Right ventricular volumetric measurements: New tricks for an old dog

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.

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.

Management of the acute respiratory distress syndrome

Significant advances have occurred in the knowledge of the pathogenesis of ARDS. It is now recognized that ARDS is a manifestation of a diffuse process that results from a complicated cascade of events following an initial insult or injury. Mechanical ventilation and PEEP are still important components of supportive therapy. To avoid ventilator-associated lung injury there is emphasis on targeting ventilator management based on measurement of pulmonary mechanics. For those with resistant hypoxia and severe pulmonary hypertension adjunctive modalities, such as prone positioning and low-dose iNO, may provide important benefit. Alternative modes of supporting gas exchange, such as with partial liquid ventilation and extracorporeal gas-exchange, may serve as rescue therapies. Advances in cell and molecular biology have contributed to a better understanding of the role of inflammatory cells and mediators that contribute to the acute lung injury and the pathophysiology of the syndrome that manifests as ARDS. Based on this new understanding, the potential targets for intervention to ameliorate the systemic inflammatory response have proliferated. Examples include the cytokine network and its receptors, antioxidants, and endothelins. Apart from the challenge of testing these agents in experimental models, it seems likely that determination of the optimum combination of agents will become an equally important endeavor. A particular challenge is to develop better methods of predicting which of the many at-risk patients will go on to full-blown ARDS and MODS, thereby targeting subgroups of patients most likely to benefit from anti-inflammatory therapies. Similarly, the adverse effects of immunosuppressive therapy may be diminished by improved, perhaps molecular, techniques to detect microbial pathogens and permit differentiation between Systemic inflammatory response syndrome and sepsis.