scholarly journals A Multilevel Bayesian Approach to Improve Effect Size Estimation in Regression Modeling of Metabolomics Data Utilizing Imputation with Uncertainty

Metabolites ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 319 ◽  
Author(s):  
Christopher E. Gillies ◽  
Theodore S. Jennaro ◽  
Michael A. Puskarich ◽  
Ruchi Sharma ◽  
Kevin R. Ward ◽  
...  
Keyword(s):  
Missing Data ◽  
Bayesian Approach ◽  
Effect Size ◽  
Bayesian Models ◽  
Regression Modeling ◽  
Multiple Hypothesis Testing ◽  
P Value ◽  
Size Estimation ◽  
Metabolomics Data ◽  
True Effect Size

To ensure scientific reproducibility of metabolomics data, alternative statistical methods are needed. A paradigm shift away from the p-value toward an embracement of uncertainty and interval estimation of a metabolite’s true effect size may lead to improved study design and greater reproducibility. Multilevel Bayesian models are one approach that offer the added opportunity of incorporating imputed value uncertainty when missing data are present. We designed simulations of metabolomics data to compare multilevel Bayesian models to standard logistic regression with corrections for multiple hypothesis testing. Our simulations altered the sample size and the fraction of significant metabolites truly different between two outcome groups. We then introduced missingness to further assess model performance. Across simulations, the multilevel Bayesian approach more accurately estimated the effect size of metabolites that were significantly different between groups. Bayesian models also had greater power and mitigated the false discovery rate. In the presence of increased missing data, Bayesian models were able to accurately impute the true concentration and incorporating the uncertainty of these estimates improved overall prediction. In summary, our simulations demonstrate that a multilevel Bayesian approach accurately quantifies the estimated effect size of metabolite predictors in regression modeling, particularly in the presence of missing data.

scholarly journals Using and Understanding Power in Psychological Research: A Survey Study

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Elizabeth Collins ◽  
Roger Watt
Keyword(s):  
Effect Size ◽  
Power Analysis ◽  
A Priori ◽  
Small Sample ◽  
Survey Study ◽  
Estimation Methods ◽  
P Value ◽  
Size Estimation ◽  
Sample Sizes ◽  
Effect Size Estimation

Statistical power is key to planning studies if understood and used correctly. Power is the probability of obtaining a statistically significant p-value, given a set alpha, sample size, and population effect size. The literature suggests that psychology studies are underpowered due to small sample sizes, and that researchers do not hold accurate intuitions about sensible sample sizes and associated levels of power. In this study, we surveyed 214 psychological researchers, and asked them about their experiences of using a priori power analysis, effect size estimation methods, post hoc power, and their understanding of what the term “power” actually means. Power analysis use was high, although participants reported difficulties with complex research designs, and effect size estimation. Participants also typically could not accurately define power. If psychological researchers are expected to compute a priori power analyses to plan their research, clearer educational material and guidelines should be made available.

Effect Size Estimation From t-Statistics in the Presence of Publication Bias

2018 ◽  
Vol 226 (1) ◽  
pp. 56-80 ◽  
Author(s):  
Rolf Ulrich ◽  
Jeff Miller ◽  
Edgar Erdfelder
Keyword(s):  
Publication Bias ◽  
Effect Size ◽  
Effect Sizes ◽  
Size Estimation ◽  
True Effect Size ◽  
True Effect ◽  
Effect Size Estimation ◽  
T Values ◽  
File Drawer

Abstract. Publication bias hampers the estimation of true effect sizes. Specifically, effect sizes are systematically overestimated when studies report only significant results. In this paper we show how this overestimation depends on the true effect size and on the sample size. Furthermore, we review and follow up methods originally suggested by Hedges (1984) , Iyengar and Greenhouse (1988) , and Rust, Lehmann, and Farley (1990) allowing the estimation of the true effect size from published test statistics (e.g., from the t-values of reported significant results). Moreover, we adapted these methods allowing meta-analysts to estimate the percentage of researchers who consign undesired results in a research domain to the file drawer. We also apply the same logic to the case when significant results tend to be underreported. We demonstrate the application of these procedures for conventional one-sample and two-sample t-tests. Finally, we provide R and MATLAB versions of a computer program to estimate the true unbiased effect size and the prevalence of publication bias in the literature.

How to Detect Publication Bias in Psychological Research

2019 ◽  
Vol 227 (4) ◽  
pp. 261-279 ◽  
Author(s):  
Frank Renkewitz ◽  
Melanie Keiner
Keyword(s):  
Publication Bias ◽  
Effect Size ◽  
Statistical Power ◽  
Type I Error ◽  
Psychological Research ◽  
Type I ◽  
True Effect Size ◽  
Questionable Research Practices ◽  
True Effect ◽  
Meta Analyses

Abstract. Publication biases and questionable research practices are assumed to be two of the main causes of low replication rates. Both of these problems lead to severely inflated effect size estimates in meta-analyses. Methodologists have proposed a number of statistical tools to detect such bias in meta-analytic results. We present an evaluation of the performance of six of these tools. To assess the Type I error rate and the statistical power of these methods, we simulated a large variety of literatures that differed with regard to true effect size, heterogeneity, number of available primary studies, and sample sizes of these primary studies; furthermore, simulated studies were subjected to different degrees of publication bias. Our results show that across all simulated conditions, no method consistently outperformed the others. Additionally, all methods performed poorly when true effect sizes were heterogeneous or primary studies had a small chance of being published, irrespective of their results. This suggests that in many actual meta-analyses in psychology, bias will remain undiscovered no matter which detection method is used.

scholarly journals Kernel weighted least square approach for imputing missing values of metabolomics data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nishith Kumar ◽  
Md. Aminul Hoque ◽  
Masahiro Sugimoto
Keyword(s):  
Missing Data ◽  
Large Scale ◽  
Missing Values ◽  
Kernel Weight ◽  
Least Square ◽  
Data Matrix ◽  
Data Imputation ◽  
Metabolomics Data ◽  
Missing Value ◽  
Missing Data Imputation

AbstractMass spectrometry is a modern and sophisticated high-throughput analytical technique that enables large-scale metabolomic analyses. It yields a high-dimensional large-scale matrix (samples × metabolites) of quantified data that often contain missing cells in the data matrix as well as outliers that originate for several reasons, including technical and biological sources. Although several missing data imputation techniques are described in the literature, all conventional existing techniques only solve the missing value problems. They do not relieve the problems of outliers. Therefore, outliers in the dataset decrease the accuracy of the imputation. We developed a new kernel weight function-based proposed missing data imputation technique that resolves the problems of missing values and outliers. We evaluated the performance of the proposed method and other conventional and recently developed missing imputation techniques using both artificially generated data and experimentally measured data analysis in both the absence and presence of different rates of outliers. Performances based on both artificial data and real metabolomics data indicate the superiority of our proposed kernel weight-based missing data imputation technique to the existing alternatives. For user convenience, an R package of the proposed kernel weight-based missing value imputation technique was developed, which is available at https://github.com/NishithPaul/tWLSA.

Abstract 3789: Nessun Dorma : Have the Risks of Rosiglitazone been Exaggerated?

Circulation ◽  
2007 ◽  
Vol 116 (suppl_16) ◽  
Author(s):  
George A Diamond ◽  
Sanjay Kaul
Keyword(s):  
Confidence Intervals ◽  
Effect Size ◽  
Fixed Effects ◽  
Meta Analysis ◽  
Cardiovascular Death ◽  
Sensitivity Analyses ◽  
Odds Ratios ◽  
True Effect Size ◽  
Index Study ◽  
The Stability

Background A highly publicized meta-analysis of 42 clinical trials comprising 27,844 diabetics ignited a firestorm of controversy by charging that treatment with rosiglitazone was associated with a “…worrisome…” 43% greater risk of myocardial infarction ( p =0.03) and a 64% greater risk of cardiovascular death ( p =0.06). Objective The investigators excluded 4 trials from the infarction analysis and 19 trials from the mortality analysis in which no events were observed. We sought to determine if these exclusions biased the results. Methods We compared the index study to a Bayesian meta-analysis of the entire 42 trials (using odds ratio as the measure of effect size) and to fixed-effects and random-effects analyses with and without a continuity correction that adjusts for values of zero. Results The odds ratios and confidence intervals for the analyses are summarized in the Table . Odds ratios for infarction ranged from 1.43 to 1.22 and for death from 1.64 to 1.13. Corrected models resulted in substantially smaller odds ratios and narrower confidence intervals than did uncorrected models. Although corrected risks remain elevated, none are statistically significant (*p<0.05). Conclusions Given the fragility of the effect sizes and confidence intervals, the charge that roziglitazone increases the risk of adverse events is not supported by these additional analyses. The exaggerated values observed in the index study are likely the result of excluding the zero-event trials from analysis. Continuity adjustments mitigate this error and provide more consistent and reliable assessments of true effect size. Transparent sensitivity analyses should therefore be performed over a realistic range of the operative assumptions to verify the stability of such assessments especially when outcome events are rare. Given the relatively wide confidence intervals, additional data will be required to adjudicate these inconclusive results.

Abstract MP50: Serum Metabolomic Profile of Incident Diabetes

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Casey M Rebholz ◽  
Bing Yu ◽  
Zihe Zheng ◽  
Patrick Chang ◽  
Adrienne Tin ◽  
...  
Keyword(s):  
Fasting Glucose ◽  
Ultra Performance Liquid Chromatography ◽  
Diabetes Risk ◽  
Incident Diabetes ◽  
P Value ◽  
Metabolic Disturbances ◽  
Isoleucine Leucine ◽  
Metabolomics Data ◽  
Metabolomic Profiling ◽  
Increased Risk

Background: Metabolomic profiling offers the potential to reveal metabolic pathways relevant to diabetes pathophysiology and to improve diabetes risk prediction. Methods: We prospectively analyzed metabolites and incident diabetes from baseline (1987-1989) through December 31, 2015 in a subset of 2,939 Atherosclerosis Risk in Communities (ARIC) Study participants with metabolomics data and without diabetes at baseline. Metabolomic profiling was conducted in stored serum specimens collected at baseline using a reverse phase, untargeted ultra-performance liquid chromatography tandem mass spectrometry approach. Results: Among the 245 named compounds we identified, 7 metabolites were significantly associated with incident diabetes after Bonferroni correction and covariate adjustment (age, sex, race, center, batch, education, blood pressures, body mass index, lipids, smoking, physical activity, history of cardiovascular disease, eGFR, fasting glucose). These 7 metabolites consisted of a xenobiotic (erythritol) and compounds involved in amino acid metabolism [isoleucine, leucine, valine, asparagine, 3-(4-hydoxyphenyl)lactate] and glucose metabolism (trehalose). Higher levels of the metabolites were associated with an increased risk of incident diabetes, with the exception of asparagine which was associated with a lower risk of diabetes (HR per 1 SD increase: 0.78, 95% CI: 0.71, 0.85; p=4.19x10 -8 ). The 7 metabolites improved the prediction of incident diabetes beyond fasting glucose and established risk factors (C statistic for model with vs. without 7 metabolites, respectively: 0.744 vs. 0.735; p-value for difference in C statistics=0.001). Conclusions: Branched chain amino acids may play a role in diabetes development. Our study is the first to report asparagine as a protective biomarker of diabetes risk. The serum metabolome reflects known and novel metabolic disturbances that improve diabetes prediction.

scholarly journals Clinical Efficacy and Target Engagement of Glutamatergic Drugs: Placebo-Controlled RCTs of Pomaglumetad and TS-134 for Reversal of Ketamine-Induced Psychotic Symptoms and PharmacoBOLD in Healthy Volunteers

2020 ◽  
Author(s):  
Joshua T. Kantrowitz ◽  
Jack Grinband ◽  
Donald C. Goff ◽  
Adrienne C. Lahti ◽  
Stephen R. Marder ◽  
...  
Keyword(s):  
Effect Size ◽  
Metabotropic Glutamate Receptor ◽  
Rating Scale ◽  
Clinical Symptoms ◽  
Anterior Cingulate ◽  
Psychotic Symptoms ◽  
High Dose ◽  
Size Estimation ◽  
Target Engagement ◽  
Dorsal Anterior Cingulate Cortex

AbstractWe tested two metabotropic glutamate receptor 2/3 (mGluR2/3) agonist prodrugs – pomaglumetad (POMA) and TS-134 – including a high-dose of POMA that was four times the dose tested in the failed phase schizophrenia III trials – in two proof of mechanism, Phase Ib studies using identical pharmacoBOLD target-engagement methodology.The POMA study was a double-blind, NIMH-sponsored, 10-day study of 80 or 320 mg/d POMA or placebo (1:1:1 ratio), designed to detect d>0.8 sd between-group effect-size differences. The TS-134 study was a single-blind, industry-sponsored, 6-day study of 20 or 60 mg/d TS-134 or placebo (5:5:2 ratio), designed to permit effect-size estimation for future studies. Primary outcomes were ketamine-induced changes in pharmacoBOLD in the dorsal anterior cingulate cortex (dACC) and Brief Psychiatric Rating Scale (BPRS).95 healthy controls were randomized to POMA and 63 to TS-134. High-dose POMA had significant within and between-group reduction in ketamine-induced BPRS total symptoms (p<0.01, d=-0.41; p=0.04, d=-0.44, respectively) but neither POMA dose significantly suppressed ketamine-induced dACC pharmacoBOLD. In contrast, low-dose TS-134 had significant/trend level, moderate to large within and between group effects on BPRS positive symptoms (p=0.02, d=-0.36; p=0.008, d=-0.82, respectively) and dACC pharmacoBOLD (p=0.004, d=-0.56; p=0.079, d=-0.50, respectively) using pooled across-study placebo data.High-dose POMA exerted significant effects on clinical symptoms, but not on target engagement, suggesting a higher dose may yet be needed. TS-134 20 mg showed evidence of symptom reduction and target engagement, indicating a curvilinear dose-response curve. These results warrant further investigation of mGluR2/3 and other glutamate-targeted treatments for schizophrenia.

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