Using copulas to enable causal inference from nonexperimental data: Tutorial and simulation studies.

Causal inference in psychological research is typically hampered by unobserved confounding. A copula-based method can be used to statistically control for this problem without the need for instruments or covariates, given relatively lenient distributional assumptions on independent variables and error terms. The current study aims to: (a) provide a user-friendly introduction to the copula method for psychology researchers, and (b) examine the degree of non-normality in the independent variables required for satisfactory performance. A Monte Carlo simulation study was used to assess the behavior of the copula method under various combinations of conditions (sample size, skewness of independent variables, effect size, and magnitude of confounding). In addition, an applied example from research on the effects of parental rearing on adult personality and life satisfaction was used to illustrate the method. Simulations revealed that the copula method performed better at higher levels of skewness in the independent variables, and that the impacts of lower skewness can be offset to some extent by larger sample size. When skewness and/or sample size is too small, the copula method is biased toward the uncorrected model. In the applied example, parental rejection/punishment predicted less adaptive personality and life satisfaction, with no evidence of confounding. For parental control/overprotection, there was evidence that confounding attenuated the estimated relationship with personality/life satisfaction. Copula adjustment is a promising method for handling unobserved confounding. The discussion focuses on how to proceed when assumptions are not quite met, and outlines potential avenues for future research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Need for Innovation in Rhinoplasty.

Rhinoplasty is a challenging surgery and results are not always perfect. There are many obstacles to achieving optimal results. Among these are inadequate instrumentation, the unpredictability of healing, imprecise planning, and many more. Furthermore, selecting patients who can most benefit from surgery is equally important. In this article, some of the more pressing areas of rhinoplasty that need innovation are discussed. From proper patient selection, to advances in education, to the standardization of training programs, to the development of sophisticated implants, the future of rhinoplasty surgery lies in continued creativity and innovation.

A cautionary note on the finite sample behavior of maximal reliability.

Several calls have been made for replacing coefficient α with more contemporary model-based reliability coefficients in psychological research. Under the assumption of unidimensional measurement scales and independent measurement errors, two leading alternatives are composite reliability and maximal reliability. Of these two, the maximal reliability statistic, or equivalently Hancock's H, has received a significant amount of attention in recent years. The difference between composite reliability and maximal reliability is that the former is a reliability index for a scale mean (or unweighted sum), whereas the latter estimates the reliability of a scale score where indicators are weighted differently based on their estimated reliabilities. The formula for the maximal reliability weights has been derived using population quantities; however, their finite-sample behavior has not been extensively examined. Particularly, there are two types of bias when the maximal reliability statistic is calculated from sample data: (a) the sample maximal reliability estimator is a positively biased estimator of population maximal reliability, and (b) the true reliability of composites formed with maximal reliability weights calculated from sample data is on average less than the population reliability. Both effects are more pronounced in small-sample scenarios (e.g., <100). We also demonstrate that the composite reliability estimator for equally weighted composite exhibits substantially less bias, which makes it a more appropriate choice for the small-sample case. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Lower Lateral Cartilages: An Anatomic and Morphological Study in Noses of Black Southern Africans.

Background: The anatomy of the nose of different ethnic groups has been widely researched in order to facilitate a better understanding of the individual nose as a foundation for improving surgical outcomes. The only anatomical research of the lower lateral cartilages (LLCs) available to the surgeon working with an African patient is to extrapolate data from studies already published on African Americans. Objectives: The aim of this descriptive cadaveric study was to assess the normal anatomy of the LLCs in noses of Black South Africans and compare this to data from studies on noses from Caucasian, Asian, Korean, and African-American populations. Methods: Ninety lower lateral cartilages of 45 cadavers of Black South Africans who did not have previous surgery or trauma to the nose were dissected. The morphological shapes and 12 standard anatomical measurements were recorded. The results were analyzed and compared to data in the literature from studies on lower lateral cartilages of Caucasian, Asian, Korean, and African-American populations. Results: A statistically significant difference was found in terms of overall cartilage dimensions, distance from nasal rim, and morphological shapes, compared to all previously studied groups, including the African-American population. There were significant differences in cartilage dimensions between males and females. This translates to clinically significant data that is useful during reconstructive and aesthetic nasal surgery on patients with a Southern African background. Conclusions: This study sets norms for alar cartilages in Black Southern Africans.

Strengthening the assessment of factorial invariance across population subgroups: a commentary on Varni et al. (2013).

OBJECTIVES: This article provides a commentary in response to "Varni et al. (Qual Life Res. doi: 10.1007/s11136-013-0370-4 , 2013)." METHODS AND RESULTS: The commentary argues that the approximate model fit indexes commonly used in maximum-likelihood confirmatory factor analysis and factorial invariance testing are seriously flawed, as they overlook potentially serious model misspecifications that could bias parameter estimates and compromise inference. CONCLUSIONS: Flexible and convenient Bayesian estimation approaches are presented that can substantially aid in: (1) resolving commonly encountered specification errors in confirmatory factor models and (2) locating specific measurement parameters that are non-invariant across population subgroups. It is recommended that these methods should be more widely adopted for evaluating the factorial invariance of patient-reported outcome measures and other types of instruments.

Pitfalls in subgroup analysis based on growth mixture models: a commentary on Van Leeuwen et al. (2012).

OBJECTIVES: This article is a brief commentary in response to "van Leeuwen et al. (Qual Life Res 21:1499-1508, 2012)" METHODS AND RESULTS: The commentary argues that in the context of mixture modeling, assigning individuals to specific subgroups for conducting a secondary set of analyses ignores the original uncertainty in group membership, thereby biasing any subsequent results and inference. CONCLUSIONS: Alternative approaches to subgroup analysis that attempt to preserve uncertainty in group membership are discussed and illustrated.

Reciprocal relationship between social support and psychological distress among a national sample of older adults: an autoregressive cross-lagged model.

In this study we examined the longitudinal relationships between five dimensions of social support and psychological distress to determine whether (1) social support was related to subsequent psychological distress levels; or (2) if distress levels were related to subsequent social support levels; or (3) if distress and support had a reciprocal (bi-directional) relationship across time. Using bivariate autoregressive cross-lagged models, we analysed data from 2,564 older adults. We found support for the reciprocal relationship between affectionate support and distress. Higher psychological distress was related to subsequently higher levels of positive social interaction, and significantly related to subsequently higher emotional/informational support. No significant relationship was found between tangible and structural support and psychological distress. This study demonstrates that different types of support are associated in correspondingly different ways with psychological distress, and that psychological distress may be important in predicting levels of social support two years later.

Improving the evaluation of model fit in confirmatory factor analysis: A commentary on Gundy, C.M., Fayers, P.M., Groenvold, M., Petersen, M. Aa., Scott, N.W., Sprangers, M.A.J., Velikov, G., Aaronson, N.K. (2011). Comparing higher-order models for the EORTC QLQ-C30. Quality of life research, doi:10.1007/s11136-011-0082-6.

This article is a brief commentary in response to "Gundy, C.M., Fayers, P.M., Groenvold, M., Petersen, M. Aa., Scott, N.W., Sprangers, M.A.J., Velikov, G., Aaronson, N.K. (2011). Comparing higher-order models for the EORTC QLQ-C30. Quality of life research, doi: 10.1007/s11136-011-0082-6 ." The commentary argues that approximate goodness-of-fit indexes cannot be used to salvage confirmatory factor models rejected by the Chi-square test of exact fit. Instead, rigorous diagnostic procedures should be used to identify and resolve the particular sources of model misspecification.

Psychometric properties, factorial structure, and measurement invariance of the English and French versions of the Medical Outcomes Study social support scale.

BACKGROUND: The Medical Outcomes Study (MOS) social support scale is a 19-item survey that measures four dimensions of functional support. The current study reports on the psychometric properties, factorial structure, and measurement invariance of the scale for a sample of English- and French-speaking Canadians aged 55 or older. DATA AND METHODS: The internal consistency and composite reliability for a congeneric measurement model of the dimensions of functional social support were examined. A confirmatory factor analysis and test of invariance across language (English = 2,642; French = 489) were also performed. RESULTS: Across both English- and French-speaking respondents, results indicated good internal consistency (Cronbach's alpha ranged from .90 to .97) and composite reliability (ranging from .93 to .97) for all dimensions of functional social support. The confirmatory factor analysis revealed acceptable fit indices for the 4-factor structure similar to the original one. The scale appears to function uniformly across both language groups. INTERPRETATION: The MOS social support scale appears to be a psychometrically sound instrument for use in research on social support with samples of English- and French-speaking older adults.

Income disparities in health-adjusted life expectancy for Canadian adults, 1991 to 2001.

BACKGROUND: Health-adjusted life expectancy is a summary measure of population health that combines mortality and morbidity data into a single index. This article profiles differences in health-adjusted life expectancy across income categories for a representative sample of the Canadian population. DATA AND METHODS: Mortality data were obtained from the 1991-2001 Canadian census mortality follow-up study, which linked a 15% sample of the 1991 adult non-institutional population with 11 years of death records from the Canadian Mortality Data Base. Information on morbidity was obtained from the Health Utilities Index Mark 3 instrument on the 2000/2001 Canadian Community Health Survey. The Sullivan method was used to compute health-adjusted life expectancy for national deciles of population ranked by income. MAIN RESULTS: For both sexes, and with few exceptions, a nearly linear gradient across income deciles emerged for health-adjusted life expectancy at age 25. Compared with people in higher-income deciles, those in lower-income deciles had fewer years of health-adjusted life expectancy. These disparities were substantially larger than those revealed by life expectancy alone. INTERPRETATION: These findings highlight the generally worse health-related quality of life of lower-income groups. The results demonstrate that assessments of socio-economic disparities in health should include the effects of both mortality and morbidity.

Neighborhood disadvantage: pathways of effects for young children.

The present study used Canadian National Longitudinal data to examine a model of the mechanisms through which the effects of neighborhood socioeconomic conditions impact young children's verbal and behavioral outcomes (N= 3,528; M age = 5.05 years, SD= 0.86). Integrating elements of social disorganization theory and family stress models, and results from structural equation models suggest that both neighborhood and family mechanisms played an important role in the transmission of neighborhood socioeconomic effects. Neighborhood disadvantage manifested its effect via lower neighborhood cohesion, which was associated with maternal depression and family dysfunction. These processes were, in turn, related to less consistent, less stimulating, and more punitive parenting behaviors, and ultimately, poorer child outcomes.

Eliciting Canadian population preferences for health states using the Classification and Measurement System of Functional Health (CLAMES).

A major objective of the Population Health Impact of Disease in Canada (PHI) research program was to obtain Canadian-specific preferences for health states associated with various diseases, in order to estimate the morbidity component of summary measures of population health embodying the Canadian experience of disease. In this study, preferences for health states were elicited from lay panels (N=146) in nine Canadian communities (Vancouver, Edmonton, Saskatoon, Toronto, Ottawa, Montréal, Québec, Moncton and Halifax); the study was conducted from January to June of 2003. Information on health states was presented to raters using the CLAssification and MEasurement System of Functional Health (CLAMES), which assesses functional capacity using 11 health status attributes, each with four to five levels ranging from normal to severely limited functioning. Preferences for 238 health states classified by CLAMES were elicited using the standard gamble (SG) technique in both individual and group exercises. Mean preferences for these health states were then used to estimate the parameters of a log-linear scoring function for CLAMES. The function provides a convenient method of computing preference scores for any health state classified by CLAMES, without the need for direct measurement in surveys. Further, the SG appears feasible in group settings.

Determinants of unacceptable waiting times for specialized services in Canada.

BACKGROUND: Much of the current evidence regarding timely access to healthcare services focuses on the duration of the waiting time as the principal determinant of wait time acceptability. We conducted the first national-level analysis of wait time acceptability in Canada to identify the determinants of unacceptable waits for specialized healthcare services, including selected demographic and socio-economic variables. METHODS: We analyzed data reported by respondents to a national survey on access to healthcare services who accessed specialized services (i.e., specialist visits, non-emergency surgery and selected diagnostic tests) during a 12-month period. We used univariate analyses and weighted logistic regression to examine the relation between wait time acceptability and selected demographic, socio-economic and health status factors for each specialized service. RESULTS: Between 17% and 29% of patients who waited for a specialized service declared that their waiting time was unacceptable. Most individuals reported waiting less than 3 months for their services. Between 10% and 19% of those who waited indicated that waiting for care affected their lives. Results of the logistic regression analyses showed that longer waits and adverse experiences during the waiting period were significantly associated with higher odds of reporting an unacceptable waiting time for all three types of specialized services. The role of socio-economic and demographic factors on wait time acceptability was varied. Individuals with lower education were consistently less likely to consider their waiting times unacceptable. Patients less than 65 years of age were more likely to consider their waiting times unacceptable for specialist visits and diagnostic tests. INTERPRETATION: Our study shows that the primary determinants of waiting time acceptability are the length of the waiting time and the effects of waiting on the patient's life. In addition, some patient characteristics, such as age and education, may play a role, pointing to the potential role of patient expectations in determining the acceptability of waits for specialized services.

Deriving utility scores for co-morbid conditions: a test of the multiplicative model for combining individual condition scores.

BACKGROUND: The co-morbidity of health conditions is becoming a significant health issue, particularly as populations age, and presents important methodological challenges for population health research. For example, the calculation of summary measures of population health (SMPH) can be compromised if co-morbidity is not taken into account. One popular co-morbidity adjustment used in SMPH computations relies on a straightforward multiplicative combination of the severity weights for the individual conditions involved. While the convenience and simplicity of the multiplicative model are attractive, its appropriateness has yet to be formally tested. The primary objective of the current study was therefore to examine the empirical evidence in support of this approach. METHODS: The present study drew on information on the prevalence of chronic conditions and a utility-based measure of health-related quality of life (HRQoL), namely the Health Utilities Index Mark 3 (HUI3), available from Cycle 1.1 of the Canadian Community Health Survey (CCHS; 2000-01). Average HUI3 scores were computed for both single and co-morbid conditions, and were also purified by statistically removing the loss of functional health due to health problems other than the chronic conditions reported. The co-morbidity rule was specified as a multiplicative combination of the purified average observed HUI3 utility scores for the individual conditions involved, with the addition of a synergy coefficient s for capturing any interaction between the conditions not explained by the product of their utilities. The fit of the model to the purified average observed utilities for the co-morbid conditions was optimized using ordinary least squares regression to estimate s. Replicability of the results was assessed by applying the method to triple co-morbidities from the CCHS cycle 1.1 database, as well as to double and triple co-morbidities from cycle 2.1 of the CCHS (2003-04). RESULTS: Model fit was optimized at s = .99 (i.e., essentially a straightforward multiplicative model). These results were closely replicated with triple co-morbidities reported on CCHS 2000-01, as well as with double and triple co-morbidities reported on CCHS 2003-04. CONCLUSION: The findings support the simple multiplicative model for computing utilities for co-morbid conditions from the utilities for the individual conditions involved. Future work using a wider variety of conditions and data sources could serve to further evaluate and refine the approach.