The initial footprint of an earthquake can be extended considerably by triggering of clustered aftershocks. Such earthquake-earthquake interactions have been studied extensively for data-rich, ...stationary natural seismicity. Induced seismicity, however, is intrinsically inhomogeneous in time and space and may have a limited catalog of events; this may hamper the distinction between human-induced background events and triggered aftershocks. Here we introduce a novel Gamma Accelerated-Failure-Time model for efficiently analyzing interevent-time distributions in such cases. It addresses the spatiotemporal variation and quantifies, per event, the probability of each event to have been triggered. Distentangling the obscuring aftershocks from the background events is a crucial step to better understand the causal relationship between operational parameters and non-stationary induced seismicity. Applied to the Groningen gas field in the North of the Netherlands, our model elucidates geological and operational drivers of seismicity and has been used to test for aftershock triggering. We find that the hazard rate in Groningen is indeed enhanced after each event and conclude that aftershock triggering cannot be ignored. In particular we find that the non-stationary interevent-time distribution is well described by our Gamma model. This model suggests that 27.0(± 8.5)% of the recorded events in the Groningen field can be attributed to triggering.
Background
To determine the utility of admission laboratory markers in the assessment and prognostication of coronavirus disease‐2019 (COVID‐19), a systematic review and meta‐analysis were conducted ...on the association between admission laboratory values in hospitalized COVID‐19 patients and subsequent disease severity and mortality.
Material and Methods
Searches were conducted in MEDLINE, Pubmed, Embase, and the WHO Global Research Database from December 1,2019 to May 1, 2020 for relevant articles. A random effects meta‐analysis was used to calculate the weighted mean difference (WMD) and 95% confidence interval (95% CI) for each of 27 laboratory markers. The impact of age and sex on WMDs was estimated using meta‐regression techniques for 11 markers.
Results
In total, 64 studies met the inclusion criteria. The most marked WMDs were for neutrophils (ANC) at 3.82 × 109/L (2.76, 4.87), lymphocytes (ALC) at −0.34 × 109/L (−0.45, −0.23), interleukin‐6 (IL‐6) at 32.59 pg/mL (23.99, 41.19), ferritin at 814.14 ng/mL (551.48, 1076.81), C‐reactive protein (CRP) at 66.11 mg/L (52.16, 80.06), D‐dimer at 5.74 mg/L (3.91, 7.58), LDH at 232.41 U/L (178.31, 286.52), and high sensitivity troponin I at 90.47 pg/mL (47.79, 133.14) when comparing fatal to nonfatal cases. Similar trends were observed comparing severe to non‐severe groups. There were no statistically significant associations between age or sex and WMD for any of the markers included in the meta‐regression.
Conclusion
The results highlight that hyper inflammation, blunted adaptive immune response, and intravascular coagulation play key roles in the pathogenesis of COVID‐19. Markers of these processes are good candidates to identify patients for early intervention and, importantly, are likely reliable regardless of age or sex in adult patients.
Abstract
Background
Due to identifiability problems, statistical inference about treatment-by-period interactions has not been discussed for stepped wedge designs in the literature thus far. ...Unidirectional switch designs (USDs) generalize the stepped wedge designs and allow for estimation and testing of treatment-by-period interaction in its many flexible design forms.
Methods
Under different forms of the USDs, we simulated binary data at both aggregated and individual levels and studied the performances of the generalized linear mixed model (GLMM) and the marginal model with generalized estimation equations (GEE) for estimating and testing treatment-by-period interactions.
Results
The parallel group design had the highest power for detecting the treatment-by-period interactions. While there was no substantial difference between aggregated-level and individual-level analysis, the GLMM had better point estimates than the marginal model with GEE. Furthermore, the optimal USD for estimating the average treatment effect was not efficient for treatment-by-period interaction and the marginal model with GEE required a substantial number of clusters to yield unbiased estimates of the interaction parameters when the correlation structure is autoregressive of order 1 (AR1). On the other hand, marginal model with GEE had better coverages than GLMM under the AR1 correlation structure.
Conclusion
From the designs and methods evaluated, in general, parallel group design with a GLMM is, preferred for estimation and testing of treatment-by-period interaction in a clustered randomized controlled trial for a binary outcome.
In clinical trials it is not uncommon to face a multiple testing problem which can have an impact on both type I and type II error rates, leading to inappropriate interpretation of trial results. ...Multiplicity issues may need to be considered at the design, analysis and interpretation stages of a trial. The proportion of trial reports not adequately correcting for multiple testing remains substantial. The purpose of this article is to provide an introduction to multiple testing issues in clinical trials, and to reduce confusion around the need for multiplicity adjustments. We use a tutorial, question-and-answer approach to address the key issues of why, when and how to consider multiplicity adjustments in trials. We summarize the relevant circumstances under which multiplicity adjustments ought to be considered, as well as options for carrying out multiplicity adjustments in terms of trial design factors including Population, Intervention/Comparison, Outcome, Time frame and Analysis (PICOTA). Results are presented in an easy-to-use table and flow diagrams. Confusion about multiplicity issues can be reduced or avoided by considering the potential impact of multiplicity on type I and II errors and, if necessary pre-specifying statistical approaches to either avoid or adjust for multiplicity in the trial protocol or analysis plan.
Highlights • Social relationships and risk of dementia: a systematic review and meta-analysis of longitudinal cohort studies. • We conducted a systematic literature review in MEDLINE, Embase and ...PsycINFO. • We included 19 longitudinal cohort studies in the systematic review & meta-analysis. • We examined the association between social relationships and incident dementia. • A lack of social interaction is associated with incident dementia. • The results are comparable with other well-established risk factors for dementia.
Abstract
Introduction
We determined if the heart rate (HR) monitoring performance of a wireless and nonadhesive belt is non‐inferior compared to standard electrocardiography (ECG). Secondary ...objective was to explore the belt's respiratory rate (RR) monitoring performance compared to chest impedance (CI).
Method
In this multicenter non‐inferiority trial, preterm and term infants were simultaneously monitored with the belt and conventional ECG/CI for 24 h. HR monitoring performance was estimated with the HR difference and ability to detect cardiac events compared to the ECG, and the incidence of HR‐data loss per second. These estimations were statistically compared to prespecified margins to confirm equivalence/non‐inferiority. Exploratory RR analyses estimated the RR trend difference and ability to detect apnea/tachypnea compared to CI, and the incidence of RR‐data loss per second.
Results
Thirty‐nine infants were included. HR monitoring with the belt was non‐inferior to the ECG with a mean HR difference of 0.03 beats per minute (bpm) (standard error SE = 0.02) (95% limits of agreement LoA: −5 to 5 bpm) (
p
< 0.001). Second, sensitivity and positive predictive value (PPV) for cardiac event detection were 94.0% (SE = 0.5%) and 92.6% (SE = 0.6%), respectively (
p
≤ 0.001). Third, the incidence of HR‐data loss was 2.1% (SE = 0.4%) per second (
p
< 0.05). The exploratory analyses of RR showed moderate trend agreement with a mean RR‐difference of 3.7 breaths/min (SE = 0.8) (LoA: −12 to 19 breaths/min), but low sensitivities and PPV's for apnea/tachypnea detection. The incidence of RR‐data loss was 2.2% (SE = 0.4%) per second.
Conclusion
The nonadhesive, wireless belt showed non‐inferior HR monitoring and a moderate agreement in RR trend compared to ECG/CI. Future research on apnea/tachypnea detection is required.
The DerSimonian–Laird (DL) weighted average method for aggregated data meta‐analysis has been widely used for the estimation of overall effect sizes. It is criticized for its underestimation of the ...standard error of the overall effect size in the presence of heterogeneous effect sizes. Due to this negative property, many alternative estimation approaches have been proposed in the literature. One of the earliest alternative approaches was developed by Hardy and Thompson (HT), who implemented a profile likelihood instead of the moment‐based approach of DL. Others have further extended this likelihood approach and proposed higher‐order likelihood inferences (e.g., Bartlett‐type corrections). In addition, corrections factors for the estimated DL standard error, like the Hartung–Knapp–Sidik–Jonkman (HKSJ) adjustment, and the restricted maximum likelihood (REML) estimation have been suggested too. Although these improvements address the uncertainty in estimating the between‐study variance better than the DL method, they all assume that the true within‐study standard errors are known and equal to the observed standard errors of the effect sizes. Here, we will treat the observed standard errors as estimators for the within‐study variability and we propose a bivariate likelihood approach that jointly estimates the overall effect size, the between‐study variance, and the potentially heteroskedastic within‐study variances. We study the performance of the proposed method by means of simulation, and compare it to DL (with and without HKSJ), HT, their higher‐order likelihood methods, and REML. Our proposed approach seems to have better or similar coverages compared to the other approaches and it appears to be less biased in the case of heteroskedastic within‐study variances when this heteroskedasticty is correlated with the effect size.
Summary Background The application of test-negative design case-control studies to assess the effectiveness of influenza vaccine has increased substantially in the past few years. The validity of ...these studies is predicated on the assumption that confounding bias by risk factors is limited by design. We aimed to assess the effectiveness of influenza vaccine in a high-risk group of elderly people. Methods We searched the Cochrane library, Medline, and Embase up to July 13, 2014, for test-negative design case-control studies that assessed the effectiveness of seasonal influenza vaccine against laboratory confirmed influenza in community-dwelling people aged 60 years or older. We used generalised linear mixed models, adapted for test-negative design case-control studies, to estimate vaccine effectiveness according to vaccine match and epidemic conditions. Findings 35 test-negative design case-control studies with 53 datasets met inclusion criteria. Seasonal influenza vaccine was not significantly effective during local virus activity, irrespective of vaccine match or mismatch to the circulating viruses. Vaccination was significantly effective against laboratory confirmed influenza during sporadic activity (odds ratio OR 0·69, 95% CI 0·48–0·99) only when the vaccine matched. Additionally, vaccination was significantly effective during regional (match: OR 0·42, 95% CI 0·30–0·60; mismatch: OR 0·57, 95% CI 0·41–0·79) and widespread (match: 0·54, 0·46–0·62; mismatch: OR 0·72, 95% CI 0·60–0·85) outbreaks. Interpretation Our findings show that in elderly people, irrespective of vaccine match, seasonal influenza vaccination is effective against laboratory confirmed influenza during epidemic seasons. Efforts should be renewed worldwide to further increase uptake of the influenza vaccine in the elderly population. Funding None.
During an infection or inflammation, several drug-metabolizing enzymes in the liver are down-regulated, including cytochrome P450 iso-enzymes. Since voriconazole is extensively metabolized by ...cytochrome P450 iso-enzymes, the metabolism of voriconazole can be influenced during inflammation via reduced clearance of the drug, resulting in higher voriconazole trough concentrations.
To investigate prospectively the influence of inflammation on voriconazole metabolism and voriconazole trough concentrations.
A prospective observational study was performed at the University Medical Center Groningen. Patients were eligible for inclusion if they were ≥18 years old and treated with voriconazole. Voriconazole and voriconazole-N-oxide concentrations were determined in discarded blood samples. To determine the degree of inflammation, C-reactive protein (CRP) concentrations were used. Subsequently, a longitudinal data analysis was performed to assess the effect of inflammation on the metabolic ratio and voriconazole trough concentration.
Thirty-four patients were included. In total 489 voriconazole trough concentrations were included in the longitudinal data analysis. This analysis showed that inflammation, reflected by CRP concentrations, significantly influenced the metabolic ratio, voriconazole trough concentration and voriconazole-N-oxide concentration (all P < 0.001), when corrected for other factors that could influence voriconazole metabolism. The metabolic ratio was decreased by 0.99229
and the voriconazole-N-oxide concentration by 0.99775
, while the voriconazole trough concentration was increased by 1.005321
, where N is the difference in CRP units (in mg/L).
This study shows that voriconazole metabolism is decreased during inflammation, resulting in higher voriconazole trough concentrations. Therefore, frequent monitoring of voriconazole serum concentrations is recommended during and following severe inflammation.
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