Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly observed and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and to assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of data on general pedigrees. Here, we review the theory of SEM for both unrelated and family data, describe the available software for SEM, and provide examples of SEM analysis.

Structural Equation Modeling

When writing a manuscript, we often use words such as perfect, strong, good or weak to name the strength of the relationship between variables. However, it is unclear where a good relationship turns into a strong one. The same strength of r is named differently by several researchers. Therefore, there is an absolute necessity to explicitly report the strength and direction of r while reporting correlation coefficients in manuscripts. This article aims to familiarize medical readers with several different correlation coefficients reported in medical manuscripts, clarify confounding aspects and summarize the naming practices for the strength of correlation coefficients.

https://www.trjemergmed.com/full-text-pdf/644

User's guide to correlation coefficients.

In this article, we shall discuss the analysis of the relationship between two quantitative outcomes, say, height and weight, which is shown graphically by a scatter plot (see Fig. 1). Fig. 1 Relationship between height and weight.

Biostatistics 104: correlational analysis

Rater agreement is important in clinical research, and Cohen’s Kappa is a widely used method for assessing inter-rater reliability; however, there are well documented statistical problems associated with the measure. In order to assess its utility, we evaluated it against Gwet’s AC1 and compared the results. This study was carried out across 67 patients (56% males) aged 18 to 67, with a mean SD of 44.13 ± 12.68 years. Nine raters (7 psychiatrists, a psychiatry resident and a social worker) participated as interviewers, either for the first or the second interviews, which were held 4 to 6 weeks apart. The interviews were held in order to establish a personality disorder (PD) diagnosis using DSM-IV criteria. Cohen’s Kappa and Gwet’s AC1 were used and the level of agreement between raters was assessed in terms of a simple categorical diagnosis (i.e., the presence or absence of a disorder). Data were also compared with a previous analysis in order to evaluate the effects of trait prevalence. Gwet’s AC1 was shown to have higher inter-rater reliability coefficients for all the PD criteria, ranging from .752 to 1.000, whereas Cohen’s Kappa ranged from 0 to 1.00. Cohen’s Kappa values were high and close to the percentage of agreement when the prevalence was high, whereas Gwet’s AC1 values appeared not to change much with a change in prevalence, but remained close to the percentage of agreement. For example a Schizoid sample revealed a mean Cohen’s Kappa of .726 and a Gwet’s AC1of .853 , which fell within the different level of agreement according to criteria developed by Landis and Koch, and Altman and Fleiss. Based on the different formulae used to calculate the level of chance-corrected agreement, Gwet’s AC1 was shown to provide a more stable inter-rater reliability coefficient than Cohen’s Kappa. It was also found to be less affected by prevalence and marginal probability than that of Cohen’s Kappa, and therefore should be considered for use with inter-rater reliability analysis.

/pdf/a-comparison-of-cohen-s-kappa-and-gwet-s-ac1-when-4nouyfwhdl.pdf

A comparison of Cohen’s Kappa and Gwet’s AC1 when calculating inter-rater reliability coefficients: a study conducted with personality disorder samples

Objective
To report on long-term mortality in anorexia nervosa (AN), bulimia nervosa (BN), binge eating disorder (BED), and eating disorder not otherwise specified (ED-NOS), causes of death, and predictors of early death.

Method
A large sample of consecutively admitted inpatients (N = 5,839) was followed-up on vital status through the German civil registry office. Of these patients 1,639 were treated for AN, 1,930 for BN, 363 for BED, and 1,907 for ED-NOS. Data from the main inpatient hospital treatment were applied to bivariate and multivariate Cox regression analyses on survival time from onset of eating disorder to death or end of observation. Standardized mortality ratios (SMR) were computed matched for age, gender, and person-years.

Results
SMR were 5.35 for AN, 1.49 for BN, 1.50 for BED, 2.39 for narrowly defined ED-NOS, and 1.70 for widely defined ED-NOS. Patients with AN died earlier than patients with BN, BED, or ED-NOS who did not differ. A diagnosis of AN, chronicity, later age of onset, not living in a relationship, and an irregular type of discharge from index inpatient treatment were major predictors of a shorter time to death. Suicidality was a univariate predictor of a shorter time to death in BN only. AN patients mostly died from natural causes related to their eating disorder.

Discussion
Mortality in AN is excessive and considerably higher than in BN, BED, and ED-NOS. © 2015 Wiley Periodicals, Inc. (Int J Eat Disord 2016; 49:391–401).

Mortality in eating disorders ‐ results of a large prospective clinical longitudinal study

Correspondence to: Y H Chan Tel: (65) 6317 2121 Fax: (65) 6317 2122 Email: chanyh@ cteru.com.sg Parametric & non-parametric tests(1) are used when the outcome response is quantitative and our interest is to determine whether there are any statistical differences between/amongst groups (which are categorical). In this article, we are going to discuss how to analyse relationships between categorical variables. Table I shows the first five cases of 200 subjects with their gender and intensity of snoring (No, At Times, Frequent and Always) and snoring status (Yes or No) recorded.

Biostatistics 103: qualitative data - tests of independence.

Biostatistics 102: quantitative data--parametric & non-parametric tests.

Biostatistics 202: logistic regression analysis.

Correspondence to: Dr Y H Chan Tel: (65) 6325 7070 Fax: (65) 6324 2700 Email: chanyh@ cteru.com.sg In the 100 series(1-4) the common univariate techniques (summarized in Table I) available for data analyses were discussed. These techniques do not allow us to take into account the effect of other covariates/ confounders (except for partial correlation(4)) in an analysis. In such situations, a Regression Model would be required.

Y H Chan

Papers

Biostatistics 104: correlational analysis

Biostatistics 103: qualitative data - tests of independence.

Biostatistics 102: quantitative data--parametric & non-parametric tests.

Biostatistics 202: logistic regression analysis.

Biostatistics 201: linear regression analysis.