Biomedical subjects

Y-K Tu

Publications and source records attributed to Y-K Tu.

4 recordsLinked to original sources

PubMed2006

What is the effect of adjusting for more than one measure of current body size on the relation between birthweight and blood pressure?

The statistical validity of the negative associations observed between birthweight and disease in later life has recently been questioned, because these associations might be due, in part, to inappropriate adjustment for current body size, creating a statistical artefact known as the 'reversal paradox'. The aim of this study was to explore the effect of adjusting for more than one measure of current body size on the association between birthweight and disease in later life using simulations and meta-analyses of empirical studies. The simulations examined the relation between birthweight and adult systolic blood pressure before and after adjusting for one, two or three measures of current body size by including current weight and subsequently adding body mass index and height in successive analytical models. Meta-analyses were then performed to compare the effect sizes observed among empirical studies reporting associations between birthweight and blood pressure before and after adjusting for one or two measures of current body size. The meta-analyses confirmed the results of the simulations - both showed that associations between birthweight and blood pressure tend to become increasingly negative following adjustment for current body size, and that this effect is enhanced after adjusting for additional measures of current body size.

Blood Pressure↗

PubMed2005

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 specialties 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.

Data Interpretation, Statistical↗

PubMed2005

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.

Analysis of Variance↗

PubMed2002

Is reduction of pocket probing depth correlated with the baseline value or is it "mathematical coupling"?

Previous studies using correlation or regression analysis have showed that treatment effects measured by the change in clinical parameters are often associated with baseline values of the same parameters. These studies, however, have a methodological weakness. Correlation/regression between baseline measures and the derived change variable invalidates the statistical procedures of testing the null hypothesis: that the coefficient of correlation/regression is zero. This is due to the phenomenon of mathematical coupling. To investigate the impact that this has on the observed correlation/regression coefficient when in reality this is zero, we used random simulations of hypothetical data to model the treatment of periodontal pockets. Results showed a strong probability of obtaining statistically significant correlation/regression coefficients. To separate this artificial effect of mathematical coupling from the true underlying biological relationship, one must apply appropriate analytical strategies to re-evaluate previous evidence within the periodontal literature.

Algorithms↗