Semiparametric Multivariate Accelerated Failure Time Model with Generalized Estimating Equations

The semiparametric accelerated failure time model is not as widely used as the Cox relative risk model mainly due to computational difficulties. Recent developments in least squares estimation and induced smoothing estimating equations provide promising tools to make the accelerate failure time models more attractive in practice. For semiparametric multivariate accelerated failure time models, we propose a generalized estimating equation approach to account for the multivariate dependence through working correlation structures. The marginal error distributions can be either identical as in sequential event settings or different as in parallel event settings. Some regression coefficients can be shared across margins as needed. The initial estimator is a rank-based estimator with Gehan's weight, but obtained from an induced smoothing approach with computation ease. The resulting estimator is consistent and asymptotically normal, with a variance estimated through a multiplier resampling method. In a simulation study, our estimator was up to three times as efficient as the initial estimator, especially with stronger multivariate dependence and heavier censoring percentage. Two real examples demonstrate the utility of the proposed method

Estimating Surface Area in Early Hominins

Height and weight-based methods of estimating surface area have played an important role in the development of the current consensus regarding the role of thermoregulation in human evolution. However, such methods may not be reliable when applied to early hominins because their limb proportions differ markedly from those of humans. Here, we report a study in which this possibility was evaluated by comparing surface area estimates generated with the best-known height and weight-based method to estimates generated with a method that is sensitive to proportional differences. We found that the two methods yield indistinguishable estimates when applied to taxa whose limb proportions are similar to those of humans, but significantly different results when applied to taxa whose proportions differ from those of humans. We also found that the discrepancy between the estimates generated by the two methods is almost entirely attributable to inter-taxa differences in limb proportions. One corollary of these findings is that we need to reassess hypotheses about the role of thermoregulation in human evolution that have been developed with the aid of height and weight-based methods of estimating body surface area. Another is that we need to use other methods in future work on fossil hominin body surface areas

Learning deterministic probabilistic automata from a model checking perspective

Probabilistic automata models play an important role in the formal design and analysis of hard- and software systems. In this area of applications, one is often interested in formal model-checking procedures for verifying critical system properties. Since adequate system models are often difficult to design manually, we are interested in learning models from observed system behaviors. To this end we adopt techniques for learning finite probabilistic automata, notably the Alergia algorithm. In this paper we show how to extend the basic algorithm to also learn automata models for both reactive and timed systems. A key question of our investigation is to what extent one can expect a learned model to be a good approximation for the kind of probabilistic properties one wants to verify by model checking. We establish theoretical convergence properties for the learning algorithm as well as for probability estimates of system properties expressed in linear time temporal logic and linear continuous stochastic logic. We empirically compare the learning algorithm with statistical model checking and demonstrate the feasibility of the approach for practical system verification

A Bayesian re-assessment of two Phase II trials of gemcitabine in metastatic nasopharyngeal cancer

The Simon two-stage minimax design is a popular statistical design used in Phase II clinical trials. The analysis of the data arising from the design typically involves the use of frequentist statistics. This paper presents an alternative, Bayesian, approach to the design and analysis of Phase II clinical trials. In particular, we consider how a Bayesian approach could have affected the design, analysis and interpretation of two parallel Phase II trials of the National Cancer Centre Singapore, on the activity of gemcitabine in chemotherapy-naïve and in previously treated patients with metastatic nasopharyngeal carcinoma. We begin by explaining the Bayesian methodology and contrasting it with the frequentist approach. We then carry out a Bayesian analysis of the trial results. The conclusions drawn using the Bayesian approach were in general agreement with those obtained from the frequentist analysis. However they had the advantage of allowing for different and potentially more useful interpretations to be made regarding the trial results, as well as for the incorporation of external sources of information. In particular, using a Bayesian trial design, we were able to take into account the results of the parallel trial results when deciding whether to continue each trial beyond the interim stage

Phase II study of single agent capecitabine in the treatment of metastatic non-pancreatic neuroendocrine tumours

BACKGROUND: This study sought to determine the safety of single agent capecitabine, a pro-drug of 5FU, in patients with metastatic non-pancreatic neuroendocrine tumours (NETs). METHODS: Multicentre phase II, first-line study design. Oral capecitabine was administered on days 1-14 of 3-week cycles. RESULTS: Treatment was safe and well tolerated. Common toxicities were diarrhoea and fatigue. CONCLUSION: The study provides evidence to support the use of capecitabine as a substitute for infusional 5FU in the management of NETs

Non-randomized therapy trial to determine the safety and efficacy of heavy ion radiotherapy in patients with non-resectable osteosarcoma

<p>Abstract</p> <p>Background</p> <p>Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. For effective treatment, local control of the tumor is absolutely critical, because the chances of long term survival are <10% and might effectively approach zero if a complete surgical resection of the tumor is not possible. Up to date there is no curative treatment protocol for patients with non-resectable osteosarcomas, who are excluded from current osteosarcoma trials, e.g. <it>EURAMOS1</it>. Local photon radiotherapy has previously been used in small series and in an uncontrolled, highly individualized fashion, which, however, documented that high dose radiotherapy can, in principle, be used to achieve local control. Generally the radiation dose that is necessary for a curative approach can hardly be achieved with conventional photon radiotherapy in patients with non-resectable tumors that are usually located near radiosensitive critical organs such as the brain, the spine or the pelvis. In these cases particle Radiotherapy (proton therapy (PT)/heavy ion therapy (HIT) may offer a promising new alternative. Moreover, compared with photons, heavy ion beams provide a higher physical selectivity because of their finite depth coverage in tissue. They achieve a higher relative biological effectiveness. Phase I/II dose escalation studies of HIT in adults with non-resectable bone and soft tissue sarcomas have already shown favorable results.</p> <p>Methods/Design</p> <p>This is a monocenter, single-arm study for patients ≥ 6 years of age with non-resectable osteosarcoma. Desired target dose is 60-66 Cobalt Gray Equivalent (Gy E) with 45 Gy PT (proton therapy) and a carbon ion boost of 15-21 GyE. Weekly fractionation of 5-6 × 3 Gy E is used. PT/HIT will be administered exclusively at the Ion Radiotherapy Center in Heidelberg. Furthermore, FDG-PET imaging characteristics of non-resectable osteosarcoma before and after PT/HIT will be investigated prospectively. Systemic disease before and after PT/HIT is targeted by standard chemotherapy protocols and is not part of this trial.</p> <p>Discussion</p> <p>The primary objectives of this trial are the determination of feasibility and toxicity of HIT. Secondary objectives are tumor response, disease free survival and overall survival. The aim is to improve outcome for patients with non-resectable osteosarcoma.</p> <p>Trail Registration</p> <p>Registration number (ClinicalTrials.gov): NCT01005043</p