Effect modification occurs when a covariate alters the relative effectiveness of treatment compared to control. It is widely understood that, when effect modification is present, treatment recommendations may vary by population and by subgroups within the population. Population-adjustment methods are increasingly used to adjust for differences in effect modifiers between study populations and to produce population-adjusted estimates in a relevant target population for decision-making. It is also widely understood that marginal and conditional estimands for non-collapsible effect measures, such as odds ratios or hazard ratios, do not in general coincide even without effect modification. However, the consequences of both non-collapsibility and effect modification together are little-discussed in the literature.In this article, we set out the definitions of conditional and marginal estimands, illustrate their properties when effect modification is present, and discuss the implications for decision-making. In particular, we show that effect modification can result in conflicting treatment rankings between conditional and marginal estimates. This is because conditional and marginal estimands correspond to different decision questions that are no longer aligned when effect modification is present. For time-to-event outcomes, the presence of covariates implies that marginal hazard ratios are time-varying, and effect modification can cause marginal hazard curves to cross. We conclude with practical recommendations for decision-making in the presence of effect modification, based on pragmatic comparisons of both conditional and marginal estimates in the decision target population. Currently, multilevel network meta-regression is the only population-adjustment method capable of producing both conditional and marginal estimates, in any decision target population.

Network meta-analysis combines aggregate data (AgD) from multiple randomised controlled trials, assuming that any effect modifiers are balanced across populations. Individual patient data (IPD) meta-regression is the "gold standard" method to relax this assumption, however IPD are frequently only available in a subset of studies. Multilevel network meta-regression (ML-NMR) extends IPD meta-regression to incorporate AgD studies whilst avoiding aggregation bias, but currently requires the aggregate-level likelihood to have a known closed form. Notably, this prevents application to time-to-event outcomes. We extend ML-NMR to individual-level likelihoods of any form, by integrating the individual-level likelihood function over the AgD covariate distributions to obtain the respective marginal likelihood contributions. We illustrate with two examples of time-to-event outcomes, showing the performance of ML-NMR in a simulated comparison with little loss of precision from a full IPD analysis, and demonstrating flexible modelling of baseline hazards using cubic M-splines with synthetic data on newly diagnosed multiple myeloma. ML-NMR is a general method for synthesising individual and aggregate level data in networks of all sizes. Extension to general likelihoods, including for survival outcomes, greatly increases the applicability of the method. R and Stan code is provided, and the methods are implemented in the multinma R package.

Backgrounds and aimsPregnant women and children are not included in Egypt’s hepatitis C virus (HCV) elimination programmes. This study assesses the cost-effectiveness of several screening and treatment strategies for pregnant women and infants in Egypt.DesignA Markov model was developed to simulate the cascade of care and HCV disease progression among pregnant women and their infants according to different screening and treatment strategies, which included: targeted versus universal antenatal screening; treatment of women in pregnancy or deferred till after breast feeding; treatment of infected children at 3 years vs 12 years. Current practice is targeted antenatal screening with deferred treatment for the mother and child. We also explored prophylactic treatment after birth for children of diagnosed HCV-infected women. Discounted lifetime cost, life expectancy (LE) and disability-adjusted life-years (DALYs) were calculated separately for women and their infants, and then combined.ResultsCurrent practice led to the highest cost (US$314.0), the lowest LE (46.3348 years) and the highest DALYs (0.0512 years) per mother–child pair. Universal screening and treatment during pregnancy followed by treatment of children at 3 years would be less expensive and more effective (cost saving) compared with current practice (US$219.3, 46.3525 and 0.0359 years). Prophylactic treatment at birth for infants born to HCV RNA-positive mothers would also be similarly cost saving, even with treatment uptake as low as 15% (US$218.6, 46.3525 and 0.0359 years). Findings were robust to reasonable changes in parameters.ConclusionUniversal screening and treatment of HCV in pregnancy, with treatment of infected infants at age 3 years is cost saving compared with current practice in the Egyptian setting.

Quantitative evidence synthesis methods aim to combine data from multiple medical trials to infer relative effects of different interventions. A challenge arises when trials report continuous outcomes on different measurement scales. To include all evidence in one coherent analysis, we require methods to `map' the outcomes onto a single scale. This is particularly challenging when trials report aggregate rather than individual data. We are motivated by a meta-analysis of interventions to prevent obesity in children. Trials report aggregate measurements of body mass index (BMI) either expressed as raw values or standardised for age and sex. We develop three methods for mapping between aggregate BMI data using known relationships between individual measurements on different scales. The first is an analytical method based on the mathematical definitions of z-scores and percentiles. The other two approaches involve sampling individual participant data on which to perform the conversions. One method is a straightforward sampling routine, while the other involves optimization with respect to the reported outcomes. In contrast to the analytical approach, these methods also have wider applicability for mapping between any pair of measurement scales with known or estimable individual-level relationships. We verify and contrast our methods using trials from our data set which report outcomes on multiple scales. We find that all methods recreate mean values with reasonable accuracy, but for standard deviations, optimization outperforms the other methods. However, the optimization method is more likely to underestimate standard deviations and is vulnerable to non-convergence.

Modelling the potential effectiveness of hepatitis C screening and treatment strategies during pregnancy in Egypt and UkraineJournal of HepatologyVol. 78Issue 5PreviewHCV test and treat campaigns currently exclude pregnant women. Pregnancy offers a unique opportunity for HCV screening and to potentially initiate direct-acting antiviral treatment. We explored HCV screening and treatment strategies in two lower middle-income countries with high HCV prevalence, Egypt and Ukraine. Full-Text PDF Open Access It has come to our attention that the affiliation of Manal Hamdy-El-Sayed was incorrectly listed as: Department of Community, Environmental, and Occupational Medicine, Faculty of Medicine, Ain Shams University, Cairo, Egypt The correct affiliation is: Department of Paediatrics and the Clinical Research Center, Faculty of Medicine, Ain Shams University, Cairo, Egypt. The corrected affiliation list is provided in this corrigendum. We apologise for any inconvenience caused.

We respond to discussant comments on our paper "Twenty years of network meta-analysis: Continuing controversies and recent developments" (https://doi.org/10.1002/jrsm.1700) and raise some additional points for consideration, including: the way in which methodological guidance is generated; integration of the estimand framework with evidence synthesis; and implications of the European Joint Clinical Assessment. We ask: what properties are required of population adjustment methods to enable transparent and consistent decision-making? We also ask why individual patient data is not routinely made available to re-imbursement authorities and clinical guideline developers.

Network meta-analysis (NMA) is an extension of pairwise meta-analysis (PMA) which combines evidence from trials on multiple treatments in connected networks. NMA delivers internally consistent estimates of relative treatment efficacy, needed for rational decision making. Over its first 20 years NMA's use has grown exponentially, with applications in both health technology assessment (HTA), primarily re-imbursement decisions and clinical guideline development, and clinical research publications. This has been a period of transition in meta-analysis, first from its roots in educational and social psychology, where large heterogeneous datasets could be explored to find effect modifiers, to smaller pairwise meta-analyses in clinical medicine on average with less than six studies. This has been followed by narrowly-focused estimation of the effects of specific treatments at specific doses in specific populations in sparse networks, where direct comparisons are unavailable or informed by only one or two studies. NMA is a powerful and well-established technique but, in spite of the exponential increase in applications, doubts about the reliability and validity of NMA persist. Here we outline the continuing controversies, and review some recent developments. We suggest that heterogeneity should be minimized, as it poses a threat to the reliability of NMA which has not been fully appreciated, perhaps because it has not been seen as a problem in PMA. More research is needed on the extent of heterogeneity and inconsistency in datasets used for decision making, on formal methods for making recommendations based on NMA, and on the further development of multi-level network meta-regression.

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.

Background & Aims: HCV test and treat campaigns currently exclude pregnant women. Pregnancy offers a unique opportunity for HCV screening and to potentially initiate direct-acting antiviral treatment. We explored HCV screening and treatment strategies in two lower middle-income countries with high HCV prevalence, Egypt and Ukraine.Methods: Country-specific probabilistic decision models were developed to simulate a cohort of pregnant women. We compared five strategies: S0, targeted risk-based screening and deferred treatment (DT) to after pregnancy/breastfeeding; S1, World Health Organization (WHO) risk-based screening and DT; S2, WHO risk-based screening and targeted treatment (treat women with risk factors for HCV vertical transmission [VT]); S3, universal screening and targeted treatment during pregnancy; S4, universal screening and treatment. Maternal and infant HCV outcomes were projected.Results: S0 resulted in the highest proportion of women undiagnosed: 59% and 20% in Egypt and Ukraine, respectively, with 0% maternal cure by delivery and VT estimated at 6.5% and 7.9%, respectively. WHO risk-based screening and DT (S1) increased the proportion of women diagnosed with no change in maternal cure or VT. Universal screening and treatment during pregnancy (S4) resulted in the highest proportion of women diagnosed and cured by delivery (65% and 70%, respectively), and lower levels of VT (3.4% and 3.6%, respectively).Conclusions: This is one of the first models to explore HCV screening and treatment strategies in pregnancy, which will be critical in informing future care and policy as more safety/efficacy data emerge. Universal screening and treatment in pregnancy could potentially improve both maternal and infant outcomes.(c) 2023 The Authors. Published by Elsevier B.V. on behalf of European Association for the Study of the Liver. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Background. Current 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. Methods. Data 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. Results. Clearance 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. Conclusions. Most 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.

To determine the extent of exposure to Zika virus (ZIKV) and chikungunya virus (CHIKV) in Jamaica, we collected serum from 584 pregnant women during 2017–2019. We found that 15.6% had antibodies against ZIKV and 83.6% against CHIKV. These results indicate potential recirculation of ZIKV but not CHIKV in the near future.

Background Network meta-analysis (NMA) and indirect comparisons combine aggregate data (AgD) from multiple studies on treatments of interest but may give biased estimates if study populations differ. Population adjustment methods such as multilevel network meta-regression (ML-NMR) aim to reduce bias by adjusting for differences in study populations using individual patient data (IPD) from 1 or more studies under the conditional constancy assumption. A shared effect modifier assumption may also be necessary for identifiability. This article aims to demonstrate how the assumptions made by ML-NMR can be assessed in practice to obtain reliable treatment effect estimates in a target population. Methods We apply ML-NMR to a network of evidence on treatments for plaque psoriasis with a mix of IPD and AgD trials reporting ordered categorical outcomes. Relative treatment effects are estimated for each trial population and for 3 external target populations represented by a registry and 2 cohort studies. We examine residual heterogeneity and inconsistency and relax the shared effect modifier assumption for each covariate in turn. Results Estimated population-average treatment effects were similar across study populations, as differences in the distributions of effect modifiers were small. Better fit was achieved with ML-NMR than with NMA, and uncertainty was reduced by explaining within- and between-study variation. We found little evidence that the conditional constancy or shared effect modifier assumptions were invalid. Conclusions ML-NMR extends the NMA framework and addresses issues with previous population adjustment approaches. It coherently synthesizes evidence from IPD and AgD studies in networks of any size while avoiding aggregation bias and noncollapsibility bias, allows for key assumptions to be assessed or relaxed, and can produce estimates relevant to a target population for decision-making. Highlights Multilevel network meta-regression (ML-NMR) extends the network meta-analysis framework to synthesize evidence from networks of studies providing individual patient data or aggregate data while adjusting for differences in effect modifiers between studies (population adjustment). We apply ML-NMR to a network of treatments for plaque psoriasis with ordered categorical outcomes. We demonstrate for the first time how ML-NMR allows key assumptions to be assessed. We check for violations of conditional constancy of relative effects (such as unobserved effect modifiers) through residual heterogeneity and inconsistency and the shared effect modifier assumption by relaxing this for each covariate in turn. Crucially for decision making, population-adjusted treatment effects can be produced in any relevant target population. We produce population-average estimates for 3 external target populations, represented by the PsoBest registry and the PROSPECT and Chiricozzi 2019 cohort studies.

Statistics in MedicineVolume 40, Issue 11 p. 2759-2763 AUTHORS REPLYOpen Access Target estimands for efficient decision making: Response to comments on "Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study" David M. Phillippo, Corresponding Author david.phillippo@bristol.ac.uk orcid.org/0000-0003-2672-7841 Bristol Medical School (Population Health Sciences), University of Bristol, Bristol, UK Correspondence David M. Phillippo, Bristol Medical School (Population Health Sciences), University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK. Email: david.phillippo@bristol.ac.ukSearch for more papers by this authorSofia Dias, orcid.org/0000-0002-2172-0221 Bristol Medical School (Population Health Sciences), University of Bristol, Bristol, UK Centre for Reviews and Dissemination, University of York, York, UKSearch for more papers by this authorAnthony E. Ades, Bristol Medical School (Population Health Sciences), University of Bristol, Bristol, UKSearch for more papers by this authorNicky J. Welton, Bristol Medical School (Population Health Sciences), University of Bristol, Bristol, UKSearch for more papers by this author David M. Phillippo, Corresponding Author david.phillippo@bristol.ac.uk orcid.org/0000-0003-2672-7841 Bristol Medical School (Population Health Sciences), University of Bristol, Bristol, UK Correspondence David M. Phillippo, Bristol Medical School (Population Health Sciences), University of Bristol, Canynge Hall, 39 Whatley Road, Bristol BS8 2PS, UK. Email: david.phillippo@bristol.ac.ukSearch for more papers by this authorSofia Dias, orcid.org/0000-0002-2172-0221 Bristol Medical School (Population Health Sciences), University of Bristol, Bristol, UK Centre for Reviews and Dissemination, University of York, York, UKSearch for more papers by this authorAnthony E. Ades, Bristol Medical School (Population Health Sciences), University of Bristol, Bristol, UKSearch for more papers by this authorNicky J. Welton, Bristol Medical School (Population Health Sciences), University of Bristol, Bristol, UKSearch for more papers by this author First published: 08 May 2021 https://doi.org/10.1002/sim.8965Citations: 1 Funding information: Medical Research Council, MR/P015298/1; MR/R025223/1 AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat We thank Remiro-Azócar, Heath, and Baio (R-AHB) for their letter to the editor,1 in response to our recent article presenting a simulation study comparing the performance of methods for population-adjusted indirect comparison.2 R-AHB discuss the important issue of target estimands with noncollapsible effect measures, expanding upon the discussion in sections 4.3 and 7 of our article.2 R-AHB distinguish between marginal and conditional treatment effect estimates and explain that matching-adjusted indirect comparison (MAIC) targets marginal effects whereas simulated treatment comparison (STC) and multilevel network meta-regression (ML-NMR) target conditional treatment effects. They conclude that "methods like MAIC are valid for population-based inference, but not "fit for purpose" when inference is at the individual level, whereas methods like ML-NMR are valid for inference at the individual level, but not designed for population-based inference." Furthermore, they assert that marginal treatment effect estimates are necessary for population-based inference as required for decision-making in Health Technology Assessment (HTA). We welcome and encourage debate of these issues, which—despite much discussion in the literature on randomized controlled trials (RCTs)3-6 and observational epidemiology7-9—have largely been overlooked in the literature on population adjustment and meta-analysis to date. However, whilst we agree with R-AHB that population-based inference is required for HTA, we disagree that methods like ML-NMR are not appropriate to obtain population-average estimates for HTA. In this response, we further clarify the use of conditional estimates to inform population-average treatment effects and why we believe these are appropriate target estimands for decision making. We also correct some important inaccuracies in R-AHB's letter regarding the characterization of the methods (in particular ML-NMR) and interpretation of our simulation study results. 1 TARGET ESTIMANDS FOR DECISION MAKING Healthcare decision making requires estimates of average relative treatment effects between each treatment of interest in the decision target population. Ideally, such estimates would be provided by a well-designed, representative RCT comparing all treatments of interest in the decision target population. To help solidify ideas and define terminology, let us first consider the analysis of this ideal RCT. The simplest analysis of this ideal RCT is an unadjusted analysis (eg, ANOVA or regression including only the main effect of treatment). However, this is not the most efficient approach. Instead, it is recommended practice to include prespecified prognostic factors in the analysis model (eg, ANCOVA or regression including main effects of covariates and treatment).3-6, 10 The adjusted analysis is more powerful and more efficient because some of the additional variation in the outcome not due to treatment has been conditioned on the covariates.3-6 The unadjusted analysis results in marginal treatment effect estimates, whereas the adjusted analysis results in conditional treatment effect estimates. Since only main effects of covariates and no interactions with treatment have been included in the adjusted analysis, the conditional treatment effects apply over the entire study population; they do not vary by covariate values and are not subgroup-specific. Thus, they can be considered population-average conditional treatment effects, and can be used to make treatment decisions for the entire population represented by this ideal RCT. When working with noncollapsible effect measures such as odds ratios or hazard ratios, conditional and marginal treatment effects do not in general coincide; conditional effects will lie further from the null.11 We should also be clear that the everyday usage of "conditional" to mean "depends upon" is misleading here: indeed, marginal estimates are more strongly dependent on the population than these conditional estimates, because the marginal estimates are affected by differences in all prognostic factors (observed and unobserved) whereas the conditional estimates will not be affected by differences in observed prognostic factors.4 R-AHB are correct that other types of conditional effects depend on specified covariate values when treatment-covariate interactions have been included in the analysis, and are then appropriate for decision-making for individuals. To avoid further confusion, we refer to these conditional effects from analyses including interactions as individual-level conditional treatment effects, and refer to conditional effects from analyses without interactions as population-average conditional treatment effects. However, R-AHB conflate these individual-level and population-average conditional treatment effects, which have different interpretations and different uses for decision making. The ML-NMR model is parameterized in terms of individual-level conditional treatment effects, since treatment-covariate interactions are included in the analysis model in order to adjust for differences in effect modifiers between studies (population adjustment). However, ML-NMR can still produce estimates of marginal and conditional population-average treatment effects by integration over the covariate joint distribution in the target population, as we have described previously2, 12 and reiterate in the following section. Both marginal and population-average conditional estimands reflect a "population-average treatment effect" as both apply to the entire population and are averaged over the distribution of covariates in the population. The marginal estimand reflects the average treatment effect over individuals in the target population without any knowledge of the distribution of prognostic covariates in the sample. The population-average conditional estimand reflects the average treatment effect over individuals in the target population accounting for the distribution of prognostic covariates. We consider that the population-average conditional treatment effect is the most appropriate target estimand for decision makers, primarily because it reflects the recommended analysis that would be undertaken in the ideal evidence scenario described above. Decision makers typically have a well-defined target population in mind, however, the marginal estimand does not make full use of this information. The population-average conditional estimand is more efficient because it accounts for the known distribution of prognostic factors in the target population.3-6 2 PRODUCING ESTIMATES OF TARGET ESTIMANDS Although the ML-NMR model is parameterized on individual-level conditional treatment effects on a given linear predictor scale, ML-NMR can produce population average estimates for a range of quantities of interest in a target population through appropriate use of integration, as described in section 2.5 of Phillippo et al.12 When the quantities of interest are population-average conditional treatments effects dab(P) between treatments b and a in population P, integration simplifies to plugging in mean covariate values x ‾ ( P ) in the target population since these are defined on the linear predictor scale, as given in equation (9) of our article:2 d a b ( P ) = ∫ X μ ( P ) + x T β 1 + β 2 , b + γ b f ( P ) ( x ) d x − ∫ X μ ( P ) + x T β 1 + β 2 , a + γ a f ( P ) ( x ) d x = x ‾ ( P ) T β 2 , b − β 2 , a + γ b − γ a , (1) where β 2 , b and β 2 , a are coefficients for effect modifier interactions, and γ b and γ a are individual-level treatment effects at the reference level of the covariates x = 0 . β 1 are coefficients for prognostic (main) effects of covariates, μ ( P ) is a distribution for baseline response in population P, f(P)(·) is the joint covariate distribution in population P with support X . This does not mean that the resulting estimate is only appropriate for individuals with the mean covariate values, as suggested by R-AHB. It just happens that the population-average conditional treatment effect estimate on the linear predictor scale is equivalent to that for individuals with the mean covariate values. Health economic models typically require population-average absolute effects, such as average event probabilities p ‾ k ( P ) on treatment k, which can be produced following Phillippo et al.12 and using the notation in our article2 as p ‾ k ( P ) = ∫ X g − 1 μ ( P ) + x T β 1 + β 2 , k + γ k f ( P ) ( x ) d x , (2) where g(·) is a suitable link function (eg, logit). Contrary to the assertion of R-AHB that ML-NMR cannot produce marginal population-average treatment effect estimates, ML-NMR can indeed estimate the marginal population-average treatment effect Δ a b ( P ) between treatments b and a in population P, simply by working with the population-average absolute effects from (2): Δ a b ( P ) = g ( p ‾ b ( P ) ) − g ( p ‾ a ( P ) ) . (3) Estimates of other summaries of marginal population-average treatment effects such as risk differences or relative risks can be produced by similar manipulation of p ‾ b ( P ) and p ‾ a ( P ) . Again, we note that dab(P), p ‾ k ( P ) , and Δ a b ( P ) are not subgroup-specific but apply over the entire target population P, since all covariates (including effect modifiers) have been integrated over. MAIC directly targets the marginal population-average treatment effect, and cannot estimate the (more efficient) population-average conditional treatment effect unless suitable adjusted estimates are available from the AgD study. As described by both R-AHB and ourselves,2 STC in typical usage estimates neither the marginal or conditional population-average treatment effect and will be biased for either estimand, because in typical use STC combines conditional and marginal effects. Moreover, Equations (2) and (3) make it clear that marginal population-average relative effects depend not only on the distribution of effect modifiers, but also on the distribution of all prognostic variables and the population baseline risk. Thus, the marginal population-average treatment effects Δ a b ( P ) depend more strongly on the population of interest than the population-average conditional treatment effects dab(P), and are less generalizable/transportable as a result.4 This is an additional concern for MAIC and STC, which produce marginal treatment effect estimates specific to the aggregate study population in a population-adjusted indirect comparison, and may not be representative of the decision target population in either prognostic factors or effect modifiers.13, 14 3 COMMENT ON SIMULATION STUDY RESULTS As we have argued above, population-average conditional treatment effects are appropriate target estimands for efficient decision making. Our simulation study2 is therefore designed to evaluate the performance of the methods against the population-average conditional treatment effects dab(P)—not the individual-level conditional treatment effects γ k as R-AHB claim. R-AHB suppose that much of the observed bias for MAIC in our simulation study is due to evaluating its performance against the wrong estimand, since MAIC targets the marginal population-average treatment effect. However, standard unadjusted Bucher indirect comparisons also target marginal estimands, and yet MAIC manages to substantially increase the bias compared with these in some scenarios. Moreover, we would expect STC to perform poorly for the same reasons, since it mixes conditional and marginal estimates. However, in our simulations STC performed well and was seen to be unbiased when the requisite assumptions were met. Intuitively, therefore, our simulation scenarios must be such that the differences between marginal and population-average conditional estimands are small. More formally, we can investigate this claim using the formula of Matthews and Badi15 for the ratio between the conditional and marginal estimands, which depends on the strength of the covariate effect and the variance within the population. Using this result, we determine that—even in the worst cases—the difference between marginal and population-average conditional estimands in the scenarios we investigated is less than 0.5%. Therefore, the performance issues demonstrated for MAIC are not due to any meaningful difference in estimands, but are due to the fundamental inability of MAIC to extrapolate and the resulting bias and instability as population overlap decreases. R-AHB contrast our results against a simulation study of their own,16 which shows that MAIC can remain unbiased (for the marginal estimand) even with only moderate overlap between populations. The observed difference in performance is due to R-AHB considering only matching covariate means across populations, whereas we consider matching both means and variances (first and second moments) which is a common approach14 and follows the original description of the method.17 The results of R-AHB suggest that matching on covariate means only might be less sensitive to reduced population overlap and may be able to tolerate lower levels of overlap before issues arise, since this is a much less exacting requirement. This has also been observed in other simulation studies.18, 19 However, the question of when, if at all, it is preferable or necessary to match higher moments between populations for MAIC remains an interesting area for further theoretical research and simulation studies. 4 CONCLUSIONS We welcome the much-needed discussion of target estimands in the letter from R-AHB, which has largely been overlooked in the population adjustment literature to date. We hope that our response has served to further clarify the issues surrounding estimands for noncollapsible effect measures. Here, we have argued the case for considering population-average conditional treatment effects as appropriate targets for efficient decision making. In their letter, R-AHB state that "methods like ML-NMR are valid for inference at the individual level, but are not designed for population-based inference." However, we have demonstrated that this is not the case: ML-NMR can indeed support inference at the individual level, but can also provide estimates of both marginal and conditional population-average treatment effects, as well as the population-average absolute effects typically required for health economic modeling. Moreover, ML-NMR is likely to be a more efficient approach than MAIC even when targeting marginal population-average treatment effects, since regression adjustment is typically more efficient than weighting.20 We have shown that the results of our simulation study, including the poor performance of MAIC in many scenarios, are not an artifact of noncollapsibility and incompatible estimands, and are pertinent regardless of whether population-average conditional or marginal estimands are of interest. R-AHB refer to ML-NMR as the "gold standard" for estimating conditional population-adjusted treatment effects from mixtures of IPD and AgD, and suggest further work extending ML-NMR to estimate marginal population-adjusted as a research priority. However, as we have described above, ML-NMR can be used to obtain marginal population-adjusted treatment effects, and we would suggest therefore that ML-NMR may also be considered the "gold standard" for estimating marginal population-average treatment effects. Analysts and decision makers should carefully consider which target estimand is most appropriate to their needs and, as we have argued, population-average conditional treatment effects as targeted by a hypothetical "ideal RCT" may be a more efficient choice than marginal estimands which do not account for known population characteristics. REFERENCES 1Remiro-Azócar A, Heath A, Baio G. Conflating marginal and conditional treatment effects: comments on 'assessing the performance of population adjustment methods for anchored indirect comparisons: a simulation study'; 2020. arXiv Statistics in Medicine arXiv:2011.06334. Google Scholar 2 Phillippo DM, Dias S, Ades AE, Welton NJ. 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AbstractBackgroundIt 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.

BACKGROUND: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. METHODS: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). RESULTS: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. CONCLUSION: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.

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-19TM 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-19TM 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% uncertainty interval 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.

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.

Abstract Objective To assess the accuracy of the AbC-19 Rapid Test lateral flow immunoassay for the detection of previous severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Design Test accuracy study. Setting Laboratory based evaluation. Participants 2847 key workers (healthcare staff, fire and rescue officers, and police officers) in England in June 2020 (268 with a previous polymerase chain reaction (PCR) positive result (median 63 days previously), 2579 with unknown previous infection status); and 1995 pre-pandemic blood donors. Main outcome measures AbC-19 sensitivity and specificity, estimated using known negative (pre-pandemic) and known positive (PCR confirmed) samples as reference standards and secondly using the Roche Elecsys anti-nucleoprotein assay, a highly sensitive laboratory immunoassay, as a reference standard in samples from key workers. Results Test result bands were often weak, with positive/negative discordance by three trained laboratory staff for 3.9% of devices. Using consensus readings, for known positive and negative samples sensitivity was 92.5% (95% confidence interval 88.8% to 95.1%) and specificity was 97.9% (97.2% to 98.4%). Using an immunoassay reference standard, sensitivity was 94.2% (90.7% to 96.5%) among PCR confirmed cases but 84.7% (80.6% to 88.1%) among other people with antibodies. This is consistent with AbC-19 being more sensitive when antibody concentrations are higher, as people with PCR confirmation tended to have more severe disease whereas only 62% (218/354) of seropositive participants had had symptoms. If 1 million key workers were tested with AbC-19 and 10% had actually been previously infected, 84 700 true positive and 18 900 false positive results would be projected. The probability that a positive result was correct would be 81.7% (76.8% to 85.8%). Conclusions AbC-19 sensitivity was lower among unselected populations than among PCR confirmed cases of SARS-CoV-2, highlighting the scope for overestimation of assay performance in studies involving only PCR confirmed cases, owing to “spectrum bias.” Assuming that 10% of the tested population have had SARS-CoV-2 infection, around one in five key workers testing positive with AbC-19 would be false positives. Study registration ISRCTN 56609224.

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. Copyright (c) 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. 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 betweenstudy variation in the risks of vertical transmission and congenital Zika syndrome. 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