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Purpose: To compare the oral health-related quality of life (OHRQoL) in a prospective, randomized crossover trial in patients with mandibular overdentures retained with two or four locators. Materials and Methods: In 30 patients with edentulous mandibles, four implants (ICX-plus implants [Medentis Medical]) were placed in the intraforaminal area. Eight weeks after transgingival healing, patients were randomly assigned to have two or four implants incorporated in the prosthesis. After 3 months, the retention concepts were switched. The patients with a two-implant-supported overdenture had four implants incorporated, whereas patients with a four-implant-supported overdenture had two retention locators taken out. After 3 more months, all four implants were retained in the implant-supported overdenture in every patient. To measure OHRQoL of the patients, the Oral Health Impact Profile 14, German version (OHIP-14 G), was used. Results: A considerable increase in OHRQoL could be seen in all patients after the prosthesis was placed on the implants. Also, a statistically significant difference of OHRQoL could be seen in the OHIP-14 G scores between two-implant and four-implant overdentures. Patients had a higher OHRQoL after incorporation of four implants in the overdenture compared with only two implants. Conclusion: Patients with implant-retained overdentures had better OHRQoL compared with those with conventional dentures. The number of incorporated implants in the locator-retained overdenture also influenced the increase in OHRQoL, with four implants having a statistically significant advantage over two implants. [ABSTRACT FROM AUTHOR]

Progression‐free survival (PFS) is a frequently used endpoint in oncological clinical studies. In case of PFS, potential events are progression and death. Progressions are usually observed delayed as they can be diagnosed not before the next study visit. For this reason potential bias of treatment effect estimates for progression‐free survival is a concern. In randomized trials and for relative treatment effects measures like hazard ratios, bias‐correcting methods are not necessarily required or have been proposed before. However, less is known on cross‐trial comparisons of absolute outcome measures like median survival times. This paper proposes a new method for correcting the assessment time bias of progression‐free survival estimates to allow a fair cross‐trial comparison of median PFS. Using median PFS for example, the presented method approximates the unknown posterior distribution by a Bayesian approach based on simulations. It is shown that the proposed method leads to a substantial reduction of bias as compared to estimates derived from maximum likelihood or Kaplan–Meier estimates. Bias could be reduced by more than 90% over a broad range of considered situations differing in assessment times and underlying distributions. By coverage probabilities of at least 94% based on the credibility interval of the posterior distribution the resulting parameters hold common confidence levels. In summary, the proposed approach is shown to be useful for a cross‐trial comparison of median PFS. [ABSTRACT FROM AUTHOR]

Abstract Background Modern radiotherapy plays an important role in therapy of advanced head and neck carcinomas. However, no clinical studies have been published addressing the effectiveness of postoperative radiotherapy in patients with small tumor (pT1, pT2) and concomitant ipsilateral metastasis of a single lymph node (pN1), which would provide a basis for a general treatment recommendation. Methods/Design The present study is a non-blinded, prospective, multi-center randomized controlled trial (RCT). As the primary clinical endpoint, overall-survival in patients receiving postoperative radiation therapy vs. patients without adjuvant therapy following curative intended surgery is compared. The aim of the study is to enroll 560 adult males and females for 1:1 randomization to one of the two treatment arms (irradiation/no irradiation). Since patients with small tumor (T1/T2) but singular lymph node metastasis are rare and the amount of patients consenting to randomization is not predictable in advance, all patients rejecting randomization will be treated as preferred and enrolled in a prospective observational study (comprehensive cohort design) after giving informed consent. This observational part of the trial will be performed with maximum consistency to the treatment and observation protocol of the RCT. Because the impact of patient preference for a certain treatment option is not calculable, parallel design of RCT and observational study may provide a maximum of evidence and efficacy for evaluation of treatment outcome. Secondary clinical endpoints are as follows: incidence and time to tumor relapse (locoregional relapse, lymph node involvement and distant metastatic spread), Quality of life as reported by EORTC (QLQ-C30 with H&N 35 module), and time from operation to orofacial rehabilitation. All tumors represent a homogeneous clinical state and therefore additional investigation of protein expression levels within resection specimen may serve for establishment of surrogate parameters of patient outcome. Conclusion The inherent challenges of a rare clinical condition (pN1) and two substantially different therapy arms would limit the practicality of a classical randomized study. The concept of a Comprehensive Cohort Design combines the preference of a randomized study, with the option of careful data interpretation within an observational study. Trial registration ClinicalTrials.gov: NCT00964977

Many authors have proposed different approaches to combine multiple endpoints in a univariate outcome measure in the literature. In case of binary or time-to-event variables, composite endpoints, which combine several event types within a single event or time-to-first-event analysis are often used to assess the overall treatment effect. A main drawback of this approach is that the interpretation of the composite effect can be difficult as a negative effect in one component can be masked by a positive effect in another. Recently, some authors proposed more general approaches based on a priority ranking of outcomes, which moreover allow to combine outcome variables of different scale levels. These new combined effect measures assign a higher impact to more important endpoints, which is meant to simplify the interpretation of results. Whereas statistical tests and models for binary and time-to-event variables are well understood, the latter methods have not been investigated in detail so far. In this paper, we will investigate the statistical properties of prioritized combined outcome measures. We will perform a systematical comparison to standard composite measures, such as the all-cause hazard ratio in case of time-to-event variables or the absolute rate difference in case of binary variables, to derive recommendations for different clinical trial scenarios. We will discuss extensions and modifications of the new effect measures, which simplify the clinical interpretation. Moreover, we propose a new method on how to combine the classical composite approach with a priority ranking of outcomes using a multiple testing strategy based on the closed test procedure. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In cluster-randomized trials, groups of individuals (clusters) are randomized to the treatments or interventions to be compared. In many of those trials, the primary objective is to compare the time for an event to occur between randomized groups, and the shared frailty model well fits clustered time-to-event data. Members of the same cluster tend to be more similar than members of different clusters, causing correlations. As correlations affect the power of a trial to detect intervention effects, the clustered design has to be considered in planning the sample size. In this publication, we derive a sample size formula for clustered time-to-event data with constant marginal baseline hazards and correlation within clusters induced by a shared frailty term. The sample size formula is easy to apply and can be interpreted as an extension of the widely used Schoenfeld's formula, accounting for the clustered design of the trial. Simulations confirm the validity of the formula and its use also for non-constant marginal baseline hazards. Findings are illustrated on a cluster-randomized trial investigating methods of disseminating quality improvement to addiction treatment centers in the USA. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The benefit of adjusting the sample size in clinical trials on the basis of treatment effects observed in interim analysis has been the subject of several recent papers. Different conclusions were drawn about the usefulness of this approach for gaining power or saving sample size, because of differences in trial design and setting. We examined the benefit of sample size adjustment in relation to trial design parameters such as 'time of interim analysis' and 'choice of stopping criteria'. We compared the adaptive weighted inverse normal method with classical group sequential methods for the most common and for optimal stopping criteria in early, half-time and late interim analyses. We found that reacting to interim data might significantly reduce average sample size in some situations, while classical approaches can out-perform the adaptive designs under other circumstances. We characterized these situations with respect to time of interim analysis and choice of stopping criteria. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: The course of multiple sclerosis (MS) shows substantial inter-individual variability. The underlying determinants of disease severity likely involve genetic and environmental factors., Objective: The aim of this study was to assess the impact of APOE and HLA polymorphisms as well as smoking and body mass index (BMI) in the very early MS course., Methods: Untreated patients ( n = 263) with a recent diagnosis of relapsing-remitting (RR) MS or clinically isolated syndrome underwent standardized magnetic resonance imaging (MRI). Genotyping was performed for single-nucleotide polymorphisms (SNPs) rs3135388 tagging the HLA-DRB1*15:01 haplotype and rs7412 (Ɛ2) and rs429358 (Ɛ4) in APOE. Linear regression analyses were applied based on the three SNPs, smoking and BMI as exposures and MRI surrogate markers for disease severity as outcomes., Results: Current smoking was associated with reduced gray matter fraction, lower brain parenchymal fraction and increased cerebrospinal fluid fraction in comparison to non-smoking, whereas no effect was observed on white matter fraction. BMI and the SNPs in HLA and APOE were not associated with structural MRI parameters., Conclusions: Smoking may have an unfavorable effect on the gray matter fraction as a potential measure of MS severity already in early MS. These findings may impact patients' counseling upon initial diagnosis of MS.

Background: Composite endpoints comprising hospital admissions and death are the primary outcome in many cardiovascular clinical trials. For statistical analysis, a Cox proportional hazards model for the time to first event is commonly applied. There is an ongoing debate on whether multiple episodes per individual should be incorporated into the primary analysis. While the advantages in terms of power are readily apparent, potential biases have been mostly overlooked so far., Methods: Motivated by a randomized controlled clinical trial in heart failure patients, we use directed acyclic graphs (DAG) to investigate potential sources of bias in treatment effect estimates, depending on whether only the first or multiple episodes are considered. The biases first are explained in simplified examples and then more thoroughly investigated in simulation studies that mimic realistic patterns., Results: Particularly the Cox model is prone to potentially severe selection bias and direct effect bias, resulting in underestimation when restricting the analysis to first events. We find that both kinds of bias can simultaneously be reduced by adequately incorporating recurrent events into the analysis model. Correspondingly, we point out appropriate proportional hazards-based multi-state models for decreasing bias and increasing power when analyzing multiple-episode composite endpoints in randomized clinical trials., Conclusions: Incorporating multiple episodes per individual into the primary analysis can reduce the bias of a treatment's total effect estimate. Our findings will help to move beyond the paradigm of considering first events only for approaches that use more information from the trial and augment interpretability, as has been called for in cardiovascular research.