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Journal of Educational Evaluation For... 2021Appropriate sample size calculation and power analysis have become major issues in research and publication processes. However, the complexity and difficulty of... (Review)
Review
Appropriate sample size calculation and power analysis have become major issues in research and publication processes. However, the complexity and difficulty of calculating sample size and power require broad statistical knowledge, there is a shortage of personnel with programming skills, and commercial programs are often too expensive to use in practice. The review article aimed to explain the basic concepts of sample size calculation and power analysis; the process of sample estimation; and how to calculate sample size using G*Power software (latest ver. 3.1.9.7; Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany) with 5 statistical examples. The null and alternative hypothesis, effect size, power, alpha, type I error, and type II error should be described when calculating the sample size or power. G*Power is recommended for sample size and power calculations for various statistical methods (F, t, χ2, Z, and exact tests), because it is easy to use and free. The process of sample estimation consists of establishing research goals and hypotheses, choosing appropriate statistical tests, choosing one of 5 possible power analysis methods, inputting the required variables for analysis, and selecting the “calculate” button. The G*Power software supports sample size and power calculation for various statistical methods (F, t, χ2, z, and exact tests). This software is helpful for researchers to estimate the sample size and to conduct power analysis.
Topics: Humans; Research Design; Sample Size; Software
PubMed: 34325496
DOI: 10.3352/jeehp.2021.18.17 -
Frontiers in Genetics 2022A polygenic risk score estimates the genetic risk of an individual for some disease or trait, calculated by aggregating the effect of many common variants associated...
A polygenic risk score estimates the genetic risk of an individual for some disease or trait, calculated by aggregating the effect of many common variants associated with the condition. With the increasing availability of genetic data in large cohort studies such as the UK Biobank, inclusion of this genetic risk as a covariate in statistical analyses is becoming more widespread. Previously this required specialist knowledge, but as tooling and data availability have improved it has become more feasible for statisticians and epidemiologists to calculate existing scores themselves for use in analyses. While tutorial resources exist for conducting genome-wide association studies and generating of new polygenic risk scores, fewer guides exist for the simple calculation and application of existing genetic scores. This guide outlines the key steps of this process: selection of suitable polygenic risk scores from the literature, extraction of relevant genetic variants and verification of their quality, calculation of the risk score and key considerations of its inclusion in statistical models, using the UK Biobank imputed data as a model data set. Many of the techniques in this guide will generalize to other datasets, however we also focus on some of the specific techniques required for using data in the formats UK Biobank have selected. This includes some of the challenges faced when working with large numbers of variants, where the computation time required by some tools is impractical. While we have focused on only a couple of tools, which may not be the best ones for every given aspect of the process, one barrier to working with genetic data is the sheer volume of tools available, and the difficulty for a novice to assess their viability. By discussing in depth a couple of tools that are adequate for the calculation even at large scale, we hope to make polygenic risk scores more accessible to a wider range of researchers.
PubMed: 35251129
DOI: 10.3389/fgene.2022.818574 -
Journal of Pathology Informatics 2017Automated calculations by laboratory information system (LIS) are efficient and accurate ways of providing calculated laboratory test results. Due to the lack of...
BACKGROUND
Automated calculations by laboratory information system (LIS) are efficient and accurate ways of providing calculated laboratory test results. Due to the lack of established advanced mathematical functions and equation logic in LIS software, calculations beyond simple arithmetic functions require a tedious workaround. Free and bioavailable testosterone (BT) calculations require a quadratic solver currently unavailable as ready to use the function on most commercial LIS platforms. We aimed to develop a module within the Epic Beaker LIS to enable automatic quadratic equation solving capability and real-time reporting of calculated free and BT values.
MATERIALS AND METHODS
We developed and implemented an advanced calculation module from the ground up using existing basic calculation programming functions in the Epic Beaker LIS. A set of calculation variables were created, and mathematical logic and functions were used to link the variables and perform the actual quadratic equation based calculations. Calculations were performed in real-time during result entry events, and calculated results populated the result components in LIS automatically.
RESULTS
Free and BT were calculated using instrument measured results of total testosterone, sex hormone binding globulin, and/or serum albumin, by applying equations widely adopted in laboratory medicine for endocrine diseases and disorders. Calculated results in Epic Beaker LIS were then compared and confirmed by manual calculations using Microsoft Excel spreadsheets and scientific calculators to have no discrepancies.
CONCLUSIONS
Automated calculations of free and BT were successfully implemented and validated, the first of such implementation for the Epic Beaker LIS platform, eliminating the need of offline manual calculations, potential transcription error, and with improved turnaround time. It may serve as a model to build similarly complex equations when the clinical need arises.
PubMed: 28828199
DOI: 10.4103/jpi.jpi_28_17 -
Journal of Diabetes Science and... Sep 2014Matching meal insulin to carbohydrate intake, blood glucose, and activity level is recommended in type 1 diabetes management. Calculating an appropriate insulin bolus... (Review)
Review
Matching meal insulin to carbohydrate intake, blood glucose, and activity level is recommended in type 1 diabetes management. Calculating an appropriate insulin bolus size several times per day is, however, challenging and resource demanding. Accordingly, there is a need for bolus calculators to support patients in insulin treatment decisions. Currently, bolus calculators are available integrated in insulin pumps, as stand-alone devices and in the form of software applications that can be downloaded to, for example, smartphones. Functionality and complexity of bolus calculators vary greatly, and the few handfuls of published bolus calculator studies are heterogeneous with regard to study design, intervention, duration, and outcome measures. Furthermore, many factors unrelated to the specific device affect outcomes from bolus calculator use and therefore bolus calculator study comparisons should be conducted cautiously. Despite these reservations, there seems to be increasing evidence that bolus calculators may improve glycemic control and treatment satisfaction in patients who use the devices actively and as intended.
Topics: Blood Glucose; Diabetes Mellitus, Type 1; Drug Dosage Calculations; Humans; Hypoglycemic Agents; Insulin
PubMed: 24876436
DOI: 10.1177/1932296814532906 -
Biomolecules Jan 2022The calculation of dissociation constants is an important problem in molecular biophysics. For such a calculation, it is important to correctly calculate both terms of...
The calculation of dissociation constants is an important problem in molecular biophysics. For such a calculation, it is important to correctly calculate both terms of the binding free energy; that is, the enthalpy and entropy of binding. Both these terms can be computed using molecular dynamics simulations, but this approach is very computationally expensive, and entropy calculations are especially slow. We develop an alternative very fast method of calculating the binding entropy and dissociation constants. The main part of our approach is based on the evaluation of movement ranges of molecules in the bound state. Then, the range of molecular movements in the bound state (here, in molecular crystals) is used for the calculation of the binding entropies and, then (using, in addition, the experimentally measured sublimation enthalpies), the crystal-to-vapor dissociation constants. Previously, we considered the process of the reversible sublimation of small organic molecules from crystals to vapor. In this work, we extend our approach by considering the dissolution of molecules, in addition to their sublimation. Similar to the sublimation case, our method shows a good correlation with experimentally measured dissociation constants at the dissolution of crystals.
Topics: Entropy; Molecular Dynamics Simulation; Thermodynamics
PubMed: 35204648
DOI: 10.3390/biom12020147 -
BMC Medical Research Methodology Jul 2022Although books and articles guiding the methods of sample size calculation for prevalence studies are available, we aim to guide, assist and report sample size...
BACKGROUND
Although books and articles guiding the methods of sample size calculation for prevalence studies are available, we aim to guide, assist and report sample size calculation using the present calculators.
RESULTS
We present and discuss four parameters (namely level of confidence, precision, variability of the data, and anticipated loss) required for sample size calculation for prevalence studies. Choosing correct parameters with proper understanding, and reporting issues are mainly discussed. We demonstrate the use of a purposely-designed calculators that assist users to make proper informed-decision and prepare appropriate report.
CONCLUSION
Two calculators can be used with free software (Spreadsheet and RStudio) that benefit researchers with limited resources. It will, hopefully, minimize the errors in parameter selection, calculation, and reporting. The calculators are available at: ( https://sites.google.com/view/sr-ln/ssc ).
Topics: Cross-Sectional Studies; Humans; Sample Size; Software
PubMed: 35907796
DOI: 10.1186/s12874-022-01694-7 -
Aorta (Stamford, Conn.) Aug 2016Reliable methods for measuring the thoracic aorta are critical for determining treatment strategies in aneurysmal disease. Z-scores are a pragmatic alternative to raw... (Review)
Review
Reliable methods for measuring the thoracic aorta are critical for determining treatment strategies in aneurysmal disease. Z-scores are a pragmatic alternative to raw diameter sizes commonly used in adult medicine. They are particularly valuable in the pediatric population, who undergo rapid changes in physical development. The advantage of the Z-score is its inclusion of body surface area (BSA) in determining whether an aorta is within normal size limits. Therefore, Z-scores allow us to determine whether true pathology exists, which can be challenging in growing children. In addition, Z-scores allow for thoughtful interpretation of aortic size in different genders, ethnicities, and geographical regions. Despite the advantages of using Z-scores, there are limitations. These include intra- and inter-observer bias, measurement error, and variations between alternative Z-score nomograms and BSA equations. Furthermore, it is unclear how Z-scores change in the normal population over time, which is essential when interpreting serial values. Guidelines for measuring aortic parameters have been developed by the American Society of Echocardiography Pediatric and Congenital Heart Disease Council, which may reduce measurement bias when calculating Z-scores for the aortic root. In addition, web-based Z-score calculators have been developed to aid in efficient Z-score calculations. Despite these advances, clinicians must be mindful of the limitations of Z-scores, especially when used to demonstrate beneficial treatment effect. This review looks to unravel the mystery of the Z-score, with a focus on the thoracic aorta. Here, we will discuss how Z-scores are calculated and the limitations of their use.
PubMed: 28097194
DOI: 10.12945/j.aorta.2016.16.014 -
Journal of Chemical Information and... Dec 2021We describe the Mass Spectrometry Adduct Calculator (MSAC), an automated Python tool to calculate the adduct ion masses of a parent molecule. Here, adduct refers to a...
We describe the Mass Spectrometry Adduct Calculator (MSAC), an automated Python tool to calculate the adduct ion masses of a parent molecule. Here, adduct refers to a version of a parent molecule [M] that is charged due to addition or loss of atoms and electrons resulting in a charged ion, for example, [M + H]. MSAC includes a database of 147 potential adducts and adduct/neutral loss combinations and their mass-to-charge ratios (/) as extracted from the NIST/EPA/NIH Mass Spectral Library (NIST17), Global Natural Products Social Molecular Networking Public Spectral Libraries (GNPS), and MassBank of North America (MoNA). The calculator relies on user-selected subsets of the combined database to calculate expected / for adducts of molecules supplied as formulas. This tool is intended to help researchers create identification libraries to collect evidence for the presence of molecules in mass spectrometry data. While the included adduct database focuses on adducts typically detected during liquid chromatography-mass spectrometry analyses, users may supply their own lists of adducts and charge states for calculating expected /. We also analyzed statistics on adducts from spectra contained in the three selected mass spectral libraries. MSAC is freely available at https://github.com/pnnl/MSAC.
Topics: Chromatography, Liquid; Mass Spectrometry
PubMed: 34842435
DOI: 10.1021/acs.jcim.1c00579 -
Vision (Basel, Switzerland) Feb 2022Smartphone apps are becoming increasingly popular in ophthalmology, one specific area of their application being toric intraocular lens (IOL) surgery for astigmatism... (Review)
Review
Smartphone apps are becoming increasingly popular in ophthalmology, one specific area of their application being toric intraocular lens (IOL) surgery for astigmatism correction. Our objective was to identify, review and objectively score smartphone apps applicable to toric IOL calculation and/or axis alignment. This review was divided into three phases. A review was conducted on four major app databases (phase I): National Health Service (NHS) Apps Library, Google Play Store, Apple App Store and Amazon Appstore. A systematic literature review (phase II) was conducted to identify studies for included apps in phase I of our study. Keywords used in both searches included: "toric lens", "toric IOL", "refraction", "astigmatism", "ophthalmology", "eye calculator", "ophthalmology calculator" and "refractive calculator". Included apps were objectively scored (phase III) by three independent reviewers using the mobile app rating scale (MARS), a validated tool that ranks the quality of mobile health apps using a calculated mean app quality (MAQ) score. Phase I of our study screened 2428 smartphone apps, of which six apps for toric IOL calculation and four apps for axis marking were eligible and were selected for quantitative analysis. Phase II of our study screened 477 studies from PubMed, Medline and Google Scholar. Three studies validating two apps (toriCAM, iToric Patwardhan) in a clinical setting as adjunct tools for preoperative axis marking were identified. Phase III ranked Toric Calculator for iPhone (Apple iOS, MAQ 4.13; average MAQ 3.34 ± 0.54) as the highest-scoring toric IOL calculator, and iToric Patwardhan (Android OS, MAQ 4.13; average MAQ 3.41 ± 0.44) was the highest-scoring axis marker in our study. Our review identified and objectively scored ten smartphone apps available for toric IOL surgery adjuncts. Toric Calculator for iPhone and iToric Patwardhan were the highest-scoring toric IOL calculator and axis marker, respectively. Current literature, though limited, suggests that axis marking smartphone apps can achieve similar levels of misalignment reduction when compared to digital systems.
PubMed: 35225972
DOI: 10.3390/vision6010013