scholarly journals Enhancing statistical power in temporal biomarker discovery through representative shapelet mining

2020 ◽  
Vol 36 (Supplement_2) ◽  
pp. i840-i848
Author(s):  
Thomas Gumbsch ◽  
Christian Bock ◽  
Michael Moor ◽  
Bastian Rieck ◽  
Karsten Borgwardt
Keyword(s):  
Organ Failure ◽  
Multiple Testing ◽  
Statistical Power ◽  
Biomarker Discovery ◽  
A Priori ◽  
Structural Diversity ◽  
Submodular Optimization ◽  
Failure Assessment ◽  
Novel Method ◽  
Real World Datasets

Abstract Motivation Temporal biomarker discovery in longitudinal data is based on detecting reoccurring trajectories, the so-called shapelets. The search for shapelets requires considering all subsequences in the data. While the accompanying issue of multiple testing has been mitigated in previous work, the redundancy and overlap of the detected shapelets results in an a priori unbounded number of highly similar and structurally meaningless shapelets. As a consequence, current temporal biomarker discovery methods are impractical and underpowered. Results We find that the pre- or post-processing of shapelets does not sufficiently increase the power and practical utility. Consequently, we present a novel method for temporal biomarker discovery: Statistically Significant Submodular Subset Shapelet Mining (S5M) that retrieves short subsequences that are (i) occurring in the data, (ii) are statistically significantly associated with the phenotype and (iii) are of manageable quantity while maximizing structural diversity. Structural diversity is achieved by pruning non-representative shapelets via submodular optimization. This increases the statistical power and utility of S5M compared to state-of-the-art approaches on simulated and real-world datasets. For patients admitted to the intensive care unit (ICU) showing signs of severe organ failure, we find temporal patterns in the sequential organ failure assessment score that are associated with in-ICU mortality. Availability and implementation S5M is an option in the python package of S3M: github.com/BorgwardtLab/S3M.

scholarly journals Noise in the process: an assessment of the evidential value of mediation effects in marketing journals

2021 ◽  
Author(s):  
Aaron Charlton ◽  
Amanda Kay Montoya ◽  
John Price ◽  
Joseph Hilgard
Keyword(s):  
Low Power ◽  
Multiple Testing ◽  
Statistical Power ◽  
A Priori ◽  
Marketing Research ◽  
Real World Data ◽  
Mediation Effects ◽  
Mediation Analyses ◽  
Evidential Value ◽  
Adequate Power

Mediation analysis plays a central role in marketing research due to its usefulness in helping to explain complex processes. Like other forms of inference, mediation analyses are susceptible to false positive results. This is particularly true when analytic decisions are based on the data, rather than a priori hypotheses. To assess the collective evidential value of mediation analyses in marketing, we used an approach first implemented by Götz and colleagues (2021) that (1) measures the relative proximity of confidence intervals to zero (RP) and (2) aggregates a related set of RP scores into a single distribution. For our analysis, we compared the RP distribution of top marketing journals (2018-20) to simulations of low power, adequate power, and null effects. We also compared the marketing journals to real-world data from Journal of Personality and Social Psychology (JPSP) (2018-20). We found that, in terms of evidential value, mediation analyses in marketing substantially deviated from simulations of adequate power and JPSP but were similar to simulations of low power and null effects. We propose study preregistration, corrections for multiple testing, and increased statistical power as solutions to increase evidence quality going forward.

scholarly journals Finding Statistically Significant Interactions between Continuous Features

2019 ◽  
Author(s):  
Mahito Sugiyama ◽  
Karsten Borgwardt
Keyword(s):  
Multiple Testing ◽  
Statistical Power ◽  
Search Space ◽  
Higher Order ◽  
P Value ◽  
Combinatorial Explosion ◽  
Feature Interactions ◽  
Binary Features ◽  
Real World Datasets ◽  
Class Variable

The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare, but the combinatorial explosion of the candidate space makes this problem extremely challenging in terms of computational efficiency and proper correction for multiple testing. While recent progress has been made regarding this challenge for binary features, we here present the first solution for continuous features. We propose an algorithm which overcomes the combinatorial explosion of the search space of higher-order interactions by deriving a lower bound on the p-value for each interaction, which enables us to massively prune interactions that can never reach significance and to thereby gain more statistical power. In our experiments, our approach efficiently detects all significant interactions in a variety of synthetic and real-world datasets.

Replication of the Superstition and Performance Study by

2014 ◽  
Vol 45 (3) ◽  
pp. 239-245 ◽  
Author(s):  
Robert J. Calin-Jageman ◽  
Tracy L. Caldwell
Keyword(s):  
Task Difficulty ◽  
Statistical Power ◽  
Meta Analysis ◽  
A Priori ◽  
Significant Heterogeneity ◽  
Performance Study ◽  
Improve Performance ◽  
Research Designs ◽  
Series Of Experiments ◽  
And Performance

A recent series of experiments suggests that fostering superstitions can substantially improve performance on a variety of motor and cognitive tasks ( Damisch, Stoberock, & Mussweiler, 2010 ). We conducted two high-powered and precise replications of one of these experiments, examining if telling participants they had a lucky golf ball could improve their performance on a 10-shot golf task relative to controls. We found that the effect of superstition on performance is elusive: Participants told they had a lucky ball performed almost identically to controls. Our failure to replicate the target study was not due to lack of impact, lack of statistical power, differences in task difficulty, nor differences in participant belief in luck. A meta-analysis indicates significant heterogeneity in the effect of superstition on performance. This could be due to an unknown moderator, but no effect was observed among the studies with the strongest research designs (e.g., high power, a priori sampling plan).

Sepsis nach elektiver Septumplastik und Nasenmuschelverkleinerung

2019 ◽  
Vol 98 (08) ◽  
pp. 571-574
Author(s):  
Katharina Stölzel ◽  
Lichun Zhang ◽  
Tordis Borowski ◽  
Heidi Olze ◽  
Tim Schroeder ◽  
...  
Keyword(s):  
Organ Failure ◽  
Assessment Score ◽  
Failure Assessment ◽  
Qsofa Score

FallberichtEin 20-jähriger adipöser Patient stellte sich im August 2018 in einer auswärtigen Klinik zur Septumplastik und Muschelverkleinerung bei Septumdeviation und Muschelhyperplasie ohne relevante Vorerkrankungen vor. Intraoperativ war keine Antibiotikagabe erfolgt. Postoperativ wurde der Patient zur weiteren Betreuung mit Doyle-Splinten und Gelaspon® auf die Normalstation verlegt. Es war Hochsommer und die Krankenzimmertemperatur bei ausgefallener Klimaanlage sehr hoch. Wie erst später fremdanamnestisch bekannt wurde, hatte der Patient eine bereits vor stationärer Aufnahme mehrere Tage bestehende, aber nicht mitgeteilte Enteritis. Am Morgen des ersten postoperativen Tages wies der Patient Zeichen der Sepsis auf: arterielle Hypotonie, Tachykardie, Tachypnoe und Desorientierung (= quick Sepsis – related organ failure assessment Score [qSOFA Score] = positiv). Es erfolgte die Gabe von Kristalloiden und bei Schwellung des Gesichtes die Entfernung der Doyle-Splinte. Ungeachtet dessen kam es zur progredienten Verschlechterung und Entwicklung eines schweren Schocks, so dass mit der Gabe von Vasopressoren begonnen wurde. Bei zusätzlich beginnender respiratorischer Erschöpfung wurde die Indikation zur Intubation gestellt. Während der Intubation kam es zur Aspiration. Zur weiteren intensivmedizinischen Versorgung erfolgte aus kapazitären Gründen die Verlegung auf die interdisziplinäre internistische Intensivstation unserer Klinik mit zunächst unklarem Infektfokus. Bei Übernahme war der Patient intubiert und beatmet (CPAP PEEP 9 mbar, Druckunterstützung 16 mbar, FiO2 0,8) und hoch katecholaminpflichtig (Noradrenalin 1 µg/kg/min, Epinephrin 0,2 µg/kg/min).

scholarly journals Establishment of a prognostic model based on the Sequential Organ Failure Assessment score for patients with first-time acute myocardial infarction

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110119
Author(s):  
Shuai Zheng ◽  
Jun Lyu ◽  
Didi Han ◽  
Fengshuo Xu ◽  
Chengzhuo Li ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Sequential Organ Failure Assessment ◽  
Organ Failure ◽  
Sofa Score ◽  
Cardiovascular Events ◽  
Failure Assessment ◽  
First Time

Objective This study aimed to identify the prognostic factors of patients with first-time acute myocardial infarction (AMI) and to establish a nomogram for prognostic modeling. Methods We studied 985 patients with first-time AMI using data from the Multi-parameter Intelligent Monitoring for Intensive Care database and extracted their demographic data. Cox proportional hazards regression was used to examine outcome-related variables. We also tested a new predictive model that includes the Sequential Organ Failure Assessment (SOFA) score and compared it with the SOFA-only model. Results An older age, higher SOFA score, and higher Acute Physiology III score were risk factors for the prognosis of AMI. The risk of further cardiovascular events was 1.54-fold higher in women than in men. Patients in the cardiac surgery intensive care unit had a better prognosis than those in the coronary heart disease intensive care unit. Pressurized drug use was a protective factor and the risk of further cardiovascular events was 1.36-fold higher in nonusers. Conclusion The prognosis of AMI is affected by age, the SOFA score, the Acute Physiology III score, sex, admission location, type of care unit, and vasopressin use. Our new predictive model for AMI has better performance than the SOFA model alone.

scholarly journals 2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Sangyoon Yi ◽  
Xianyang Zhang ◽  
Lu Yang ◽  
Jinyan Huang ◽  
Yuanhang Liu ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Statistical Power ◽  
Association Studies ◽  
Control Procedure ◽  
Multiple Testing Correction ◽  
New Approach ◽  
False Discovery ◽  
Traditional Procedure ◽  
Extensive Evaluation ◽  
Confounder Adjustment

AbstractOne challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%.

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