How to analyze the Visual Analogue Scale: Myths, truths and clinical relevance

2016 ◽  
Vol 13 (1) ◽  
pp. 67-75 ◽  
Author(s):  
Gillian Z. Heller ◽  
Maurizio Manuguerra ◽  
Roberta Chow
Keyword(s):  
Visual Analogue Scale ◽  
Statistical Methods ◽  
Analogue Scale ◽  
Small Sample Size ◽  
Ordinal Regression ◽  
Small Sample ◽  
Published Data ◽  
Data Set ◽  
Vas Scores ◽  
Ordinal Level

AbstractBackground and aimsThe Visual Analogue Scale (VAS) is a popular tool for the measurement of pain. A variety of statistical methods are employed for its analysis as an outcome measure, not all of them optimal or appropriate. An issue which has attracted much discussion in the literature is whether VAS is at a ratio or ordinal level of measurement. This decision has an influence on the appropriate method of analysis. The aim of this article is to provide an overview of current practice in the analysis of VAS scores, to propose a method of analysis which avoids the shortcomings of more traditional approaches, and to provide best practice recommendations for the analysis of VAS scores.MethodsWe report on the current usage of statistical methods, which fall broadly into two categories: those that assume a probability distribution for VAS, and those that do not. We give an overview of these methods, and propose continuous ordinal regression, an extension of current ordinal regression methodology, which is appropriate for VAS at an ordinal level of measurement. We demonstrate the analysis of a published data set using a variety of methods, and use simulation to compare the power of the various methods to detect treatment differences, in differing pain situations.ResultsWe demonstrate that continuous ordinal regression provides the most powerful statistical analysis under a variety of conditions.Conclusions and Implications We recommend that in the situation in which no covariates besides treatment group are included in the analysis, distribution-free methods (Wilcoxon, Mann–Whitney) be used, as their power is indistinguishable from that of the proposed method. In the situation in which there are covariates which affect VAS, the proposed method is optimal. However, in this case, if the VAS scores are not concentrated around either extreme of the scale, normal-distribution methods (t-test, linear regression) are almost as powerful, and are recommended as a pragmatic choice. In the case of small sample size and VAS skewed to either extreme of the scale, the proposed method has vastly superior power to other methods.

scholarly journals Analgesic efficacy of meloxicam with or without a buprenorphine patch in cats after ovariohysterectomy

2018 ◽  
Vol 63 (No. 6) ◽  
pp. 279-286
Author(s):  
SY Heo ◽  
SJ Kim ◽  
NS Kim
Keyword(s):  
Visual Analogue Scale ◽  
Analogue Scale ◽  
Small Sample Size ◽  
Analgesic Efficacy ◽  
Small Sample ◽  
Visual Analogue Scale Pain ◽  
Pain Scores ◽  
Analogue Scale Pain ◽  
Group A ◽  
Group B

The purpose of this prospective double blind clinical study was to evaluate the analgesic efficacy of meloxicam with/without a buprenorphine patch for pain management after ovariohysterectomy in cats. Cats were randomly divided into two groups: ten cats were treated with meloxicam s.c. after ovariohysterectomy (Group A), and eight cats were treated with s.c. meloxicam and a 20 µg/h buprenorphine transdermal patch (Group B). For patch treatment, the cat’s hair was clipped on the left side in the thoracic area. Pain scores were assessed at 0.5, 1, 2, 4, 6, 8, 24 and 30 h post-ovariohysterectomy extubation. To evaluate postoperative pain, 4A-VET pain scale and visual analogue scale pain scores were used. In addition, blood was collected from all cats to determine the cortisol levels at –2 h and at 0.5, 4, 6 and 24 h after extubation. The 4A-VET scores for Group B were significantly lower at 1, 4, 6, 8, 24 and 30 h than the scores for Group A. The visual analogue scale pain scores for Group B were significantly lower at 4, 6, 24 and 30 h than the scores for Group A. Serum cortisol concentrations were not significantly different between Groups A and B at any of the measured intervals. There was a significant positive correlation between postoperative visual analogue scale and 4A-VET pain scores in both groups. Our results should be subject to careful interpretation as the study was limited by its small sample size and by observer subjectivity.

scholarly journals Preference for Alternate-Day Versus Conventional In-Center Dialysis: A Health Utility Elicitation

2020 ◽  
Vol 7 ◽  
pp. 205435812091442
Author(s):  
Rafael J. Solimano ◽  
James Lineen ◽  
David M. J. Naimark
Keyword(s):  
Visual Analogue Scale ◽  
Analogue Scale ◽  
Small Sample Size ◽  
Health Centre ◽  
Small Sample ◽  
Health State ◽  
Single Center ◽  
Utility Elicitation ◽  
Utility Values ◽  
Elicitation Methods

Background: Mortality rates for patients on hemodialysis (HD) continue to be high, in particular, following the long interdialytic period, yet thrice-weekly conventional HD (CHD) is still an almost universal regimen. Alternate-day dialysis (ADD) may have advantages over the current schedule because it would eliminate the long interdialytic break. A preliminary, as yet unpublished, patient simulation and cost-utility analysis compared CHD versus ADD and demonstrated that the economic attractiveness of ADD was sensitive, in particular, to patients’ preference for ADD versus CHD. To date, this preference has not been elicited. Objective: To elicit utilities for both CHD and ADD using 3 standard elicitation methods among a prevalent cohort of patients on CHD. Design: This study is a single-center survey of patient preferences (utilities). Setting: This study took place within the dialysis units of Sunnybrook Health Centre, a university-affiliated teaching hospital in Toronto, Ontario, Canada, which encompasses 174 patients on in-center HD. Patients: Those older than 18 years of age, on thrice-weekly HD, were included in this study. Measurements: Descriptive statistics were used to summarize patient characteristics and the utility values generated. A multiple linear regression was performed to determine an association between participant characteristics and the utility ratio. Methods: Via standardized face-to-face interviews by a single investigator, 3 utility elicitation methods, visual analogue scale (VAS), time trade-off (TTO), and standard gamble (SG), were administered to generate utilities for each patient for their current health state of CHD (thrice-weekly). After completing this task, we provided each patient with a concise summary regarding the current literature on how ADD may impact their health. Finally, patients were asked to envision their health while on an ADD regimen while repeating the VAS, TTO, and SG. Results: We recruited 65 participants. The mean utilities of CHD versus ADD were similar for all 3 methods. Visual analogue scale, TTO, and SG had utility values of 0.6 ± 0.2, 0.6 ± 0.3, and 0.7 ± 0.3, and 0.6 ± 0.2, 0.7 ± 0.3, and 0.7 ± 0.3 for CHD and ADD, respectively. The ratio for CHD to ADD was 1.1 ± 0.4, 1.1 ± 0.5, and 1.0 ± 0.2 for VAS, TTO, and SG, respectively. Limitations: Small sample size from a single center, where not all participants agreed to participate, wide variability in participant responses and requiring patients to conceptually imagine life on ADD may have affected our results. Conclusions: Compared with CHD, there was no difference in the preference toward ADD which demonstrates promise that adopting an alternate-day schedule may be acceptable to patients. Furthermore, with the generation of a utility for ADD, this will allow for more precise estimates in future simulation studies of the economic attractiveness of ADD. Trial registration: Not required as this article is not a systematic review nor does it report the results of a health care intervention.

scholarly journals The reproducibility of subjective appetite scores

1995 ◽  
Vol 73 (4) ◽  
pp. 517-530 ◽  
Author(s):  
Anne Raben ◽  
ANNA TAGLIABUE ◽  
Arne Astrup
Keyword(s):  
Visual Analogue Scale ◽  
Food Consumption ◽  
Analogue Scale ◽  
Prior Experience ◽  
Random Noise ◽  
Normal Weight ◽  
Vas Scores ◽  
The Mean ◽  
The Difference ◽  
Mean Differences

Although subjective appetite scores are widely used, studies on the reproducibility of this method are scarce. In the present study nine healthy, normal weight, young men recorded their subjective appetite sensations before and during 5 h after two different test meals A and B. The subjects tested each meal twice and in randomized order. Visual analogue scale (VAS) scores, 10 cm in length, were used to assess hunger, satiety, fullness, prospective food consumption and palatability of the meals. Plasma glucose and lactate concentrations were determined concomitantly. The repeatability was investigated for fasting values, Δ-mean 5 h and mean 5 h values, Δ-peak/nadir and peak/nadir values. Although the profiles of the postprandial responses were similar, the coefficients of repeatability (CR = 2SD) on the mean differences were large, ranging from 2·86 to 5.24 cm for fasting scores, 1·36 to 1·88 cm for mean scores, 2·98 to 5·42 cm for Δ-mean scores, and 3·16 to 6·44 cm for peak and Δ-peak scores. For palatability ratings the CK values varied more, ranging from 2·38 (taste) to 8·70 cm (aftertaste). Part of the difference in satiety ratings could be explained by the differences in palatability ratings. However, the low reproducibility may also be caused by a conditioned satiation or hunger due to the subjects' prior experience of the meals and therefore not just be a reflection of random noise. It is likely, however, that the variation in appetite ratings is due both to methodological day-to-day variation and to biological day-to-day variation in subjective appetite sensations.

scholarly journals Comparison of statistical methods for analysis of small sample sizes for detecting the differences in efficacy between treatments for knee osteoarthritis

2020 ◽  
Author(s):  
Chia-Lung Shih ◽  
Te-Yu Hung
Keyword(s):  
Knee Osteoarthritis ◽  
Sample Size ◽  
Statistical Methods ◽  
Statistical Power ◽  
Permutation Test ◽  
Small Sample Size ◽  
Small Sample ◽  
T Test ◽  
Sample Sizes ◽  
Knee Oa

Abstract Background A small sample size (n < 30 for each treatment group) is usually enrolled to investigate the differences in efficacy between treatments for knee osteoarthritis (OA). The objective of this study was to use simulation for comparing the power of four statistical methods for analysis of small sample size for detecting the differences in efficacy between two treatments for knee OA. Methods A total of 10,000 replicates of 5 sample sizes (n=10, 15, 20, 25, and 30 for each group) were generated based on the previous reported measures of treatment efficacy. Four statistical methods were used to compare the differences in efficacy between treatments, including the two-sample t-test (t-test), the Mann-Whitney U-test (M-W test), the Kolmogorov-Smirnov test (K-S test), and the permutation test (perm-test). Results The bias of simulated parameter means showed a decreased trend with sample size but the CV% of simulated parameter means varied with sample sizes for all parameters. For the largest sample size (n=30), the CV% could achieve a small level (<20%) for almost all parameters but the bias could not. Among the non-parametric tests for analysis of small sample size, the perm-test had the highest statistical power, and its false positive rate was not affected by sample size. However, the power of the perm-test could not achieve a high value (80%) even using the largest sample size (n=30). Conclusion The perm-test is suggested for analysis of small sample size to compare the differences in efficacy between two treatments for knee OA.

A Multi-Linear Statistical Method for Discriminant Analysis of 2D Frontal Face Images

2012 ◽  
pp. 18-33 ◽  
Author(s):  
Carlos Eduardo Thomaz ◽  
Vagner do Amaral ◽  
Gilson Antonio Giraldi ◽  
Edson Caoru Kitani ◽  
João Ricardo Sato ◽  
...  
Keyword(s):  
Small Sample Size ◽  
Face Image ◽  
Small Sample ◽  
High Dimensional ◽  
Data Set ◽  
Linear Discriminant ◽  
Face Images ◽  
Linear Framework ◽  
Facial Changes ◽  
2D Data

This chapter describes a multi-linear discriminant method of constructing and quantifying statistically significant changes on human identity photographs. The approach is based on a general multivariate two-stage linear framework that addresses the small sample size problem in high-dimensional spaces. Starting with a 2D data set of frontal face images, the authors determine a most characteristic direction of change by organizing the data according to the patterns of interest. These experiments on publicly available face image sets show that the multi-linear approach does produce visually plausible results for gender, facial expression and aging facial changes in a simple and efficient way. The authors believe that such approach could be widely applied for modeling and reconstruction in face recognition and possibly in identifying subjects after a lapse of time.

scholarly journals SSMD: a semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data

2020 ◽  
Author(s):  
Xiaoyu Lu ◽  
Szu-Wei Tu ◽  
Wennan Chang ◽  
Changlin Wan ◽  
Jiashi Wang ◽  
...  
Keyword(s):  
Small Sample Size ◽  
Cell Types ◽  
Small Sample ◽  
Training Data ◽  
Mouse Tissue ◽  
Marker Genes ◽  
Specific Cell ◽  
Cell Type ◽  
Data Set ◽  
Transcriptomics Data

Abstract Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different data set scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment. SSMD is featured by (i) a novel nonparametric method to discover data set-specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including: (i) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment; (ii) diverse experimental platforms of mouse transcriptomics data; (iii) small sample size and limited training data source and (iv) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing with state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD.

scholarly journals Deep generative models in DataSHIELD

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Stefan Lenz ◽  
Moritz Hess ◽  
Harald Binder
Keyword(s):  
Genetic Variant ◽  
Small Sample Size ◽  
Synthetic Data ◽  
Routine Data ◽  
Original Data ◽  
Generative Models ◽  
Small Sample ◽  
Generative Adversarial Networks ◽  
Artificial Data ◽  
Data Set

Abstract Background The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In Germany, for example, it is not possible to pool routine data from different hospitals for research purposes without the consent of the patients. Methods The DataSHIELD software provides an infrastructure and a set of statistical methods for joint, privacy-preserving analyses of distributed data. The contained algorithms are reformulated to work with aggregated data from the participating sites instead of the individual data. If a desired algorithm is not implemented in DataSHIELD or cannot be reformulated in such a way, using artificial data is an alternative. Generating artificial data is possible using so-called generative models, which are able to capture the distribution of given data. Here, we employ deep Boltzmann machines (DBMs) as generative models. For the implementation, we use the package “BoltzmannMachines” from the Julia programming language and wrap it for use with DataSHIELD, which is based on R. Results We present a methodology together with a software implementation that builds on DataSHIELD to create artificial data that preserve complex patterns from distributed individual patient data. Such data sets of artificial patients, which are not linked to real patients, can then be used for joint analyses. As an exemplary application, we conduct a distributed analysis with DBMs on a synthetic data set, which simulates genetic variant data. Patterns from the original data can be recovered in the artificial data using hierarchical clustering of the virtual patients, demonstrating the feasibility of the approach. Additionally, we compare DBMs, variational autoencoders, generative adversarial networks, and multivariate imputation as generative approaches by assessing the utility and disclosure of synthetic data generated from real genetic variant data in a distributed setting with data of a small sample size. Conclusions Our implementation adds to DataSHIELD the ability to generate artificial data that can be used for various analyses, e.g., for pattern recognition with deep learning. This also demonstrates more generally how DataSHIELD can be flexibly extended with advanced algorithms from languages other than R.

scholarly journals A Systematic Review of the Nickel Content of the Normal Human Prostate Gland

2021 ◽  
Vol 2020 ◽  
Author(s):  
Vladimir Zaichick
Keyword(s):  
Small Sample Size ◽  
Prostate Gland ◽  
Nickel Content ◽  
Small Sample ◽  
Prostatic Tissue ◽  
Cochrane Library ◽  
Published Data ◽  
Systematic Analysis ◽  
High Data ◽  
Ni Content

Introduction: The prostate gland is subject to various disorders. The etiology and pathogenesis of these diseases remain not well understood. Moreover, despite technological advancements, the differential diagnosis of prostate disorders has become progressively more complex and controversial. It was suggested that the nickel (Ni) level in prostatic tissue plays an important role in prostatic carcinogenesis and its measurement may be useful as a cancer biomarker. These suggestions promoted more detailed studies of the Ni content in the prostatic tissue of healthy subjects. Materials and methods: The present study evaluated by systematic analysis the published data for Ni content analyzed in prostatic tissue of “normal” glands. This evaluation reviewed 1889 studies, all of which were published in the years from 1921 to 2020 and were located by searching the databases Scopus, PubMed, MEDLINE, ELSEVIER-EMBASE, Cochrane Library, and the Web of Science. The articles were analyzed and “Median of Means” and “Range of Means” were used to examine heterogeneity of the measured Ni content in prostates of apparently healthy men. Results: The objective analysis was performed on data from the 20 studies, which included 743 subjects. It was found that the range of means of prostatic Ni content reported in the literature for “normal” gland varies widely from 0.030 mg/kg to 4.50 mg/kg with median of means 0.625 mg/kg on a wet mass basis. Conclusion: Because of small sample size and high data heterogeneity, we recommend other primary studies be performed.

scholarly journals Effect of intraarticular steroid injection in addition to physical modalities in osteoarthritis knee

2015 ◽  
Vol 23 (1) ◽  
pp. 48-54
Author(s):  
Md Jahidul Islam ◽  
MM Jalal Uddin ◽  
Md Shahadat Hossain ◽  
Md Ruhul Amin ◽  
Md Moshiur Rahman ◽  
...  
Keyword(s):  
Visual Analogue Scale ◽  
Range Of Motion ◽  
Analogue Scale ◽  
Small Sample ◽  
Therapeutic Exercise ◽  
Womac Index ◽  
Group A ◽  
Pre Treatment ◽  
Group B ◽  
Physical Modalities

Context: Osteoarthritis (OA) is the most common form of arthritis accounting for about 30% of general physician visits. Intrarticular (IA) corticosteroid injections have been used for decades in clinical practice for pain relief and control of local inflammation in OA. In the present study a combined therapy of long acting intra-articular injection in addition to physical modalities of OA knee was given to find out the functional improvement and clinical outcome of the patient. Methods: It was a prospective interventional non-randomized clinical study conducted in the Department of Physical Medicine & Rehabilitation, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, from October, 2011 to March, 2012. Fifty four patients between 35 and 75 years without consideration of gender with a history of not less than three months knee pain with radiographic confirmation of primary osteoarthritis were selected purposefully. Then they were divided randomly in group A and B, having 27 patients in each group. Group A received NSAID (non steroidal anti-inflammatory drugs) i.e. aceclofenac 100mg twice daily for 10 days + omeprazol 20mg twice daily for 10 days + MWD (micro wave diathermy 20 minutes for 14 days. + therapeutic exercise + ADL (activities of daily living), while Group B received 80mg intraarticular triamcinolon acetonide injection once followed by NSAID i.e. aceclofenac 100mg twice daily for 10 days + omeprazol 20mg twice daily for 10 days + MWD 20 minutes for 14 days. + therapeutic exercise + ADL. In both groups the patients were observed for six weeks. Results: The mean of age of patients in group A and B were 52.33±9.62 years and 52.29±9.67 years respectively. In group A, 9 (33.3%) were male and 18 (66.7%) were female. In group B, 10 (37.0%) were male and 18 (63.0%) were female. Mean visual analogue scale (VAS) during pre treatment in group A and group B were 6.22±1.60 and 7.15±1.56 respectively. Mean range of motion (ROM) during pre treatment in group A and group B were 117.33±13.05 and 112.37±19.01 respectively. Mean time taken to walk 50 feet during pre treatment in group A and group B were 18.22±2.39 and 18.81±2.13 minutes respectively. Mean Western Ontario and Mc Master Universities (WOMAC) index in group A and group B were 60.85±15.86 and 67.33±16.33 minutes respectively. After treatment in both groups visual analogue scale (VAS), range of motion (ROM), time taken to walk 50 feet and Western Ontario and Mc Master Universities (WOMAC) index gradually decreased and range of motion (ROM) gradually increased, which were statistically significant. However, the study conducted with small sample size in a single centre in Dhaka city, which may not be representative for the whole country. DOI: http://dx.doi.org/10.3329/jdmc.v23i1.22694 J Dhaka Medical College, Vol. 23, No.1, April, 2014, Page 48-54

scholarly journals Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Zhihua Wang ◽  
Yongbo Zhang ◽  
Huimin Fu
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Small Sample Size ◽  
Computational Effort ◽  
Small Sample ◽  
Grey Theory ◽  
Data Set ◽  
Rolling Mechanism ◽  
Short Term Forecasting ◽  
Prediction Approach

Reasonable prediction makes significant practical sense to stochastic and unstable time series analysis with small or limited sample size. Motivated by the rolling idea in grey theory and the practical relevance of very short-term forecasting or 1-step-ahead prediction, a novel autoregressive (AR) prediction approach with rolling mechanism is proposed. In the modeling procedure, a new developed AR equation, which can be used to model nonstationary time series, is constructed in each prediction step. Meanwhile, the data window, for the next step ahead forecasting, rolls on by adding the most recent derived prediction result while deleting the first value of the former used sample data set. This rolling mechanism is an efficient technique for its advantages of improved forecasting accuracy, applicability in the case of limited and unstable data situations, and requirement of little computational effort. The general performance, influence of sample size, nonlinearity dynamic mechanism, and significance of the observed trends, as well as innovation variance, are illustrated and verified with Monte Carlo simulations. The proposed methodology is then applied to several practical data sets, including multiple building settlement sequences and two economic series.

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