Use and Misuse of Statistical Methods in the Journal of Korean Academy of Nursing Administration

Purpose: To do nursing research effectively requires an understanding of fundamental principles of statistical methods. In this article, some key statistical methods which are commonly used in nursing research are identified and summarized. Methods: Ninety-two original articles from the Journal of Korean Academy of Nursing Administration were reviewed. Statistical methods were classified and summarized for usage in research and occurrence of common errors. Results: Among the original articles reviewed, 58 statistical usages contained errors. Most errors were found in linear regression analysis, Pearson correlation analysis, and chi-square test. From the detection of statistical errors in usage, suggestions for appropriate statistical methods were made. Conclusion: In order to improve validity of original articles in the Journal of Korean Academy of Nursing Administration, clearly stated statistical usage and close editorial attention to statistical methods are needed. Understanding statistical methods is part of the process that researchers must use to determine both quality and usefulness of the research. Research findings will be used to guide nursing practice and reduce uncertainty in decision making. However, to understand how to interpret research results, it is important to be able to understand basic statistical concepts. Researchers should also choose statistical methods that match their purposes.ope

Variable Selection for Propensity Score Models Considering the Correlations between Covariates

Objectives: In the covariate selection for propensity score model (PSM), including all the covariates that can be observed has been recommended. However, there are problems that appear multi collinearity and do not obtain the matching number needed using over fitted propensity score model. In this study, we studied the method of variable selection for PSM considering the correlations between covariates. Methods: All the covariates were classified according to the relation with treatment and outcome and generated considering the correlations each other. We examined the odds ratio and MSE (mean squared error) of PSM and the matching number of simulated data. Results: When there are correlations among covariates included in PSM, the matching number decreased as the correlation of covariates was stronger. Also, the larger the strength of correlation among covariates was, the smaller MSE was and the matching number was. Conclusions: When including covariates in PSM, we found that it is more efficient to examine the correlation of covariates, treatment variable, and outcome variable than using all the covariates observed.ope

Linkage Disequilibrium Analysis of Quantitative Trait Locus Associated with Lipid Profiles

Background and Objectives: The common methods of genetic association analysis are sensitive to population stratification, which may easily lead to a spurious association result. We used a regression approach based for linkage disequilibrium to perform a high resolution genetic association analysis. Subjects and Methods: We applied a regression approach that can increase the resolution of quantitative traits that are related with cardiovascular diseases. The population data was composed of 543 males and 876 females without cardiovascular diseases, and it was obtained from a cardiovascular genome center. We used information about linkage disequilibrium between the marker and trait locus, and we added the covariates to model their effects. Results: We found that this regression approach has the merit of analyzing genetic association based on linkage disequilibrium. In the analysis of the male group, the total cholesterol was significantly in linkage disequilibrium with CETP3 (p=0.002), and triglyceride was significantly in linkage disequilibrium with ACE8 (p=0.037), APOA1-1 (p=0.031), APOA5-1 (p=0.001), APOA5-2 (p=0.001) and LIPC4 (p=0.022). HDL-cholesterol was significantly in linkage disequilibrium with ACE7 (p=0.002), ACE8 (p=0.008), ACE10 (p=0.003), APOA5-2 (p=0.022), and MTP1 (p=0.001). In the female group, total cholesterol was significantly associated with APOA5-1 (p=0.020), APOA5-2 (p=0.001), and LIPC1 (p=0.016), and triglyceride was significantly associated with APOA5-1 (p=0.009), APOA5-2 (p=0.001), and CETP5 (p=0.049). LDL-cholesterol was significantly associated with APOA5-2 (p=0.004), and HDL-cholesterol was significantly associated with LIPC1 (p=0.004). Conclusion: We used a regression-based method to perform high resolution linkage disequilibrium analysis of a quantitative trait locus that's associated with lipid profiles. This method of using a single marker, as applied in this paper, was well suited for analysis of genetic association. Because of the simplicity, the method can also be easily performed by routine statistical analysis software.ope

A mixture of experts model for the diagnosis of liver cirrhosis by measuring the liver stiffness.

OBJECTIVES: The mixture-of-experts (ME) network uses a modular type of neural network architecture optimized for supervised learning. This model has been applied to a variety of areas related to pattern classification and regression. In this research, we applied a ME model to classify hidden subgroups and test its significance by measuring the stiffness of the liver as associated with the development of liver cirrhosis. METHODS: The data used in this study was based on transient elastography (Fibroscan) by Kim et al. We enrolled 228 HBsAg-positive patients whose liver stiffness was measured by the Fibroscan system during six months. Statistical analysis was performed by R-2.13.0. RESULTS: A classical logistic regression model together with an expert model was used to describe and classify hidden subgroups. The performance of the proposed model was evaluated in terms of the classification accuracy, and the results confirmed that the proposed ME model has some potential in detecting liver cirrhosis. CONCLUSIONS: This method can be used as an important diagnostic decision support mechanism to assist physicians in the diagnosis of liver cirrhosis in patients.ope

Low-dose abdominal CT for evaluating suspected appendicitis

BACKGROUND: Computed tomography (CT) has become the predominant test for diagnosing acute appendicitis in adults. In children and young adults, exposure to CT radiation is of particular concern. We evaluated the rate of negative (unnecessary) appendectomy after low-dose versus standard-dose abdominal CT in young adults with suspected appendicitis. METHODS: In this single-institution, single-blind, noninferiority trial, we randomly assigned 891 patients with suspected appendicitis to either low-dose CT (444 patients) or standard-dose CT (447 patients). The median radiation dose in terms of dose-length product was 116 mGy·cm in the low-dose group and 521 mGy·cm in the standard-dose group. The primary end point was the percentage of negative appendectomies among all nonincidental appendectomies, with a noninferiority margin of 5.5 percentage points. Secondary end points included the appendiceal perforation rate and the proportion of patients with suspected appendicitis who required additional imaging. RESULTS: The negative appendectomy rate was 3.5% (6 of 172 patients) in the low-dose CT group and 3.2% (6 of 186 patients) in the standard-dose CT group (difference, 0.3 percentage points; 95% confidence interval, -3.8 to 4.6). The two groups did not differ significantly in terms of the appendiceal perforation rate (26.5% with low-dose CT and 23.3% with standard-dose CT, P=0.46) or the proportion of patients who needed additional imaging tests (3.2% and 1.6%, respectively; P=0.09). CONCLUSIONS: Low-dose CT was noninferior to standard-dose CT with respect to negative appendectomy rates in young adults with suspected appendicitis. (Funded by GE Healthcare Medical Diagnostics and others; ClinicalTrials.gov number, NCT00913380.).ope

Analysis of Genetic Association with Lipid Profiles Adjusting for Lipid-lowering Drug Effects

Objective: The population-based genetic association studies of continuous traits can be seriously distorted when the traits are subject to the effects of certain treatment. Without appropriate adjustment of treatment effect, the results of analyses may be fundamentally flawed. So, we proposed an statistical approach based on censored normal regression to adjust a treatment effect and applied this method to real data. Methods: We used data consisting of 1,687 individuals(male 884, female 983) who have the information of lipid profiles(total cholesterol, triglyceride, low density lipoprotein) and single nucleotide polymorphisms(SNPs) from Yonsei cardiovascular genome center. We used a censored normal regression method to analyze the genetic association adjusted for lipid-lowering drug effects and compared its performance to that of ordinary multiple linear regression. Results: The results of our study provided that the performance of censored normal regression is more powerful than that of multiple linear regression for analysis of genetic association in serum lipid profiles. There were significant genetic association with lipid profiles in each 7 SNPs described in our real data. Conclusion: We have demonstrated that censored normal regression approach for genetic association analysis can effectively adjust the distorting effect of lipid-lowering drug and recover a marked loss in statistical power.ope

The Effect of Plant Sterol on Serum Cholesterol in Hypercholesterolemia Patients

Background and Objectives : Phytosterols(Plant sterols) have been known to reduce serum cholesterol concentrations by inhibiting the absorption of both dietary and biliary cholesterol from the small intestines. In consideration of the lack of evaluation in Korea into the hypercholesterolemic effect of plant sterols, this study investigated the effect of plant sterol containing beverage on blood lipid profiles in hypercholesterolemic patients. Materials and Methods : Forty-five hypercholesterolemic patients(fasting LDL-cholesterol>130 mg/dL) were fed either a placebo beverage for 4 weeks or a test beverage containing plant sterols for 8 weeks in a single-blind, randomized, cross-over study. The subjects were instructed to maintain the same amount of dietary fat and cholesterol intake during the study. After 4 weeks of plant sterols treatment, the dose of plant sterols was doubled (3.2 g/d) for subjects whose LDL-cholesterol reduction rate had not been reduced by 15%. Results : The study population consisted 45 patients(15 males, 30 females, mean age 56) who completed the whole protocol. At baseline, the subjects' mean dietary intake of saturated fat was 11.12 g, and cholesterol was 135.2 mg. After 8 weeks treatment with plant sterols, serum concentrations of total cholesterol and LDL-cholesterol were significantly reduced by 4.38%(p= 0.039), and 8.28%(p=0.036), respectively. However, the HDL-cholesterol and triglyceride levels/concentrations did not change significantly. Two-thirds of the subjects responded to treatment with plant sterols, and the mean reduction rates in LDL-cholesterol and total cholesterol levels/concentrations of those subjects were 14.1% and 9.2% respectively. Conclusion : Our findings indicate that plant sterols significantly reduce serum total cholesterol and LDL-cholesterol concentrations and further suggest that plant sterols are also effective for those with low cholesterol intake.ope

How to Develop, Validate, and Compare Clinical Prediction Models Involving Radiological Parameters: Study Design and Statistical Methods.

Clinical prediction models are developed to calculate estimates of the probability of the presence/occurrence or future course of a particular prognostic or diagnostic outcome from multiple clinical or non-clinical parameters. Radiologic imaging techniques are being developed for accurate detection and early diagnosis of disease, which will eventually affect patient outcomes. Hence, results obtained by radiological means, especially diagnostic imaging, are frequently incorporated into a clinical prediction model as important predictive parameters, and the performance of the prediction model may improve in both diagnostic and prognostic settings. This article explains in a conceptual manner the overall process of developing and validating a clinical prediction model involving radiological parameters in relation to the study design and statistical methods. Collection of a raw dataset; selection of an appropriate statistical model; predictor selection; evaluation of model performance using a calibration plot, Hosmer-Lemeshow test and c-index; internal and external validation; comparison of different models using c-index, net reclassification improvement, and integrated discrimination improvement; and a method to create an easy-to-use prediction score system will be addressed. This article may serve as a practical methodological reference for clinical researchers.ope

Association of Depressive Mood and Frailty With Mortality and Health Care Utilization: Korean National Cohort Study

OBJECTIVES: To investigate the association of depressive mood and frailty with mortality and health care utilization (HCU) and identify the coexisting effect of depressive mood and frailty in older adults. DESIGN: A retrospective study using nationwide longitudinal cohort data. SETTING AND PARTICIPANTS: A total of 27,818 older adults age 66 years from the National Screening Program for Transitional Ages between 2007 and 2008, part of the National Health Insurance Service-Senior cohort. METHODS: Depressive mood and frailty were measured by the Geriatric Depression Scale and Timed Up and Go test, respectively. Outcomes were mortality and HCU, including long-term care services (LTCS), hospital admissions, and total length of stay (LOS) from the index date to December 31, 2015. Cox proportional hazards regression and zero-inflated negative binomial regression were performed to identify differences in outcomes by depressive mood and frailty. RESULTS: Participants with depressive mood and frailty represented 50.9% and 2.4%, respectively. The prevalence of mortality and LTCS use in the overall participants was 7.1% and 3.0%, respectively. More than 3 hospital admissions (36.7%) and total LOS above 15 days (53.2%) were the most common. Depressive mood was associated with LTCS use [hazard ratio (HR) 1.22, 95% confidence interval (CI) 1.05-1.42] and hospital admissions [incidence rate ratio (IRR) 1.05, 95% CI 1.02-1.08]. Frailty had associations with mortality risk (HR 1.96, 95% CI 1.44-2.68), LTCS use (HR 4.86, 95% CI 3.45-6.84), and LOS (IRR 1.30, 95% CI 1.06-1.60). The coexistence of depressive mood and frailty was associated with increased LOS (IRR 1.55, 95% CI 1.16-2.07). CONCLUSIONS AND IMPLICATIONS: Our findings highlight the need to focus on depressive mood and frailty to reduce mortality and HCU. Identifying combined problems in older adults may contribute to healthy aging by reducing adverse health outcomes and the burden of health care costs. Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.ope

A Study on Comparison of Generalized Kappa Statistics in Agreement Analysis

Agreement analysis is conducted to assess reliability among rating results performed repeatedly on the same subjects by one or more raters. The kappa statistic is commonly used when rating scales are categorical. The simple and weighted kappa statistics are used to measure the degree of agreement between two raters, and the generalized kappa statistics to measure the degree of agreement among more than two raters. In this paper, we compare the performance of four different generalized kappa statistics proposed by Fleiss (1971), Conger (1980), Randolph (2005), and Gwet (2008a). We also examine how sensitive each of four generalized kappa statistics can be to the marginal probability distribution as to whether marginal balancedness and/or homogeneity hold or not. The performance of the four methods is compared in terms of the relative bias and coverage rate through simulation studies in various scenarios with different numbers of raters, subjects, and categories. A real data example is also presented to illustrate the four methods.ope