Valerie Clerehugh

Problems of correlations between explanatory variables in multiple regression analyses in the dental literature

Multivariable analysis is a widely used statistical methodology for investigating associations amongst clinical variables. However, the problems of collinearity and multicollinearity, which can give rise to spurious results, have in the past frequently been disregarded in dental research. This article illustrates and explains the problems which may be encountered, in the hope of increasing awareness and understanding of these issues, thereby improving the quality of the statistical analyses undertaken in dental research. Three examples from different clinical dental specialities are used to demonstrate how to diagnose the problem of collinearity/multicollinearity in multiple regression analyses and to illustrate how collinearity/multicollinearity can seriously distort the model development process. Lack of awareness of these problems can give rise to misleading results and erroneous interpretations. Multivariable analysis is a useful tool for dental research, though only if its users thoroughly understand the assumptions and limitations of these methods. It would benefit evidence-based dentistry enormously if researchers were more aware of both the complexities involved in multiple regression when using these methods and of the need for expert statistical consultation in developing study design and selecting appropriate statistical methodologies.

Statistical Power for Analyses of Changes in Randomized Controlled Trials

Randomized controlled trials (RCTs) are widely recommended as the most useful study design to generate reliable evidence and guidance to daily practices in medicine and dentistry. However, it is not well-known in dental research that different statistical methods of data analysis can yield substantial differences in study power. In this study, computer simulations are used to explore how using different univariate and multivariate statistical methods of analyzing change in continuous outcome variables affects study power, and the sample size required for RCTs. Results show that, in general, analysis of covariance (ANCOVA) yields greater power than other statistical methods in testing the superiority of one treatment over another, or in testing the equivalence between two treatments. Therefore, ANCOVA should be used in preference to change score or percentage change score to reduce type II error rates.

Direct and Indirect Effects of Interdental Hygiene in a Clinical Trial

Many randomized controlled trials (RCTs) in dental research test the efficacy of interventions on more than one outcome variable. Univariate methods, such as the t test or analysis of covariance, cannot evaluate the efficacy of interventions on multiple outcomes simultaneously. The aim of this study was to use structural equation modeling (SEM) to re-analyze a RCT, comparing the effects of pre-curved interdental brushes and flossing on probing pocket depth (PPD), plaque indices, and bleeding on probing (BOP) measured at baseline, intermediate, and final examinations. Results of SEM showed that the observed greater reduction in PPD and BOP in persons using interdental brushing than in those flossing is due mainly to the greater efficiency in plaque removal with the interdental brushes (indirect effect) rather than to the compression of the interdental papillae (direct effect). In contrast, smokers showed less BOP at baseline but also less improvement in BOP through direct effects.

Partial least squares path modelling for relations between baseline factors and treatment outcomes in periodontal regeneration.

BACKGROUND Some clinical outcome variables in periodontal research are mathematically coupled, and it is not feasible to include all the mathematically coupled variables in an ordinary least squares (OLS) regression analysis. The simplest solution to this problem is to drop at least one of the mathematically coupled variables. However, this solution is not satisfactory when the mathematically coupled variables have distinctive clinical implications. MATERIAL AND METHODS Partial least squares (PLS) methods were used to analyse data from a study on guided tissue regeneration. Relationships between characteristics of baseline lesions and treatment outcomes after 1 year were analysed using PLS, and the results were compared with those from OLS regression. RESULTS PLS analysis suggested that there were multiple dimensions in the characteristics of baseline lesion: vertical dimension was positively associated with probing pocket depth (PPD) reduction and clinical attachment level (CAL) gain, whilst horizontal dimension was negatively associated with the outcome. Baseline gingival recession had a negative association with PPD reduction but a small positive one with CAL gain. CONCLUSION PLS analysis provides new insights into the relationships between baseline characteristics of infrabony defects and periodontal treatment outcomes. The hypothesis of multiple dimensions in baseline lesions needs to be validated by further analysis of different datasets.