Seminars in Orthodontics最新文献

Missing data are a common issue in medical research. We aim to explain in non-technical language the issues and concepts around missing data, as well as discuss common methods for handling missing data. Specifically, our objectives are to answer the following questions: (1) What are missing data and why should we care about them? (2) What are the missingness mechanisms and how do they impact statistical analysis? (3) How can we explore missing values in our datasets? (4) What are ad-hoc methods for dealing with missing values and are they valid? (5) What is multiple imputation? (6) What should we consider when conducting a multiple imputation analysis? (7) Is multiple imputation always needed? (8) How should we report an analysis with missing data? We illustrate discussions with examples from an orthodontic study.

Network meta-analysis has been widely used to address the limitations of traditional pairwise meta-analysis. Network meta-analysis incorporates all available evidence into a general statistical framework for comparing multiple treatments. The original Bayesian approach offers a statistical framework to address heterogeneity in the evidence and complexity in the data structure when clinical trials with more than two treatment groups are included. Alternative frequentist approaches have been developed and implemented in commonly used statistical software. The aim of this article is to provide a non-technical introduction to the statistical models and assumptions of network meta-analysis, such as consistency and transitivity, for the orthodontics and dental research community. An example was used to demonstrate how to conduct a network meta-analysis and how to use GRADE and CINeMA tools for placing confidence in the NMA effect estimates in a network meta-analysis. The statistical theory behind network meta-analysis is complex, so we strongly encourage close collaboration between orthodontists and experienced statisticians when planning and conducting a network meta-analysis. Network meta-analysis has been proven to be a very useful tool for evidence synthesis because it improves the efficiency of comparative effectiveness research and the quality of decision-making.

It is essential to conduct a reliability examination even if the method was considered reliable in the past, as it may not be reliable in a new study conducted by different researchers using different materials. The current article highlights the importance of reliability examination in orthodontic studies and explains which assessment methods are more appropriate than others. Several fallacies in reporting and interpreting reliability are also discussed. In addition, the article presents examples of reliability examination for one-, two-, and three-dimensional data using graphic visualization in a tutorial format.

Randomized controlled trials (RCTs) are generally considered the highest level of evidence and the preferred approach to comparing the effectiveness of different treatments. However, the cost of an RCT can be very high, and it may be considered unethical to randomly assign patients to treatments that have no real benefits or even may cause harm. For rare events, it may take a long time and require a large number of patients to observe a sufficient number of outcomes. RCTs may have low external validity or generalizability. Observational studies provide valuable alternatives, particularly for developing predictive models and assessing the effectiveness of interventions. This article aims to provide a general introduction to the advantages and disadvantages of two major observational study designs, namely cohort and case-control studies. Cohort studies compare the outcomes of exposed and unexposed groups over time. However, the nonrandom allocation may lead to confounding bias. Propensity score matching and statistical adjustment are often used to address this problem, but they cannot deal with unmeasured confounders. Case-control studies select participants based on their outcomes and retrospectively collect information on the exposure levels of the case and control groups. We will discuss methods to minimize or adjust for confounding bias, such as propensity score matching and statistical adjustment.

Longitudinal growth data with repeated measurements of distances and angles on radiographs are usually collected to study skeletal and dental changes throughout childhood and adolescence. The analysis of longitudinal data usually requires sophisticated statistical methods and modeling techniques because repeated measurements made on the same subject violate the assumption of independence underlying classical statistical tests. Advanced methods, such as multilevel modeling, must be used to account for the correlations between repeated measurements. In this article, we describe four statistical models for the analysis of growth data: linear multilevel model, curvilinear multilevel model, multilevel Preece-Baines model, and super imposition by translation and rotation (SITAR) model. We use data of 42 children on the mandibular length obtained from the archives at the AAOF Craniofacial Growth Legacy Collection for demonstration. Our analyses showed that although the multilevel curvilinear model appears to fit the data well from a statistical perspective, the Preece-Baines model and the SITAR model provide additional insights into mandibular growth. The SITAR model suggests two growth peaks which is consistent with the current understanding of mandibular growth and deserves more attention from orthodontic researchers.

Research reports need to provide complete, accurate, and transparent information to allow readers to easily understand and critically assess the study results. Poor reporting makes studies unable to be synthesized in systematic reviews, fail to inform clinical practice, and compromise evidence-based clinical decision making. Evidence suggested the reporting quality of orthodontic clinical studies was poor, which caused a large amount of avoidable research waste. Reporting guidelines (RGs) are developed to guide and standardize the reporting of specific study types and improve their reporting quality. This article introduces the commonly used RGs in orthodontic clinical studies and illustrates the relationship between the existing RGs and their extensions. The majority of extensions are those to the CONSORT and PRISMA guidelines. The EQUATOR Network is an online library of RGs and education resources, and authors can use it to find appropriate RGs. Although a large number of RGs and extensions have been published, involving various study types, the reporting quality of orthodontic clinical studies still needs to be improved. Active strategies to strengthen the implementation of RGs are necessary to fill the gaps between RG publication and the quality improvement of studies. Other issues including selective reporting and spin, structure format of abstracts, and artificial intelligence in reporting are also discussed. Language models such as ChatGPT have largely changed scientific research and reporting in the era of artificial intelligence. Authors are strongly recommended to always be transparent in reporting and responsible for the content of their studies.

Accelerated orthodontics (AO) is emerging as a revolutionary approach in achieving desired orthodontic results in a shorter timeframe. AO modalities, both invasive and non-invasive promise to bring about rapid orthodontic tooth movement (OTM) transformations through targeted bone remodeling. From micro-osteoperforations facilitating bone remodeling to photobiomodulation enhancing cellular activity, the armamentarium of accelerated orthodontics promises to not only shorten treatment times but also potentially unlock novel therapeutic avenues for complex malocclusions. This burgeoning field, however, necessitates rigorous scientific scrutiny to optimize protocols, mitigate potential iatrogenic effects, and ultimately deliver on the promise of a faster, more efficacious, and patient-centric orthodontic experience. This paper offers a comprehensive review of AO, exploring its potential benefits and drawbacks, analysing the effectiveness of popular techniques, and providing insights for informed decision-making by delving into the science behind AO, evaluating clinical evidence, such as, transient pain, root resorption, and periodontal considerations. Also, this paper aims to equip patients and Orthodontists with a deeper understanding of this evolving field.

Censoring occurs when we do not observe exactly the value that we are interested in, but we only learn about some bounds for it. For instance, an observation is right-censored (left-censored) when it is smaller (larger) than the true value.

Censoring is most often encountered when observing a time to event, i.e., the time that elapses between a well-defined starting moment until a particular event of interest (for example, the age until the first dental caries). However, it may apply to any measurement or observation. For instance, left- and right-censoring applies to diagnostic assays with, respectively, a lower and an upper limit of detection.

The presence of censored observations has important consequences for the statistical analysis. This is because, in such a case, the use of classical statistics (such as, e.g., the sample mean) or statistical models (such as, e.g., linear regression) will result in biased results. Analysis of data that include censored observations requires the use of methods that take explicitly into account censoring. Collectively, in medicine, these methods are referred to as survival analysis. In this article, we provide a review of the basic (parametric and non-parametric) statistical methods of survival analysis.

Evidence synthesis of primary orthodontic studies is crucial in advancing the dental and orthodontic field. The quality of conclusions delivered to the end-users of systematic reviews is contingent upon the appropriateness and diligence of the systematic review methods. The article provides a description of the core components of pairwise meta-analysis, a statistical tool that synthesises the findings of several related primary studies. Emphasis is placed on the features and good selection practices of the available meta-analysis models, proper visualisation of the results and the concept of statistical heterogeneity. A real-life systematic review is used to exemplify the introduced methods.