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Statistical power analysis and sample size planning for moderated mediation models
Conditional process models, including moderated mediation models and mediated moderation models, are widely used in behavioral science research. However, few studies have examined approaches to conduct statistical power analysis for such models and there is also a lack of software packages that provide such power analysis functionalities. In this paper, we introduce new simulation-based methods for power analysis of conditional process models with a focus on moderated mediation models. These simulation-based methods provide intuitive ways for sample-size planning based on regression coefficients in a moderated mediation model as well as selected variance and covariance components. We demonstrate how the methods can be applied to five commonly used moderated mediation models using a simulation study, and we also assess the performance of the methods through the five models. We implement our approaches in the WebPower R package and also in Web apps to ease their application.
Journal Article
Safety and efficacy of total parenteral nutrition versus total enteral nutrition for patients with severe acute pancreatitis: a meta-analysis
Objective
This study was performed to systematically compare the safety and efficacy of total enteral nutrition (TEN) and total parenteral nutrition (TPN) for patients with severe acute pancreatitis (SAP).
Methods
The PubMed database was searched up to January 2017, and nine studies were retrieved. These studies were selected according to specific eligibility criteria. The methodological quality of each trial was assessed, and the study design, interventions, participant characteristics, and final results were then analyzed by Review Manager 5.3 (The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, Denmark).
Results
Nine relevant randomized controlled trials involving 500 patients (244 patients in the TEN group and 256 patients in the TPN group) were included in the meta-analysis. Pooled analysis showed a significantly lower mortality rate in the TEN than TPN group [odds ratio (OR), 0.31; 95% confidence interval (CI), 0.18–0.54]. The duration of hospitalization was significantly shorter in the TEN than TPN group (mean difference, −0.59; 95% CI, −2.56–1.38). Compared with TPN, TEN had a lower risk of pancreatic infection and related complications (OR, 0.41; 95% CI, 0.22–0.77), organ failure (OR, 0.17; 95% CI, 0.06–0.52), and surgical intervention (OR, 0.17; 95% CI, 0.05–0.62).
Conclusions
This meta-analysis indicates that TEN is safer and more effective than TPN for patients with SAP. When both TEN and TPN have a role in the management of SAP, TEN is the preferred option.
Journal Article
Non-response bias assessment in logistics survey research: use fewer tests?
Purpose
– The purpose of this paper is to consider the concepts of individual and complete statistical power used for multiple testing and shows their relevance for determining the number of statistical tests to perform when assessing non-response bias.
Design/methodology/approach
– A statistical power analysis of 55 survey-based research papers published in three prestigious logistics journals (International Journal of Physical Distribution and Logistics Management, Journal of Business Logistics, Transportation Journal) over the last decade was conducted.
Findings
– Results show that some of the low complete power levels encountered could have been avoided if fewer tests had been used in the assessment of non-response bias.
Originality/value
– The research offers important recommendations to scholars engaged in survey research as they assess the effects of non-respondents on research findings. By following the recommended strategies for testing non-response bias, researchers can improve the statistical power of their findings.
Journal Article
Statistical power analysis for the social and behavioral sciences : basic and advanced techniques
\"This will be the first book to demonstrate the application of power analysis to the newer more advanced techniques such as hierarchical linear modeling, meta-analysis, and structural equation modelling that are increasingly popular in behavioral and social science research\"-- Provided by publisher.
Book
Development of a General Statistical Analytical System Using Nationally Standardized Medical Information
In Japan, since the Next Generation Medical Infrastructure Act regarding anonymized medical data contributing to R&D came into force in 2018, it is expected to exploit medical data for R&D. The Millennial Medical Record Project has been collected a large amount of standardized medical data of a number of hospitals stored in a database under the act. In order for users to widely exploit the medical data when carrying out trial-and-error, there is a difficulty of data access because of a highly secured management of non-anonymous medical data. To solve the data access problem, we develop a general statistical analytical system for executing a variety of statistical significance tests with statistical power analysis in an environment of trial-and-error for users’ analyses without programming. In the analytical system, the front-end is a registration form as the input and the analysis results as the output on Microsoft Excel, and the back-end is based on Python, R and SQL. Although the fixed registration form covers limited application for the analysis, since the analysis results using the stored Millennial Medical Record data is provided in a short time without collecting the necessary data for the analysis, the exploitation of medical data could widely and rapidly promote by medical experts/researchers in the manner of trial-and-error. The developed system could apply to make protocols for clinical research and clinical trial, and the potential to discover real-world evidence could be increased.
Journal Article
Haplin power analysis: a software module for power and sample size calculations in genetic association analyses of family triads and unrelated controls
Background
Log-linear and multinomial modeling offer a flexible framework for genetic association analyses of offspring (child), parent-of-origin and maternal effects, based on genotype data from a variety of child-parent configurations. Although the calculation of statistical power or sample size is an important first step in the planning of any scientific study, there is currently a lack of software for genetic power calculations in family-based study designs. Here, we address this shortcoming through new implementations of power calculations in the
R
package Haplin, which is a flexible and robust software for genetic epidemiological analyses. Power calculations in Haplin can be performed analytically using the asymptotic variance-covariance structure of the parameter estimator, or else by a straightforward simulation approach. Haplin performs power calculations for child, parent-of-origin and maternal effects, as well as for gene-environment interactions. The power can be calculated for both single SNPs and haplotypes, either autosomal or X-linked. Moreover, Haplin enables power calculations for different child-parent configurations, including (but not limited to) case-parent triads, case-mother dyads, and case-parent triads in combination with unrelated control-parent triads.
Results
We compared the asymptotic power approximations to the power of analysis attained with Haplin. For external validation, the results were further compared to the power of analysis attained by the EMIM software using data simulations from Haplin. Consistency observed between Haplin and EMIM across various genetic scenarios confirms the computational accuracy of the inference methods used in both programs. The results also demonstrate that power calculations in Haplin are applicable to genetic association studies using either log-linear or multinomial modeling approaches.
Conclusions
Haplin provides a robust and reliable framework for power calculations in genetic association analyses for a wide range of genetic effects and etiologic scenarios, based on genotype data from a variety of child-parent configurations.
Journal Article
Replication, effect sizes and identifying the biological impacts of pesticides on bees under field conditions
1. Honeybees have world-wide importance as crop pollinators. To ensure their persistence in agricultural systems, statistically robust field trials of plant protection products are vital. 2. We consider the implications of regulations from the European Food Safety Authority that require the detection of a 7% effect size change in bee colony sizes under field conditions. 3. Based on a power analysis, we argue that the necessary levels of replication (68 replicates) may pose practical constraints to field testing. 4. Policy implications. Regulatory studies benefit from data sources collated over a range of spatial scales, from laboratory to landscapes. Basing effect size thresholds solely on expert judgement, as has been done, may be inappropriate. Rather, definition through experimental or simulation studies that assess the biological consequences of changes in colony size for bee populations is required. This has implications for regulatory bodies outside the European Union.
Journal Article