Spontaneous Clearance of Vertically Acquired Hepatitis C Infection: Implications for Testing and Treatment.

BACKGROUND: Current guidelines recommend that infants born to women with hepatitis C virus (HCV) viremia be 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 that suggest that 25%-40% of vertically acquired HCV infections clear spontaneously within 4-5 years. METHODS: Data on 179 infants with HCV RNA and/or anti-HCV evidence of vertically acquired infection in 3 prospective European cohorts were investigated. Ages at clearance of infection were estimated taking account of interval censoring and delayed entry. We also investigated clearance in initially HCV RNA-negative infants in whom RNA was not detectable until after 6 weeks. RESULTS: Clearance rates were initially high then declined slowly. Apparently, many infections clear before they can be confirmed. An estimated 65.9% (95% credible interval [CrI], 50.1-81.6) of confirmed infections cleared by 5 years, at a median 12.4 (CrI, 7.1-18.9) months. If treatment were to begin at age 6 months, 18 months, or 3 years, at least 59.0% (CrI, 42.0-76.9), 39.7% (CrI, 17.9-65.9), and 20.9% (CrI, 4.6-44.8) of those treated would clear without treatment. In 7 (6.6%) confirmed infections, RNA was not detectable until after 6 weeks and not until after 6 months in 2 (1.9%). However, all such cases subsequently cleared. CONCLUSIONS: Most confirmed infection cleared by age 3 years. Treatment before age 3, if it was available, would avoid loss to follow-up but would result in substantial overtreatment.

Antenatal Seroprevalence of Zika and Chikungunya Viruses, Kingston Metropolitan Area, Jamaica, 2017-2019.

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.

Assessing the performance of population adjustment methods for anchored indirect comparisons: A simulation study.

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 with 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.

Threshold Analysis as an Alternative to GRADE for Assessing Confidence in Guideline Recommendations Based on Network Meta-analyses.

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.

Quantifying how diagnostic test accuracy depends on threshold in a meta-analysis.

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.

Has Chlamydia trachomatis prevalence in young women in England, Scotland and Wales changed? Evidence from national probability surveys.

We evaluate the utility of the National Surveys of Attitudes and Sexual Lifestyles (Natsal) undertaken in 2000 and 2010, before and after the introduction of the National Chlamydia Screening Programme, as an evidence source for estimating the change in prevalence of Chlamydia trachomatis (CT) in England, Scotland and Wales. Both the 2000 and 2010 surveys tested urine samples for CT by Nucleic Acid Amplification Tests (NAATs). We examined the sources of uncertainty in estimates of CT prevalence change, including sample size and adjustments for test sensitivity and specificity, survey non-response and informative non-response. In 2000, the unadjusted CT prevalence was 4.22% in women aged 18-24 years; in 2010, CT prevalence was 3.92%, a non-significant absolute difference of 0.30 percentage points (95% credible interval -2.8 to 2.0). In addition to uncertainty due to small sample size, estimates were sensitive to specificity, survey non-response or informative non-response, such that plausible changes in any one of these would be enough to either reverse or double any likely change in prevalence. Alternative ways of monitoring changes in CT incidence and prevalence over time are discussed.

Population Adjustment Methods for Indirect Comparisons: A Review of National Institute for Health and Care Excellence Technology Appraisals.

OBJECTIVES: Indirect 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). METHODS: We reviewed NICE TAs published between 01/01/2010 and 20/04/2018. RESULTS: Population 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. CONCLUSIONS: Population 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.

Use of a random effects meta-analysis in the design and analysis of a new clinical trial.

In designing a randomized controlled trial, it has been argued that trialists should consider existing evidence about the likely intervention effect. One approach is to form a prior distribution for the intervention effect based on a meta-analysis of previous studies and then power the trial on its ability to affect the posterior distribution in a Bayesian analysis. Alternatively, methods have been proposed to calculate the power of the trial to influence the "pooled" estimate in an updated meta-analysis. These two approaches can give very different results if the existing evidence is heterogeneous, summarised using a random effects meta-analysis. We argue that the random effects mean will rarely represent the trialist's target parameter, and so, it will rarely be appropriate to power a trial based on its impact upon the random effects mean. Furthermore, the random effects mean will not generally provide an appropriate prior distribution. More appropriate alternatives include the predictive distribution and shrinkage estimate for the most similar study. Consideration of the impact of the trial on the entire random effects distribution might sometimes be appropriate. We describe how beliefs about likely sources of heterogeneity have implications for how the previous evidence should be used and can have a profound impact on the expected power of the new trial. We conclude that the likely causes of heterogeneity among existing studies need careful consideration. In the absence of explanations for heterogeneity, we suggest using the predictive distribution from the meta-analysis as the basis for a prior distribution for the intervention effect.

Proportion of Tubal Factor Infertility due to Chlamydia: Finite Mixture Modeling of Serum Antibody Titers.

In this study, we examined whether the proportion of tubal factor infertility (TFI) that is attributable to Chlamydia trachomatis, the population excess fraction (PEF), can be estimated from serological data using finite mixture modeling. Whole-cell inclusion immunofluorescence serum antibody titers were recorded among infertile women seen at St. Michael's Hospital in Bristol, United Kingdom, during the period 1985-1995. Women were classified as TFI cases or controls based on laparoscopic examination. Finite mixture models were used to identify the number of component titer distributions and the proportion of serum samples in each, from which estimates of PEF were derived. Four titer distributions were identified. The component at the highest titer was found only in samples from women with TFI, but there was also an excess of the second-highest titer component in TFI cases. Minimum and maximum estimates of the PEF were 28.0% (95% credible interval: 6.9, 50.0) and 46.8% (95% credible interval: 23.2, 64.1). Equivalent estimates based on the standard PEF formula from case-control studies were 0% and over 65%. Finite mixture modeling can be applied to serological data to obtain estimates of the proportion of reproductive damage attributable to C. trachomatis Further studies using modern assays in contemporary, representative populations should be undertaken.

Pelvic inflammatory disease and salpingitis: incidence of primary and repeat episodes in England.

Pelvic inflammatory disease (PID) and more specifically salpingitis (visually confirmed inflammation) is the primary cause of tubal factor infertility and is an important risk factor for ectopic pregnancy. The risk of these outcomes increases following repeated episodes of PID. We developed a homogenous discrete-time Markov model for the distribution of PID history in the UK. We used a Bayesian framework to fully propagate parameter uncertainty into the model outputs. We estimated the model parameters from routine data, prospective studies, and other sources. We estimated that for women aged 35-44 years, 33·6% and 16·1% have experienced at least one episode of PID and salpingitis, respectively (diagnosed or not) and 10·7% have experienced one salpingitis and no further PID episodes, 3·7% one salpingitis and one further PID episode, and 1·7% one salpingitis and ⩾2 further PID episodes. Results are consistent with numerous external data sources, but not all. Studies of the proportion of PID that is diagnosed, and the proportion of PIDs that are salpingitis together with the severity distribution in different diagnostic settings and of overlap between routine data sources of PID would be valuable.

Proportion of Pelvic Inflammatory Disease Cases Caused by Chlamydia trachomatis: Consistent Picture From Different Methods.

BACKGROUND: Pelvic inflammatory disease (PID) is a leading cause of both tubal factor infertility and ectopic pregnancy. Chlamydia trachomatis is an important risk factor for PID, but the proportion of PID cases caused by C. trachomatis is unclear. Estimates of this are required to evaluate control measures. METHODS: We consider 5 separate methods of estimating age-group-specific population excess fractions (PEFs) of PID due to C. trachomatis, using routine data, surveys, case-control studies, and randomized controlled trials, and apply these to data from the United Kingdom before introduction of the National Chlamydia Screening Programme. RESULTS: As they are informed by randomized comparisons and national exposure and outcome estimates, our preferred estimates of the proportion of PID cases caused by C. trachomatis are 35% (95% credible interval [CrI], 11%-69%) in women aged 16-24 years and 20% (95% CrI, 6%-38%) in women aged 16-44 years in the United Kingdom. There is a fair degree of consistency between adjusted estimates of PEF, but all have wide 95% CrIs. The PEF decreases from 53.5% (95% CrI, 15.6%-100%) in women aged 16-19 years to 11.5% (95% CrI, 3.0%-25.7%) in women aged 35-44 years. CONCLUSIONS: The PEFs of PID due to C. trachomatis decline steeply with age by a factor of around 5-fold between younger and older women. Further studies of the etiology of PID in different age groups are required.

Symptom lead times in lung and colorectal cancers: what are the benefits of symptom-based approaches to early diagnosis?

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.

Minimal clinically important difference on the Beck Depression Inventory--II according to the patient's perspective.

BACKGROUND: The 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. METHOD: We 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'. RESULTS: Improvement 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%. CONCLUSIONS: MCID 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.

A Bayesian framework to account for uncertainty due to missing binary outcome data in pairwise meta-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.

Recapture or precapture? Fallibility of standard capture-recapture methods in the presence of referrals between sources.

Capture-recapture methods, largely developed in ecology, are now commonly used in epidemiology to adjust for incomplete registries and to estimate the size of difficult-to-reach populations such as problem drug users. Overlapping lists of individuals in the target population, taken from administrative data sources, are considered analogous to overlapping "captures" of animals. Log-linear models, incorporating interaction terms to account for dependencies between sources, are used to predict the number of unobserved individuals and, hence, the total population size. A standard assumption to ensure parameter identifiability is that the highest-order interaction term is 0. We demonstrate that, when individuals are referred directly between sources, this assumption will often be violated, and the standard modeling approach may lead to seriously biased estimates. We refer to such individuals as having been "precaptured," rather than truly recaptured. Although sometimes an alternative identifiable log-linear model could accommodate the referral structure, this will not always be the case. Further, multiple plausible models may fit the data equally well but provide widely varying estimates of the population size. We demonstrate an alternative modeling approach, based on an interpretable parameterization and driven by careful consideration of the relationships between the sources, and we make recommendations for capture-recapture in practice.

Regional differences in the frequency of the c.985A>G ACADM mutation: findings from a meta-regression of genotyping and screening studies.

Several countries include medium-chain acyl-CoA dehydrogenase (MCAD) deficiency, a rare autosomal recessive disease, in their newborn screening programmes despite prevalence uncertainty. We estimated the frequency of its most common mutation, c.985A>G, tested for regional differences and compared screening and genotype frequencies. We identified 43 studies reporting the frequency of c.985A>G over 10 million individuals, and pooled frequency data using a novel Bayesian approach. We found significant variation in the frequency of the mutation across regions supporting a reported founder effect. The proportion of c.985A>G homozygotes was highest in Western Europe with 4.1 (95%CI: 2.8-5.6) per 100,000 individuals, then the New World (3.2, 95%CI: 2.0-4.7), Southern (1.2, 95%CI: 0.6-2.0) and Eastern European regions (0.9, 95%CI: 0.5-1.7). No cases with the mutation were identified in Asian and Middle Eastern regions. Significant differences were found in some countries between the genotype and screening allele frequency of c.985A>G. Our predictions could inform the frequency of the mutation by region and our approach could apply to other genetic conditions.

Simultaneous multioutcome synthesis and mapping of treatment effects to a common scale.

OBJECTIVES: A new method is presented for both synthesizing treatment effects on multiple outcomes subject to measurement error and estimating coherent mapping coefficients between all outcomes. It can be applied to sets of trials reporting different combinations of patient- or clinician-reported outcomes, including both disease-specific measures and generic health-related quality-of-life measures. It is underpinned by a structural equation model that includes measurement error and latent common treatment effect factor. Treatment effects can be expressed on any of the test instruments that have been used. METHODS: This is illustrated in a synthesis of eight placebo-controlled trials of TNF-α inhibitors in ankylosing spondylitis, each reporting treatment effects on between two and five of a total six test instruments. RESULTS: The method has advantages over other methods for synthesis of multiple outcome data, including standardization and multivariate normal synthesis. Unlike standardization, it allows synthesis of treatment effect information from test instruments sensitive to different underlying constructs. It represents a special case of previously proposed multivariate normal models for evidence synthesis, but unlike the former, it also estimates mappings. Combining synthesis and mapping as a single operation makes more efficient use of available data than do current mapping methods and generates treatment effects that are consistent with the mappings. A limitation, however, is that it can only generate mappings to and from those instruments on which some trial data exist. CONCLUSIONS: The method should be assessed in a wide range of data sets on different clinical conditions, before it can be used routinely in health technology assessment.

Incidence of Chlamydia trachomatis infection in women in England: two methods of estimation.

Information on the incidence of Chlamydia trachomatis (CT) is essential for models of the effectiveness and cost-effectiveness of screening programmes. We developed two independent estimates of CT incidence in women in England: one based on an incidence study, with estimates 'recalibrated' to the general population using data on setting-specific relative risks, and allowing for clearance and re-infection during follow-up; the second based on UK prevalence data, and information on the duration of CT infection. The consistency of independent sources of data on incidence, prevalence and duration, validates estimates of these parameters. Pooled estimates of the annual incidence rate in women aged 16-24 and 16-44 years for 2001-2005 using all these data were 0·05 [95% credible interval (CrI) 0·035-0·071] and 0·021 (95% CrI 0·015-0·028), respectively. Although, the estimates apply to England, similar methods could be used in other countries. The methods could be extended to dynamic models to synthesize, and assess the consistency of data on contact and transmission rates.

Mapping between measurement scales in meta-analysis, with application to measures of body mass index in children.

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 standardized for age and sex. We develop three methods for mapping between aggregate BMI data using known or estimated relationships between measurements on different scales at the individual level. 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 simulation studies and 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.