precision in seroprevalence estimates. If lateral flow devices were used for individual risk assessment, devices with maximum specificity would be preferable. However, if, for an example, 20% of the tested population had antibodies, we estimate that around 1 in 20 positive results on the most specific device would be incorrect.

Tests for disease often produce a continuous measure, such as the concentration of some biomarker in a blood sample. In clinical practice, a threshold C is selected such that results, say, greater than C are declared positive and those less than C negative. Measures of test accuracy such as sensitivity and specificity depend crucially on C, and the optimal value of this threshold is usually a key question for clinical practice. Standard methods for meta-analysis of test accuracy (i) do not provide summary estimates of accuracy at each threshold, precluding selection of the optimal threshold, and furthermore, (ii) do not make use of all available data. We describe a multinomial meta-analysis model that can take any number of pairs of sensitivity and specificity from each study and explicitly quantifies how accuracy depends on C. Our model assumes that some prespecified or Box-Cox transformation of test results in the diseased and disease-free populations has a logistic distribution. The Box-Cox transformation parameter can be estimated from the data, allowing for a flexible range of underlying distributions. We parameterise in terms of the means and scale parameters of the two logistic distributions. In addition to credible intervals for the pooled sensitivity and specificity across all thresholds, we produce prediction intervals, allowing for between-study heterogeneity in all parameters. We demonstrate the model using two case study meta-analyses, examining the accuracy of tests for acute heart failure and preeclampsia. We show how the model can be extended to explore reasons for heterogeneity using study-level covariates.

Background\ud Screening for SARS-CoV-2 antibodies is under way in some key worker groups; how this adds to self-reported COVID-19 illness is unclear. In this study, we investigate the association between self-reported belief of COVID-19 illness and seropositivity. \ud \ud Methods\ud Cross-sectional study of three key worker streams comprising (A) Police and Fire & Rescue (2 sites) (B) healthcare workers (1 site) and (C) healthcare workers with previously positive PCR result (5 sites). We collected self-reported signs and symptoms of COVID-19 and compared this with serology results from two SARS-CoV-2 immunoassays (Roche Elecsys® and EUROIMMUN). \ud \ud Results\ud Between 01 and 26 June, we recruited 2847 individuals (Stream A: 1,247, Stream B: 1,546 and Stream C: 154). Amongst those without previous positive PCR tests, 687/2,579 (26%) reported belief they had COVID-19, having experienced compatible symptoms; however, only 208 (30.3%) of these were seropositive on both immunoassays. Both immunoassays had high sensitivities relative to previous PCR positivity (>93%); there was also limited decline in antibody titres up to 110 days post symptom onset. Symptomatic but seronegative individuals had differing symptom profiles and shorter illnesses than seropositive individuals. \ud \ud Conclusion\ud Non-COVID-19 respiratory illness may have been mistaken for COVID-19 during the outbreak; laboratory testing is more specific than self-reported key worker beliefs in ascertaining past COVID-19 disease.

IntroductionZika virus (ZIKV) infection in pregnancy has been associated with microcephaly and severe neurological damage to the fetus. Our aim is to document the risks of adverse pregnancy and birth outcomes and the prevalence of laboratory markers of congenital infection in deliveries to women experiencing ZIKV infection during pregnancy, using data from European Commission-funded prospective cohort studies in 20 centres in 11 countries across Latin America and the Caribbean.Methods and analysisWe will carry out a centre-by-centre analysis of the risks of adverse pregnancy and birth outcomes, comparing women with confirmed and suspected ZIKV infection in pregnancy to those with no evidence of infection in pregnancy. We will document the proportion of deliveries in which laboratory markers of congenital infection were present. Finally, we will investigate the associations of trimester of maternal infection in pregnancy, presence or absence of maternal symptoms of acute ZIKV infection and previous flavivirus infections with adverse outcomes and with markers of congenital infection. Centre-specific estimates will be pooled using a two-stage approach.Ethics and disseminationEthical approval was obtained at each centre. Findings will be presented at international conferences and published in peer-reviewed open access journals and discussed with local public health officials and representatives of the national Ministries of Health, Pan American Health Organization and WHO involved with ZIKV prevention and control activities.

Background Immune correlates of protection from COVID-19 are important, but incompletely understood. Methods We conducted a prospective cohort study in 2,826 participants working in hospitals and Fire and Police services in England, UK during the pandemic(ISRCTN5660922). Of these, 2,672 were unselected volunteers recruited irrespective of previous SARS-CoV-2 RT-PCR test results, and 154 others were recruited separately specifically because they previously tested positive. At recruitment in June 2020, we measured numbers of interferon-γ secreting, SARS-CoV-2 responsive T cells using T-SPOT®Discovery SARS-CoV-2 kits (Oxford Immunotec Ltd), and antibodies to SARS-CoV-2 proteins using commercial immunoassays. We then described time to microbiologically confirmed SARS-CoV-2 infection, stratified by immunological parameters. Results T cells responsive to the spike (S), nuclear (N) and membrane proteins (M) dominated the responses measured. Using the sum of the spots (responsive cells within each well of 250,000 peripheral blood mononuclear cells) for S, N and M antigens minus the control, the 2,672 unselected participants were divided into those with higher responses (n=669, 25.4%; median 30 spots (IQR 18,54)) and those with low responses (n=2016, 76.7%, median 3 (IQR 1,6)), the cutoff we derived being 12 spots. Of the participants with higher T cell responses, 367 (53%) had detectable antibodies against the N or S proteins. During a median of 118 days follow-up, 20 participants with lower T cell responses developed COVID-19, compared with none in the population with high T cell responses (log-rank test, p=6×10−3). Conclusions Peripheral blood SARS-CoV-2 responsive T cell numbers are associated with risk of developing COVID-19.

Summary Standard network meta-analysis (NMA) and indirect comparisons combine aggregate data from multiple studies on treatments of interest, assuming that any effect modifiers are balanced across populations. Population adjustment methods relax this assumption using individual patient data from one or more studies. However, current matching-adjusted indirect comparison and simulated treatment comparison methods are limited to pairwise indirect comparisons and cannot predict into a specified target population. Existing meta-regression approaches incur aggregation bias. We propose a new method extending the standard NMA framework. An individual level regression model is defined, and aggregate data are fitted by integrating over the covariate distribution to form the likelihood. Motivated by the complexity of the closed form integration, we propose a general numerical approach using quasi-Monte-Carlo integration. Covariate correlation structures are accounted for by using copulas. Crucially for decision making, comparisons may be provided in any target population with a given covariate distribution. We illustrate the method with a network of plaque psoriasis treatments. Estimated population-average treatment effects are similar across study populations, as differences in the distributions of effect modifiers are small. A better fit is achieved than a random effects NMA, uncertainty is substantially reduced by explaining within- and between-study variation, and estimates are more interpretable.

Our understanding of congenital infections is based on prospective studies of women infected during pregnancy. The EU has funded three consortia to study Zika virus, each including a prospective study of pregnant women. Another multi-centre study has been funded by the US National Institutes of Health. This Personal View describes the study designs required to research Zika virus, and questions whether funding academics in the EU and USA to work with collaborators in outbreak areas is an effective strategy. 3 years after the 2015-16 Zika virus outbreaks, these collaborations have taught us little about vertical transmission of the virus. In the time taken to approve funding, agree contracts, secure ethics approval, and equip laboratories, Zika virus had largely disappeared. By contrast, prospective studies based on local surveillance and standard-of-care protocols have already provided valuable data. Threats to fetal and child health pose new challenges for global preparedness requiring support for the design and implementation of locally appropriate protocols. These protocols can answer the key questions earlier than externally designed studies and at lower cost. Local protocols can also provide a framework for recruitment of unexposed controls that are required to study less specific outcomes. Other priorities include accelerated development of non-invasive tests, and longer-term storage of neonatal and antenatal samples to facilitate retrospective reconstruction of cohort studies.

Background: Zika virus (ZIKV) infection has been associated with congenital microcephaly and other neurodevelopmental abnormalities. There is little published research on the effect of maternal ZIKV infection in a non-endemic European region. We aimed to describe the outcomes of pregnant travelers diagnosed as ZIKV-infected in Spain, and their exposed children. Methods: This prospective observational cohort study of nine referral hospitals enrolled pregnant women (PW) who travelled to endemic areas during their pregnancy or the two previous months, or those whose sexual partners visited endemic areas in the previous 6 months. Infants of ZIKV-infected mothers were followed for about two years. Results: ZIKV infection was diagnosed in 163 PW, 112 (70%) were asymptomatic and 24 (14.7%) were confirmed cases. Among 143 infants, 14 (9.8%) had adverse outcomes during follow-up, three had a congenital Zika syndrome (CZS), and 11 other potential Zika-related outcomes. The overall incidence of CZS was 2.1% (95%CI: 0.4&ndash, 6.0%), but among infants born to ZIKV-confirmed mothers, this increased to 15.8% (95%CI: 3.4&ndash, 39.6%). Conclusions: A nearly 10% overall risk of neurologic and hearing adverse outcomes was found in ZIKV-exposed children born to a ZIKV-infected traveler PW. Longer-term follow-up of these children is needed to assess whether there are any later-onset manifestations.

Active learning is a powerful pedagogical tool in science classes. The physical classroom space is, however, often overlooked as a key component of pedagogical design. We present two mini-case studies of science faculty at the Pennsylvania State University who are using active learning techniques in the Bluebox, an experimental learning space designed for maximum flexibility and active learning pedagogy. These vignettes are exemplars of how a thoughtfully designed learning space can support and even enhance active learning in science classes. We conclude by making three design recommendations to consider when creating new classrooms or enhancing existing ones.

Summary Network meta-analysis (NMA) pools evidence on multiple treatments to estimate relative treatment effects. Included studies are typically assessed for risk of bias; however, this provides no indication of the impact of potential bias on a decision based on the NMA. We propose methods to derive bias adjustment thresholds which measure the smallest changes to the data that result in a change of treatment decision. The methods use efficient matrix operations and can be applied to explore the consequences of bias in individual studies or aggregate treatment contrasts, in both fixed and random-effects NMA models. Complex models with multiple types of data input are handled by using an approximation to the hypothetical aggregate likelihood. The methods are illustrated with a simple NMA of thrombolytic treatments and a more complex example comparing social anxiety interventions. An accompanying R package is provided.

Standard methods for indirect comparisons and network meta-analysis are based on aggregate data, with the key assumption that there is no difference between the trials in the distribution of effect-modifying variables. Methods which relax this assumption are becoming increasingly common for submissions to reimbursement agencies, such as the National Institute for Health and Care Excellence (NICE). These methods use individual patient data from a subset of trials to form population-adjusted indirect comparisons between treatments, in a specific target population. Recently proposed population adjustment methods include the Matching-Adjusted Indirect Comparison (MAIC) and the Simulated Treatment Comparison (STC). Despite increasing popularity, MAIC and STC remain largely untested. Furthermore, there is a lack of clarity about exactly how and when they should be applied in practice, and even whether the results are relevant to the decision problem. There is therefore a real and present risk that the assumptions being made in one submission to a reimbursement agency are fundamentally different to—or even incompatible with—the assumptions being made in another for the same indication. We describe the assumptions required for population-adjusted indirect comparisons, and demonstrate how these may be used to generate comparisons in any given target population. We distinguish between anchored and unanchored comparisons according to whether a common comparator arm is used or not. Unanchored comparisons make much stronger assumptions, which are widely regarded as infeasible. We provide recommendations on how and when population adjustment methods should be used, and the supporting analyses that are required to provide statistically valid, clinically meaningful, transparent and consistent results for the purposes of health technology appraisal. Simulation studies are needed to examine the properties of population adjustment methods and their robustness to breakdown of assumptions.

The ECF sigma family was identified 23 years ago as a distinct group of σ70-like factors. ECF sigma factors have since emerged as a major form of bacterial signal transduction that can be grouped into over 50 phylogenetically distinct subfamilies. Advances in our understanding of these sigma factors and the signaling pathways governing their activity have elucidated conserved features as well as aspects that have evolved over time. All ECF sigma factors are predicted to share a common streamlined domain structure and mode of promoter interaction. The activity of most ECF sigma factors is controlled by an anti-sigma factor. The nature of the anti-sigma factor and the activating signaling pathways appear to be conserved within ECF families, while considerable diversity exists between different families.

BACKGROUND: Depression is usually managed in primary care, but most antidepressant trials are of patients from secondary care mental health services, with eligibility criteria based on diagnosis and severity of depressive symptoms. Antidepressants are now used in a much wider group of people than in previous regulatory trials. We investigated the clinical effectiveness of sertraline in patients in primary care with depressive symptoms ranging from mild to severe and tested the role of severity and duration in treatment response.METHODS: The PANDA study was a pragmatic, multicentre, double-blind, placebo-controlled randomised trial of patients from 179 primary care surgeries in four UK cities (Bristol, Liverpool, London, and York). We included patients aged 18 to 74 years who had depressive symptoms of any severity or duration in the past 2 years, where there was clinical uncertainty about the benefit of an antidepressant. This strategy was designed to improve the generalisability of our sample to current use of antidepressants within primary care. Patients were randomly assigned (1:1) with a remote computer-generated code to sertraline or placebo, and were stratified by severity, duration, and site with random block length. Patients received one capsule (sertraline 50 mg or placebo orally) daily for one week then two capsules daily for up to 11 weeks, consistent with evidence on optimal dosages for efficacy and acceptability. The primary outcome was depressive symptoms 6 weeks after randomisation, measured by Patient Health Questionnaire, 9-item version (PHQ-9) scores. Secondary outcomes at 2, 6 and 12 weeks were depressive symptoms and remission (PHQ-9 and Beck Depression Inventory-II), generalised anxiety symptoms (Generalised Anxiety Disorder Assessment 7-item version), mental and physical health-related quality of life (12-item Short-Form Health Survey), and self-reported improvement. All analyses compared groups as randomised (intention-to-treat). The study is registered with EudraCT, 2013-003440-22 (protocol number 13/0413; version 6.1) and ISRCTN, ISRCTN84544741, and is closed to new participants.FINDINGS: Between Jan 1, 2015, and Aug 31, 2017, we recruited and randomly assigned 655 patients-326 (50%) to sertraline and 329 (50%) to placebo. Two patients in the sertraline group did not complete a substantial proportion of the baseline assessment and were excluded, leaving 653 patients in total. Due to attrition, primary outcome analyses were of 550 patients (266 in the sertraline group and 284 in the placebo group; 85% follow-up that did not differ by treatment allocation). We found no evidence that sertraline led to a clinically meaningful reduction in depressive symptoms at 6 weeks. The mean 6-week PHQ-9 score was 7·98 (SD 5·63) in the sertraline group and 8·76 (5·86) in the placebo group (adjusted proportional difference 0·95, 95% CI 0·85-1·07; p=0·41). However, for secondary outcomes, we found evidence that sertraline led to reduced anxiety symptoms, better mental (but not physical) health-related quality of life, and self-reported improvements in mental health. We observed weak evidence that depressive symptoms were reduced by sertraline at 12 weeks. We recorded seven adverse events-four for sertraline and three for placebo, and adverse events did not differ by treatment allocation. Three adverse events were classified as serious-two in the sertraline group and one in the placebo group. One serious adverse event in the sertraline group was classified as possibly related to study medication.INTERPRETATION: Sertraline is unlikely to reduce depressive symptoms within 6 weeks in primary care but we observed improvements in anxiety, quality of life, and self-rated mental health, which are likely to be clinically important. Our findings support the prescription of SSRI antidepressants in a wider group of participants than previously thought, including those with mild to moderate symptoms who do not meet diagnostic criteria for depression or generalised anxiety disorder.FUNDING: National Institute for Health Research.

ObjectivesIndirect comparisons via a common comparator (anchored comparisons) are commonly used in health technology assessment. However, common comparators may not be available, or the comparison may be biased due to differences in effect modifiers between the included studies. Recently proposed population adjustment methods aim to adjust for differences between study populations in the situation where individual patient data are available from at least one study, but not all studies. They can also be used when there is no common comparator or for single-arm studies (unanchored comparisons). We aim to characterise the use of population adjustment methods in technology appraisals (TAs) submitted to the United Kingdom National Institute for Health and Care Excellence (NICE).MethodsWe reviewed NICE TAs published between 01/01/2010 and 20/04/2018.ResultsPopulation adjustment methods were used in 7 percent (18/268) of TAs. Most applications used unanchored comparisons (89 percent, 16/18), and were in oncology (83 percent, 15/18). Methods used included matching-adjusted indirect comparisons (89 percent, 16/18) and simulated treatment comparisons (17 percent, 3/18). Covariates were included based on: availability, expert opinion, effective sample size, statistical significance, or cross-validation. Larger treatment networks were commonplace (56 percent, 10/18), but current methods cannot account for this. Appraisal committees received results of population-adjusted analyses with caution and typically looked for greater cost effectiveness to minimise decision risk.ConclusionsPopulation adjustment methods are becoming increasingly common in NICE TAs, although their impact on decisions has been limited to date. Further research is needed to improve upon current methods, and to investigate their properties in simulation studies.

Guideline development requires the synthesis of evidence on several treatments of interest, typically by using network meta-analysis (NMA). Because treatment effects may be estimated imprecisely or be based on evidence lacking internal or external validity, guideline developers must assess the robustness of recommendations made on the basis of the NMA to potential limitations in the evidence. Such limitations arise because the observed estimates differ from the true effects of interest, for example, because of study biases, sampling variation, or issues of relevance. The widely used GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework aims to assess the quality of evidence supporting a recommendation by using a structured series of qualitative judgments. This article argues that GRADE approaches proposed for NMA are insufficient for the purposes of guideline development, because the influence of the evidence on the final recommendation is not taken into account. It outlines threshold analysis as an alternative approach, demonstrating the method with 2 examples of clinical guidelines from the National Institute for Health and Care Excellence (NICE) in the United Kingdom. Threshold analysis quantifies precisely how much the evidence could change (for any reason, such as potential biases, or simply sampling variation) before the recommendation changes, and what the revised recommendation would be. If it is judged that the evidence could not plausibly change by more than this amount, then the recommendation is considered robust; otherwise, it is sensitive to plausible changes in the evidence. In this manner, threshold analysis directly informs decision makers and guideline developers of the robustness of treatment recommendations.

Background: When there are structural relationships between outcomes reported in different trials, separate analyses of each outcome do not provide a single coherent analysis, which is required for decision-making. For example, trials of intrapartum anti-bacterial prophylaxis (IAP) to prevent early onset group B streptococcal (EOGBS) disease can report three treatment effects: the effect on bacterial colonisation of the newborn, the effect on EOGBS, and the effect on EOGBS conditional on newborn colonisation. These outcomes are conditionally related, or nested, in a multi-state model. This paper shows how to exploit these structural relationships, providing a single coherent synthesis of all the available data, while checking to ensure that different sources of evidence are consistent. Results: Overall, the use of IAP reduces the risk of EOGBS (RR: 0.03; 95% Credible Interval (CrI): 0.002-0.13). Most of the treatment effect is due to the prevention of colonisation in newborns of colonised mothers (RR: 0.08, 95% CrI: 0.04-0.14). Node-splitting demonstrated that the treatment effect calculated using only direct evidence was consistent with that predicted from the remaining evidence (p=0.15). The findings accorded with previously published separate meta-analyses of the different outcomes, once these are re-analysed correctly accounting for zero cells. Conclusion: Multiple outcomes should be synthesised together where possible, taking account of their structural relationships. This generates an internally coherent analysis, suitable for decision making, in which estimates of each of the treatment effects are based on all available evidence (direct and indirect). Separate meta-analyses of each outcome have none of these properties.

BackgroundSeroprevalence surveys of Chlamydia trachomatis (CT) antibodies are promising for estimating age-specific CT cumulative incidence, however accurate estimates require improved understanding of antibody response to CT infection.MethodsWe used GUMCAD, England's national sexually transmitted infection (STI) surveillance system, to select sera taken from female STI clinic attendees on the day of or after a chlamydia diagnosis. Serum specimens were collected from laboratories and tested anonymously on an indirect and a double-antigen ELISA, both of which are based on the CT-specific Pgp3 antigen. We used cross-sectional and longitudinal descriptive analyses to explore the relationship between seropositivity and a) cumulative number of chlamydia diagnoses and b) time since most recent chlamydia diagnosis.Results919 samples were obtained from visits when chlamydia was diagnosed and 812 during subsequent follow-up visits. Pgp3 seropositivity using the indirect ELISA increased from 57.1% (95% confidence interval: 53.2-60.7) on the day of a first-recorded chlamydia diagnosis to 89.6% (95%CI: 79.3-95.0) on the day of a third or higher documented diagnosis. With the double-antigen ELISA, the increase was from 61.1% (95%CI: 53.2-60.7) to 97.0% (95%CI: 88.5-99.3). Seropositivity decreased with time since CT diagnosis on only the indirect assay, to 49.3% (95%CI: 40.9-57.7) two or more years after a first diagnosis and 51.9% (95%CI: 33.2-70.0) after a repeat diagnosis.ConclusionSeropositivity increased with cumulative number of infections, and decreased over time after diagnosis on the indirect ELISA, but not on the double-antigen ELISA. This is the first study to demonstrate the combined impact of number of chlamydia diagnoses, time since diagnosis, and specific ELISA on Pgp3 seropositivity. Our findings are being used to inform models estimating age-specific chlamydia incidence over time using serial population-representative serum sample collections, to enable accurate public health monitoring of chlamydia.

Background and objectivesThe evidence base supporting the National Chlamydia Screening Programme, initiated in 2003, has been questioned repeatedly, with little consensus on modelling assumptions, parameter values or evidence sources to be used in cost-effectiveness analyses. The purpose of this project was to assemble all available evidence on the prevalence and incidence ofChlamydia trachomatis(CT) in the UK and its sequelae, pelvic inflammatory disease (PID), ectopic pregnancy (EP) and tubal factor infertility (TFI) to review the evidence base in its entirety, assess its consistency and, if possible, arrive at a coherent set of estimates consistent with all the evidence.MethodsEvidence was identified using ‘high-yield’ strategies. Bayesian Multi-Parameter Evidence Synthesis models were constructed for separate subparts of the clinical and population epidemiology of CT. Where possible, different types of data sources were statistically combined to derive coherent estimates. Where evidence was inconsistent, evidence sources were re-interpreted and new estimates derived on a post-hoc basis.ResultsAn internally coherent set of estimates was generated, consistent with a multifaceted evidence base, fertility surveys and routine UK statistics on PID and EP. Among the key findings were that the risk of PID (symptomatic or asymptomatic) following an untreated CT infection is 17.1% [95% credible interval (CrI) 6% to 29%] and the risk of salpingitis is 7.3% (95% CrI 2.2% to 14.0%). In women aged 16–24 years, screened at annual intervals, at best, 61% (95% CrI 55% to 67%) of CT-related PID and 22% (95% CrI 7% to 43%) of all PID could be directly prevented. For women aged 16–44 years, the proportions of PID, EP and TFI that are attributable to CT are estimated to be 20% (95% CrI 6% to 38%), 4.9% (95% CrI 1.2% to 12%) and 29% (95% CrI 9% to 56%), respectively. The prevalence of TFI in the UK in women at the end of their reproductive lives is 1.1%: this is consistent with all PID carrying a relatively high risk of reproductive damage, whether diagnosed or not. Every 1000 CT infections in women aged 16–44 years, on average, gives rise to approximately 171 episodes of PID and 73 of salpingitis, 2.0 EPs and 5.1 women with TFI at age 44 years.Conclusions and research recommendationsThe study establishes a set of interpretations of the major studies and study designs, under which a coherent set of estimates can be generated. CT is a significant cause of PID and TFI. CT screening is of benefit to the individual, but detection and treatment of incident infection may be more beneficial. Women with lower abdominal pain need better advice on when to seek early medical attention to avoid risk of reproductive damage. The study provides new insights into the reproductive risks of PID and the role of CT. Further research is required on the proportions of PID, EP and TFI attributable to CT to confirm predictions made in this report, and to improve the precision of key estimates. The cost-effectiveness of screening should be re-evaluated using the findings of this report.FundingThe Medical Research Council grant G0801947.

BACKGROUND AND AIMS:Capture-recapture (CRC) analysis is recommended for estimating the prevalence of problem drug use or people who inject drugs (PWID). We aim to demonstrate how naïve application of CRC can lead to highly misleading results, and to suggest how the problems might be overcome.METHODS:We present a case study of estimating the prevalence of PWID in Bristol, UK, applying CRC to lists in contact with three services. We assess: (i) sensitivity of results to different versions of the dominant (treatment) list: specifically, to inclusion of non-incident cases and of those who were referred directly from one of the other services; (ii) the impact of accounting for a novel covariate, housing instability; (iii) consistency of CRC estimates with drug-related mortality data. We then formally incorporate the drug-related mortality data and lower bounds for prevalence alongside the CRC, in a single coherent model.RESULTS:Five of eleven models fitted the full data equally well but generated widely varying prevalence estimates, from 2740 (95% CI 2670, 2840) to 6890 (95% CI 3740, 17680). Results were highly sensitive to inclusion of non-incident cases, demonstrating the presence of considerable heterogeneity, and were sensitive to a lesser extent to inclusion of direct referrals. A reduced dataset including only incident cases and excluding referrals could be fitted by simpler models, and led to much greater consistency in estimates. Accounting for housing stability improved model fit considerably more than did the standard covariates of age and gender. External data provided validation of results and aided model selection, generating a final estimate of the number of PWID in Bristol in 2011 of 2770 (95% Cr-I 2570, 3110), or 0.9% (95% Cr-I 0.9, 1.0%) of the population aged 15-64 years.CONCLUSIONS:Steps can be taken to reduce bias in capture-recapture analysis, including consideration of data sources, reduction of lists to less heterogeneous sub-samples, use of covariates, and formal incorporation of external data. This article is protected by copyright. All rights reserved.

Network meta-analysis enables the simultaneous synthesis of a network of clinical trials comparing any number of treatments. Potential inconsistencies between estimates of relative treatment effects are an important concern, and several methods to detect inconsistency have been proposed. This paper is concerned with the node-splitting approach, which is particularly attractive because of its straightforward interpretation, contrasting estimates from both direct and indirect evidence. However, node-splitting analyses are labour-intensive because each comparison of interest requires a separate model. It would be advantageous if node-splitting models could be estimated automatically for all comparisons of interest.We present an unambiguous decision rule to choose which comparisons to split, and prove that it selects only comparisons in potentially inconsistent loops in the network, and that all potentially inconsistent loops in the network are investigated. Moreover, the decision rule circumvents problems with the parameterisation of multi-arm trials, ensuring that model generation is trivial in all cases. Thus, our methods eliminate most of the manual work involved in using the node-splitting approach, enabling the analyst to focus on interpreting the results. (C) 2015 The Authors Research Synthesis Methods Published by John Wiley & Sons Ltd.

Bayesian statistical approaches to mixed treatment comparisons (MTCs) are becoming more popular due to their flexibility and interpretability. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (though richer than standard meta-analysis, comparing only two treatments) and researchers often choose study arms based upon which treatments emerge as superior in previous trials. In this paper, we summarize existing hierarchical Bayesian methods for MTCs with a single outcome, and introduce novel Bayesian approaches for multiple outcomes simultaneously, rather than in separate MTC analyses. We do this by incorporating partially observed data and its correlation structure between outcomes through contrast- and arm-based parameterizations that consider any unobserved treatment arms as missing data to be imputed. We also extend the model to apply to all types of generalized linear model outcomes, such as count or continuous responses. We offer a simulation study under various missingness mechanisms (e.g., MCAR, MAR, and MNAR) providing evidence that our models outperform existing models in terms of bias, MSE, and coverage probability, then illustrate our methods with a real MTC dataset. We close with a discussion of our results, several contentious issues in MTC analysis, and a few avenues for future methodological development.

BackgroundThe Beck Depression Inventory, 2nd edition (BDI-II) is widely used in research on depression. However, the minimal clinically important difference (MCID) is unknown. MCID can be estimated in several ways. Here we take a patient-centred approach, anchoring the change on the BDI-II to the patient's global report of improvement.MethodWe used data collected (n = 1039) from three randomized controlled trials for the management of depression. Improvement on a ‘global rating of change’ question was compared with changes in BDI-II scores using general linear modelling to explore baseline dependency, assessing whether MCID is best measured in absolute terms (i.e. difference) or as percent reduction in scores from baseline (i.e. ratio), and receiver operator characteristics (ROC) to estimate MCID according to the optimal threshold above which individuals report feeling ‘better’.ResultsImprovement in BDI-II scores associated with reporting feeling ‘better’ depended on initial depression severity, and statistical modelling indicated that MCID is best measured on a ratio scale as a percentage reduction of score. We estimated a MCID of a 17.5% reduction in scores from baseline from ROC analyses. The corresponding estimate for individuals with longer duration depression who had not responded to antidepressants was higher at 32%.ConclusionsMCID on the BDI-II is dependent on baseline severity, is best measured on a ratio scale, and the MCID for treatment-resistant depression is larger than that for more typical depression. This has important implications for clinical trials and practice.

Decision makers in different health care settings need to weigh the benefits and harms of alternative treatment strategies. Such health care decisions include marketing authorization by regulatory agencies, practice guideline formulation by clinical groups, and treatment selection by prescribers and patients in clinical practice. Multiple criteria decision analysis (MCDA) is a family of formal methods that help make explicit the tradeoffs that decision makers accept between the benefit and risk outcomes of different treatment options. Despite the recent interest in MCDA, certain methodological aspects are poorly understood. This paper presents 7 guidelines for applying MCDA in benefit-risk assessment and illustrates their use in the selection of a statin drug for the primary prevention of cardiovascular disease. We provide guidance on the key methodological issues of how to define the decision problem, how to select a set of nonoverlapping evaluation criteria, how to synthesize and summarize the evidence, how to translate relative measures to absolute ones that permit comparisons between the criteria, how to define suitable scale ranges, how to elicit partial preference information from the decision makers, and how to incorporate uncertainty in the analysis. Our example on statins indicates that fluvastatin is likely to be the most preferred drug by our decision maker and that this result is insensitive to the amount of preference information incorporated in the analysis.

Missing outcome data are a common threat to the validity of the results from randomised controlled trials (RCTs), which, if not analysed appropriately, can lead to misleading treatment effect estimates. Studies with missing outcome data also threaten the validity of any meta‐analysis that includes them. A conceptually simple Bayesian framework is proposed, to account for uncertainty due to missing binary outcome data in meta‐analysis. A pattern‐mixture model is fitted, which allows the incorporation of prior information on a parameter describing the missingness mechanism. We describe several alternative parameterisations, with the simplest being a prior on the probability of an event in the missing individuals. We describe a series of structural assumptions that can be made concerning the missingness parameters. We use some artificial data scenarios to demonstrate the ability of the model to produce a bias‐adjusted estimate of treatment effect that accounts for uncertainty. A meta‐analysis of haloperidol versus placebo for schizophrenia is used to illustrate the model. We end with a discussion of elicitation of priors, issues with poor reporting and potential extensions of the framework. Our framework allows one to make the best use of evidence produced from RCTs with missing outcome data in a meta‐analysis, accounts for any uncertainty induced by missing data and fits easily into a wider evidence synthesis framework for medical decision making. © 2015 The Authors. Statistics in MedicinePublished by John Wiley & Sons Ltd.

Objective: Trials often may report several similar outcomes measured on different test instruments. We explored a method for synthesising treatment effect information both within and between trials and for reporting treatment effects on a common scale as an alternative to standardisation Study design: We applied a procedure that simultaneously estimates a pooled treatment effect and the “mapping” ratios between the treatment effects on test instruments in a connected network. Standardised and non-standardised treatment effects were compared. The methods were illustrated in a dataset of 22 trials of selective serotonin reuptake inhibitors against placebo for social anxiety disorder, each reporting treatment effects on between one and six of a total nine test instruments. Results: Ratios of treatment effects on different test instruments varied from trial to trial, with a coefficient of variation of 18% (95% credible interval 11–29%). Standardised effect models fitted the data less well, and standardised treatment effects were estimated with less relative precision than non-standardised effects and with greater relative heterogeneity. Conclusion: Simultaneous synthesis of treatment effects and mapping to a common scale make fewer assumptions than standardising by dividing effects by the sample standard deviation, allow results to be reported on a common scale, and deliver estimates with superior relative precision. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

Background: Individuals with undiagnosed lung and colorectal cancers present with non-specific symptoms in primary care more often than matched controls. Increased access to diagnostic services for patients with symptoms generates more early-stage diagnoses, but the mechanisms for this are only partially understood.Methods: We re-analysed a UK-based case–control study to estimate the Symptom Lead Time (SLT) distribution for a range of potential symptom criteria for investigation. Symptom Lead Time is the time between symptoms caused by cancer and eventual diagnosis, and is analogous to Lead Time in a screening programme. We also estimated the proportion of symptoms in lung and colorectal cancer cases that are actually caused by the cancer.Results: Mean Symptom Lead Times were between 4.1 and 6.0 months, with medians between 2.0 and 3.2 months. Symptom Lead Time did not depend on stage at diagnosis, nor which criteria for investigation are adopted. Depending on the criteria, an estimated 27–48% of symptoms in individuals with as yet undiagnosed lung cancer, and 12–32% with undiagnosed colorectal cancer are not caused by the cancer.Conclusions: In most cancer cases detected by a symptom-based programme, the symptoms are caused by cancer. These cases have a short lead time and benefit relatively little. However, in a significant minority of cases cancer detection is serendipitous. This group experiences the benefits of a standard screening programme, a substantial mean lead time and a higher probability of early-stage diagnosis

CsrA of Escherichia coli is an RNA-binding protein that globally regulates a wide variety of cellular processes and behaviors, including carbon metabolism, motility, biofilm formation, and the stringent response. CsrB and CsrC are small RNAs (sRNAs) that sequester CsrA, thereby preventing CsrA-mRNA interaction. RpoE (σ E ) is the extracytoplasmic stress response sigma factor of E. coli . Previous RNA sequencing (RNA-seq) studies identified rpoE mRNA as a CsrA target. Here, we explored the regulation of rpoE by CsrA and found that CsrA represses rpoE translation. Gel mobility shift, footprint, and toeprint studies identified three CsrA binding sites in the rpoE leader transcript, one of which overlaps the rpoE Shine-Dalgarno (SD) sequence, while another overlaps the rpoE translation initiation codon. Coupled in vitro transcription-translation experiments showed that CsrA represses rpoE translation by binding to these sites. We further demonstrate that σ E indirectly activates the transcription of csrB and csrC , leading to increased sequestration of CsrA, such that repression of rpoE by CsrA is reduced. We propose that the Csr system fine-tunes the σ E -dependent cell envelope stress response. We also identified a 51-amino-acid coding sequence whose stop codon overlaps the rpoE start codon and demonstrate that rpoE is translationally coupled with this upstream open reading frame (ORF51). The loss of coupling reduces rpoE translation by more than 50%. Identification of a translationally coupled ORF upstream of rpoE suggests that this previously unannotated protein may participate in the cell envelope stress response. In keeping with existing nomenclature, we named ORF51 rseD , resulting in an operon arrangement of rseD-rpoE-rseA-rseB-rseC . IMPORTANCE CsrA posttranscriptionally represses genes required for bacterial stress responses, including the stringent response, catabolite repression, and the RpoS (σ S )-mediated general stress response. We show that CsrA represses the translation of rpoE , encoding the extracytoplasmic stress response sigma factor, and that σ E indirectly activates the transcription of csrB and csrC , resulting in reciprocal regulation of these two global regulatory systems. These findings suggest that extracytoplasmic stress leads to derepression of rpoE translation by CsrA, and CsrA-mediated repression helps reset RpoE abundance to prestress levels once envelope damage is repaired. The discovery of an ORF, rseD , translationally coupled with rpoE adds further complexity to translational control of rpoE .

Background Depressive symptoms are usually managed within primary care and antidepressant medication constitutes the first-line treatment. It remains unclear at present which people are more likely to benefit from antidepressant medication. This paper describes the protocol for a randomised controlled trial (PANDA) to investigate the severity and duration of depressive symptoms that are associated with a clinically significant response to sertraline compared to placebo, in people presenting to primary care with depression. Methods/design PANDA is a randomised, double blind, placebo controlled trial in which participants are individually randomised to sertraline or placebo. Eligible participants are those who are between the ages of 18 to 74; have presented to primary care with depression or low mood during the past 2 years; have not received antidepressant or anti-anxiety medication in the 8 weeks prior to enrolment in the trial and there is clinical equipoise about the benefits of selective serotonin reuptake inhibitor (SSRI) medication. Participants who consent to participate in the trial are randomised to receive either sertraline or matching placebo, starting at 50 mg daily for 1 week, increasing to 100 mg daily for up to 11 weeks (with the option of increasing to 150 mg if required). Participants, general practitioners (GPs) and the research team will be blind to treatment allocation. The primary outcome will be depressive symptoms measured by the Patient Health Questionnaire-9 (PHQ-9) at 6 weeks post randomisation, measured as a continuous outcome. Secondary outcomes include depressive symptoms measured with the PHQ-9 at 2 and 12 weeks as a continuous outcome and at 2, 6 and 12 weeks as a binary outcome; follow-up scores on depressive symptoms measured with the Beck Depression Inventory-II, anxiety symptoms measured by the Generalized Anxiety Disorder-7 and quality of life measured with the Euroqol-5D-5L and Short Form-12; emotional processing task scores measured at baseline, 2 and 6 weeks; and costs associated with healthcare use, time off work and personal costs. Discussion The PANDA trial uses a simple self-administered measure to establish the severity and duration of depressive symptoms associated with a clinically significant response to sertraline. The evidence from the trial will inform primary care prescribing practice by identifying which patients are more likely to benefit from antidepressants. Trial registration Controlled Trials ISRCTN Registry, ISRCTN84544741. Registered on 20 March 2014. EudraCT Number: 2013-003440-22; Protocol Number: 13/0413 (version 6.1). Electronic supplementary material The online version of this article (doi:10.1186/s13063-017-2253-4) contains supplementary material, which is available to authorized users.