BLIND PARALIND SPACE-TIME MULTIUSER DETECTION FOR ASYNCHRONOUS CDMA SYSTEM

2009 ◽  
Vol 18 (03) ◽  
pp. 503-517 ◽  
Author(s):  
XIAOFEI ZHANG ◽  
GAOPENG FENG ◽  
DAZHUAN XU ◽  
XIN GAO
Keyword(s):  
Matched Filter ◽  
Minimum Mean Square Error ◽  
Space Time ◽  
Small Sample ◽  
Cdma System ◽  
Channel Fading ◽  
Detection Problem ◽  
Small Sample Sizes ◽  
Asynchronous Cdma ◽  
Error Method

This paper links the asynchronous CDMA system signal detection problem to the parallel profiles with linear dependencies (PARALIND), and derives a deterministic blind PARALIND algorithm whose performance is close to nonblind space-time minimum mean square error method. The proposed algorithm has the better performance than space-time matched filter, supports small sample sizes, and even works well in array error condition. Most notably, it does not require knowledge of the direction of arrival and channel fading information.

Integrating Visual and Bayesian Statistical Analyses in Single Case Experimental Research to Evaluate the Effectiveness and Magnitude of a Comprehensive Behavioral Intervention

2018 ◽  
Author(s):  
Prathiba Natesan ◽  
Smita Mehta
Keyword(s):  
Behavioral Intervention ◽  
Effect Size ◽  
Rate Ratio ◽  
Visual Analysis ◽  
Single Case ◽  
Small Sample ◽  
Statistical Analyses ◽  
Data Types ◽  
Ratio Effect ◽  
Small Sample Sizes

Single case experimental designs (SCEDs) have become an indispensable methodology where randomized control trials may be impossible or even inappropriate. However, the nature of SCED data presents challenges for both visual and statistical analyses. Small sample sizes, autocorrelations, data types, and design types render many parametric statistical analyses and maximum likelihood approaches ineffective. The presence of autocorrelation decreases interrater reliability in visual analysis. The purpose of the present study is to demonstrate a newly developed model called the Bayesian unknown change-point (BUCP) model which overcomes all the above-mentioned data analytic challenges. This is the first study to formulate and demonstrate rate ratio effect size for autocorrelated data, which has remained an open question in SCED research until now. This expository study also compares and contrasts the results from BUCP model with visual analysis, and rate ratio effect size with nonoverlap of all pairs (NAP) effect size. Data from a comprehensive behavioral intervention are used for the demonstration.

No Evidence that Experiencing Physical Warmth Promotes Interpersonal Warmth: Two Failures to Replicate Williams and Bargh (2008)

2018 ◽  
Author(s):  
Christopher Chabris ◽  
Patrick Ryan Heck ◽  
Jaclyn Mandart ◽  
Daniel Jacob Benjamin ◽  
Daniel J. Simons
Keyword(s):  
Null Hypothesis ◽  
Small Sample ◽  
Sample Sizes ◽  
Double Blind ◽  
Bayesian Analyses ◽  
Physical Warmth ◽  
Small Sample Sizes ◽  
Interpersonal Warmth

Williams and Bargh (2008) reported that holding a hot cup of coffee caused participants to judge a person’s personality as warmer, and that holding a therapeutic heat pad caused participants to choose rewards for other people rather than for themselves. These experiments featured large effects (r = .28 and .31), small sample sizes (41 and 53 participants), and barely statistically significant results. We attempted to replicate both experiments in field settings with more than triple the sample sizes (128 and 177) and double-blind procedures, but found near-zero effects (r = –.03 and .02). In both cases, Bayesian analyses suggest there is substantially more evidence for the null hypothesis of no effect than for the original physical warmth priming hypothesis.

scholarly journals Non-Invasive Monitoring of Adrenocortical Activity in Three Sympatric Desert Gerbil Species

Animals ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 75
Author(s):  
Álvaro Navarro-Castilla ◽  
Mario Garrido ◽  
Hadas Hawlena ◽  
Isabel Barja
Keyword(s):  
Small Sample ◽  
Post Injection ◽  
Control Groups ◽  
Treatment Groups ◽  
Adrenocortical Activity ◽  
Non Invasive ◽  
Endocrine Status ◽  
Corticosterone Metabolites ◽  
Small Sample Sizes ◽  
Fecal Corticosterone Metabolites

The study of the endocrine status can be useful to understand wildlife responses to the changing environment. Here, we validated an enzyme immunoassay (EIA) to non-invasively monitor adrenocortical activity by measuring fecal corticosterone metabolites (FCM) in three sympatric gerbil species (Gerbillus andersoni, G. gerbillus and G. pyramidum) from the Northwestern Negev Desert’s sands (Israel). Animals included into treatment groups were injected with adrenocorticotropic hormone (ACTH) to stimulate adrenocortical activity, while control groups received a saline solution. Feces were collected at different intervals and FCM were quantified by an EIA. Basal FCM levels were similar in the three species. The ACTH effect was evidenced, but the time of FCM peak concentrations appearance differed between the species (6–24 h post-injection). Furthermore, FCM peak values were observed sooner in G. andersoni females than in males (6 h and 18 h post-injection, respectively). G. andersoni and G. gerbillus males in control groups also increased FCM levels (18 h and 48 h post-injection, respectively). Despite the small sample sizes, our results confirmed the EIA suitability for analyzing FCM in these species as a reliable indicator of the adrenocortical activity. This study also revealed that close species, and individuals within a species, can respond differently to the same stressor.

scholarly journals Use of Antiplatelet Agents Decreases the Positive Predictive Value of Fecal Immunochemical Tests for Colorectal Cancer but Does Not Affect Their Sensitivity

2021 ◽  
Vol 11 (6) ◽  
pp. 497
Author(s):  
Yoonsuk Jung ◽  
Eui Im ◽  
Jinhee Lee ◽  
Hyeah Lee ◽  
Changmo Moon
Keyword(s):  
Colorectal Cancer ◽  
Large Scale ◽  
Screening Program ◽  
Antiplatelet Agents ◽  
Population Based ◽  
Small Sample ◽  
Antithrombotic Agents ◽  
Predictive Values ◽  
Prescription Rates ◽  
Small Sample Sizes

Previous studies have evaluated the effects of antithrombotic agents on the performance of fecal immunochemical tests (FITs) for the detection of colorectal cancer (CRC), but the results were inconsistent and based on small sample sizes. We studied this topic using a large-scale population-based database. Using the Korean National Cancer Screening Program Database, we compared the performance of FITs for CRC detection between users and non-users of antiplatelet agents and warfarin. Non-users were matched according to age and sex. Among 5,426,469 eligible participants, 768,733 used antiplatelet agents (mono/dual/triple therapy, n = 701,683/63,211/3839), and 19,569 used warfarin, while 4,638,167 were non-users. Among antiplatelet agents, aspirin, clopidogrel, and cilostazol ranked first, second, and third, respectively, in terms of prescription rates. Users of antiplatelet agents (3.62% vs. 4.45%; relative risk (RR): 0.83; 95% confidence interval (CI): 0.78–0.88), aspirin (3.66% vs. 4.13%; RR: 0.90; 95% CI: 0.83–0.97), and clopidogrel (3.48% vs. 4.88%; RR: 0.72; 95% CI: 0.61–0.86) had lower positive predictive values (PPVs) for CRC detection than non-users. However, there were no significant differences in PPV between cilostazol vs. non-users and warfarin users vs. non-users. For PPV, the RR (users vs. non-users) for antiplatelet monotherapy was 0.86, while the RRs for dual and triple antiplatelet therapies (excluding cilostazol) were 0.67 and 0.22, respectively. For all antithrombotic agents, the sensitivity for CRC detection was not different between users and non-users. Use of antiplatelet agents, except cilostazol, may increase the false positives without improving the sensitivity of FITs for CRC detection.

scholarly journals Perioperative digital behaviour change interventions for reducing alcohol consumption, improving dietary intake, increasing physical activity and smoking cessation: a scoping review

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Katarina Åsberg ◽  
Marcus Bendtsen
Keyword(s):  
Physical Activity ◽  
Smoking Cessation ◽  
Behaviour Change ◽  
Scoping Review ◽  
Text Messaging ◽  
Text Message ◽  
Feasibility Studies ◽  
Small Sample ◽  
Small Sample Sizes ◽  
Randomised Controlled

Abstract Background Evidence suggests that unhealthy lifestyle behaviours are modifiable risk factors for postoperative complications. Digital behaviour change interventions (DBCIs), for instance text messaging programs and smartphone apps, have shown promise in achieving lifestyle behaviour change in a wide range of clinical populations, and it may therefore be possible to reduce postoperative complications by supporting behaviour change perioperatively using digital interventions. This scoping review was conducted in order to identify existing research done in the area of perioperative DBCIs for reducing alcohol consumption, improving dietary intake, increasing physical activity and smoking cessation. Main text This scoping review included eleven studies covering a range of surgeries: bariatric, orthopaedic, cancer, transplantation and elective surgery. The studies were both randomised controlled trials and feasibility studies and investigated a diverse set of interventions: one game, three smartphone apps, one web-based program and five text message interventions. Feasibility studies reported user acceptability and satisfaction with the behaviour change support. Engagement data showed participation rates ranged from 40 to 90%, with more participants being actively engaged early in the intervention period. In conclusion, the only full-scale randomised controlled trial (RCT), text messaging ahead of bariatric surgery did not reveal any benefits with respect to adherence to preoperative exercise advice when compared to a control group. Two of the pilot studies, one text message intervention, one game, indicated change in a positive direction with respect to alcohol and tobacco outcomes, but between group comparisons were not done due to small sample sizes. The third pilot-study, a smartphone app, found between group changes for physical activity and alcohol, but not with respect to smoking cessation outcomes. Conclusion This review found high participant satisfaction, but shows recruitment and timing-delivery issues, as well as low retention to interventions post-surgery. Small sample sizes and the use of a variety of feasibility outcome measures prevent the synthesis of results and makes generalisation difficult. Future research should focus on defining standardised outcome measures, enhancing patient engagement and improving adherence to behaviour change prior to scheduled surgery.

scholarly journals Candida infective endocarditis: A retrospective study of patient characteristics and risk factors for death in 703 U.S. cases, 2015-2019

2020 ◽  
Author(s):  
Jonathan P Huggins ◽  
Samuel Hohmann ◽  
Michael Z David
Keyword(s):  
Risk Factors ◽  
Hospital Mortality ◽  
Liver Failure ◽  
Patient Characteristics ◽  
Medication Administration ◽  
Small Sample ◽  
Risk Of Death ◽  
Opiate Abuse ◽  
Small Sample Sizes ◽  
Valve Pathology

Abstract Background Candida endocarditis is a rare, sometimes fatal complication of candidemia. Past investigations of this condition are limited by small sample sizes. We used the Vizient clinical database to report on characteristics of patients with Candida endocarditis and to examine risk factors for in-hospital mortality. Methods This was a multicenter, retrospective cohort study of 703 inpatients admitted to 179 United States hospitals between October 2015 and April 2019. We reviewed demographic, diagnostic, medication administration, and procedural data from each patient’s initial encounter. Univariate and multivariate logistic regression analyses were used to identify predictors of in-hospital mortality. Results Of 703 patients, 114 (16.2%) died during the index encounter. One hundred and fifty-eight (22.5%) underwent an intervention on a cardiac valve. On multivariate analysis, acute and subacute liver failure was the strongest predictor of death (OR 9.2, 95% CI 4.8 –17.7). Female sex (OR 1.9, 95% CI 1.2 – 3.0), transfer from an outside medical facility (OR 1.8, 95% CI 1.1 – 2.8), aortic valve pathology (OR 2.7, 95% CI 1.5 – 4.9), hemodialysis (OR 2.1, 95% CI 1.1 – 4.0), cerebrovascular disease (OR 2.2, 95% CI 1.2 – 3.8), neutropenia (OR 2.5, 95% CI 1.3 – 4.8), and alcohol abuse (OR 2.9, 95% CI 1.3 – 6.7) were also associated with death on adjusted analysis, whereas opiate abuse was associated with a lower odds of death (OR 0.5, 95% CI 0.2 – 0.9). Conclusions We found that the inpatient mortality rate was 16.2% among patients with Candida endocarditis. Acute and subacute liver failure was associated with a high risk of death while opiate abuse was associated with a lower risk of death.

scholarly journals Viscoelastometric Testing to Assess Hemostasis of COVID-19: A Systematic Review

2021 ◽  
Vol 10 (8) ◽  
pp. 1740
Author(s):  
Marion Bareille ◽  
Michaël Hardy ◽  
Jonathan Douxfils ◽  
Stéphanie Roullet ◽  
Dominique Lasne ◽  
...  
Keyword(s):  
Systematic Review ◽  
Mechanical Strength ◽  
Small Sample ◽  
The Common ◽  
Small Sample Sizes ◽  
Definition Of ◽  
Reported Data ◽  
Prophylactic Anticoagulation ◽  
The Impact ◽  
Heparin Neutralization

Infection by SARS-CoV-2 is associated with a high risk of thrombosis. The laboratory documentation of hypercoagulability and impaired fibrinolysis remains a challenge. Our aim was to assess the potential usefulness of viscoelastometric testing (VET) to predict thrombotic events in COVID-19 patients according to the literature. We also (i) analyzed the impact of anticoagulation and the methods used to neutralize heparin, (ii) analyzed whether maximal clot mechanical strength brings more information than Clauss fibrinogen, and (iii) critically scrutinized the diagnosis of hypofibrinolysis. We performed a systematic search in PubMed and Scopus databases until December 31st, 2020. VET methods and parameters, and patients’ features and outcomes were extracted. VET was performed for 1063 patients (893 intensive care unit (ICU) and 170 non-ICU, 44 studies). There was extensive heterogeneity concerning study design, VET device used (ROTEM, TEG, Quantra and ClotPro) and reagents (with non-systematic use of heparin neutralization), timing of assay, and definition of hypercoagulable state. Notably, only 4 out of 25 studies using ROTEM reported data with heparinase (HEPTEM). The common findings were increased clot mechanical strength mainly due to excessive fibrinogen component and impaired to absent fibrinolysis, more conspicuous in the presence of an added plasminogen activator. Only 4 studies out of the 16 that addressed the point found an association of VETs with thrombotic events. So-called functional fibrinogen assessed by VETs showed a variable correlation with Clauss fibrinogen. Abnormal VET pattern, often evidenced despite standard prophylactic anticoagulation, tended to normalize after increased dosing. VET studies reported heterogeneity, and small sample sizes do not support an association between the poorly defined prothrombotic phenotype of COVID-19 and thrombotic events.

scholarly journals G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Florent Le Borgne ◽  
Arthur Chatton ◽  
Maxime Léger ◽  
Rémi Lenain ◽  
Yohann Foucher
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Statistical Power ◽  
Small Sample ◽  
Causal Effects ◽  
Small Samples ◽  
Support Vector ◽  
Sample Sizes ◽  
Super Learner ◽  
Small Sample Sizes

AbstractIn clinical research, there is a growing interest in the use of propensity score-based methods to estimate causal effects. G-computation is an alternative because of its high statistical power. Machine learning is also increasingly used because of its possible robustness to model misspecification. In this paper, we aimed to propose an approach that combines machine learning and G-computation when both the outcome and the exposure status are binary and is able to deal with small samples. We evaluated the performances of several methods, including penalized logistic regressions, a neural network, a support vector machine, boosted classification and regression trees, and a super learner through simulations. We proposed six different scenarios characterised by various sample sizes, numbers of covariates and relationships between covariates, exposure statuses, and outcomes. We have also illustrated the application of these methods, in which they were used to estimate the efficacy of barbiturates prescribed during the first 24 h of an episode of intracranial hypertension. In the context of GC, for estimating the individual outcome probabilities in two counterfactual worlds, we reported that the super learner tended to outperform the other approaches in terms of both bias and variance, especially for small sample sizes. The support vector machine performed well, but its mean bias was slightly higher than that of the super learner. In the investigated scenarios, G-computation associated with the super learner was a performant method for drawing causal inferences, even from small sample sizes.

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