Andrew Blance

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.

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.

The efficacy of a plasma arc light in orthodontic bonding: a randomized controlled clinical trial

Objective To evaluate the clinical performance of a plasma arc light (Ortho LITE, 3M Unitek, Monrovia, CA, USA) against a conventional tungsten–quartz halogen curing light (Visilux 2, 3M Unitek, Monrovia, CA, USA) for direct orthodontic bonding. Design A single centre prospective randomized controlled clinical trial. Setting The Orthodontic Department at St Lukes Hospital, Bradford, UK. Subjects and methods Forty-three consecutive patients requiring fixed appliances from the orthodontic waiting list. A split mouth technique was adopted; with quadrants randomly assigned to either the plasma arc light or the conventional halogen curing light and bonded directly with APC pre-adjusted edgewise brackets (3M Unitek, Monrovia, CA, USA). Main outcome measure Bracket failures. Secondary outcome measures Time taken to bond-up the appliances, patient sensitivity or discomfort during curing and time to replace failed brackets were investigated. Results No statistically significant difference in bracket failure rates over the full course of treatment was found between the plasma arc light (6.7%; 95% CI 4.5–10.0) and the halogen curing light (9.5%; 95% CI 6.8–13.1). There was no statistically significant difference in bracket survival times. The bond-up times were typically reduced by 204 seconds per patient with the plasma arc light. There were no differences in patient reported sensitivity or discomfort or rebond times. Conclusion The plasma arc light is a viable clinical alternative to the conventional halogen curing light with benefits for both the clinician and patient due to reduced bonding times.

Are general dental practitioners effective in the management of non-apnoeic snoring using mandibular advancement appliances?

Objective This study assessed the effectiveness of general dental practitioners (GDPs) in the management of subjects with non-apnoeic snoring using a mandibular advancement appliance (MAA), following a one day training course.Subjects and methods Sixty subjects suffering from simple, non-apnoeic snoring were treated by 15 GDPs, in three hospital centres, using a monobloc mandibular advancement appliance design. All GDPs attended a one day training course prior to the study which covered theoretical and practical training in the use of mandibular advancement appliances. Snoring and level of disturbance were assessed using a questionnaire completed by their sleeping partner before and after a three month treatment period. Daytime sleepiness was assessed by the patients using the Epworth sleepiness scale questionnaire (ESS) before and after a three month treatment period. In addition, patients completed an outcome questionnaire, to assess side-effects experienced from the MAA.Results A success rate of 48% (95% CI 0.35, 0.61) was achieved in partner-assessed snoring and disturbance levels, following a three month period of MAA treatment. The median ESS score reduced from 9 to 7.5 (95% CI 0, 3). General dental practitioners experienced problems during protrusive bite registrations, with 10% being judged inadequate.Conclusion GDPs were not effective in the management of non-apnoeic snoring using a monobloc appliance after a one day training course. Further training and/or selection of a different design of appliance should be considered for GDPs to become highly competent in this area.

Not necessarily strength in numbers, more a case of size does matter!

A few years ago, I was approached with the consideration that this journal wished to undertake a formal statistical review of every scientific paper before publication. At the time, I saw this as an ambitious step on behalf of the Journal of Orthodontics. Nonetheless, it was an opportunity that I considered most worthy of support. Now, taking a moment to pause and reflect back over this period I am pleased with what we have achieved. Due credit must be given to all those involved, not least to all those authors who have taken the statistical reviewers comments and embraced the opportunities that this extra critical review presents. Having seen many papers before and after the review process, I personally have found satisfaction in the small (but valuable) role this process can play in raising the overall level of scientific quality. It is my hope that both the authors and you, the reader, agree. While not wishing to take anything away from the positives, I find myself thinking of two issues that frequently arise, namely a priori sample size calculations and clustered data. A priori (before commencement of the study) sample size calculations are important for several reasons. Arguably most importantly is that from a clinical perspective it is unethical to inconvenience patients: if the sample size is too small then the study is unlikely to find evidence of the effect it seeks, while if the sample size is too large then more patients than required participate. In both cases, there are resource issues both in terms of time and financially. Having just argued that the biggest driver for a priori sample size calculations is an ethical one, I find it of grave concern to observe so many manuscripts where mention of the determination of sample size is either simply omitted or, more worryingly, never conducted. In the UK, set against the backdrop of National Research Ethics Committee (NRES) and the fact the journal now requests the ethical approval number for all published studies where applicable, this really is quite surprising. The solution perhaps lies with us all; ethics committees in the approval process, researchers in their reporting, reviewers in their critique and readers in their critical appraisal. The second issue is the handling of clustered observations. Dentists are very familiar with this kind of data, due to the natural hierarchy of the mouth; sites are clustered around a tooth, which in turn are clustered in a within an individual mouth. Clustering such as this poses many interesting statistical challenges, as the assumption of independence that underlies most of what might be considered ‘traditional’ statistical methods is violated. Perhaps because of the familiarity with this hierarchy however, the statistical issues are often still not fully appreciated within dental research. The consequence of the lack of independence is that the number of observations is not equal to the true sample size. In fact, the true sample size is somewhere between the number of independent units/patients and the number of observations, with the exact value depending on the level of similarity within patients. Thus, the effective sample size is reduced. If the lack of independence is erroneously ignored, then the result is underestimated standard errors attached to any estimate. Consequently, any assessment of statistical significance will be erroneous. Such data require more sophisticated methods in their analysis. There are several suitable methods, of which multilevel modelling (MLM) and generalized estimating equations (GEE) are two examples. The bottom-line is that where observations lack independence, then appropriate methods should be employed.

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.