scholarly journals A Bayesian Rate Ratio Effect Size to Quantify Intervention Effects for Count Data in Single Case Experimental Research

2020 ◽  
pp. 019874292093070 ◽  
Author(s):  
Prathiba Natesan Batley ◽  
Smita Shukla Mehta ◽  
John H. Hitchcock
Keyword(s):  
Effect Size ◽  
Count Data ◽  
Rate Ratio ◽  
Single Case ◽  
Real Data ◽  
Small Sample ◽  
Ratio Effect ◽  
Intervention Efficacy ◽  
Small Sample Sizes ◽  
Meta Analyses

Single case experimental design (SCED) is an indispensable methodology when evaluating intervention efficacy. Despite long-standing success with using visual analyses to evaluate SCED data, this method has limited utility for conducting meta-analyses. This is critical because meta-analyses should drive practice and policy in behavioral disorders, more than evidence derived from individual SCEDs. Even when analyzing data from individual studies, there is merit to using multiple analytic methods since statistical analyses in SCED can be challenging given small sample sizes and autocorrelated data. These complexities are exacerbated when using count data, which are common in SCEDs. Bayesian methods can be used to develop new statistical procedures that may address these challenges. The purpose of the present study was to formulate a within-subject Bayesian rate ratio effect size (BRR) for autocorrelated count data which obviates the need for small sample corrections. This effect size is the first step toward building a between-subject rate ratio that can be used for meta-analyses. We illustrate this within-subject effect size using real data for an ABAB design and provide codes for practitioners who may want to compute BRR.

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.

Clinical outcomes of cancer patients with COVID-19: A systematic review and meta-analysis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18600-e18600
Author(s):  
Maryam Alasfour ◽  
Salman Alawadi ◽  
Malak AlMojel ◽  
Philippos Apolinario Costa ◽  
Priscila Barreto Coelho ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Cancer Patients ◽  
Clinical Outcomes ◽  
Meta Analysis ◽  
Small Sample ◽  
Prediction Intervals ◽  
Invasive Ventilation ◽  
Newcastle Ottawa Scale ◽  
Small Sample Sizes ◽  
Meta Analyses

e18600 Background: Patients with coronavirus disease 2019 (COVID-19) and cancer have worse clinical outcomes compared to those without cancer. Primary studies have examined this population, but most had small sample sizes and conflicting results. Prior meta-analyses exclude most US and European data or only examine mortality. The present meta-analysis evaluates the prevalence of several clinical outcomes in cancer patients with COVID-19, including new emerging data from Europe and the US. Methods: A systematic search of PubMED, medRxiv, JMIR and Embase by two independent investigators included peer-reviewed papers and preprints up to July 8, 2020. The primary outcome was mortality. Other outcomes were ICU and non-ICU admission, mild, moderate and severe complications, ARDS, invasive ventilation, stable, and clinically improved rates. Study quality was assessed through the Newcastle–Ottawa scale. Random effects model was used to derive prevalence rates, their 95% confidence intervals (CI) and 95% prediction intervals (PI). Results: Thirty-four studies (N = 4,371) were included in the analysis. The mortality prevalence rate was 25.2% (95% CI: 21.1–29.7; 95% PI: 9.8-51.1; I 2 = 85.4), with 11.9% ICU admissions (95% CI: 9.2-15.4; 95% PI: 4.3-28.9; I 2= 77.8) and 25.2% clinically stable (95% CI: 21.1-29.7; 95% PI: 9.8-51.1; I 2 = 85.4). Furthermore, 42.5% developed severe complications (95% CI: 30.4-55.7; 95% PI: 8.2-85.9; I 2 = 94.3), with 22.7% developing ARDS (95% CI: 15.4-32.2; 95% PI: 5.8-58.6; I 2 = 82.4), and 11.3% needing invasive ventilation (95% CI: 6.7-18.4; 95% PI: 2.3-41.1; I 2 = 79.8). Post-follow up, 49% clinically improved (95% CI: 35.6-62.6; 95% PI: 9.8-89.4; I 2 = 92.5). All outcomes had large I 2 , suggesting high levels of heterogeneity among studies, and wide PIs indicating high variability within outcomes. Despite this variability, the mortality rate in cancer patients with COVID-19, even at the lower end of the PI (9.8%), is higher than the 2% mortality rate of the non-cancer with COVID-19 population, but not as high as what other meta-analyses conclude, which is around 25%. Conclusions: Patients with cancer who develop COVID-19 have a higher probability of mortality compared to the general population with COVID-19, but possibly not as high as previous studies have shown. A large proportion of them developed severe complications, but a larger proportion recovered. Prevalence of mortality and other outcomes published in prior meta-analyses did not report prediction intervals, which compromises the clinical utilization of such results.

scholarly journals Cluster Wild Bootstrapping to Handle Dependent Effect Sizes in Meta-Analysis with a Small Number of Studies

2021 ◽  
Author(s):  
Megha Joshi ◽  
James E Pustejovsky ◽  
S. Natasha Beretvas
Keyword(s):  
Effect Size ◽  
Type I Error ◽  
Meta Analysis ◽  
Error Rates ◽  
Small Sample ◽  
Type I ◽  
Hypothesis Tests ◽  
Type I Error Rates ◽  
Meta Analyses ◽  
Small Sample Correction

The most common and well-known meta-regression models work under the assumption that there is only one effect size estimate per study and that the estimates are independent. However, meta-analytic reviews of social science research often include multiple effect size estimates per primary study, leading to dependence in the estimates. Some meta-analyses also include multiple studies conducted by the same lab or investigator, creating another potential source of dependence. An increasingly popular method to handle dependence is robust variance estimation (RVE), but this method can result in inflated Type I error rates when the number of studies is small. Small-sample correction methods for RVE have been shown to control Type I error rates adequately but may be overly conservative, especially for tests of multiple-contrast hypotheses. We evaluated an alternative method for handling dependence, cluster wild bootstrapping, which has been examined in the econometrics literature but not in the context of meta-analysis. Results from two simulation studies indicate that cluster wild bootstrapping maintains adequate Type I error rates and provides more power than extant small sample correction methods, particularly for multiple-contrast hypothesis tests. We recommend using cluster wild bootstrapping to conduct hypothesis tests for meta-analyses with a small number of studies. We have also created an R package that implements such tests.

scholarly journals True and false positive rates for different criteria of evaluating statistical evidence from clinical trials

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Don van Ravenzwaaij ◽  
John P. A. Ioannidis
Keyword(s):  
Clinical Trials ◽  
Confidence Intervals ◽  
False Positive ◽  
Evaluation Criteria ◽  
Small Sample ◽  
Statistical Evidence ◽  
Bayes Factors ◽  
True Positive ◽  
Small Sample Sizes ◽  
Meta Analyses

Abstract Background Until recently a typical rule that has often been used for the endorsement of new medications by the Food and Drug Administration has been the existence of at least two statistically significant clinical trials favoring the new medication. This rule has consequences for the true positive (endorsement of an effective treatment) and false positive rates (endorsement of an ineffective treatment). Methods In this paper, we compare true positive and false positive rates for different evaluation criteria through simulations that rely on (1) conventional p-values; (2) confidence intervals based on meta-analyses assuming fixed or random effects; and (3) Bayes factors. We varied threshold levels for statistical evidence, thresholds for what constitutes a clinically meaningful treatment effect, and number of trials conducted. Results Our results show that Bayes factors, meta-analytic confidence intervals, and p-values often have similar performance. Bayes factors may perform better when the number of trials conducted is high and when trials have small sample sizes and clinically meaningful effects are not small, particularly in fields where the number of non-zero effects is relatively large. Conclusions Thinking about realistic effect sizes in conjunction with desirable levels of statistical evidence, as well as quantifying statistical evidence with Bayes factors may help improve decision-making in some circumstances.

scholarly journals Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: findings from the ENIGMA Epigenetics Working Group

2019 ◽  
Author(s):  
Tianye Jia ◽  
Congying Chu ◽  
Yun Liu ◽  
Jenny van Dongen ◽  
Evangelos Papastergios ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Nucleus Accumbens ◽  
Large Scale ◽  
Biomarker Discovery ◽  
Association Studies ◽  
Meta Analysis ◽  
Hippocampal Volume ◽  
Small Sample ◽  
Small Sample Sizes ◽  
Meta Analyses

AbstractDNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc)—three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.

scholarly journals Relaxation therapy as a treatment for tics in patients with Tourette syndrome: a systematic literature review

2019 ◽  
Vol 41 (5) ◽  
pp. 1011-1017 ◽  
Author(s):  
Florence Tilling ◽  
Andrea E. Cavanna
Keyword(s):  
Literature Review ◽  
Tourette Syndrome ◽  
Systematic Literature Review ◽  
Treatment Strategies ◽  
Small Sample ◽  
Relaxation Therapy ◽  
Behavioural Treatment ◽  
Study Protocols ◽  
Small Sample Sizes ◽  
Meta Analyses

Abstract Background Tourette syndrome (TS) is a neurodevelopmental condition characterized by the presence of multiple motor and phonic tics, often associated with co-morbid behavioural problems. Tics can be modulated by environmental factors and are characteristically exacerbated by psychological stress, among other factors. This observation has led to the development of specific behavioural treatment strategies, including relaxation therapy. Objective This review aimed to assess the efficacy of relaxation therapy to control or reduce tic symptoms in patients with TS. Methods We conducted a systematic literature review of original studies on the major scientific databases, including Medline, EMBASE, and PsycInfo, according to the standards outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Outcomes measures included both tic severity and tic frequency. Results Our literature search identified three controlled trials, with a total number of 40 participants (range: 6–18 participants). In all three studies, relaxation therapy decreased the severity and/or the frequency of tic symptoms. However, the only trial comparing relaxation therapy to two other behavioural techniques found relaxation therapy to be the least effective intervention, as it reduced the number of tics by 32% compared to 44% with self-monitoring and 55% with habit reversal. Discussion The results of this systematic literature review provide initial evidence for the use of relaxation therapy as a behavioural treatment intervention for tics in patients with TS. Caution is needed in the interpretation of these findings, because the reviewed trials had small sample sizes and there was high heterogeneity across the study protocols.

scholarly journals Comparing “Visual” Effect Size Indices for Single-Case Designs

Methodology ◽  
2010 ◽  
Vol 6 (2) ◽  
pp. 49-58 ◽  
Author(s):  
Rumen Manolov ◽  
Antonio Solanas ◽  
David Leiva
Keyword(s):  
Effect Size ◽  
Visual Analysis ◽  
Psychological Treatment ◽  
Single Case ◽  
Data Sets ◽  
Intervention Effect ◽  
Applied Study ◽  
Data Points ◽  
Single Case Designs ◽  
Meta Analyses

Effect size indices are indispensable for carrying out meta-analyses and can also be seen as an alternative for making decisions about the effectiveness of a treatment in an individual applied study. The desirable features of the procedures for quantifying the magnitude of intervention effect include educational/clinical meaningfulness, calculus easiness, insensitivity to autocorrelation, low false alarm, and low miss rates. Three effect size indices related to visual analysis are compared according to the aforementioned criteria. The comparison is made by means of data sets with known parameters: degree of serial dependence, presence or absence of general trend, and changes in level and/or in slope. The percent of nonoverlapping data showed the highest discrimination between data sets with and without intervention effect. In cases when autocorrelation or trend is present, the percentage of data points exceeding the median may be a better option to quantify the effectiveness of a psychological treatment.

scholarly journals Exercise and Nutrition Impact on Osteoporosis and Sarcopenia—The Incidence of Osteosarcopenia: A Narrative Review

Nutrients ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 4499
Author(s):  
Sousana K. Papadopoulou ◽  
Konstantinos Papadimitriou ◽  
Gavriela Voulgaridou ◽  
Evridiki Georgaki ◽  
Eudoxia Tsotidou ◽  
...  
Keyword(s):  
Older Adults ◽  
Protective Role ◽  
Scientific Data ◽  
Bone Diseases ◽  
Small Sample ◽  
Narrative Review ◽  
Prevention Of Osteoporosis ◽  
Small Sample Sizes ◽  
Meta Analyses

Osteoporosis and sarcopenia are diseases which affect the myoskeletal system and often occur in older adults. They are characterized by low bone density and loss of muscle mass and strength, factors which reduce the quality of life and mobility. Recently, apart from pharmaceutical interventions, many studies have focused on non-pharmaceutical approaches for the prevention of osteoporosis and sarcopenia with exercise and nutrition to being the most important and well studied of those. The purpose of the current narrative review is to describe the role of exercise and nutrition on prevention of osteoporosis and sarcopenia in older adults and to define the incidence of osteosarcopenia. Most of the publications which were included in this review show that resistance and endurance exercises prevent the development of osteoporosis and sarcopenia. Furthermore, protein and vitamin D intake, as well as a healthy diet, present a protective role against the development of the above bone diseases. However, current scientific data are not sufficient for reaching solid conclusions. Although the roles of exercise and nutrition on osteoporosis and sarcopenia seem to have been largely evaluated in literature over the recent years, most of the studies which have been conducted present high heterogeneity and small sample sizes. Therefore, they cannot reach final conclusions. In addition, osteosarcopenia seems to be caused by the effects of osteoporosis and sarcopenia on elderly. Larger meta-analyses and randomized controlled trials are needed designed based on strict inclusion criteria, in order to describe the exact role of exercise and nutrition on osteoporosis and sarcopenia.

Effectiveness of Outdoor Exercise Parks on Health Outcomes in Older Adults—A Mixed-Methods Systematic Review and Meta-Analysis

2020 ◽  
pp. 1-13
Author(s):  
Yoke Leng Ng ◽  
Keith D. Hill ◽  
Pazit Levinger ◽  
Elissa Burton
Keyword(s):  
Physical Activity ◽  
Systematic Review ◽  
Older Adults ◽  
Meta Analysis ◽  
Small Sample ◽  
Activity Levels ◽  
Single Leg Stance ◽  
Small Sample Sizes ◽  
Meta Analyses

The objective of this systematic review was to examine the effectiveness of outdoor exercise park equipment on physical activity levels, physical function, psychosocial outcomes, and quality of life of older adults living in the community and to evaluate the evidence of older adults’ use of outdoor exercise park equipment. A search strategy was conducted from seven databases. Nine articles met the inclusion criteria. The study quality results were varied. Meta-analyses were undertaken for two physical performance tests: 30-s chair stand test and single-leg stance. The meta-analysis results were not statistically significant. It was not possible to conclude whether exercise parks were effective at improving levels of physical activity. The review shows that older adults value the benefits of health and social interaction from the use of exercise parks. Findings should be interpreted with caution due to the small sample sizes and the limited number of studies.

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