Estimation of the Piecewise Exponential Model by Bayesian P-Splines via Gibbs Sampling: Robustness and Reliability of Posterior Estimates

In the investigation of disease dynamics, the effect of covariates on the hazard function is a major topic. Some recent smoothed estimation methods have been proposed, both frequentist and Bayesian, based on the relationship between penalized splines and mixed models theory. These approaches are also motivated by the possibility of using automatic procedures for determining the optimal amount of smoothing. However, estimation algorithms involve an analytically intractable hazard function, and thus require ad-hoc software routines. We propose a more user-friendly alternative, consisting in regularized estimation of piecewise exponential models by Bayesian P-splines. A further facilitation is that widespread Bayesian software, such as WinBUGS, can be used. The aim is assessing the robustness of this approach with respect to different prior functions and penalties. A large dataset from breast cancer patients, where results from validated clinical studies are available, is used as a benchmark to evaluate the reliability of the estimates. A second dataset from a small case series of sarcoma patients is used for evaluating the performances of the PE model as a tool for exploratory analysis. Concerning breast cancer data, the estimates are robust with respect to priors and penalties, and consistent with clinical knowledge. Concerning soft tissue sarcoma data, the estimates of the hazard function are sensitive with respect to the prior for the smoothing parameter, whereas the estimates of regression coefficients are robust. In conclusion, Gibbs sampling results an efficient computational strategy. The issue of the sensitivity with respect to the priors concerns only the estimates of the hazard function, and seems more likely to occur when non-large case series are investigated, calling for tailored solutions

Virtual dissection by ultrasound: probe handling in the first year of medical education

Objectives The aim of the present study was to assess the educational plan of first-year students of medicine by analyzing their scores in ultrasound body scanning. Methods Since 2009, the San Paolo Medical School (Milan, Italy) has vertically integrated the study of anatomy with ultrasound-assisted virtual body dissection. Three modules were supplied: musculoskeletal system, heart and abdomen pelvis. 653 first-year students were trained. The students alternated as mutual model and operator. A skillfulness score was assigned to each student. The scores were consequently listed. Nonparametric exact multiple contrast tests were employed to determine relative group effects. Results Statistical analysis showed that: no gender-related differences were found (0:49; p = 0.769); peer learners performed less well than peer tutors (0.677; p = 0); between modules, scores in the musculoskeletal system (pMS = 0.726) tend to be higher (p < 0.001) than those obtained in the heart and abdomen pelvis (pH = 0.398; pAP = 0.375 p = 0.270); significant differences were found compared to the beginning of the project\u2019s academic year. Conclusion The students considered this didactic course an engaging and exciting approach. Acceptance of peer teaching was extraordinarily high. Autonomous exercitation allowed the students to improve self-criticism and enhance their own skills. The level of expertise obtained by peer tutors and by peer learners can be considered satisfactory. The main objective of training future physicians on personal stethoechoscope with the necessary competence seems to have been successfully started

The acute impact of chemotherapy on the cognitive and emotional domains and on quality of life of older cancer patients

Background There are data suggesting that chemotherapy may have a negative impact on cognitive function; on the other hand, aging itself causes changes in the cognitive domain. This area is commonly evaluated in the context of a comprehensive geriatric assessment by the Mini-mental state examination (MMS). Aim We conducted a pilot study to evaluate the acute effects of chemotherapy on cognition in elderly patients (age >70) affected by solid tumors and to evaluate its effect on quality of life and emotional domain. Methods Cognitive function was measured before chemotherapy start and at 12 weeks (or chemotherapy interruption, whichever occurred first) with a battery of neuropsychological tests administered to assess attention, learning and memory, language and reading , visuo-constructional ability, problem salving and general intelligence. Quality of life and the emotional domain were evaluated by the FACT-G questionnaire and the Geriatric Depression Scale, respectively. Results A total of 30 patients, median age 76 (range 70-82) are evaluable. Baseline values of the test are reported. Data on changes in test scores after chemotherapy, as well as impact of chemotherapy on emotion and quality of life will be presented. Conclusion These results will be the basis to design a more complete and adequately sized study

Estimation of a Piecewise Exponential Model by Bayesian P-splines Techniques for Prognostic Assessment and Prediction

Methods for fitting survival regression models with a penalized smoothed hazard function have been recently discussed, even though they could be cumbersome. A simpler alternative which does not require specific software packages could be fitting a penalized piecewise exponential model. In this work the implementation of such strategy in Win-BUGS is illustrated, and preliminary results are reported concerning the application of Bayesian P-splines techniques. The technique is applied to a pre-specified model in which the number and positions of knots were fixed on the basis of clinical knowledge, thus defining a non-standard smoothing problem

Selection of artificial neural network models for survival analysis with genetic algorithms

In follow-up clinical studies, the main time end-point is the failure from a specific starting point (e.g. treatment, surgery). A deeper investigation concerns the causes of failure. Statistical analysis typically focuses on the study of the cause specific hazard functions of possibly censored survival data. In the framework of discrete time models and competing risks, a multilayer perceptron was already proposed as an extension of generalized linear models with multinomial errors using a non-linear predictor (PLANNCR). According to standard practice, weight-decay was adopted to modulate model complexity. A Genetic Algorithm is considered for the complexity control of PLANNCR allowing to regularize independently each parameter of the model. The ICOMP information criterion is used as fitness function. To demonstrate the criticality and the benefits of the technique an application to a case series of 1793 women with primary breast cancer without axillary lymph node involvement is presented

Artificial neural network for the joint modelling of discrete cause-specific hazards

Objective: Artificial neural network (ANN) based regression methods have been introduced for modelling censored survival data to account for complex prognostic patterns. In the framework of ANN extensions of generalized linear models for survival data, PLANN is a partial logistic ANN, suitable for smoothed discrete hazard estimation as a function of time and covariates. An extension of PLANN for competing risks analysis (PLANNCR) is now proposed for discrete or grouped survival times, resorting to the multinomial likelihood. Methods and materials: PLANNCR is built by assigning input nodes to the explanatory variables with the time interval treated as an ordinal variable. The logistic function is used as activation for the hidden nodes of the network, whereas the softmax, which corresponds to the canonical link of generalized linear models for polytomous regression, is adopted for multiple output nodes, to provide a smoothed estimation of discrete conditional event probabilities for each event. The Kullback-Leibler distance is used as error function for the target vectors, amounting to half of the deviance of a multinomial logistic regression model. PLANNCR can jointly model non-linear, non-proportional and non-additive effects on cause-specific hazards (CSHs). The degree of smoothing is modulated by the number of hidden nodes and penalization of the error function (weight decay). Model optimisation is achieved by quasi-Newton algorithms, while non-linear cross-validation (NCV) and the Network Information Criterion (NIC) were adopted for model selection. PLANNCR was applied to data on 1793 women with primary invasive breast cancer, histologically N-, who underwent surgery at the Milan Cancer Institute between 1981 and 1986. Results: Differential effects of covariates and time on the shape of the CSH for the three main failure causes, namely intra-breast tumor recurrences, distant metastases and contralateral breast cancer, have been enlightened. Conclusions: PLANNCR can be suitably adopted in an exploratory framework for a thorough evaluation of the disease dynamics in the presence of competing risks