Category: Statistics

Psychol Methods. 2021 Oct 14. doi: 10.1037/met0000429. Online ahead of print.

ABSTRACT

The replicability crisis has drawn attention to numerous weaknesses in psychology and social science research practice. In this work we focus on three issues that cannot be addressed with replication alone, and which deserve more attention: Functional misspecification, structural misspecification, and unreliable interpretation of results. We demonstrate a number of possible consequences via simulation, and provide recommendations for researchers to improve their research practice. Psychologists and social scientists should engage with these areas of analytical and statistical improvement, as they have the potential to seriously hinder scientific progress. Every research question and hypothesis may present its own unique challenges, and it is only through an awareness and understanding of varied statistical methods for predictive and causal modeling, that researchers will have the tools with which to appropriately address them. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

PMID:34647760 | DOI:10.1037/met0000429

J Neurosurg Sci. 2021 Oct 14. doi: 10.23736/S0390-5616.21.05477-1. Online ahead of print.

ABSTRACT

INTRODUCTION: The subarachnoid hemorrhage due to a ruptured brain aneurysm is a neurological emergency with high mobility and mortality. Hypertensive states are related to a bad prognosis and a higher risk of a ruptured aneurysm. However, the relationship between the blood pressure variability with the aneurysmal subarachnoid hemorrhage and its prognosis is quite unknown.

EVIDENCE ACQUISITION: A systematic review was performed across the databases. The following descriptors and related were used for the search: “blood pressure”, “arterial pressure”, variability, subarachnoid hemorrhage, hemorrhage, aneurysmal, aneurysmal subarachnoid hemorrhage. The following data were extracted: Glasgow Outcome Scale or Modified Rankin Scale, and blood pressure variabilities to categorize the prognosis.

EVIDENCE SYNTHESIS: 5 studies were selected. The blood pressure variability and the related outcome were assessed by mean systolic blood pressure and minimum systolic blood. The meta-analysis of mean systolic blood pressure (cut-off >95.3 mmHg) showed an odds ratio of 11.23 (CI 95%: 4,423 to 28,537) (p=<0.001), predicting the good outcome after the aneurysmal subarachnoid hemorrhage. The pooled analysis revealed AUC of the ROC predicting the good outcome was statistically significant (AUC:0.85, p<0.001). The pooled data analysis of minimum systolic blood pressure revealed an odds ratio of 6.43 (CI 95%:2.834-14.589, P <0,001) and AUC of the pooled ROC 0.931 (CI95%:0,851 to 1,000, P <0,001) to predict poor outcome. The funnel plot through Egger’s test for the analysis showed different grades of asymmetry.

CONCLUSIONS: The blood pressure variability (mean and minimum systolic blood pressure) is a good predictor and parameter in the aneurysmal subarachnoid hemorrhage prognosis and outcome prediction.

PMID:34647715 | DOI:10.23736/S0390-5616.21.05477-1

Psychol Methods. 2021 Oct 14. doi: 10.1037/met0000422. Online ahead of print.

ABSTRACT

Theories can be represented as statistical models for empirical testing. There is a vast literature on model selection and multimodel inference that focuses on how to assess which statistical model, and therefore which theory, best fits the available data. For example, given some data, one can compare models on various information criterion or other fit statistics. However, what these indices fail to capture is the full range of counterfactuals. That is, some models may fit the given data better not because they represent a more correct theory, but simply because these models have more fit propensity-a tendency to fit a wider range of data, even nonsensical data, better. Current approaches fall short in considering the principle of parsimony (Occam’s Razor), often equating it with the number of model parameters. Here we offer a toolkit for researchers to better study and understand parsimony through the fit propensity of structural equation models. We provide an R package (ockhamSEM) built on the popular lavaan package. To illustrate the importance of evaluating fit propensity, we use ockhamSEM to investigate the factor structure of the Rosenberg Self-Esteem Scale. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

PMID:34647757 | DOI:10.1037/met0000422

J Neurosurg Sci. 2021 Oct 14. doi: 10.23736/S0390-5616.21.05402-3. Online ahead of print.

ABSTRACT

INTRODUCTION: Post craniotomy pain management with opioids is challenging due to their side effects, which might mask neurological deterioration symptoms. Recently, intravenous (IV) acetaminophen has been tested in this population. This meta-analysis aimed to synthesize evidence from published randomized controlled trials (RCTs) about the efficacy of IV acetaminophen in reducing pain scores in postoperative craniotomy patients.

EVIDENCE ACQUISITION: A computer literature search of PubMed, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials (CENTRAL) was conducted. We selected RCTs comparing IV acetaminophen versus placebo for postoperative pain management in craniotomy patients. Data on the visual analog scale (VAS), opioid requirements, hospital stay, and patients’ satisfaction weexretr acted and pooled as standardized mean difference (SMD) with the corresponding 95% confidence intervals (CI) in the meta-analysis model.

EVIDENCE SYNTHESIS: Five RCTs, with a total of 493 patients, were pooled in the final analysis. Patients in the IV acetaminophen group had significantly lower VAS pain scores compared to the placebo group (SMD -0.28, 95% CI [-0.46 to -0.10]). However, in terms of opioid requirement, hospital stay, and patients’ satisfaction, there were no statistically significant differences between both groups (P>0.05).

CONCLUSIONS: This meta-analysis provides class one evidence that IV Acetaminophen can significantly reduce postoperative pain in craniotomy patients with an excellent safety profile; however, there are not benefits in terms of hospital stay, opioid requirement, or patients’ satisfaction.

PMID:34647710 | DOI:10.23736/S0390-5616.21.05402-3

Minerva Pediatr (Torino). 2021 Oct 14. doi: 10.23736/S2724-5276.21.05896-1. Online ahead of print.

ABSTRACT

BACKGROUND: Cerebral palsy (CP) is the most common cause of physical disability in childhood defined as a group of permanent disorders of movement. The aim of this study was to determine the effects of 12-week aquatic exercise program on gross motor function, swimming skills, and walking ability in children with cerebral palsy.

METHODS: Eighteen children (Mean ± SD age: 12.3 ± 3 years) with cerebral palsy classified at Levels I, II and III on the Gross Motor Function Classification System were allocated to one group, where the first 12 weeks were a control period while another 12 weeks were an experimental period. The participants underwent the same battery of tests focusing gross motor function, swimming skills, and walking ability on three occasions.

RESULTS: Control period was stable with no significant changes in any of measurements. After the 12-week experimental program, a statistically significant improvement was determined in gross motor function (p=0.005), swimming skills (p=0.000), walking endurance and walking (p=0.000). No significant differences (p>0.05) were observed for walking efficiency.

CONCLUSIONS: The 12-week aquatic exercise program (3/week, 60 minutes), combining Halliwick method, swimming and walking activities may improve the gross motor function, swimming skills, walking endurance and velocity in ambulatory children with cerebral palsy.

PMID:34647700 | DOI:10.23736/S2724-5276.21.05896-1

J Evid Based Med. 2021 Oct 14. doi: 10.1111/jebm.12451. Online ahead of print.

NO ABSTRACT

PMID:34647687 | DOI:10.1111/jebm.12451

Stat Med. 2021 Oct 14. doi: 10.1002/sim.9219. Online ahead of print.

ABSTRACT

Seasonal influenza infects between 10 and 50 million people in the United States every year. Accurate forecasts of influenza and influenza-like illness (ILI) have been named by the CDC as an important tool to fight the damaging effects of these epidemics. Multi-model ensembles make accurate forecasts of seasonal influenza, but current operational ensemble forecasts are static: they require an abundance of past ILI data and assign fixed weights to component models at the beginning of a season, but do not update weights as new data on component model performance is collected. We propose an adaptive ensemble that (i) does not initially need data to combine forecasts and (ii) finds optimal weights which are updated week-by-week throughout the influenza season. We take a regularized likelihood approach and investigate this regularizer’s ability to impact adaptive ensemble performance. After finding an optimal regularization value, we compare our adaptive ensemble to an equal-weighted and static ensemble. Applied to forecasts of short-term ILI incidence at the regional and national level, our adaptive model outperforms an equal-weighted ensemble and has similar performance to the static ensemble using only a fraction of the data available to the static ensemble. Needing no data at the beginning of an epidemic, an adaptive ensemble can quickly train and forecast an outbreak, providing a practical tool to public health officials looking for a forecast to conform to unique features of a specific season.

PMID:34647627 | DOI:10.1002/sim.9219

J Med Virol. 2021 Oct 14. doi: 10.1002/jmv.27394. Online ahead of print.

ABSTRACT

Like most vaccines, the effectiveness of COVID-19 vaccines developed so far is not 100% and a small percentage of fully vaccinated individuals still develop symptomatic or asymptomatic SARS-CoV-2 infections. ¹ In addition, the emerging of novel mutations is likely to help the virus evade vaccines especially in regions with low vaccination coverage. ² This article is protected by copyright. All rights reserved.

PMID:34647629 | DOI:10.1002/jmv.27394

Sci Rep. 2021 Oct 13;11(1):20314. doi: 10.1038/s41598-021-98321-0.

ABSTRACT

Validation of algorithms for selecting patients from healthcare administrative databases (HAD) is recommended. This PATHFINDER study section is aimed at testing algorithms to select rheumatoid arthritis (RA) patients from Tuscan HAD (THAD) and assessing RA diagnosis time interval between the medical chart date and that of THAD. A population was extracted from THAD. The information of the medical charts at the Rheumatology Unit of Pisa University Hospital represented the reference. We included first ever users of biologic disease modifying anti-rheumatic drugs (bDMARDs) between 2014 and 2016 (index date) with at least a specialist visit at the Rheumatology Unit of the Pisa University Hospital recorded from 2013 to the index date. Out of these, we tested four index tests (algorithms): (1) RA according to hospital discharge records or emergency department admissions (ICD-9 code, 714*); (2) RA according to exemption code from co-payment (006); (3) RA according to hospital discharge records or emergency department admissions AND RA according to exemption code from co-payment; (4) RA according to hospital discharge records or emergency department admissions OR RA according to exemption code from co-payment. We estimated sensitivity, specificity, positive and negative predicted values (PPV and NPV) with 95% confidence interval (95% CI) and the RA diagnosis median time interval (interquartile range, IQR). Two sensitivity analyses were performed. Among 277 reference patients, 103 had RA. The fourth algorithm identified 96 true RA patients, PPV 0.78 (95% CI 0.70-0.85), sensitivity 0.93 (95% CI 0.86-0.97), specificity 0.84 (95% CI 0.78-0.90), and NPV 0.95 (95% CI 0.91-0.98). The sensitivity analyses confirmed performance. The time measured between the actual RA diagnosis date recorded in medical charts and that assumed in THAD was 2.2 years (IQR 0.5-8.4). In conclusion, this validation showed the fourth algorithm as the best. The time interval elapsed between the actual RA diagnosis date in medical charts and that extrapolated from THAD has to be considered in the design of future studies.

PMID:34645838 | DOI:10.1038/s41598-021-98321-0

Sci Rep. 2021 Oct 13;11(1):20304. doi: 10.1038/s41598-021-99175-2.

ABSTRACT

Community detection is a fundamental procedure in the analysis of network data. Despite decades of research, there is still no consensus on the definition of a community. To analytically test the realness of a candidate community in weighted networks, we present a general formulation from a significance testing perspective. In this new formulation, the edge-weight is modeled as a censored observation due to the noisy characteristics of real networks. In particular, the edge-weights of missing links are incorporated as well, which are specified to be zeros based on the assumption that they are truncated or unobserved. Thereafter, the community significance assessment issue is formulated as a two-sample test problem on censored data. More precisely, the Logrank test is employed to conduct the significance testing on two sets of augmented edge-weights: internal weight set and external weight set. The presented approach is evaluated on both weighted networks and un-weighted networks. The experimental results show that our method can outperform prior widely used evaluation metrics on the task of individual community validation.

PMID:34645850 | DOI:10.1038/s41598-021-99175-2