On repeated diagnostic testing in screening for a medical condition: How often should the diagnostic test be repeated?

In screening large populations a diagnostic test is frequently used repeatedly. An example is screening for bowel cancer using the fecal occult blood test (FOBT) on several occasions such as at 3 or 6 days. The question that is addressed here is how often should we repeat a diagnostic test when screening for a specific medical condition. Sensitivity is often used as a performance measure of a diagnostic test and is considered here for the individual application of the diagnostic test as well as for the overall screening procedure. The latter can involve an increasingly large number of repeated applications, but how many are sufficient? We demonstrate the issues involved in answering this question using real data on bowel cancer at St Vincents Hospital in Sydney. As data are only available for those testing positive at least once, an appropriate modeling technique is developed on the basis of the zero-truncated binomial distribution which allows for population heterogeneity. The latter is modeled using discrete nonparametric maximum likelihood. If we wish to achieve an overall sensitivity of 90%, the FOBT should be repeated for 2 weeks instead of the 1 week that was used at the time of the survey. A simulation study also shows consistency in the sense that bias and standard deviation for the estimated sensitivity decrease with an increasing number of repeated occasions as well as with increasing sample size.

Nonparametric estimation of the random effects distribution for the risk or rate ratio in rare events meta-analysis with the arm-based and contrast-based approaches.

Rare events are events which occur with low frequencies. These often arise in clinical trials or cohort studies where the data are arranged in binary contingency tables. In this article, we investigate the estimation of effect heterogeneity for the risk-ratio parameter in meta-analysis of rare events studies through two likelihood-based nonparametric mixture approaches: an arm-based and a contrast-based model. Maximum likelihood estimation is achieved using the EM algorithm. Special attention is given to the choice of initial values. Inspired by the classification likelihood, a strategy is implemented which repeatably uses random allocation of the studies to the mixture components as choice of initial values. The likelihoods under the contrast-based and arm-based approaches are compared and differences are highlighted. We use simulations to assess the performance of these two methods. Under the design of sampling studies with nested treatment groups, the results show that the nonparametric mixture model based on the contrast-based approach is more appropriate in terms of model selection criteria such as AIC and BIC. Under the arm-based design the results from the arm-based model performs well although in some cases it is also outperformed by the contrast-based model. Comparisons of the estimators are provided in terms of bias and mean squared error. Also included in the comparison is the mixed Poisson regression model as well as the classical DerSimonian-Laird model (using the Mantel-Haenszel estimator for the common effect). Using simulation, estimating effect heterogeneity in the case of the contrast-based method appears to behave better than the compared methods although differences become negligible for large within-study sample sizes. We illustrate the methodologies using several meta-analytic data sets in medicine.

Retrospective evaluation of factors affecting successful fit testing of respiratory protective equipment during the early phase of COVID-19.

OBJECTIVES: Respiratory protective equipment is critical to protect healthcare workers from COVID-19 infection, which includes filtering facepiece respirators (FFP3). There are reports of fitting issues within healthcare workers, although the factors affecting fitting outcomes are largely unknown. This study aimed to evaluate factors affecting respirator fitting outcomes. DESIGN: This is a retrospective evaluation study. We conducted a secondary analysis of a national database of fit testing outcomes in England between July and August 2020. SETTINGS: The study involves National Health Service (NHS) hospitals in England. PARTICIPANTS: A total of 9592 observations regarding fit test outcomes from 5604 healthcare workers were included in the analysis. INTERVENTION: Fit testing of FFP3 on a cohort of healthcare workers in England, working in the NHS. PRIMARY AND SECONDARY OUTCOME MEASURES: Primary outcome measure was the fit testing result, that is, pass or fail with a specific respirator. Key demographics, including age, gender, ethnicity and face measurements of 5604 healthcare workers, were used to compare fitting outcomes. RESULTS: A total of 9592 observations from 5604 healthcare workers were included in the analysis. A mixed-effects logistic regression model was used to determine the factors which affected fit testing outcome. Results showed that males experienced a significantly (p<0.05) higher fit test success than females (OR 1.51; 95% CI 1.27 to 1.81). Those with non-white ethnicities demonstrated significantly lower odds of successful respirator fitting; black (OR 0.65; 95% CI 0.51 to 0.83), Asian (OR 0.62; 95% CI 0.52 to 0.74) and mixed (OR 0.60; 95% CI 0.45 to 0.79. CONCLUSION: During the early phase of COVID-19, females and non-white ethnicities were less likely to have a successful respirator fitting. Further research is needed to design new respirators which provide equal opportunity for comfortable, effective fitting of these devices.

Modeling COVID-19 contact-tracing using the ratio regression capture-recapture approach.

Contact-tracing is one of the most effective tools in infectious disease outbreak control. A capture-recapture approach based upon ratio regression is suggested to estimate the completeness of case detection. Ratio regression has been recently developed as flexible tool for count data modeling and has proved to be successful in the capture-recapture setting. The methodology is applied here to Covid-19 contact tracing data from Thailand. A simple weighted straight line approach is used which includes the Poisson and geometric distribution as special cases. For the case study data of contact tracing for Thailand, a completeness of 83% could be found with a 95% confidence interval of 74%-93%.

Estimating risk and rate ratio in rare events meta-analysis with the Mantel-Haenszel estimator and assessing heterogeneity.

Meta-analysis of binary outcome data faces often a situation where studies with a rare event are part of the set of studies to be considered. These studies have low occurrence of event counts to the extreme that no events occur in one or both groups to be compared. This raises issues how to estimate validly the summary risk or rate ratio across studies. A preferred choice is the Mantel-Haenszel estimator, which is still defined in the situation of zero studies unless all studies have zeros in one of the groups to be compared. For this situation, a modified Mantel-Haenszel estimator is suggested and shown to perform well by means of simulation work. Also, confidence interval estimation is discussed and evaluated in a simulation study. In a second part, heterogeneity of relative risk across studies is investigated with a new chi-square type statistic which is based on a conditional binomial distribution where the conditioning is on the event margin for each study. This is necessary as the conventional Q-statistic is undefined in the occurrence of zero studies. The null-distribution of the proposed Q-statistic is obtained by means of a parametric bootstrap as a chi-square approximation is not valid for rare events meta-analysis, as bootstrapping of the null-distribution shows. In addition, for the effect heterogeneity situation, confidence interval estimation is considered using a nonparametric bootstrap procedure. The proposed techniques are illustrated at hand of three meta-analytic data sets.

On the equivalence of one-inflated zero-truncated and zero-truncated one-inflated count data likelihoods.

One-inflation in zero-truncated count data has recently found considerable attention. There are currently two views in the literature. In the first approach, the untruncated model is considered as one-inflated whereas in the second approach the truncated model is viewed as one-inflated. Here, we show that both models have identical model spaces as well as identical maximum likelihoods. Consequences of population size estimation are illuminated, and the findings are illustrated at hand of two case studies.

Fewer COVID-19 Neurological Complications with Dexamethasone and Remdesivir.

OBJECTIVE: The objective of this study was to assess the impact of treatment with dexamethasone, remdesivir or both on neurological complications in acute coronavirus diease 2019 (COVID-19). METHODS: We used observational data from the International Severe Acute and emerging Respiratory Infection Consortium World Health Organization (WHO) Clinical Characterization Protocol, United Kingdom. Hospital inpatients aged ≥18 years with laboratory-confirmed severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) infection admitted between January 31, 2020, and June 29, 2021, were included. Treatment allocation was non-blinded and performed by reporting clinicians. A propensity scoring methodology was used to minimize confounding. Treatment with remdesivir, dexamethasone, or both was assessed against the standard of care. The primary outcome was a neurological complication occurring at the point of death, discharge, or resolution of the COVID-19 clinical episode. RESULTS: Out of 89,297 hospital inpatients, 64,088 had severe COVID-19 and 25,209 had non-hypoxic COVID-19. Neurological complications developed in 4.8% and 4.5%, respectively. In both groups, neurological complications were associated with increased mortality, intensive care unit (ICU) admission, worse self-care on discharge, and time to recovery. In patients with severe COVID-19, treatment with dexamethasone (n = 21,129), remdesivir (n = 1,428), and both combined (n = 10,846) were associated with a lower frequency of neurological complications: OR = 0.76 (95% confidence interval [CI] = 0.69-0.83), OR = 0.69 (95% CI = 0.51-0.90), and OR = 0.54 (95% CI = 0.47-0.61), respectively. In patients with non-hypoxic COVID-19, dexamethasone (n = 2,580) was associated with less neurological complications (OR = 0.78, 95% CI = 0.62-0.97), whereas the dexamethasone/remdesivir combination (n = 460) showed a similar trend (OR = 0.63, 95% CI = 0.31-1.15). INTERPRETATION: Treatment with dexamethasone, remdesivir, or both in patients hospitalized with COVID-19 was associated with a lower frequency of neurological complications in an additive manner, such that the greatest benefit was observed in patients who received both drugs together. ANN NEUROL 2023;93:88-102.

Estimating the undetected infections in the Monkeypox outbreak.

While the number of detected Monkeypox infections are widely available, an understanding of the extent of undetected cases is urgently needed for an effective tackling of its spread. The aim of this study is to estimate the true number of Monkeypox (detected and undetected) infections in most affected countries. The question being asked is: How many cases have actually occurred? We propose a lower bound estimator for the true number of Monkeypox cases. The estimator is data-driven and can be easily computed from the cumulative distributions of weekly cases. We focused on the ratio of the total estimated cases to the observed cases on July 31, 2022: The proportion of undetected cases was relevant in all countries, with countries whose estimated true number of infections could be more than three times the observed one. We provided a practical contribution to the understanding of the current Monkeypox wave and reliable estimates on how many undetected cases are going around in several countries, where the epidemic spreads differently.

A randomised controlled trial of a digital intervention (Renewed) to support symptom management, wellbeing and quality of life in cancer survivors.

BACKGROUND: Many cancer survivors following primary treatment have prolonged poor quality of life. AIM: To determine the effectiveness of a bespoke digital intervention to support cancer survivors. DESIGN: Pragmatic parallel open randomised trial. SETTING: UK general practices. METHODS: People having finished primary treatment (<= 10 years previously) for colo-rectal, breast or prostate cancers, with European-Organization-for-Research-and-Treatment-of-Cancer QLQ-C30 score <85, were randomised by online software to: 1)detailed 'generic' digital NHS support ('LiveWell';n=906), 2) a bespoke complex digital intervention ('Renewed';n=903) addressing symptom management, physical activity, diet, weight loss, distress, or 3) 'Renewed-with-support' (n=903): 'Renewed' with additional brief email and telephone support. RESULTS: Mixed linear regression provided estimates of the differences between each intervention group and generic advice: at 6 months (primary time point: n's respectively 806;749;705) all groups improved, with no significant between-group differences for EORTC QLQ-C30, but global health improved more in both intervention groups. By 12 months there were: small improvements in EORTC QLQ-C30 for Renewed-with-support (versus generic advice: 1.42, 95% CIs 0.33-2.51); both groups improved global health (12 months: renewed: 3.06, 1.39-4.74; renewed-with-support: 2.78, 1.08-4.48), dyspnoea, constipation, and enablement, and lower NHS costs (generic advice £265: in comparison respectively £141 (153-128) and £77 (90-65) lower); and for Renewed-with-support improvement in several other symptom subscales. No harms were identified. CONCLUSION: Cancer survivors quality of life improved with detailed generic online support. Robustly developed bespoke digital support provides limited additional short term benefit, but additional longer term improvement in global health enablement and symptom management, with substantially lower NHS costs.

Combined statistical decision limits based on two GH-2000 scores for the detection of growth hormone misuse.

The growth hormone-2000 biomarker method, based on the measurements of insulin-like growth factor-I and the amino-terminal pro-peptide of type III collagen, has been developed as a powerful technique for the detection of growth hormone misuse by athletes. Insulin-like growth factor-I and amino-terminal pro-peptide of type III collagen are combined in gender-specific formulas to create the growth hormone-2000 score, which is used to determine whether growth hormone has been administered. To comply with World Anti-Doping Agency regulations, each analyte must be measured by two methods. Insulin-like growth factor-I and amino-terminal pro-peptide of type III collagen can be measured by a number of approved methods, each leading to its own growth hormone-2000 score. Single decision limits for each growth hormone-2000 score have been introduced and developed by Bassett, Erotokritou-Mulligan, Holt, Böhning and their co-authors in a series of papers. These have been incorporated into the guidelines of the World Anti-Doping Agency. A joint decision limit was constructed based on the sample correlation between the two growth hormone-2000 scores generated from an available sample to increase the sensitivity of the biomarker method. This paper takes this idea further into a fully developed statistical approach. It constructs combined decision limits when two growth hormone-2000 scores from different assay combinations are used to decide whether an athlete has been misusing growth hormone. The combined decision limits are directly related to tolerance regions and constructed using a Bayesian approach. It is also shown to have highly satisfactory frequentist properties. The new approach meets the required false-positive rate with a pre-specified level of certainty.

Can digital rectal examination be used to detect cauda equina compression in people presenting with acute cauda equina syndrome? A systematic review and meta-analysis of diagnostic test accuracy studies.

BACKGROUND: Digital rectal examination (DRE) is a commonly used test to help identify people with cauda equina compression (CEC). OBJECTIVE: To determine the diagnostic accuracy of DRE in assessment of anal tone, squeeze, sensation and reflexes, as predictors of CEC. DESIGN: A systematic review to investigate the diagnostic accuracy of DRE to detect CEC compared with lumbar Magnetic Resonance Imaging (MRI). METHOD: Six electronic databases were searched from inception to 6 July 2020 for studies published in English. Two assessors independently performed screening, data extraction and risk of bias assessment (QUADAS-2). Meta-analysis was performed using STATA-16. RESULTS: Six studies were included (n = 741). The sensitivity of anal tone was low across all studies (range: 0.23 to 0.53) with moderate quality evidence against the use of DRE of anal tone. One study on anal sensation found no correlation with CEC using Kendall's tau test: p = 0.102 and another found sensation had low test accuracy. One study identified sensitivity: 0.29 and specificity: 0.96 for anal squeeze, while another identified sensitivity: 0.38 and specificity: 0.6 for anal reflexes. CONCLUSION: The diagnostic accuracy of DRE of anal tone to detect CEC is low and carries a high risk of false reassurance. It is therefore not recommended in any clinical setting. More research is needed to determine the diagnostic accuracy of DRE of anal squeeze, sensation and reflexes and if done the results should be interpreted with caution.

Estimation of Effect Heterogeneity in Rare Events Meta-Analysis.

The paper outlines several approaches for dealing with meta-analyses of count outcome data. These counts are the accumulation of occurred events, and these events might be rare, so a special feature of the meta-analysis is dealing with low counts including zero-count studies. Emphasis is put on approaches which are state of the art for count data modelling including mixed log-linear (Poisson) and mixed logistic (binomial) regression as well as nonparametric mixture models for count data of Poisson and binomial type. A simulation study investigates the performance and capability of discrete mixture models in estimating effect heterogeneity. The approaches are exemplified on a meta-analytic case study investigating the acceptance of bibliotherapy.

Bayesian spatio-temporal joint disease mapping of Covid-19 cases and deaths in local authorities of England.

The overwhelming spatio-temporal nature of the spread of the ongoing Covid-19 pandemic demands urgent attention of data analysts and model developers. Modelling results obtained from analytical tool development are essential to understand the ongoing pandemic dynamics with a view to helping the public and policy makers. The pandemic has generated data on a huge number of interesting statistics such as the number of new cases, hospitalisations and deaths in many spatio-temporal resolutions for the analysts to investigate. The multivariate nature of these data sets, along with the inherent spatio-temporal dependencies, poses new challenges for modellers. This article proposes a two-stage hierarchical Bayesian model as a joint bivariate model for the number of cases and deaths observed weekly for the different local authority administrative regions in England. An adaptive model is proposed for the weekly Covid-19 death rates as part of the joint bivariate model. The adaptive model is able to detect possible step changes in death rates in neighbouring areas. The joint model is also used to evaluate the effects of several socio-economic and environmental covariates on the rates of cases and deaths. Inclusion of these covariates points to the presence of a north-south divide in both the case and death rates. Nitrogen dioxide, the only air pollution measure used in the model, is seen to be significantly positively associated with the number cases, even in the presence of the spatio-temporal random effects taking care of spatio-temporal dependencies present in the data. The proposed models provide excellent fits to the observed data and are seen to perform well for predicting the location specific number of deaths a week in advance. The structure of the models is very general and the same framework can be used for modelling other areally aggregated temporal statistics of the pandemics, e.g. the rate of hospitalisation.

Novel Use of Capture-Recapture Methods to Estimate Completeness of Contact Tracing during an Ebola Outbreak, Democratic Republic of the Congo, 2018-2020.

Despite its critical role in containing outbreaks, the efficacy of contact tracing, measured as the sensitivity of case detection, remains an elusive metric. We estimated the sensitivity of contact tracing by applying unilist capture-recapture methods on data from the 2018-2020 outbreak of Ebola virus disease in the Democratic Republic of the Congo. To compute sensitivity, we applied different distributional assumptions to the zero-truncated count data to estimate the number of unobserved case-patients with any contacts and infected contacts. Geometric distributions were the best-fitting models. Our results indicate that contact tracing efforts identified almost all (n = 792, 99%) of case-patients with any contacts but only half (n = 207, 48%) of case-patients with infected contacts, suggesting that contact tracing efforts performed well at identifying contacts during the listing stage but performed poorly during the contact follow-up stage. We discuss extensions to our work and potential applications for the ongoing coronavirus pandemic.

Delayed antibiotic prescribing for respiratory tract infections: individual patient data meta-analysis.

OBJECTIVE: To assess the overall effect of delayed antibiotic prescribing on average symptom severity for patients with respiratory tract infections in the community, and to identify any factors modifying this effect. DESIGN: Systematic review and individual patient data meta-analysis. DATA SOURCES: Cochrane Central Register of Controlled Trials, Ovid Medline, Ovid Embase, EBSCO CINAHL Plus, and Web of Science. ELIGIBILITY CRITERIA FOR STUDY SELECTION: Randomised controlled trials and observational cohort studies in a community setting that allowed comparison between delayed versus no antibiotic prescribing, and delayed versus immediate antibiotic prescribing. MAIN OUTCOME MEASURES: The primary outcome was the average symptom severity two to four days after the initial consultation measured on a seven item scale (ranging from normal to as bad as could be). Secondary outcomes were duration of illness after the initial consultation, complications resulting in admission to hospital or death, reconsultation with the same or worsening illness, and patient satisfaction rated on a Likert scale. RESULTS: Data were obtained from nine randomised controlled trials and four observational studies, totalling 55 682 patients. No difference was found in follow-up symptom severity (seven point scale) for delayed versus immediate antibiotics (adjusted mean difference -0.003, 95% confidence interval -0.12 to 0.11) or delayed versus no antibiotics (0.02, -0.11 to 0.15). Symptom duration was slightly longer in those given delayed versus immediate antibiotics (11.4 v 10.9 days), but was similar for delayed versus no antibiotics. Complications resulting in hospital admission or death were lower with delayed versus no antibiotics (odds ratio 0.62, 95% confidence interval 0.30 to 1.27) and delayed versus immediate antibiotics (0.78, 0.53 to 1.13). A significant reduction in reconsultation rates (odds ratio 0.72, 95% confidence interval 0.60 to 0.87) and an increase in patient satisfaction (adjusted mean difference 0.09, 0.06 to 0.11) were observed in delayed versus no antibiotics. The effect of delayed versus immediate antibiotics and delayed versus no antibiotics was not modified by previous duration of illness, fever, comorbidity, or severity of symptoms. Children younger than 5 years had a slightly higher follow-up symptom severity with delayed antibiotics than with immediate antibiotics (adjusted mean difference 0.10, 95% confidence interval 0.03 to 0.18), but no increased severity was found in the older age group. CONCLUSIONS: Delayed antibiotic prescribing is a safe and effective strategy for most patients, including those in higher risk subgroups. Delayed prescribing was associated with similar symptom duration as no antibiotic prescribing and is unlikely to lead to poorer symptom control than immediate antibiotic prescribing. Delayed prescribing could reduce reconsultation rates and is unlikely to be associated with an increase in symptoms or illness duration, except in young children. STUDY REGISTRATION: PROSPERO CRD42018079400.

Upper bound estimators of the population size based on ordinal models for capture-recapture experiments.

Capture-recapture studies have attracted a lot of attention over the past few decades, especially in applied disciplines where a direct estimate for the size of a population of interest is not available. Epidemiology, ecology, public health, and biodiversity are just a few examples. The estimation of the number of unseen units has been a challenge for theoretical statisticians, and considerable progress has been made in providing lower bound estimators for the population size. In fact, it is well known that consistent estimators for this cannot be provided in the very general case. Considering a case where capture-recapture studies are summarized by a frequency of frequencies distribution, we derive a simple upper bound of the population size based on the cumulative distribution function. We introduce two estimators of this bound, without any specific parametric assumption on the distribution of the observed frequency counts. The behavior of the proposed estimators is investigated using several benchmark datasets and a large-scale simulation experiment based on the scheme discussed by Pledger.

The identity of two meta-analytic likelihoods and the ignorability of double-zero studies.

In meta-analysis, the conventional two-stage approach computes an effect estimate for each study in the first stage and proceeds with the analysis of effect estimates in the second stage. For counts of events as outcome, the risk ratio is often the effect measure of choice. However, if the meta-analysis includes many studies with no events the conventional method breaks down. As an alternative one-stage approach, a Poisson regression model and a conditional binomial model can be considered where no event studies do not cause problems. The conditional binomial model excludes double-zero studies, whereas this is seemingly not the case for the Poisson regression approach. However, we show here that both models lead to the same likelihood inference and double-zero studies (in contrast to single-zero studies) do not contribute in either case to the likelihood.

The effects of recombinant human insulin-like growth factor-1/insulin-like growth factor binding protein-3 administration on lipid and carbohydrate metabolism in recreational athletes.

OBJECTIVE: Previous studies suggested that recombinant human IGF-1 (rhIGF-1) administration affects carbohydrate and lipid metabolism in healthy people and in people with diabetes. This study aimed to determine the effects of rhIGF-1/rhIGF binding protein-3 (rhIGFBP-3) administration on glucose homeostasis and lipid metabolism in healthy recreational athletes. DESIGN AND SETTING: Randomized, double-blind, placebo-controlled rhIGF-1/rhIGFBP-3 administration study at Southampton General Hospital, UK. PARTICIPANTS: 56 recreational athletes (30 men, 26 women). METHODS: Participants were randomly assigned to receive placebo, low-dose rhIGF-1/rhIGFBP-3 (30 mg/day) or high-dose rhIGF-1/rhIGFBP-3 (60 mg/day) for 28 days. The following variables were measured before and immediately after the treatment period: fasting lipids, glucose, insulin, C-peptide and glycated haemoglobin. The homeostatic model assessment (HOMA-IR) was used to estimate insulin sensitivity and indirect calorimetry to assess substrate oxidation rates. The general linear model approach was used to compare treatment group changes with the placebo group. RESULTS: Compared with the placebo group, there was a significant reduction in fasting triglycerides in participants treated with high-dose rhIGF-1/rhIGFBP-3 (p = .030), but not in the low-dose group (p = .390). In women, but not in men, there were significant increases in total cholesterol (p = .003), HDL cholesterol (p = .001) and LDL cholesterol (p = .008). These lipid changes were associated with reduced fasting insulin (p = .010), C-peptide (p = .001) and HOMA-IR (p = .018) in women and reduced C-peptide (p = .046) in men. CONCLUSIONS: rhIGF-1/rhIGFBP-3 administration for 28 days reduced insulin concentration, improved insulin sensitivity and had significant effects on lipid profile including decreased fasting triglycerides.

The combined effect of alcohol and body mass index on risk of chronic liver disease: A systematic review and meta-analysis of cohort studies.

BACKGROUND & AIMS: Increasingly populations are both overweight/obese and consume alcohol. The risk of liver disease from the combination of these factors is unclear. We performed a systematic review and meta-analysis to address this important gap in evidence. Protocol registered with PROSPERO(CRD42016046508). METHODS: We performed electronic searches of Ovid Medline, Embase Classic + Embase, until 17th June 2020 for cohort studies of adults without pre-existing liver disease. Primary outcome was morbidity/mortality from chronic liver disease. Exposures were alcohol consumption categorised as within or above UK recommended limits (14 units/112 g per week) and BMI categorised as normal, overweight or obese. Non-drinkers were excluded. A Poisson regression log-linear model was used to test for statistical interaction between alcohol and BMI and to conduct a one-stage meta-analysis. RESULTS: Searches identified 3129 studies-16 were eligible. Of these, nine cohorts (1,121,514 participants) had data available and were included in the analysis. The Poisson model showed no significant statistical interaction between alcohol consumption and BMI on the risk of chronic liver disease. Compared to normal weight participants drinking alcohol within UK recommended limits, relative risk of chronic liver disease in overweight participants drinking above limits was 3.32 (95% CI 2.88 to 3.83) and relative risk in obese participants drinking above limits was 5.39 (95% CI 4.62 to 6.29). CONCLUSIONS: This meta-analysis demonstrated a significantly increased risk of chronic liver disease in participants who were both overweight/obese and consumed alcohol above UK recommended limits. This evidence should inform advice given to patients and risk stratification by healthcare professionals.

Comparison of normal distribution-based and nonparametric decision limits on the GH-2000 score for detecting growth hormone misuse (doping) in sport.

This paper is motivated by the GH-2000 biomarker test, though the discussion is applicable to other diagnostic tests. The GH-2000 biomarker test has been developed as a powerful technique to detect growth hormone misuse by athletes, based on the GH-2000 score. Decision limits on the GH-2000 score have been developed and incorporated into the guidelines of the World Anti-Doping Agency (WADA). These decision limits are constructed, however, under the assumption that the GH-2000 score follows a normal distribution. As it is difficult to affirm the normality of a distribution based on a finite sample, nonparametric decision limits, readily available in the statistical literature, are viable alternatives. In this paper, we compare the normal distribution-based and nonparametric decision limits. We show that the decision limit based on the normal distribution may deviate significantly from the nominal confidence level 1-α or nominal FPR γ when the distribution of the GH-2000 score departs only slightly from the normal distribution. While a nonparametric decision limit does not assume any specific distribution of the GH-2000 score and always guarantees the nominal confidence level and FPR, it requires a much larger sample size than the normal distribution-based decision limit. Due to the stringent FPR of the GH-2000 biomarker test used by WADA, the sample sizes currently available are much too small, and it will take many years of testing to have the minimum sample size required, in order to use the nonparametric decision limits. Large sample theory about the normal distribution-based and nonparametric decision limits is also developed in this paper to help understanding their behaviours when the sample size is large.