An overview of applied robust methods

Factors Influencing Tooth Sensitivity: Insights From the Hospital Universiti Sains Malaysia Using Bootstrap-Enhanced Ordinal Regression

Introduction Tooth sensitivity, or dentin hypersensitivity (DH), is characterized by sharp, sudden pain in response to stimuli such as cold, heat, sweet, or acidic foods and drinks. In Malaysia, there is limited understanding of the epidemiological aspects of tooth sensitivity, necessitating focused research. The condition results from the exposure of dentinal tubules transmitting stimuli to nerves within the pulp, with contributing factors including gingival recession, enamel erosion, and periodontal disease. This study aims to investigate the factors associated with tooth sensitivity among patients at the Hospital Universiti Sains Malaysia (USM) using advanced statistical methods. Methods This study employed a computational research design to develop an ordinal regression and bootstrap methodology using the RStudio software (Posit PBC, Boston, MA) to analyze secondary data from the Hospital Universiti Sains Malaysia. Six variables were analyzed: tooth wear severity, patient's age, gender, smoking status, alcohol status, and type of toothbrush. The study was conducted in three phases: 1) the development of an ordinal regression model, 2) the development of algorithms for ordinal regression and bootstrap method, and 3) validation using tooth sensitivity data. Results The analysis revealed that the replication with 1000 samples provided the most precise estimates with small standard errors (SE) and consistently significant effects across variables. Tooth sensitivity was influenced by age, toothpaste type, toothbrush type, and brushing frequency. Conclusion The study highlights the importance of considering multiple variables such as age, toothpaste type, toothbrush type, and brushing frequency in understanding tooth sensitivity. The combined ordinal regression and bootstrap technique significantly improved the model's accuracy, providing valuable insights for dental health professionals. These findings underscore the need for specific guidelines on oral hygiene practices to manage and reduce the risk of tooth sensitivity.

Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites?

Observational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and diagnosis of individuals/participants. But regression analysis with weighted composites has been known to yield attenuated regression coefficients when predictors contain errors. Contrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the signal-to-noise ratio (SNR). In particular, the SNR for the regression coefficient via the least squares (LS) method with equally weighted composites is mathematically greater than that by CB-SEM if the items for each factor are parallel, even when the SEM model is correctly specified and estimated by an efficient method. Analytical, numerical and empirical results also show that LS regression using weighted composites performs as well as or better than the normal maximum likelihood method for CB-SEM under many conditions even when the population distribution is multivariate normal. Results also show that the LS regression coefficients become more efficient when considering the sampling errors in the weights of composites than those that are conditional on weights.

Which method is more powerful in testing the relationship of theoretical constructs? A meta comparison of structural equation modeling and path analysis with weighted composites

Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite scores is preferred for prediction and diagnosis of individuals. While path analysis with composite scores has been criticized for yielding biased parameter estimates, recent literature pointed out that the population values of parameters in a latent-variable model depend on artificially assigned scales. Consequently, bias in parameter estimates is not a well-grounded concept for models involving latent constructs. This article compares path analysis with composite scores against SEM with respect to effect size and statistical power in testing the significance of the path coefficients, via the z- or t-statistics. The data come from many sources with various models that are substantively determined. Results show that SEM is not as powerful as path analysis even with equally weighted composites. However, path analysis with Bartlett-factor scores and the partial least-squares approach to SEM perform the best with respect to effect size and power.