Bayesian joint ordinal and survival modeling for breast cancer risk assessment.

We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional-odds cumulative logit model. Time-to-event is modeled through a left-truncated proportional-hazards model, which incorporates information of the longitudinal marker as well as baseline covariates. Both longitudinal and survival processes are connected by means of a common vector of random effects. General inferences are discussed under the Bayesian approach and include the posterior distribution of the probabilities associated to each longitudinal category and the assessment of the impact of the baseline covariates and the longitudinal marker on the hazard function. The flexibility provided by the joint model makes possible to dynamically estimate individual event-free probabilities and predict future longitudinal marker values. The model is applied to the assessment of breast cancer risk in women attending a population-based screening program. The longitudinal ordinal marker is mammographic breast density measured with the Breast Imaging Reporting and Data System (BI-RADS) scale in biennial screening exams. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Bayesian analysis of a disability model for lung cancer survival.

Bayesian reasoning, survival analysis and multi-state models are used to assess survival times for Stage IV non-small-cell lung cancer patients and the evolution of the disease over time. Bayesian estimation is done using minimum informative priors for the Weibull regression survival model, leading to an automatic inferential procedure. Markov chain Monte Carlo methods have been used for approximating posterior distributions and the Bayesian information criterion has been considered for covariate selection. In particular, the posterior distribution of the transition probabilities, resulting from the multi-state model, constitutes a very interesting tool which could be useful to help oncologists and patients make efficient and effective decisions.

Bayesian methods in cost-effectiveness studies: objectivity, computation and other relevant aspects.

In a probabilistic sensitivity analysis (PSA) of a cost-effectiveness (CE) study, the unknown parameters are considered as random variables. A crucial question is what probabilistic distribution is suitable for synthesizing the available information (mainly data from clinical trials) about these parameters. In this context, the important role of Bayesian methodology has been recognized, where the parameters are of a random nature. We explore, in the context of CE analyses, how formal objective Bayesian methods can be implemented. We fully illustrate the methodology using two CE problems that frequently appear in the CE literature. The results are compared with those obtained with other popular approaches to PSA. We find that the discrepancies can be quite marked, specially when the number of patients enrolled in the simulated cohort under study is large. Finally, we describe in detail the numerical methods that need to be used to obtain the results.

Analysis of the renal transplant waiting list in the País Valencià (Spain).

In this paper we analyse the renal transplant waiting list of the País Valencià in Spain, using Queueing theory. The customers of this queue are patients with end-stage renal failure waiting for a kidney transplant. We set up a simplified model to represent the flow of the customers through the system, and perform Bayesian inference to estimate parameters in the model. Finally, we consider several scenarios by tuning the estimations achieved and computationally simulate the behaviour of the queue under each one. The results indicate that the system could reach equilibrium at some point in the future and the model forecasts a slow decrease in the size of the waiting list in the short and middle term.

Ataxias cerebelosas autosómicas recesivas. Clasificación, aspectos genéticos y fisiopatología / Autosomal recessive cerebellar ataxias. Their classification, genetic features and pathophysiology

Introducción y desarrollo. Las ataxias cerebelosas autosómicas recesivas (ARCA) son un grupo heterogéneo de trastornos neurológicos raros que afectan a los sistemas nerviosos central y periférico y, en algunos casos, a otros sistemas y órganos. Estos trastornos suelen tener una edad de inicio antes de los 20 años de edad. En base a criterios patogénicos se pueden distinguir cinco grupos de ARCA: ataxias congénitas (trastorno del desarrollo), ataxias mitocondriales, ataxias asociadas a defectos metabólicos, ataxias asociadas a defectos en la reparación del ADN y ataxias degenerativas de mecanismo patogénico desconocido. Las formas más frecuentes en la población caucásica son la ataxia de Friefreich y la ataxia-telangiectasia. Otras formas más raras son la abetalipoproteinemia, la ataxia con deficiencia de vitamina E (AVED), la ataxia con apraxia oculomotora tipos 1 (AOA1) y 2 (AOA2), la ataxia precoz con reflejos conservados, la ataxia espástica de Charlevoix-Saguenay y el síndrome de Joubert. La prevalencia de las ARCA se estima en siete casos por 100.000 habitantes. Estas enfermedades se deben a mutaciones en genes específicos, algunos de los cuales y sus proteínas se han identificado, como FRDA (frataxina) en la ataxia de Friedreich, APTX (aprataxina) en la AOA1, aTTP (proteína transferidora de a-tocoferol) en la AVED y STX (senataxina) en la AOA2. Como se trata de trastornos autonómicos recesivos, no suele haber antecedentes en la familia de los enfermos y ambos progenitores están sanos. Conclusiones. La mayoría de las ataxias cerebelosas no tienen tratamiento específico, excepto algunas debidas a déficit de coenzima Q10 y la abetalipoproteinemia. El diagnóstico clínico debe confirmarse mediante las pruebas complementarias de neuroimagen (tomografía axial computarizada, resonancia magnética), el examen electrofisiológico y el análisis de mutaciones del gen causante si éste se ha identificado. El diagnóstico clínico y genético correcto son muy importantes para ofrecer un pronóstico y un consejo genético apropiados y, en ocasiones, un tratamiento farmacológico (AU)

Introduction and development. Autosomal recessive cerebellar ataxias (ARCA) are a heterogeneous group of rare neurological disorders involving both central and peripheral nervous system, and in some case other systems and organs. They use to have early onset before the age of 20. Based on pathogenic mechanisms five main types may be distinguished: congenital (developmental disorder), mitochondrial ataxias, ataxias associated with metabolic disorders, ataxias with a DNA repair defect, and degenerative ataxia with unknown pathogenesis. The most frequent in Caucasian population are Friedreich ataxia and ataxia-telangiectasia. Other forms are much less common, and include abetaliproteinemia, ataxia with vitamin E deficiency (AVED), ataxia with oculomotor apraxia types 1 (AOA1) and 2 (AOA2), early onset cerebellar ataxia with retained reflexes, Charlevoix-Saguenay spastic ataxia, and Joubert syndrome. The prevalence of ARCA has been estimated to 7 in 100,000 inhabitants. These diseases are due to mutations in specific genes, some of which and its encoded proteins have been identified, such as FRDA (frataxin) in Friedreich ataxia, APTX (aprataxin) in AOA1, αTTP (α-tocopherol transfer protein) in AVED, and STX (senataxin) in AOA2. Due to autosomal recessive inheritance, previous familial history of affected individuals unlikely. Conclusions. Most of these cerebellar ataxias have no specific treatment with exception of the ataxia associated with deficiency coenzyme Q10 and abetalipoproteinemia. Clinical diagnosis must be confirmed by ancillary tests such as neuroimaging (magnetic resonance, scanning), electrophysiological examination, and mutation analysis when the causative gene has been identified. Correct clinical and genetic diagnosis is important for appropriate prognosis and genetic counseling and, in some instances, pharmacological treatment (AU)

[Autosomal recessive cerebellar ataxias. Their classification, genetic features and pathophysiology]. / Ataxias cerebelosas autosómicas recesivas. Clasificación, aspectos genéticos y fisiopatología.

INTRODUCTION AND DEVELOPMENT: Autosomal recessive cerebellar ataxias (ARCA) are a heterogeneous group of rare neurological disorders involving both central and peripheral nervous system, and in some case other systems and organs. They use to have early onset before the age of 20. Based on pathogenic mechanisms five main types may be distinguished: congenital (developmental disorder), mitochondrial ataxias, ataxias associated with metabolic disorders, ataxias with a DNA repair defect, and degenerative ataxia with unknown pathogenesis. The most frequent in Caucasian population are Friedreich ataxia and ataxia-telangiectasia. Other forms are much less common, and include abetaliproteinemia, ataxia with vitamin E deficiency (AVED), ataxia with oculomotor apraxia types 1 (AOA1) and 2 (AOA2), early onset cerebellar ataxia with retained reflexes, Charlevoix-Saguenay spastic ataxia, and Joubert syndrome. The prevalence of ARCA has been estimated to 7 in 100,000 inhabitants. These diseases are due to mutations in specific genes, some of which and its encoded proteins have been identified, such as FRDA (frataxin) in Friedreich ataxia, APTX (aprataxin) in AOA1, alphaTTP (alpha-tocopherol transfer protein) in AVED, and STX (senataxin) in AOA2. Due to autosomal recessive inheritance, previous familial history of affected individuals unlikely. CONCLUSIONS: Most of these cerebellar ataxias have no specific treatment with exception of the ataxia associated with deficiency coenzyme Q10 and abetalipoproteinemia. Clinical diagnosis must be confirmed by ancillary tests such as neuroimaging (magnetic resonance, scanning), electrophysiological examination, and mutation analysis when the causative gene has been identified. Correct clinical and genetic diagnosis is important for appropriate prognosis and genetic counseling and, in some instances, pharmacological treatment.

Influence of the ovary on parameters of LH secretion during the recovery from buserelin-induced desensitization.

This study examined the effect of the ovary on LH pulsatility and on the secretory performance of gonadotrophs during the phase of recovery after treatment with buserelin, a GnRH analogue. We included 12 patients, who received buserelin (1.2 mg/day, intranasally for 3 months) as a reductive therapy for uterine leiomyomatosis prior to hysterectomy. Six patients were oophorectomized and the other 6 patients had their ovaries preserved. LH was measured in samples taken basally up to 36 days after suppression of buserelin. LH pulsatility was studied on day 9 along a 24-h cycle, and the response of the hormone to a double-stimulus GnRH test on days 0, 9, 20, and 34. The concentration of LH reached normal premenopausal levels after an average of 2 weeks in women with ovaries but increased until 4-5 weeks in oophorectomized patients. The pulsatility of LH on day 9 was similar for both groups, but parameters related to LH amplitude or to baseline secretory activity of gonadotrophs were higher in the oophorectomized women. The response of LH to the GnRH tests was also significantly higher in the oophorectomized group from day 9. The conclusions are as follows. (1) At the early stage of recovery from desensitization, as represented by day 9, LH pulsatility was not substantially influenced by the presence or absence of the ovary. (2) There was an increase in parameters related to the amplitude of the LH bursts in the oophorectomized women. Although a higher amplitude of the endogenous GnRH pulses cannot be discarded, most probably that difference is due to a higher sensitivity at a pituitary level, as reflected by the GnRH stimulation tests.

Long-term prediction of birth weight.

On the basis of the hypothesis that undisturbed individual growth in fetal life keeps a constant proportional difference with the standard population 50th percentile, birth weight can be predicted with a single sonographic exploration after the 16th week of pregnancy. Data on 135 singleton pregnancies with accurate dates and delivery at term were used. Sonography was performed between the 17th and 36th weeks of pregnancy, in every case at least 4 weeks before delivery. The observed measurements of BPD, FL, and AC were used for the prediction of their values on the day of delivery, applying the Hadlock equation for the estimation of birth weight. The mean error of birth weight predictions was -1 +/- 11% (SD), with a correlation coefficient between observed and predicted birth weights of 0.62 (P < 0.001). The accuracy was not influenced by the gestational age at the time of exploration. The model underpredicts birth weight of larger fetuses while overpredicting that of lighter ones, but a part of the error could be explained by a change in the growth pattern after modeling. Regardless of the classification considered (i.e., gestational age at sonography or birth weight), over 83% of predictions had an error below 15%. The underlying hypothesis could be useful clinically in modeling and monitoring fetal growth, allowing application of the results of remote sonographic explorations in clinical management. In addition, being able to project fetal weight to the 40th week also is valuable in improving the patient's understanding of fetal growth.