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Our study aims to contribute to existing knowledge by evaluating patients with low back pain to provide a more accurate relationship between the diameter of the intervertebral foramen and the clinical, demographic, and lumbar spine anatomic factors such as age, sex, body mass index, the Zurich Claudication Questionnaire, facet joint, intervertebral disc, ligamentum flavum, and spinal canal.We studied 90 consecutive patients who had undergone evaluation for low back pain. We used magnetic resonance imaging to assess the cross-sectional areas of the intervertebral foramina at each level of the lumbar spine together with the ligamentum flavum area and the dural sac cross-sectional area (DSCSA) measurements. The presence of disc and facet joint degeneration was evaluated and data on symptoms were obtained.Age (P0.0001), lumbar disc degeneration grade (P = 0.016), and DSCSA (P0.0001) were found to statistically significantly influence the foraminal area (FA). The mean FA at all lumbar levels increased with increasing DSCSA. The mean FA decreased with age at all levels except L5/S1. Lumbar disc degeneration grade 1-3 increased the mean FA at L5/S1, but not at other levels. No statistically significant effects of the side of the measurement, sex, body mass index, Zurich Claudication Questionnaire score, ligamentum flavum area, or facet joint degeneration were found.The results of the present study allow us to quantify the effect of age, DSCSA, and lumbar disc degeneration grade on the FA.

Purpose: To investigate the clinical relevance of PTEN in HER2-amplified and HER2-nonamplified disease.Experimental Design: We assessed PTEN status in two large adjuvant breast cancer trials (BCIRG-006 and BCIRG-005) using a PTEN immunohistochemical (IHC) assay that was previously validated in a panel of 33 breast cancer cell lines and prostate cancer tissues with known PTEN gene deletion.Results: In the HER2-positive patient population, absence of tumor cell PTEN staining occurred at a rate of 5.4% and was independent of ER/PR status. In contrast, 15.9% of HER2-negative patients exhibited absence of PTEN staining with the highest frequency seen in triple-negative breast cancer (TNBC) subgroup versus ER/PR-positive patients (35.1% vs. 10.9%). Complete absence of PTEN staining in tumor cells was associated with poor clinical outcome in HER2-positive disease. Those patients whose cancers demonstrated absent PTEN staining had a significant decrease in disease-free survival (DFS) and overall survival (OS) compared with patients with tumors exhibiting any PTEN staining patterns (low, moderate, or high). Trastuzumab appeared to provide clinical benefit even for patients lacking PTEN staining. In the HER2-negative population, there were no statistically significant differences in clinical outcome based on PTEN status.Conclusions: This study is the largest to date examining PTEN status in breast cancer and the data suggest that the rate and significance of PTEN status differ between HER2-positive and HER2-negative disease. Furthermore, the data clearly suggest that HER2-positive patients with PTEN loss still benefit from trastuzumab. Clin Cancer Res; 21(9); 2065–74. ©2015 AACR.

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Supplemental Figure 2. Clinical outcome based on PTEN data availability. Kaplan Meier analysis was performed for patients with PTEN data available compared to patients without PTEN data available. Disease free survival (A, C) and overall survival (B, D) are shown for BCIRG 006 (HER2-amplified) (A, B) and BCIRG 005 (HER2-non-amplified) (C, D).

Supplemental Figure 3a. PTEN IHC assay validation and scoring. PTEN IHC was performed on a panel of FFPE breast cancer cell pellets of known PTEN status confirmed by western immunoblot. (A.) Western immunoblot analyses for MDA-MB-468, CAL-148, MDA-MB-361, MT-3, and MDA-MB-436 human breast cancer cell lines are illustrated above with PTEN IHC illustrated below.

Supplemental Figure 4b. A serial section shows the same microscopic field after processing for PTEN localization by immunohistochemistry. Note the lack of immunostaining in the tumor cells.

Supplemental Figure 1. Clinical trial schemata. Diagrammatic representation of the BCIRG 005 (A) and BCIRG 006 (B) adjuvant breast cancer clinical trials

Rationale: The observed isotope distribution is an important attribute for the identification of peptides and proteins in mass spectrometry–based proteomics. Sulphur atoms have a very distinctive elemental isotope definition, and therefore, the presence of sulphur atoms has a substantial effect on the isotope distribution of biomolecules. Hence, knowledge of the number of sulphur atoms can improve the identification of peptides and proteins. Methods: In this paper, we conducted a theoretical investigation on the isotope properties of sulphur-containing peptides. We proposed a gradient boosting approach to predict the number of sulphur atoms based on the aggregated isotope distribution. We compared prediction accuracy and assessed the predictive power of the features using the mass and isotope abundance information from the first three, five and eight aggregated isotope peaks. Results: Mass features alone are not sufficient to accurately predict the number of sulphur atoms. However, we reach near-perfect prediction when we include isotope abundance features. The abundance ratios of the eighth and the seventh, the fifth and the fourth, and the third and the second aggregated isotope peaks are the most important abundance features. The mass difference between the eighth, the fifth or the third aggregated isotope peaks and the monoisotopic peak are the most predictive mass features. Conclusions: Based on the validation analysis it can be concluded that the prediction of the number of sulphur atoms based on the isotope profile fails, because the isotope ratios are not measured accurately. These results indicate that it is valuable for future instrument developments to focus more on improving spectral accuracy to measure peak intensities of higher-order isotope peaks more accurately.

The accelerated failure-time (AFT) model has been long recognized as a useful alternative to the proportional-hazards (PH) model. Semi-parametric AFT model has been known since 1981. Its use has been hampered by the difficulty in solving the estimating equations for the model's coefficients. In recent years, however, important developments have taken place regarding the methods of solving the equations. In this article, we briefly review the developments, focusing mainly on rank-based estimation. We conduct a simulation study that directly focuses on the applicability of the model in the context of (cancer) clinical trials. We also investigate the robustness of the AFT model to the omission of covariates. Finally, we conduct a meta-analysis of multiple clinical trials in gastric cancer to illustrate the benefits of the use of the model in practice.

In RCTs with an interest in a long-term efficacy endpoint, the follow-up time necessary to observe the endpoint may be substantial. In order to reduce the expected duration of such trials, early-outcome data may be collected to enrich an interim analysis aimed at stopping the trial early for efficacy. We propose to extend such a design with an additional interim analysis using solely early-outcome data in order to expedite the evaluation of treatment's efficacy. We evaluate the potential gain in operating characteristics (power, expected trial duration, and expected sample size) when introducing such an early interim analysis, in function of the properties of the early outcome as a surrogate for the long-term endpoint. In the context of a longitudinal age-related macular degeneration (ARMD) ophthalmology trial, results show potentially substantial gains in both the expected trial duration and the expected sample size. A prerequisite, though, is that the treatment effect on the early outcome has to be strongly correlated with the treatment effect on the long-term endpoint, that is, that the early outcome is a validated surrogate for the long-term endpoint.

The method of generalized pairwise comparisons (GPC) is a multivariate extension of the well-known non-parametric Wilcoxon-Mann-Whitney test. It allows comparing two groups of observations based on multiple hierarchically ordered endpoints, regardless of the number or type of the latter. The summary measure, "net benefit," quantifies the difference between the probabilities that a random observation from one group is doing better than an observation from the opposite group. The method takes into account the correlations between the endpoints. We have performed a simulation study for the case of two hierarchical endpoints to evaluate the impact of their correlation on the type-I error probability and power of the test based on GPC. The simulations show that the power of the GPC test for the primary endpoint is modified if the secondary endpoint is included in the hierarchical GPC analysis. The change in power depends on the correlation between the endpoints. Interestingly, a decrease in power can occur, regardless of whether there is any marginal treatment effect on the secondary endpoint. It appears that the overall power of the hierarchical GPC procedure depends, in a complex manner, on the entire variance-covariance structure of the set of outcomes. Any additional factors (such as thresholds of clinical relevance, drop out, or censoring scheme) will also affect the power and will have to be taken into account when designing a trial based on the hierarchical GPC procedure.

Background/Aims Autotransplantation of developing premolars is an established treatment to replace missing teeth in the anterior maxilla in growing patients with a reported success rate of over 90%. The normal shape of the alveolus is observed after transplantation, but data on the presence and amount of alveolar bone after healing has not been previously reported. The aim of this study was to look for potential differences in alveolar bone dimensions between sites where autotransplanted premolars replaced missing incisors and control sites of contralateral incisors. Material/Methods There were 11 patients aged between 10 and 12 years five months (mean age: 10 years and 7 months) who underwent autotransplantation of a premolar to replace a central incisor. Cone Beam Computed Tomography (CBCT) performed at least 1 year after transplantation served to evaluate bone at sites of autotransplanted premolars and controls (contralateral maxillary central incisor). The thickness of the labial bone, plus the height and width of the alveolar process were measured on scans and compared at transplant and control sites. Results Mean thicknesses of the labial bone at the transplant and control sites were 0.78 mm and 0.82 mm respectively. Mean alveolar bone height was 15.15 mm at the transplant sites and 15.12 mm at the control sites. The mean marginal thickness of the alveolus was 7.75 mm at the transplant sites and 7.98 mm at the control sites. Mean thicknesses of the alveolus for half of its vertical dimension at the transplant and control sites were 7.54 mm and 8.03 mm, respectively. Conclusion The mean values of bone thickness, width and height of the alveolar process at sites of transplanted premolars were comparable to the mean values for the control incisors. Successful autotransplantation of developing premolars to replace missing central incisors allowed preservation of alveolar bone in the anterior maxilla. Plakwicz, P (corresponding author), Med Univ Warsaw, Dept Periodontol, Fac Dent, Ul Binieckiego 6, PL-02097 Warsaw, Poland. info@plakwicz.com

Background: We explore frequentist operating characteristics of a Bayesian adaptive design that allows continuous early stopping for futility. In particular, we focus on the power versus sample size relationship when more patients are accrued than originally planned. Methods: We consider the case of a phase II single-arm study and a Bayesian phase II outcome-adaptive randomization design. For the former, analytical calculations are possible; for the latter, simulations are conducted. Results: Results for both cases show a decrease in power with an increasing sample size. It appears that this effect is due to the increasing cumulative probability of incorrectly stopping for futility. Conclusion: The increase in cumulative probability of incorrectly stopping for futility is related to the continuous nature of the early stopping, which increases the number of interim analyses with accrual. The issue can be addressed by, for instance, delaying the start of testing for futility, reducing the number of futility tests to be performed or by setting stricter criteria for concluding futility.

Immuno-oncology is a buoyant field of research, with recently developed drugs showing unprecedented response rates and/or a hope for a meaningful prolongation of the overall survival of some patients. These promising clinical developments have also pointed to the need of adapting statistical methods to best describe and test for treatment effects in randomized clinical trials. We review adaptations to tumor response and progression criteria for immune therapies. Survival may be the endpoint of choice for clinical trials in some tumor types, and the search for surrogate endpoints is likely to continue to try and reduce the duration and size of clinical trials. In situations for which hazards are likely to be non-proportional, weighted logrank tests may be preferred as they have substantially more power to detect late separation of survival curves. Alternatively, there is currently much interest in accelerated failure time models, and in capturing treatment effect by the difference in restricted mean survival times between randomized groups. Finally, generalized pairwise comparisons offer much promise in the field of immuno-oncology, both to detect late emerging treatment effects and as a general approach to personalize treatment choices through a benefit/risk approach.

Deriving chemical formulas of organic molecules, based on spectral information, with heuristic rules is a commonly recurring task. The computational effort and the potentially extensive list of candidate formulas put a strain on the downstream analysis. In this paper, we introduce a set of redefined heuristics based on the hydrogen and halogen rules that reduce the computational burden and the number of candidate formulas for organic molecules, such as peptides and lipids.

Background/Aims A risk-based approach to clinical research may include a central statistical assessment of data quality. We investigated the operating characteristics of unsupervised statistical monitoring aimed at detecting atypical data in multicenter experiments. The approach is premised on the assumption that, save for random fluctuations and natural variations, data coming from all centers should be comparable and statistically consistent. Unsupervised statistical monitoring consists of performing as many statistical tests as possible on all trial data, in order to detect centers whose data are inconsistent with data from other centers. Methods We conducted simulations using data from a large multicenter trial conducted in Japan for patients with advanced gastric cancer. The actual trial data were contaminated in computer simulations for varying percentages of centers, percentages of patients modified within each center and numbers and types of modified variables. The unsupervised statistical monitoring software was run by a blinded team on the contaminated data sets, with the purpose of detecting the centers with contaminated data. The operating characteristics (sensitivity, specificity and Youden’s J-index) were calculated for three detection methods: one using the p-values of individual statistical tests after adjustment for multiplicity, one using a summary of all p-values for a given center, called the Data Inconsistency Score, and one using both of these methods. Results The operating characteristics of the three methods were satisfactory in situations of data contamination likely to occur in practice, specifically when a single or a few centers were contaminated. As expected, the sensitivity increased for increasing proportions of patients and increasing numbers of variables contaminated. The three methods showed a specificity better than 93% in all scenarios of contamination. The method based on the Data Inconsistency Score and individual p-values adjusted for multiplicity generally had slightly higher sensitivity at the expense of a slightly lower specificity. Conclusions The use of brute force (a computer-intensive approach that generates large numbers of statistical tests) is an effective way to check data quality in multicenter clinical trials. It can provide a cost-effective complement to other data-management and monitoring techniques.

Many candidate surrogate endpoints are currently assessed using a 2-level statistical approach, which consists in checking whether (1) the potential surrogate is associated with the final endpoint in individual patients and (2) the effect of treatment on the surrogate can be used to reliably predict the effect of treatment on the final endpoint. In some situations, condition (1) is fulfilled but condition (2) is not. We use concepts of causal inference to explain this apparently paradoxical situation, illustrating this review with 2 contrasting examples in operable breast cancer: the example of pathological complete response (pCR) and that of disease-free survival (DFS). In a previous meta-analysis, pCR has been shown to be a strong and independent prognostic factor for event-free survival (EFS) and overall survival (OS) after neoadjuvant treatment of operable breast cancer. Yet, in randomized trials, the effects of experimental treatments on pCR have not translated into predictable effects on EFS or OS, making pCR an “individual-level” surrogate, but not a “trial-level” surrogate. In contrast, DFS has been shown to be an acceptable surrogate for OS at both the individual and trial levels in early, HER2-positive breast cancer. The distinction between the prognostic and predictive roles of a tentative surrogate, not always made in the literature, avoids unnecessary confusion and allows better understanding of what it takes to validate a surrogate endpoint that is truly able to replace a final endpoint.

The method of generalized pairwise comparisons (GPC) is an extension of the well-known nonparametric Wilcoxon-Mann-Whitney test for comparing two groups of observations. Multiple generalizations of Wilcoxon-Mann-Whitney test and other GPC methods have been proposed over the years to handle censored data. These methods apply different approaches to handling loss of information due to censoring: ignoring noninformative pairwise comparisons due to censoring (Gehan, Harrell, and Buyse); imputation using estimates of the survival distribution (Efron, Péron, and Latta); or inverse probability of censoring weighting (IPCW, Datta and Dong). Based on the GPC statistic, a measure of treatment effect, the "net benefit," can be defined. It quantifies the difference between the probabilities that a randomly selected individual from one group is doing better than an individual from the other group. This paper aims at evaluating GPC methods for censored data, both in the context of hypothesis testing and estimation, and providing recommendations related to their choice in various situations. The methods that ignore uninformative pairs have comparable power to more complex and computationally demanding methods in situations of low censoring, and are slightly superior for high proportions (40%) of censoring. If one is interested in estimation of the net benefit, Harrell's c index is an unbiased estimator if the proportional hazards assumption holds. Otherwise, the imputation (Efron or Peron) or IPCW (Datta, Dong) methods provide unbiased estimators in case of proportions of drop-out censoring up to 60%.