Forecasting using a large number of predictors: Is Bayesian regression a valid alternative to principal components?

This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range of prior choices. Moreover, we study the asymptotic properties of the Bayesian regression under Gaussian prior under the assumption that data are quasi collinear to establish a criterion for setting parameters in a large cross-section. JEL Classification: C11, C13, C33, C53Bayesian VAR, large cross-sections, Lasso regression, principal components, ridge regression

Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?

This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range of prior choices. Moreover, we study the asymptotic properties of the Bayesian regression under Gaussian prior under the assumption that data are quasi collinear to establish a criterion for setting parameters in a large cross-section. --Bayesian VAR,ridge regression,Lasso regression,principal components,large cross-sections

A Regularized Method for Selecting Nested Groups of Relevant Genes from Microarray Data

Gene expression analysis aims at identifying the genes able to accurately predict biological parameters like, for example, disease subtyping or progression. While accurate prediction can be achieved by means of many different techniques, gene identification, due to gene correlation and the limited number of available samples, is a much more elusive problem. Small changes in the expression values often produce different gene lists, and solutions which are both sparse and stable are difficult to obtain. We propose a two-stage regularization method able to learn linear models characterized by a high prediction performance. By varying a suitable parameter these linear models allow to trade sparsity for the inclusion of correlated genes and to produce gene lists which are almost perfectly nested. Experimental results on synthetic and microarray data confirm the interesting properties of the proposed method and its potential as a starting point for further biological investigationsComment: 17 pages, 8 Post-script figure

Sparse and stable Markowitz portfolios

We consider the problem of portfolio selection within the classical Markowitz meanvariance optimizing framework, which has served as the basis for modern portfolio theory for more than 50 years. Efforts to translate this theoretical foundation into a viable portfolio construction algorithm have been plagued by technical difficulties stemming from the instability of the original optimization problem with respect to the available data. Often, instabilities of this type disappear when a regularizing constraint or penalty term is incorporated in the optimization procedure. This approach seems not to have been used in portfolio design until very recently. To provide such a stabilization, we propose to add to the Markowitz objective function a penalty which is proportional to the sum of the absolute values of the portfolio weights. This penalty stabilizes the optimization problem, automatically encourages sparse portfolios, and facilitates an effective treatment of transaction costs. We implement our methodology using as our securities two sets of portfolios constructed by Fama and French: the 48 industry portfolios and 100 portfolios formed on size and book-to-market. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve portfolio comprising equal investments in each available asset. In addition to their excellent performance, these portfolios have only a small number of active positions, a desirable feature for small investors, for whom the fixed overhead portion of the transaction cost is not negligible. JEL Classification: G11, C00Penalized Regression, Portfolio Choice, Sparse Portfolio

Quantum-inspired classification based on quantum state discrimination

We present quantum-inspired algorithms for classification tasks inspired by the problem of quantum state discrimination. By construction, these algorithms can perform multiclass classification, prevent overfitting, and generate probability outputs. While they could be implemented on a quantum computer, we focus here on classical implementations of such algorithms. The training of these classifiers involves Semi-Definite Programming. We also present a relaxation of these classifiers that utilizes Linear Programming (but that can no longer be interpreted as a quantum measurement). Additionally, we consider a classifier based on the Pretty Good Measurement (PGM) and show how to implement it using an analogue of the so-called Kernel Trick, which allows us to study its performance on any number of copies of the input state. We evaluate these classifiers on the MNIST and MNIST-1D datasets and find that the PGM generally outperforms the other quantum-inspired classifiers and performs comparably to standard classifiers.Comment: 19 pages, 4 figure

Future of the drug label:Perspectives from a multistakeholder dialogue

'Regulating drugs does not end when market access has been granted. Monitoring drugs over the life-cycle has become state of the art, inherent to evolving legislation and societal need. Here, we explore how the drug label could move along in a changing playing-field, and become a sustainable label for the future. A dialogue between academia, government, the pharmaceutical industry, and patient/societal organizations was organized by the Regulatory Science Network Netherlands, RSNN. This is their view.

Sex Proportionality in Pre-clinical and Clinical Trials: An Evaluation of 22 Marketing Authorization Application Dossiers Submitted to the European Medicines Agency

This study assessed to what extent women were included in all phases of drug development; whether the clinical studies in the marketing authorization application dossiers include information per sex; and explored whether there are differences between women and men in the drugs' efficacy and safety. Data were extracted from dossiers submitted to the European Medicines Agency. Twenty-two dossiers of drugs approved between 2011 and 2015 for the treatment of various diseases were included. Female animals were included in only 9% of the pharmacodynamics studies, but female and male animals were included in all toxicology studies. Although fewer women than men were included in the clinical studies used to evaluate pharmacokinetics (PK) (29 to 40% women), all dossiers contained sex-specific PK parameter estimations. In the phase III trials, inclusion of women was proportional to disease prevalence for depression, epilepsy, thrombosis, and diabetes [participation to prevalence ratio (PPR) range: 0.91–1.04], but women were considered underrepresented for schizophrenia, hepatitis C, hypercholesterolemia, HIV, and heart failure (PPR range: 0.49-0.74). All dossiers contained sex-specific subgroup analyses of efficacy and safety. There seemed to be higher efficacy for women in one dossier and a trend toward lower efficacy in another dossier. More women had adverse events in both treatment (73.0 vs. 70.6%, p < 0.001) and placebo groups (69.5 vs. 65.5%, p < 0.001). In conclusion, women were included throughout all phases of clinical drug research, and sex-specific information was available in the evaluated dossiers. The included number of women was, however, not always proportional to disease prevalence rates

Amnioinfusion Compared With No Intervention in Women With Second-Trimester Rupture of Membranes A Randomized Controlled Trial

OBJECTIVE: To assess the effectiveness of amnioinfusion in women with second-trimester preterm prelabor rupture of membranes. METHODS: We performed a nationwide, multicenter, open-label, randomized controlled trial, the PPROM: Expectant Management versus Induction of Labor-III (PPROMEXIL-III) trial, in women with singleton pregnancies and preterm prelabor rupture of membranes at 16 0/7 to 24 0/7 weeks of gestation with oligohydramnios (single deepest pocket less than 20 mm). Participants were allocated to transabdominal amnioinfusion or no intervention in a oneto- one ratio by a web-based system. If the single deepest pocket was less than 20 mm on follow-up visits, amnioinfusion was repeated weekly until 28 0/7 weeks of gestation. The primary outcome was perinatal mortality. We needed 56 women to show a reduction in perinatal mortality from 70% to 35% (b error 0.20, two-sided a error 0.05). RESULTS: Between June 15, 2012, and January 13, 2016, we randomized 28 women to amnioinfusion and 28 to no intervention. One woman was enrolled before the trial registration date (June 19, 2012). Perinatal mortality rates were 18 of 28 (64%) in the amnioinfusion group vs 21 of 28 (75%) in the no intervention group (relative risk 0.86, 95% CI 0.601.22, P5.39). CONCLUSION: In women with second-trimester preterm prelabor rupture of membranes and oligohydramnios, we found no reduction in perinatal mortality after amnioinfusion

Midtrimester preterm prelabour rupture of membranes (PPROM):expectant management or amnioinfusion for improving perinatal outcomes (PPROMEXIL - III trial)

BACKGROUND: Babies born after midtrimester preterm prelabour rupture of membranes (PPROM) are at risk to develop neonatal pulmonary hypoplasia. Perinatal mortality and morbidity after this complication is high. Oligohydramnios in the midtrimester following PPROM is considered to cause a delay in lung development. Repeated transabdominal amnioinfusion with the objective to alleviate oligohydramnios might prevent this complication and might improve neonatal outcome. METHODS/DESIGN: Women with PPROM and persisting oligohydramnios between 16 and 24 weeks gestational age will be asked to participate in a multi-centre randomised controlled trial. Intervention: random allocation to (repeated) abdominal amnioinfusion (intervention) or expectant management (control). The primary outcome is perinatal mortality. Secondary outcomes are lethal pulmonary hypoplasia, non-lethal pulmonary hypoplasia, survival till discharge from NICU, neonatal mortality, chronic lung disease (CLD), number of days ventilatory support, necrotizing enterocolitis (NEC), periventricular leucomalacia (PVL) more than grade I, severe intraventricular hemorrhage (IVH) more than grade II, proven neonatal sepsis, gestational age at delivery, time to delivery, indication for delivery, successful amnioinfusion, placental abruption, cord prolapse, chorioamnionitis, fetal trauma due to puncture. The study will be evaluated according to intention to treat. To show a decrease in perinatal mortality from 70% to 35%, we need to randomise two groups of 28 women (two sided test, β-error 0.2 and α-error 0.05). DISCUSSION: This study will answer the question if (repeated) abdominal amnioinfusion after midtrimester PPROM with associated oligohydramnios improves perinatal survival and prevents pulmonary hypoplasia and other neonatal morbidities. Moreover, it will assess the risks associated with this procedure. TRIAL REGISTRATION: NTR3492 Dutch Trial Register (http://www.trialregister.nl)

Preventing preterm birth with progesterone: costs and effects of screening low risk women with a singleton pregnancy for short cervical length, the Triple P study

Contains fulltext : 97255.pdf (postprint version ) (Open Access)BACKGROUND: Women with a short cervical length in mid-trimester pregnancy have a higher risk of preterm birth and therefore a higher rate of neonatal mortality and morbidity. Progesterone can potentially decrease the number of preterm births and lower neonatal mortality and morbidity. Previous studies showed good results of progesterone in women with either a history of preterm birth or a short cervix. However, it is unknown whether screening for a short cervix and subsequent treatment in mid trimester pregnancy is effective in low risk women. METHODS/DESIGN: We plan a combined screen and treat study among women with a singleton pregnancy without a previous preterm birth. In these women, we will measure cervical length at the standard anomaly scan performed between 18 and 22 weeks. Women with cervical length </= 30 mm at two independent measurements will be randomly allocated to receive either vaginal progesterone tablets or placebo between 22 and 34 weeks. The primary outcome of this trial is adverse neonatal condition, defined as a composite outcome of neonatal mortality and severe morbidity. Secondary outcomes are time to delivery, preterm birth rate before 32, 34 and 37 weeks, days of admission in neonatal intensive care unit, maternal morbidity, maternal admission days for preterm labour and costs. We will assess growth, physical condition and neurodevelopmental outcome of the children at two years of age. DISCUSSION: This study will provide evidence for the usefulness and cost-effectiveness of screening for short cervical length at the 18-22 weeks and subsequent progesterone treatment among low risk women. TRIAL REGISTRATION: Netherlands Trial Register (NTR): NTR207