Review

Authors: Hyun Kang

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

Review

Authors: Signe Schmidt, Kirsten Nørgaard

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

Authors: Jiada James Zhan, Rebecca A Hodge, Anne L Dunlop...

BACKGROUND

Few standardized and open-source tools exist for calculating dietary pattern indexes from dietary intake data in epidemiological and clinical studies. Miscalculations of dietary indexes, with suspected erroneous findings, are occasionally noted in the literature.

OBJECTIVE

The primary aim is to develop and validate dietaryindex, a user-friendly and versatile R package that standardizes the calculation of dietary indexes.

METHODS

Dietaryindex utilizes a two-step process: an initial calculation of serving size for each food and nutrient category, followed by the calculation of individual dietary indexes. It includes generic functions that accept any preprocessed serving sizes of food groups and nutrients, with the standard serving sizes defined according to the methodologies used in well-known prospective cohort studies. For ease of use, dietaryindex also offers one-step functions that directly reference common datasets and tools, including the National Health and Nutrition Examination Survey (NHANES) and Block Food Frequency Questionnaire, eliminating the need for data preprocessing. At least two independent researchers validated the serving size definitions and scoring algorithms of dietaryindex.

RESULTS

Dietaryindex can calculate multiple dietary indexes of high interest in research, including Healthy Eating Index (HEI) - 2020, Alternative Healthy Eating Index 2010, Dietary Approaches to Stop Hypertension Index, Alternate Mediterranean Diet Score, Dietary Inflammatory Index, American Cancer Society 2020 dietary index, and Planetary Health Diet Index from the EAT-Lancet Commission. In our validation process, dietaryindex demonstrated full accuracy (100%) in all generic functions with two-decimal rounding precision in comparison to hand-calculated results. Similarly, using NHANES 2017-2018 data and ASA24 and DHQ3 example data, the HEI2015 outputs from dietaryindex aligned (99.95%-100%) with results using the SAS codes from the National Cancer Institute.

CONCLUSIONS

Dietaryindex is a user-friendly, versatile, and validated informatics tool for standardized dietary index calculations. We have open-sourced all the validation files and codes with detailed tutorials on GitHub (https://github.com/jamesjiadazhan/dietaryindex).

PubMed: 37609152
DOI: 10.1101/2023.08.07.548466

Authors: Karla Hemming, Jessica Kasza, Richard Hooper...

It has long been recognized that sample size calculations for cluster randomized trials require consideration of the correlation between multiple observations within the same cluster. When measurements are taken at anything other than a single point in time, these correlations depend not only on the cluster but also on the time separation between measurements and additionally, on whether different participants (cross-sectional designs) or the same participants (cohort designs) are repeatedly measured. This is particularly relevant in trials with multiple periods of measurement, such as the cluster cross-over and stepped-wedge designs, but also to some degree in parallel designs. Several papers describing sample size methodology for these designs have been published, but this methodology might not be accessible to all researchers. In this article we provide a tutorial on sample size calculation for cluster randomized designs with particular emphasis on designs with multiple periods of measurement and provide a web-based tool, the Shiny CRT Calculator, to allow researchers to easily conduct these sample size calculations. We consider both cross-sectional and cohort designs and allow for a variety of assumed within-cluster correlation structures. We consider cluster heterogeneity in treatment effects (for designs where treatment is crossed with cluster), as well as individually randomized group-treatment trials with differential clustering between arms, for example designs where clustering arises from interventions being delivered in groups. The calculator will compute power or precision, as a function of cluster size or number of clusters, for a wide variety of designs and correlation structures. We illustrate the methodology and the flexibility of the Shiny CRT Calculator using a range of examples.

Topics: Cluster Analysis; Cross-Over Studies; Humans; Randomized Controlled Trials as Topic; Research Design; Sample Size

PubMed: 32087011
DOI: 10.1093/ije/dyz237

Authors: Michael C Chung, Saurabh Gombar, Run Zhang Shi...

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

Authors: Sergiy O Garbuzynskiy, Alexei V Finkelstein

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

Authors: Lin Naing, Rusli Bin Nordin, Hanif Abdul Rahman...

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

Review

Authors: Yarrow Scantling-Birch, Hasan Naveed, Ritika Mukhija...

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

Review

Authors: Andres Elovic, Ali Pourmand

MDCalc offers all healthcare professionals a quick and well-designed tool to look up for popular clinical calculators that are supported by evidence-based medicine. The app allows you to select your speciality and have related calculations at a press of a button. The app offers hundreds of clinical decision tools including risk scores, algorithms, equations, diagnostic criteria, formulas, classifications, dosing calculators, and more at your fingertips.

PubMed: 31025219
DOI: 10.1007/s10278-019-00218-y