A multivariable normal tissue complication probability model for predicting radiation-induced hypothyroidism in nasopharyngeal carcinoma patients in the modern radiotherapy era.

Radiation-induced hypothyroidism (RHT) is a common long-term complication for nasopharyngeal carcinoma (NPC) survivors. A model using clinical and dosimetric factors for predicting risk of RHT could suggest a proper dose-volume parameters for the treatment planning in an individual level. We aim to develop a multivariable normal tissue complication probability (NTCP) model for RHT in NPC patients after intensity-modulated radiotherapy or volumetric modulated arc therapy. The model was developed using retrospective clinical data and dose-volume data of the thyroid and pituitary gland based on a standard backward stepwise multivariable logistic regression analysis and was then internally validated using 10-fold cross-validation. The final NTCP model consisted of age, pretreatment thyroid-stimulating hormone and mean thyroid dose. The model performance was good with an area under the receiver operating characteristic curve of 0.749 on an internal (200 patients) and 0.812 on an external (25 patients) validation. The mean thyroid dose at ≤45 Gy was suggested for treatment plan, owing to an RHT incidence of 2% versus 61% in the >45 Gy group.

Binary Response Analysis Using Logistic Regression in Dentistry.

Multivariate analysis with binary response is extensively utilized in dental research due to variations in dichotomous outcomes. One of the analyses for binary response variable is binary logistic regression, which explores the associated factors and predicts the response probability of the binary variable. This article aims to explain the statistical concepts of binary logistic regression analysis applicable to the field of dental research, including model fitting, goodness of fit test, and model validation. Moreover, interpretation of the model and logistic regression are also discussed with relevant examples. Practical guidance is also provided for dentists and dental researchers to enhance their basic understanding of binary logistic regression analysis.

The Trend of Analytical Approaches in Dental Research.

OBJECTIVES: The aims of this article are to examine statistical approaches employed in international dental articles published in 2018 and 2019, as well as to examine relationships among analytical approaches, journal rankings, and types of research. MATERIALS AND METHODS: International dental journals published in 2018 and 2019 were selected from the four quartiles (Q1-Q4) of journal rankings using a stratified random sampling. All original articles in a randomly sampled issue of each selected journal were reviewed to explore employed statistical approaches and to examine relationships among analytical approaches, journal rankings, and types of research. RESULTS: One hundred and twenty-eight English-written international journals listed according to SCImago Journal Rank were selected, consisting 969 original articles. Significant differences in the use of statistics were found among the four quartiles and between types of research. The articles in Q1 tended to use more advanced analysis but lower descriptive analytics than other quartiles. The narrative approach was highly used in laboratory-based articles (18.66%), whereas clinical research was likely to use more descriptive (92.32%) and advanced analyses (26.30%). The data also found no remarkable differences in the patterns of the three most common statistical use among the four quartiles. CONCLUSION: This research revealed statistical use in international dental journals, which will enable educators to consider statistical content to be included in dental curricula, either for undergraduate or for postgraduate programs.

Assessment of the confidence interval in the multivariable normal tissue complication probability model for predicting radiation-induced liver disease in primary liver cancer.

We developed a confidence interval-(CI) assessing model in multivariable normal tissue complication probability (NTCP) modeling for predicting radiation-induced liver disease (RILD) in primary liver cancer patients using clinical and dosimetric data. Both the mean NTCP and difference in the mean NTCP (ΔNTCP) between two treatment plans of different radiotherapy modalities were further evaluated and their CIs were assessed. Clinical data were retrospectively reviewed in 322 patients with hepatocellular carcinoma (n = 215) and intrahepatic cholangiocarcinoma (n = 107) treated with photon therapy. Dose-volume histograms of normal liver were reduced to mean liver dose (MLD) based on the fraction size-adjusted equivalent uniform dose. The most predictive variables were used to build the model based on multivariable logistic regression analysis with bootstrapping. Internal validation was performed using the cross-validation leave-one-out method. Both the mean NTCP and the mean ΔNTCP with 95% CIs were calculated from computationally generated multivariate random sets of NTCP model parameters using variance-covariance matrix information. RILD occurred in 108/322 patients (33.5%). The NTCP model with three clinical and one dosimetric parameter (tumor type, Child-Pugh class, hepatitis infection status and MLD) was most predictive, with an area under the receiver operative characteristics curve (AUC) of 0.79 (95% CI 0.74-0.84). In eight clinical subgroups based on the three clinical parameters, both the mean NTCP and the mean ΔNTCP with 95% CIs were able to be estimated computationally. The multivariable NTCP model with the assessment of 95% CIs has potential to improve the reliability of the NTCP model-based approach to select the appropriate radiotherapy modality for each patient.

Step-up procedures for non-inferiority tests with multiple experimental treatments.

Non-inferiority (NI) trials are becoming more popular. The NI of a new treatment compared with a standard treatment is established when the new treatment maintains a substantial fraction of the treatment effect of the standard treatment. A valid NI trial is also required to show assay sensitivity, the demonstration of the standard treatment having the expected effect with a size comparable to those reported in previous placebo-controlled studies. A three-arm NI trial is a clinical study that includes a new treatment, a standard treatment and a placebo. Most of the statistical methods developed for three-arm NI trials are designed for the existence of only one new treatment. Recently, a single-step procedure was developed to deal with NI trials with multiple new treatments with the overall familywise error rate controlled at a specified level. In this article, we extend the single-step procedure to two new step-up procedures for NI trials with multiple new treatments. A comparative study of test power shows that both proposed step-up procedures provide a significant improvement of power when compared to the single-step procedure. One of the two proposed step-up procedures also allows the flexibility of allocating different error rates between the sensitivity hypothesis and the NI hypotheses so that the assignment of fewer patients to the placebo becomes possible when designing NI trials. We illustrate the new procedures using data from a clinical trial.

Normal probability plots with confidence.

Normal probability plots are widely used as a statistical tool for assessing whether an observed simple random sample is drawn from a normally distributed population. The users, however, have to judge subjectively, if no objective rule is provided, whether the plotted points fall close to a straight line. In this paper, we focus on how a normal probability plot can be augmented by intervals for all the points so that, if the population distribution is normal, then all the points should fall into the corresponding intervals simultaneously with probability 1-α. These simultaneous 1-α probability intervals provide therefore an objective mean to judge whether the plotted points fall close to the straight line: the plotted points fall close to the straight line if and only if all the points fall into the corresponding intervals. The powers of several normal probability plot based (graphical) tests and the most popular nongraphical Anderson-Darling and Shapiro-Wilk tests are compared by simulation. Based on this comparison, recommendations are given in Section 3 on which graphical tests should be used in what circumstances. An example is provided to illustrate the methods.

Recursive confidence band construction for an unknown distribution function.

Given a sample X1,...,Xn of independent observations from an unknown continuous distribution function F, the problem of constructing a confidence band for F is considered, which is a fundamental problem in statistical inference. This confidence band provides simultaneous inferences on all quantiles and also on all of the cumulative probabilities of the distribution, and so they are among the most important inference procedures that address the issue of multiplicity. A fully nonparametric approach is taken where no assumptions are made about the distribution function F. Historical approaches to this problem, such as Kolmogorov's famous () procedure, represent some of the earliest inference methodologies that address the issue of multiplicity. This is because a confidence band at a given confidence level 1-α allows inferences on all of the quantiles of the distribution, and also on all of the cumulative probabilities, at that specified confidence level. In this paper it is shown how recursive methodologies can be employed to construct both one-sided and two-sided confidence bands of various types. The first approach operates by putting bounds on the cumulative probabilities at the data points, and a recursive integration approach is described. The second approach operates by providing bounds on certain specified quantiles of the distribution, and its implementation using recursive summations of multinomial probabilities is described. These recursive methodologies are illustrated with examples, and R code is available for their implementation.

Simultaneous inference for several quantiles of a normal population with applications.

A common statistical problem is to make inference about the mean of a normally distributed population. While the mean and the variance are important quantities, many real problems require information on certain quantiles of the population which combine both the mean and variance. Motivated by two recent applications, we consider simultaneous inference for more than one quantile of interest. In this paper, a set of exact 1-α level simultaneous confidence intervals for several quantiles of a normally distributed population is constructed, based on a simple random sample from that population. The critical constants for achieving an exact 1-α simultaneous coverage probability can be computed efficiently using numerical quadrature involving only a one-dimensional integral combined with standard search algorithms. The proposed methods are illustrated with an example. Several further research problems are identified.

Extension of three-arm non-inferiority studies to trials with multiple new treatments.

Non-inferiority (NI) trials are becoming increasingly popular. The main purpose of NI trials is to assert the efficacy of a new treatment compared with an active control by demonstrating that the new treatment maintains a substantial fraction of the treatment effect of the control. Most of the statistical testing procedures in this area have been developed for three-arm NI trials in which a new treatment is compared with an active control in the presence of a placebo. However, NI trials frequently involve comparisons of several new treatments with a control, such as in studies involving different doses of a new drug or different combinations of several new drugs. In seeking an adequate testing procedure for such cases, we use a new approach that modifies existing testing procedures to cover circumstances in which several new treatments are present. We also give methods and algorithms to produce the optimal sample size configuration. In addition, we also discuss the advantages of using different margins for the assay sensitivity test between the active control and the placebo and the NI test between the new treatments and the active control. We illustrate the new approach by using data from a clinical trial.

Wheelchair use by veterans newly prescribed a manual wheelchair.

OBJECTIVE: To describe the characteristics of the wheelchairs, the users, and their wheelchair use among persons newly prescribed a manual wheelchair. DESIGN: Cohort study. SETTING: Veterans Affairs teaching hospital. PARTICIPANTS: Ninety-nine consecutive, cognitively intact veterans prescribed a manual wheelchair. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Self-reported difficulty transferring into and propelling the wheelchair; and bathroom mobility method. RESULTS: Study patients had a mean age of 66 and a mean of 10 comorbid medical conditions. Parkinsonism, osteoporosis, joint replacement, and amputation were uncommon (<30% of patients), but had a high impact on need for a wheelchair (when present were reported by >50% of patients as causing need for a wheelchair). Falls and arthritis were common (>50% of patients) and highly impacted need for a wheelchair. At 1 month, over 30% of patients had wheelchairs that did not meet common criteria for wheelchair fit; 36% and 61%, respectively, reported difficulty transferring and propelling the wheelchair. The wheelchairs were used for bathroom mobility by 38% of the patients. CONCLUSIONS: The typical manual wheelchair recipient in this study sample was old with multiple medical problems. Despite provision of manual wheelchairs by trained professionals and availability of diverse wheelchair types, new wheelchair users commonly reported difficulty using the wheelchair.

Operability region equivalence: simultaneous confidence bands for the equivalence of two regression models over restricted regions.

In many scientific problems the purpose of comparing two linear regression models is to demonstrate that they have only negligible differences and so can be regarded as being practically equivalent. The frequently used statistical approach of testing the homogeneity null hypothesis of the two models by using a partial F test is not appropriate for this purpose. In this paper, a simultaneous confidence band is proposed which provides an upper bound on the largest possible difference between the two models, in units of the standard error of the observations, over a given region of the covariates. This is demonstrated to be a more practical method for assessing the equivalence of the two regression models.