Isaac Gravestock
University of Zurich
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Featured researches published by Isaac Gravestock.
Journal of Hand Surgery (European Volume) | 2014
Florian S. Frueh; Viviane Sylvie Kunz; Isaac Gravestock; Leonhard Held; Mathias Haefeli; Pietro Giovanoli; Maurizio Calcagni
PURPOSE To compare early passive mobilization (EPM) with controlled active motion (CAM) after flexor tendon surgery in zones 1 and 2. METHODS We performed a retrospective analysis of collected data of all patients receiving primary flexor tendon repair in zones 1 and 2 from 2006 to 2011, during which time 228 patients were treated, and 191 patients with 231 injured digits were eligible for study. Exclusion criteria were replantation, finger revascularization, age younger than 16 years, rehabilitation by means other than EPM or CAM, and missing information regarding postoperative rehabilitation. This left 132 patients with 159 injured fingers for analysis. The primary endpoint was the comparison of total active motion (TAM) values 4 and 12 weeks after surgery between the EPM and the CAM protocols. The analysis of TAM measurements under the rehabilitation protocols was conducted using t-tests and further linear modeling. We defined rupture rate and the assessment of adhesion/infection as secondary endpoints. RESULTS There was a statistically significant difference between the TAM values of the EPM and the CAM protocols 4 weeks after surgery. At 12 weeks, however, there was no significant difference between the 2 protocols. Older age and injuries with finger fractures were associated with lower TAM values. Rupture rates were 5% (CAM) and 7% (EPM), which were not statistically different. CONCLUSIONS This study showed a favorable effect of CAM protocol on TAM 4 weeks after surgery. The percent rupture rate was slightly lower in the patients with CAM than in the patients with EPM regime. Further studies are required to confirm our results and to investigate whether faster recovery of TAM is associated with shorter time out of work. TYPE OF STUDY/LEVEL OF EVIDENCE Therapeutic III.
Statistical Science | 2015
Leonhard Held; Daniel Sabanés Bové; Isaac Gravestock
Bayesian model selection poses two main challenges: the specification of parameter priors for all models, and the computation of the resulting Bayes factors between models. There is now a large literature on automatic and objective parameter priors in the linear model. One important class are g-priors, which were recently extended from linear to generalized linear models (GLMs). We show that the resulting Bayes factors can be approximated by test-based Bayes factors (Johnson [Scand. J. Stat. 35 (2008) 354–368]) using the deviance statistics of the models. To estimate the hyperparameter g, we propose empirical and fully Bayes approaches and link the former to minimum Bayes factors and shrinkage estimates from the literature. Furthermore, we describe how to approximate the corresponding posterior distribution of the regression coefficients based on the standard GLM output. We illustrate the approach with the development of a clinical prediction model for 30-day survival in the GUSTO-I trial using logistic regression.
Pharmaceutical Statistics | 2017
Isaac Gravestock; Leonhard Held
Incorporating historical information into the design and analysis of a new clinical trial has been the subject of much discussion as a way to increase the feasibility of trials in situations where patients are difficult to recruit. The best method to include this data is not yet clear, especially in the case when few historical studies are available. This paper looks at the power prior technique afresh in a binomial setting and examines some previously unexamined properties, such as Box P values, bias, and coverage. Additionally, it proposes an empirical Bayes-type approach to estimating the prior weight parameter by marginal likelihood. This estimate has advantages over previously criticised methods in that it varies commensurably with differences in the historical and current data and can choose weights near 1 when the data are similar enough. Fully Bayesian approaches are also considered. An analysis of the operating characteristics shows that the adaptive methods work well and that the various approaches have different strengths and weaknesses.
Statistics in Medicine | 2016
Leonhard Held; Isaac Gravestock; Daniel Sabanés Bové
There is now a large literature on objective Bayesian model selection in the linear model based on the g-prior. The methodology has been recently extended to generalized linear models using test-based Bayes factors. In this paper, we show that test-based Bayes factors can also be applied to the Cox proportional hazards model. If the goal is to select a single model, then both the maximum a posteriori and the median probability model can be calculated. For clinical prediction of survival, we shrink the model-specific log hazard ratio estimates with subsequent calculation of the Breslow estimate of the cumulative baseline hazard function. A Bayesian model average can also be employed. We illustrate the proposed methodology with the analysis of survival data on primary biliary cirrhosis patients and the development of a clinical prediction model for future cardiovascular events based on data from the Second Manifestations of ARTerial disease (SMART) cohort study. Cross-validation is applied to compare the predictive performance with alternative model selection approaches based on Harrells c-Index, the calibration slope and the integrated Brier score. Finally, a novel application of Bayesian variable selection to optimal conditional prediction via landmarking is described. Copyright
Jmir mhealth and uhealth | 2018
Simon E Thurnheer; Isaac Gravestock; Giuseppe Pichierri; Johann Steurer; Jakob M. Burgstaller
Background Pain is a common condition with a significant physical, psychosocial, and economic impact. Due to enormous progress in mobile device technology as well as the increase in smartphone ownership in the general population, mobile apps can be used to monitor patients with pain and support them in pain management. Objective The aim of this review was to assess the efficacy of smartphone or computer tablet apps in the management of patients with pain. Methods In December 2017, a literature search was performed in the following databases: MEDLINE, EMBASE, CINAHL, Cochrane, and PsycINFO. In addition, a bibliography search was conducted. We included studies with at least 20 participants per arm that evaluated the effects of apps on smartphones or computer tablets on improvement in pain. Results A total of 15 studies with 1962 patients met the inclusion criteria. Of these, 4 studies examined the effect of mobile apps on pain management in an in-clinic setting and 11 in an out-clinic setting. The majority of the original studies reported beneficial effects of the use of a pain app. Severity of pain decreased in most studies where patients were using an app compared with patients not using an app. Other outcomes, such as worst pain or quality of life showed improvements in patients using an app. Due to heterogeneity between the original studies—patient characteristics, app content, and study setting—a synthesis of the results by statistical methods was not performed. Conclusions Apps for pain management may be beneficial for patients, particularly in an out-clinic setting. Studies have shown that pain apps are workable and well liked by patients and health care professionals. There is no doubt that in the near future, mobile technologies will develop further. Medicine could profit from this development as indicated by our results, but there is a need for more scientific inputs. It is desirable to know which elements of apps or additional devices and tools may improve usability and help patients in pain management.
European Journal of Cardio-Thoracic Surgery | 2018
Selim Mosbahi; Dushaj Stak; Isaac Gravestock; Jakob M. Burgstaller; Johann Steurer; Friedrich S. Eckstein; Enrico Ferrari; Denis Berdajs
This systemic review of the literature and meta-analysis examined the current state of the evidence in long-term outcomes for and/or against aortic valve reimplantation (RAV) versus composite valve graft (CVG) intervention in patients with an acute type A dissection. Descriptive statistics were used to summarize the baseline characteristics of patients across studies. A random-effects metaregression was performed across study arms with logit-transformed proportions weighted by the study size for each of these outcomes. The results are presented as odds ratios with the RAV procedure as compared to the CVG procedure, including 95% confidence intervals (CIs) and P-values. Further outcomes are summarized with medians, interquartile ranges and the range and number of patients at risk. A total of 27 retrospective studies that included a combined 3058 patients were analysed. In-hospital mortality was in favour of the RAV procedure, which was 2% vs 8% for the CVG procedure. Survival rate at midterm was 98.8% (95% CI 91.7-100%) for RAV and 81.3% (CI 78.5-83.9%) for CVG. Freedom from valve-related reintervention was 100% (CI 93.7-100%) for RAV and 94.6% (CI 86.7-99.1%) for CVG. For an acute type A aortic dissection in the mid-term period, RAV provides a superior outcome over CVG, both in terms of aortic-valve-related reintervention and survival rate.
Clinical Infectious Diseases | 2018
Marlieke de Kraker; Harriet Sommer; Femke de Velde; Isaac Gravestock; E. Weiss; Alexandra McAleenan; Stavros Nikolakopoulos; Ohad Amit; Teri Ashton; Jan Beyersmann; Leonhard Held; A. M. Lovering; Alasdair P. MacGowan; Johan W. Mouton; Jean-François Timsit; David Wilson; Martin Wolkewitz; Esther Bettiol; Aaron Dane; Stéphan Juergen Harbarth
Abstract Innovations are urgently required for clinical development of antibacterials against multidrug-resistant organisms. Therefore, a European, public-private working group (STAT-Net; part of Combatting Bacterial Resistance in Europe [COMBACTE]), has reviewed and tested several innovative trials designs and analytical methods for randomized clinical trials, which has resulted in 8 recommendations. The first 3 focus on pharmacokinetic and pharmacodynamic modeling, emphasizing the pertinence of population-based pharmacokinetic models, regulatory procedures for the reassessment of old antibiotics, and rigorous quality improvement. Recommendations 4 and 5 address the need for more sensitive primary end points through the use of rank-based or time-dependent composite end points. Recommendation 6 relates to the applicability of hierarchical nested-trial designs, and the last 2 recommendations propose the incorporation of historical or concomitant trial data through Bayesian methods and/or platform trials. Although not all of these recommendations are directly applicable, they provide a solid, evidence-based approach to develop new, and established, antibacterials and address this public health challenge.
Biometrical Journal | 2018
Isaac Gravestock; Leonhard Held
Incorporating historical information into the design and analysis of a new clinical trial has been the subject of much recent discussion. For example, in the context of clinical trials of antibiotics for drug resistant infections, where patients with specific infections can be difficult to recruit, there is often only limited and heterogeneous information available from the historical trials. To make the best use of the combined information at hand, we consider an approach based on the multiple power prior that allows the prior weight of each historical study to be chosen adaptively by empirical Bayes. This choice of weight has advantages in that it varies commensurably with differences in the historical and current data and can choose weights near 1 if the data from the corresponding historical study are similar enough to the data from the current study. Fully Bayesian approaches are also considered. The methods are applied to data from antibiotics trials. An analysis of the operating characteristics in a binomial setting shows that the proposed empirical Bayes adaptive method works well, compared to several alternative approaches, including the meta-analytic prior.
World Neurosurgery | 2018
Nils H. Ulrich; Isaac Gravestock; Ulrike Held; Khoschy Schawkat; Giuseppe Pichierri; Maria M. Wertli; Sebastian Winklhofer; Mazda Farshad; François Porchet; Johann Steurer; Jakob M. Burgstaller
arXiv: Methodology | 2017
Isaac Gravestock; Leonhard Held