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Yu-Kang Tu

Publications and source records attributed to Yu-Kang Tu.

18 recordsLinked to original sources
PubMed2007

Revisiting the relation between change and initial value: a review and evaluation.

The relation between initial disease status and subsequent change following treatment has attracted great interest in clinical research. However, statisticians have repeatedly warned against correlating/regressing change with baseline due to two methodological concerns known as mathematical coupling and regression to the mean. Oldham's method and Blomqvist's formula are the two most often adopted methods to rectify these problems. The aims of this article are to review briefly the proposed solutions in the statistical and psychological literature, and to clarify the popular misconception that Blomqvist's formula is superior to Oldham's method. We argue that this misconception is due to a failure to recognize that the heterogeneity of individual responses to treatment is a source of regression to the mean in the analysis of the relation between change and initial value. Furthermore, we demonstrate how each method actually answers different research questions, and how confusion arises when this is not always understood.

Antihypertensive Agents↗
PubMed2006

Misuses of correlation and regression analyses in orthodontic research: the problem of mathematical coupling.

INTRODUCTION: The aim of this article was to encourage good practice in the statistical analyses of orthodontic research data. Our objective was to highlight the statistical problems caused by mathematical coupling (MC) in correlation and regression analyses. These statistical problems are among the most common pitfalls in orthodontic research when exploring associations among clinical variables. This article will show why these problems arise and how they can be avoided and overcome. METHODS: Four orthodontic journals were electronically and manually searched for articles that used correlation and regression analyses. Studies that seemed to suffer from MC in their statistical analyses were identified and carefully examined. RESULTS: Several examples from our search illustrate that MC in correlation and regression analyses can potentially cause misleading results. More appropriate statistical methods are available and should be used to eliminate confusing results and improve any subsequent interpretations. Because many clinical and radiographic variables used in orthodontic research are correlated due to direct or indirect MC, interpretation of studies in the literature needs to be cautious. CONCLUSIONS: Correlation and regression analyses are useful tools in orthodontic research when their assumptions and limitations are recognized. However, greater care is required in formulating research questions and experimental designs. It is prudent to seek statistical advice when orthodontic research involves complex data analyses.

Analysis of Variance↗
PubMed2006

Evaluating the quality of active-control trials in periodontal research.

AIM: The increasing popularity of randomized-controlled trials (RCTs) has raised the issue of their quality. Frequently overlooked are the differences between superiority and equivalence trials. The purpose of this study was to apply specific methodological criteria to evaluate the quality of active-control trials using studies that compared guided tissue regeneration (GTR) with enamel matrix derivatives (EMD). MATERIALS AND METHODS: Seven RCTs were identified in the literature. Standard methodological criteria and seven additional criteria for trials using active-control groups were used to evaluate the quality of the seven RCTs. RESULTS: Two trials were considered as superiority trials. The remaining five provided no clear statement of their research aim. However, two claimed that EMD and GTR were equally effective, because their results failed to show a significant difference between EMD and GTR. Most trials did not meet the majority of the design criteria. CONCLUSIONS: The general lack of compliance with quality criteria might place doubt on the value of these trials and may render any conclusions questionable. It is therefore important to distinguish clearly between superiority trials and equivalence trials, and to incorporate appropriate additional criteria in the design of future RCTs with active-control groups.

Dental Enamel Proteins↗
PubMed2005

Why evidence for the fetal origins of adult disease might be a statistical artifact: the "reversal paradox" for the relation between birth weight and blood pressure in later life.

Some researchers have recently questioned the validity of associations between birth weight and health in later life. They argue that these associations might be due in part to inappropriate statistical adjustment for variables on the causal pathway (such as current body size), which creates an artifactual statistical effect known as the "reversal paradox." Computer simulations were conducted for three hypothetical relations between birth weight and adult blood pressure. The authors examined the effect of statistically adjusting for different correlations between current weight and birth weight and between current weight and adult blood pressure to assess their impact on associations between birth weight and blood pressure. When there was no genuine relation between birth weight and blood pressure, adjustment for current weight created an inverse association whose size depended on the magnitude of the positive correlations between current weight and birth weight and between current weight and blood pressure. When there was a genuine inverse relation between birth weight and blood pressure, the association was exaggerated following adjustment for current weight, whereas a positive relation between birth weight and blood pressure could be reversed after adjusting for current weight. Thus, researchers must consider the reversal paradox when adjusting for variables that lie within causal pathways.

Adult↗
PubMed2005

The problem of analysing the relationship between change and initial value in oral health research.

The relationship between initial disease status and subsequent change following treatment has attracted great interest in dental research. However, medical statisticians have repeatedly warned against correlating/regressing change with baseline because of two methodological concerns known as mathematical coupling and regression to the mean. In general, mathematical coupling occurs when one variable directly or indirectly contains the whole or part of another, and the two variables are then analyzed by using correlation or regression. Consequently, the statistical procedure of testing the null hypothesis - that the coefficient of correlation or the slope of regression is zero - may become inappropriate. Regression to the mean occurs with any variable that fluctuates within an individual or a population, either owing to measurement error and/or to physiological variation. The aim of this article was to clarify the conceptual confusion around mathematical coupling and regression to the mean within the statistical literature, and to correct a popular misconception about the correct analysis of the relationship between change and initial value. As examples that use inappropriate methods to analyze the relationship between change and baseline are still found in leading dental journals, this article seeks to help oral health researchers understand these problems and explain how to overcome them.

Algorithms↗
PubMed2005

The relationship between baseline value and its change: problems in categorization and the proposal of a new method.

Oral health researchers have shown great interest in the relationship between the initial status of diseases and subsequent changes following treatment. Two main approaches have been adopted to provide evidence of a positive association between baseline values and their changes following treatment. One approach is to use correlation or regression to test the relationship between baseline measurements and subsequent change (correlation/regression approach). The second approach is to categorize the lesions into subgroups, according to threshold values, and subsequently compare the treatment effects across the two (or more) subgroups (categorization approach). However, the correlation/regression approach suffers a methodological weakness known as mathematical coupling. Consequently, the statistical procedure of testing the null hypothesis becomes inappropriate. Categorization seems to avoid the problem of mathematical coupling, although it still suffers regression to the mean. We show, first, how the appropriate null hypothesis may be established to analyze the relationship between baseline values and change in the correlation approach and, second, we use computer simulations to investigate the impact of regression to the mean on the significance testing of the differences in the average treatment effects (or average baseline values) in the categorization approach. Data available from previous literature are reanalyzed by testing the appropriate null hypotheses and the results are compared to those from testing the usual (incorrect) null hypothesis. The results indicate that both the correlation and categorization approaches can give rise to misleading conclusions and that more appropriate methods, such as Oldham's method and our new approach of deriving the correct null hypothesis, should be adopted.

Algorithms↗
PubMed2005

A multilevel modelling solution to mathematical coupling.

Owing to mathematical coupling, statistical analyses relating change to baseline values using correlation or regression are erroneous, where the statistical procedure of testing the null hypothesis becomes invalid. Alternatives, such as Oldham's method and the variance ratio test, have been advocated, although these are limited in the presence of measurement errors with non-constant variance. Furthermore, such methods prohibit the consideration of additional covariates (e.g., treatment group within trials) or confounders (e.g., age and gender). This study illustrates the more sophisticated approach of multilevel modelling (MLM) which overcomes these limitations and provides a comprehensive solution to the analysis of change with respect to baseline values. Although mathematical coupling is widespread throughout applied research, one particular area where several studies have suggested a strong relationship between baseline disease severity and treatment effect is guided tissue regeneration (GTR) within dental research. For illustration, we use GTR studies where the original data were found to be available in the literature for reanalysis. We contrast the results from an MLM approach and Oldham's method with the standard (incorrect) approach that suffers from mathematical coupling. MLM provides a robust solution when relating change to baseline and is capable of simultaneously dealing with complex error structures and additional covariates and/or potential confounders.

Data Interpretation, Statistical↗
PubMed2005

A Bayesian analysis of amalgam restorations in the Royal Air Force using the counting process approach with nested frailty effects.

Survival analysis methods are increasingly used in dental research to measure risk of tooth eruption and caries as well as life spans of amalgam restorations. Analyses have been extended to account for lack of independence in the data, which arises from the clustering of observations within units such as tooth-surfaces, teeth and subjects. There are various analytical strategies and modelling approaches now available to us in dealing with clustered dental data. In this article, the modelling strategy of Cox's proportional hazards regression is formulated using the counting process approach, which can easily be extended to include time-variant covariates as well as nested random frailty effects. A semi-parametric Bayesian method is presented for the analysis of the proposed model. The methodology is applied to an analysis of nested clustered data on life-span of amalgam restorations in the UK Royal Air Force. These data have previously been analysed using a non-Bayesian approach. The Gibbs sampler, a Markov chain Monte Carlo method, is used to generate samples from the marginal posterior distribution of the parameters of this Bayesian model.

Bayes Theorem↗
PubMed2004

Ratio variables in regression analysis can give rise to spurious results: illustration from two studies in periodontology.

OBJECTIVES: For over a century, statisticians have highlighted concerns about the inappropriate use of ratio variables in correlation and regression analysis. However, little attention has been paid to these concerns in medical and dental research. The use of ratio variables in correlation and regression analysis can give rise to spurious results due to inappropriate model specification and mathematical coupling, leading to serious misinterpretation of data and consequently to incorrect study conclusions. METHODS: Data were reanalysed from two recently published articles: one on the efficacy of guided tissue regeneration on root coverage; the other a randomised controlled trial comparing three surgical approaches in the treatment of periodontal infrabony defects. The reanalysis was performed to examine whether the assumptions behind the correlation/regression analyses have been seriously violated in these two studies, and to see if the interpretation of results is tenable. RESULTS: Use of ratio variables seriously violated the assumptions underpinning the statistical methods utilised in these two studies, and consequently the conclusions were substantially misleading. Recommendations made in these studies were not tenable. CONCLUSIONS: The reanalyses illustrate how the inappropriate use of ratio variables remains prevalent in dental research, leading to incorrect interpretation of the evidence. This emphasises the need for collaboration between clinicians and statisticians to avoid the risk of yielding erroneous conclusions from flawed statistical analyses.

Alveolar Bone Loss↗
PubMed2004

Mathematical coupling can undermine the statistical assessment of clinical research: illustration from the treatment of guided tissue regeneration.

OBJECTIVES: Previous periodontal literature has shown that there is a strong relationship between treatment effects, such as guided tissue regeneration (GTR), and baseline disease severity. However, relating change to baseline values using correlation or regression is methodologically flawed due to mathematical coupling, where the statistical procedure of testing the null hypothesis-that the coefficient of correlation or slope of regression is equal to zero-becomes erroneous. The aim of this study is to investigate if baseline disease severity is genuinely associated with the treatment outcome of intrabony defects using GTR after adjustment for mathematical coupling. In particular, we seek to demonstrate the potential effect that mathematical coupling has in distorting the results from the statistical analyses of trials of dental treatment, using data from the periodontal literature on GTR. The erroneous results arising from the use of simple correlation and regression techniques to analyse this association will be demonstrated, also the methodological flaw where the statistical procedure tests the null hypothesis-that the coefficient of correlation or the slope of regression is equal to zero. METHODS: Three main periodontal journals were electronically and manually searched to extract the data for the clinical outcomes of pocket probing depth (PPD) and lifetime cumulative attachment loss (LCAL) in the studies using GTR. The relationship between clinical outcomes and baseline measurements were reanalysed using Oldham's method and the variance ratio test. RESULTS: The results of these analyses were compared with those from the papers where the authors used the standard approach of correlation or regression. This shows that mathematical coupling caused spurious correlations between baseline disease severity and treatment effect. Ten out of 12 studies for PPD and nine out of 14 for LCAL initially claimed a significant positive relationship; after using either of the more appropriate statistical methods of adjustment, only three correlations in each group of studies remained significant. CONCLUSIONS: Previous evidence suggesting an association between baseline disease severity and treatment effect for GTR is challenged and therefore needs to be critically reviewed. All future clinical research should avoid using mathematically coupled data in correlation or regression analysis. In seeking to examine the bivariate association between baseline and subsequent change, Oldham's method is recommended.

Alveolar Bone Loss↗
PubMed2004

Collinearity in linear regression is a serious problem in oral health research.

The aim of this article is to encourage good practice in the statistical analysis of dental research data. Our objective is to highlight the statistical problems of collinearity and multicollinearity. These are among the most common statistical pitfalls in oral health research when exploring the relationship between clinical variables using multiple regression analysis. We hope that this article will show why these problems arise and how they can be avoided and overcome. Examples from the periodontal literature will be used to illustrate how collinearity and multicollinearity can seriously distort the model development process as a result of the phenomenon of mathematical coupling. Knowledge of these problems can help to eliminate misleading results and improve any subsequent interpretations. Regression analyses are useful tools in oral health research when their limitations are recognized. However, care is required in planning and it is worthwhile seeking statistical advice when formulating the study's research questions.

Algorithms↗
PubMed2004

The application of multilevel modeling in the analysis of longitudinal periodontal data--part I: absolute levels of disease.

BACKGROUND: Statistical analyses of periodontal data that average site measurements to subject mean values are unable to explore the site-specific nature of periodontal diseases. Multilevel modeling (MLM) overcomes this, taking hierarchical structure into account. MLM was used to investigate longitudinal relationships between the outcomes of lifetime cumulative attachment loss (LCAL) and probing depth (PD) in relation to potential risk factors for periodontal disease progression. METHODS: One hundred males (mean age 17 years) received a comprehensive periodontal examination at baseline and at 12 and 30 months. The resulting data were analyzed in two stages. In stage one (reported here), the absolute levels of disease were analyzed in relation to potential risk factors; in stage two (reported in a second paper), changes in disease patterns over time were analyzed in relation to the same risk factors. Each approach yielded substantially different insights. RESULTS: For absolute levels of disease, subject-level risk factors (covariates) had limited prediction for LCAL/PD throughout the 30-month observation period. Tooth position demonstrated a near linear relationship for both outcomes, with disease increasing from anterior to posterior teeth. Sites with subgingival calculus and bleeding on probing demonstrated more LCAL and PD, and supragingival calculus had an apparently protective effect. Covariates had more "explanatory power" for the variation in PD than for the variation in LCAL, suggesting that LCAL and PD might be generally associated with a different profile of covariates. CONCLUSION: This study provides, for a relatively young cohort, considerable insights into the factors associated with early-life periodontal disease and its progression at all levels of the natural hierarchy of sites within teeth within subjects.

Adolescent↗
PubMed2004

The application of multilevel modeling in the analysis of longitudinal periodontal data--part II: changes in disease levels over time.

BACKGROUND: The aim of this study was to investigate the longitudinal relationships between the outcome measurements of changes in lifetime cumulative attachment loss (cLCAL) and changes in probing depth (cPD) in relation to potential risk factors or other risk markers for periodontal disease progression from a cohort of 100 young males. In order to account for the hierarchical data structure, and to explore explicitly the site, tooth, and subject levels simultaneously, multilevel modeling was undertaken. METHODS: The analyses were undertaken in two parts. Within a previous article, the absolute levels of disease were analyzed in relation to potential risk factors; within this article, changes in disease are analyzed in relation to these factors. Each analytical approach yielded substantively different insights. RESULTS: Subject-level risk factors had limited predictive value for cLCAL/cPD throughout the 30-month observation period. Tooth position demonstrated a near linear relationship for both outcomes, with disease increasing from anterior to posterior teeth. Supragingival plaque had no significant effect on cLCAL/cPD, while subgingival calculus and bleeding on probing were negatively associated with cLCAL/cPD. In contrast to the outcomes LCAL/PD, supragingival calculus had no significant protective effect on cLCAL/cPD. There was no significant influence of smoking in this cohort. CONCLUSIONS: This study provides, for a relatively young cohort, considerable insights into the factors associated with longitudinal patterns of early-life periodontal disease at all levels of the natural hierarchy of sites within teeth within subjects. Furthermore, it is demonstrated how multilevel modeling can provide considerable insight into some of the inconsistencies and controversies found in the previous periodontal literature.

Adolescent↗
PubMed2003

The influence of the distance from the contact point to the crest of bone on the presence of the interproximal dental papilla.

BACKGROUND: Loss of the interproximal dental papilla may cause functional and, especially in the maxillary anterior region, phonetic and severe esthetic problems. The purpose of this study was to investigate whether the distance from the contact point to the bone crest on standardized periapical radiographs of the maxillary anterior teeth could be correlated with the presence of the interproximal papilla in Taiwanese patients. METHODS: In total, 200 interproximal sites of maxillary anterior teeth in 45 randomly selected patients were examined. Selected subjects were adult Taiwanese with fully erupted permanent dentition. The presence of the interproximal papilla was determined visually. If there was no visible space apical to the contact area, the papilla was recorded as being present. The distance from the contact point to the crest of bone was measured on standardized periapical radiographs using a paralleling technique with a RinnXCP holder. RESULTS: Data revealed that when the distance from the contact point to the bone crest on standardized periapical radiographs was 5 mm or less, the papillae were almost 100% present. When the distance was 6 mm, 51% of the papillae were present, and when the distance was 7 mm or greater, only 23% of the papillae were present. CONCLUSION: The distance from the contact point to the bone crest on standardized periapical radiographs of the maxillary anterior teeth is highly associated with the presence or absence of the interproximal papilla in Taiwanese patients, and is a useful guide for clinical evaluation.

Adult↗