Search for Large Rapidity Gap Events in e^+ e^- Annihilation

We investigate the cross-section for the production of a low-mass colour-singlet cluster in $e^+e^-$ annihilation with a large rapidity gap between the colour-singlet cluster and the other jets. It is argued that such events are the cross-channel analogue of large-rapidity-gap events in deep-inelastic scattering, and therefore could in principle be used to investigate the analytic continuation of the BFKL pomeron to the positive-$t$ kinematic regime, where one would expect the trajectory to pass through glueball states. The cross section can be calculated in perturbative QCD, so that the infrared scale arising from non-perturbative effects, which prevents an exponential fall-off with rapidity gap in the case of deep-inelastic scattering, is absent in $e^+ e^-$ annihilation. Correspondingly, the cross section for such events decreases rapidly with increasing rapidity gap.Comment: LATEX file - 21 pages + 15 figure

Initiator Types and the Causal Question of the Prevalent New-User Design: A Simulation Study

New-user designs restricting to treatment initiators have become the preferred design for studying drug comparative safety and effectiveness using nonexperimental data. This design reduces confounding by indication and healthy-adherer bias at the cost of smaller study sizes and reduced external validity, particularly when assessing a newly approved treatment compared with standard treatment. The prevalent new-user design includes adopters of a new treatment who switched from or previously used standard treatment (i.e., the comparator), expanding study sample size and potentially broadening the study population for inference. Previous work has suggested the use of time-conditional propensity-score matching to mitigate prevalent user bias. In this study, we describe 3 "types"of initiators of a treatment: new users, direct switchers, and delayed switchers. Using these initiator types, we articulate the causal questions answered by the prevalent new-user design and compare them with those answered by the new-user design. We then show, using simulation, how conditioning on time since initiating the comparator (rather than full treatment history) can still result in a biased estimate of the treatment effect. When implemented properly, the prevalent new-user design estimates new and important causal effects distinct from the new-user design

A WARNING ABOUT USING PREDICTED VALUES TO ESTIMATE DESCRIPTIVE MEASURES

In a recent article in the Journal, Ogburn et al. highlighted the issues with using predicted values when estimating associations or effects. While the authors cautioned against using predicted values to estimate associations or effects, they noted that predictions can be useful for descriptive purposes. In this work, we highlight the issues with using individual-level predicted values to estimate population-level descriptive parameter

Fluoroquinolone antibiotics and tendon injury in adolescents

OBJECTIVES: To estimate the association between fluoroquinolone use and tendon injury in adolescents. METHODS: We conducted an active-comparator, new-user cohort study using population-based claims data from 2000 to 2018. We included adolescents (aged 12-18 years) with an outpatient prescription fill for an oral fluoroquinolone or comparator broad-spectrum antibiotic. The primary outcome was Achilles, quadricep, patellar, or tibial tendon rupture identified by diagnosis and procedure codes. Tendinitis was a secondary outcome. We used weighting to adjust for measured confounding and a negative control outcome to assess residual confounding. RESULTS: The cohort included 4.4 million adolescents with 7.6 million fills for fluoroquinolone (275 767 fills) or comparator (7 365 684) antibiotics. In the 90 days after the index antibiotic prescription, there were 842 tendon ruptures and 16 750 tendinitis diagnoses (crude rates 0.47 and 9.34 per 1000 person-years, respectively). The weighted 90-day tendon rupture risks were 13.6 per 100 000 fluoroquinolone-treated adolescents and 11.6 per 100 000 comparator-treated adolescents (fluoroquinolone-associated excess risk: 1.9 per 100 000 adolescents; 95% confidence interval 22.6 to 6.4); the corresponding number needed to treat to harm was 52 632. For tendinitis, the weighted 90-day risks were 200.8 per 100 000 fluoroquinolone-treated adolescents and 178.1 per 100 000 comparator-treated adolescents (excess risk: 22.7 per 100 000; 95% confidence interval 4.1 to 41.3); the number needed to treat to harm was 4405. CONCLUSIONS: The excess risk of tendon rupture associated with fluoroquinolone treatment was extremely small, and these events were rare. The excess risk of tendinitis associated with fluoroquinolone treatment was also small. Other more common potential adverse drug effects may be more important to consider for treatment decision-making, particularly in adolescents without other risk factors for tendon injury

Nondifferential Treatment Misclassification Biases Toward the Null? Not a Safe Bet for Active Comparator Studies

Active comparator studies are increasingly common, particularly in pharmacoepidemiology. In such studies, the parameter of interest is a contrast (difference or ratio) in the outcome risks between the treatment of interest and the selected active comparator. While it may appear treatment is dichotomous, treatment is actually polytomous as there are at least 3 levels: no treatment, the treatment of interest, and the active comparator. Because misclassification may occur between any of these groups, independent nondifferential treatment misclassification may not be toward the null (as expected with a dichotomous treatment). In this work, we describe bias from independent nondifferential treatment misclassification in active comparator studies with a focus on misclassification that occurs between each active treatment and no treatment. We derive equations for bias in the estimated outcome risks, risk difference, and risk ratio, and we provide bias correction equations that produce unbiased estimates, in expectation. Using data obtained from US insurance claims data, we present a hypothetical comparative safety study of antibiotic treatment to illustrate factors that influence bias and provide an example probabilistic bias analysis using our derived bias correction equations

Meta-Analysis and Sparse-Data Bias

Meta-analyses are undertaken to combine information from a set of studies, often in settings where some of the individual study-specific estimates are based on relatively small study samples. Finite sample bias may occur when maximum likelihood estimates of associations are obtained by fitting logistic regression models to sparse data sets. Here we show that combining information from small studies by undertaking a meta-analytical summary of logistic regression estimates can propagate such sparse-data bias. In simulations, we illustrate 2 challenges encountered in meta-analyses of logistic regression results in settings of sparse data: 1) bias in the summary meta-analytical result and 2) confidence interval coverage that can worsen rather than improve, in terms of being less than nominal, as the number of studies in the meta-analysis increases

Missing Outcome Data in Epidemiologic Studies

Missing data are pandemic and a central problem for epidemiology. Missing data reduce precision and can cause notable bias. There remain too few simple published examples detailing types of missing data and illustrating their possible impact on results. Here we take an example randomized trial that was not subject to missing data and induce missing data to illustrate 4 scenarios in which outcomes are 1) missing completely at random, 2) missing at random with positivity, 3) missing at random without positivity, and 4) missing not at random. We demonstrate that accounting for missing data is generally a better strategy than ignoring missing data, which unfortunately remains a standard approach in epidemiology

Validation of a 5-Year Mortality Prediction Model among U.S. Medicare Beneficiaries

BACKGROUND/OBJECTIVES: A claims-based model predicting 5-year mortality (Lund-Lewis) was developed in a 2008 cohort of North Carolina (NC) Medicare beneficiaries and included indicators of comorbid conditions, frailty, disability, and functional impairment. The objective of this study was to validate the Lund-Lewis model externally within a nationwide sample of Medicare beneficiaries. DESIGN: Retrospective validation study. SETTING: U.S. Medicare population. PARTICIPANTS: From a random sample of Medicare beneficiaries, we created four annual cohorts from 2008 to 2011 of individuals aged 66 and older with an office visit in that year. The annual cohorts ranged from 1.13 to 1.18 million beneficiaries. MEASUREMENTS: The outcome was 5-year all-cause mortality. We assessed clinical indicators in the 12 months before the qualifying office visit and estimated predicted 5-year mortality for each beneficiary in the nationwide sample by applying estimates derived in the original NC cohort. Model performance was assessed by quantifying discrimination, calibration, and reclassification metrics compared with a model fit on a comorbidity score. RESULTS: Across the annual cohorts, 5-year mortality ranged from 24.4% to 25.5%. The model had strong discrimination (C-statistics ranged across cohorts from.823 to.826). Reclassification measures showed improvement over a comorbidity score model for beneficiaries who died but reduced performance among beneficiaries who survived. The calibration slope ranged from.83 to.86; the model generally predicted a higher risk than observed. CONCLUSION: The Lund-Lewis model showed strong and consistent discrimination in a national U.S. Medicare sample, although calibration indicated slight overfitting. Future work should investigate methods for improving model calibration and evaluating performance within specific disease settings

A synthesis of evidence for the effects of interventions to conserve peatland vegetation: Overview and critical discussion

Peatlandsare valuable but threatenedecosystems. Intervention to tackle direct threats is often necessary, but should be informed by scientific evidence to ensure it is effective and efficient. Herewe discuss a recent synthesis of evidence for the effects of interventions to conserve peatland vegetation -a fundamental component of healthy, functioning peatland ecosystems. The synthesis is unique in its broad scope (global evidence for a comprehensive list of 125 interventions) and practitioner-focused outputs (short narrative summaries in plain English, integrated into a searchable online database). Systematicliteraturesearches, supplemented by recommendationsfrom an international advisory board, identified162 publications containing 296 distinct tests of 66 of the interventions. Most of the articles studiedopen bogs or fens in Europe or North America. Only 36 interventions weresupported by sufficient evidence to assess their overall effectiveness. Mostof these interventions(85%) hadpositiveeffects, overall,on peatland vegetation-although this figure is likelyto have beeninflated by publication bias. We discuss how to use the synthesis, critically,to informconservation decisions.Reflecting on the content of the synthesiswe make suggestions for the future of peatland conservation,from monitoring overappropriate timeframes to routinely publishing resultsto build up the evidence base.MAVA, Arcadi