Bayesian Linear Regression

The paper is concerned with Bayesian analysis under prior-data conflict, i.e. the situation when observed data are rather unexpected under the prior (and the sample size is not large enough to eliminate the influence of the prior). Two approaches for Bayesian linear regression modeling based on conjugate priors are considered in detail, namely the standard approach also described in Fahrmeir, Kneib & Lang (2007) and an alternative adoption of the general construction procedure for exponential family sampling models. We recognize that - in contrast to some standard i.i.d. models like the scaled normal model and the Beta-Binomial / Dirichlet-Multinomial model, where prior-data conflict is completely ignored - the models may show some reaction to prior-data conflict, however in a rather unspecific way. Finally we briefly sketch the extension to a corresponding imprecise probability model, where, by considering sets of prior distributions instead of a single prior, prior-data conflict can be handled in a very appealing and intuitive way

Topics in inference and decision-making with partial knowledge

Two essential elements needed in the process of inference and decision-making are prior probabilities and likelihood functions. When both of these components are known accurately and precisely, the Bayesian approach provides a consistent and coherent solution to the problems of inference and decision-making. In many situations, however, either one or both of the above components may not be known, or at least may not be known precisely. This problem of partial knowledge about prior probabilities and likelihood functions is addressed. There are at least two ways to cope with this lack of precise knowledge: robust methods, and interval-valued methods. First, ways of modeling imprecision and indeterminacies in prior probabilities and likelihood functions are examined; then how imprecision in the above components carries over to the posterior probabilities is examined. Finally, the problem of decision making with imprecise posterior probabilities and the consequences of such actions are addressed. Application areas where the above problems may occur are in statistical pattern recognition problems, for example, the problem of classification of high-dimensional multispectral remote sensing image data

Information Extraction, Data Integration, and Uncertain Data Management: The State of The Art

Information Extraction, data Integration, and uncertain data management are different areas of research that got vast focus in the last two decades. Many researches tackled those areas of research individually. However, information extraction systems should have integrated with data integration methods to make use of the extracted information. Handling uncertainty in extraction and integration process is an important issue to enhance the quality of the data in such integrated systems. This article presents the state of the art of the mentioned areas of research and shows the common grounds and how to integrate information extraction and data integration under uncertainty management cover

Dealing with missing standard deviation and mean values in meta-analysis of continuous outcomes: a systematic review

Background: Rigorous, informative meta-analyses rely on availability of appropriate summary statistics or individual participant data. For continuous outcomes, especially those with naturally skewed distributions, summary information on the mean or variability often goes unreported. While full reporting of original trial data is the ideal, we sought to identify methods for handling unreported mean or variability summary statistics in meta-analysis. Methods: We undertook two systematic literature reviews to identify methodological approaches used to deal with missing mean or variability summary statistics. Five electronic databases were searched, in addition to the Cochrane Colloquium abstract books and the Cochrane Statistics Methods Group mailing list archive. We also conducted cited reference searching and emailed topic experts to identify recent methodological developments. Details recorded included the description of the method, the information required to implement the method, any underlying assumptions and whether the method could be readily applied in standard statistical software. We provided a summary description of the methods identified, illustrating selected methods in example meta-analysis scenarios. Results: For missing standard deviations (SDs), following screening of 503 articles, fifteen methods were identified in addition to those reported in a previous review. These included Bayesian hierarchical modelling at the meta-analysis level; summary statistic level imputation based on observed SD values from other trials in the meta-analysis; a practical approximation based on the range; and algebraic estimation of the SD based on other summary statistics. Following screening of 1124 articles for methods estimating the mean, one approximate Bayesian computation approach and three papers based on alternative summary statistics were identified. Illustrative meta-analyses showed that when replacing a missing SD the approximation using the range minimised loss of precision and generally performed better than omitting trials. When estimating missing means, a formula using the median, lower quartile and upper quartile performed best in preserving the precision of the meta-analysis findings, although in some scenarios, omitting trials gave superior results. Conclusions: Methods based on summary statistics (minimum, maximum, lower quartile, upper quartile, median) reported in the literature facilitate more comprehensive inclusion of randomised controlled trials with missing mean or variability summary statistics within meta-analyses

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

B

Treatments for women with gestational diabetes mellitus: an overview of Cochrane systematic reviews

Background Successful treatments for gestational diabetes mellitus (GDM) have the potential to improve health outcomes for women with GDM and their babies. Objectives To provide a comprehensive synthesis of evidence from Cochrane systematic reviews of the benefits and harms associated with interventions for treating GDM on women and their babies. Methods We searched the Cochrane Database of Systematic Reviews (5 January 2018) for reviews of treatment/management for women with GDM. Reviews of pregnant women with pre-existing diabetes were excluded. Two overview authors independently assessed reviews for inclusion, quality (AMSTAR; ROBIS), quality of evidence (GRADE), and extracted data. Main results We included 14 reviews. Of these, 10 provided relevant high-quality and low-risk of bias data (AMSTAR and ROBIS) from 128 randomised controlled trials (RCTs), 27 comparisons, 17,984 women, 16,305 babies, and 1441 children. Evidence ranged from high to very low-quality (GRADE). Only one effective intervention was found for treating women with GDM. Effective Lifestyle versus usual care Lifestyle intervention versus usual care probably reduces large-for-gestational age (risk ratio (RR) 0.60, 95% confidence interval (CI) 0.50 to 0.71; 6 RCTs, N = 2994; GRADE moderate-quality). Promising No evidence for any outcome for any comparison could be classified to this category. Ineffective or possibly harmful Lifestyle versus usual care Lifestyle intervention versus usual care probably increases the risk of induction of labour (IOL) suggesting possible harm (average RR 1.20, 95% CI 0.99 to 1.46; 4 RCTs, N = 2699; GRADE moderate-quality). Exercise versus control Exercise intervention versus control for return to pre-pregnancy weight suggested ineffectiveness (body mass index, BMI) MD 0.11 kg/m², 95% CI -1.04 to 1.26; 3 RCTs, N = 254; GRADE moderate-quality). Insulin versus oral therapy Insulin intervention versus oral therapy probably increases the risk of IOL suggesting possible harm (RR 1.3, 95% CI 0.96 to 1.75; 3 RCTs, N = 348; GRADE moderate-quality). Probably ineffective or harmful interventions Insulin versus oral therapy For insulin compared to oral therapy there is probably an increased risk of the hypertensive disorders of pregnancy (RR 1.89, 95% CI 1.14 to 3.12; 4 RCTs, N = 1214; GRADE moderate-quality). Inconclusive Lifestyle versus usual care The evidence for childhood adiposity kg/m² (RR 0.91, 95% CI 0.75 to 1.11; 3 RCTs, N = 767; GRADE moderate-quality) and hypoglycaemia was inconclusive (average RR 0.99, 95% CI 0.65 to 1.52; 6 RCTs, N = 3000; GRADE moderate-quality). Exercise versus control The evidence for caesarean section (RR 0.86, 95% CI 0.63 to 1.16; 5 RCTs, N = 316; GRADE moderate quality) and perinatal death or serious morbidity composite was inconclusive (RR 0.56, 95% CI 0.12 to 2.61; 2 RCTs, N = 169; GRADE moderate-quality). Insulin versus oral therapy The evidence for the following outcomes was inconclusive: pre-eclampsia (RR 1.14, 95% CI 0.86 to 1.52; 10 RCTs, N = 2060), caesarean section (RR 1.03, 95% CI 0.93 to 1.14; 17 RCTs, N = 1988), large-for-gestational age (average RR 1.01, 95% CI 0.76 to 1.35; 13 RCTs, N = 2352), and perinatal death or serious morbidity composite (RR 1.03; 95% CI 0.84 to 1.26; 2 RCTs, N = 760). GRADE assessment was moderate-quality for these outcomes. Insulin versus diet The evidence for perinatal mortality was inconclusive (RR 0.74, 95% CI 0.41 to 1.33; 4 RCTs, N = 1137; GRADE moderate-quality). Insulin versus insulin The evidence for insulin aspart versus lispro for risk of caesarean section was inconclusive (RR 1.00, 95% CI 0.91 to 1.09; 3 RCTs, N = 410; GRADE moderate quality). No conclusions possible No conclusions were possible for: lifestyle versus usual care (perineal trauma, postnatal depression, neonatal adiposity, number of antenatal visits/admissions); diet versus control (pre-eclampsia, caesarean section); myo-inositol versus placebo (hypoglycaemia); metformin versus glibenclamide (hypertensive disorders of pregnancy, pregnancy-induced hypertension, death or serious morbidity composite, insulin versus oral therapy (development of type 2 diabetes); intensive management versus routine care (IOL, large-for-gestational age); post- versus pre-prandial glucose monitoring (large-for-gestational age). The evidence ranged from moderate-, low- and very low quality. Authors’ conclusions Currently there is insufficient high-quality evidence about the effects on health outcomes of relevance for women with GDM and their babies for many of the comparisons in this overview comparing treatment interventions for women with GDM. Lifestyle changes (including as a minimum healthy eating, physical activity and self-monitoring of blood sugar levels) was the only intervention that showed possible health improvements for women and their babies. Lifestyle interventions may result in fewer babies being large. Conversely, in terms of harms, lifestyle interventions may also increase the number of inductions. Taking insulin was also associated with an increase in hypertensive disorders, when compared to oral therapy. There was very limited information on long-term health and health services costs. Further high-quality research is needed

What information on measurement uncertainty should be communicated to clinicians, and how?

open6embargoed_20190202Mario, Plebani*; Laura, Sciacovelli; Daniela, Bernardi; Ada, Aita; Giorgia, Antonelli; Andrea, PadoanPlebani, Mario; Sciacovelli, Laura; Bernardi, Daniela; Aita, Ada; Antonelli, Giorgia; Padoan, Andre

Imprecise probability models for inference in exponential families

When considering sampling models described by a distribution from an exponential family, it is possible to create two types of imprecise probability models. One is based on the corresponding conjugate distribution and the other on the corresponding predictive distribution. In this paper, we show how these types of models can be constructed for any (regular, linear, canonical) exponential family, such as the centered normal distribution. To illustrate the possible use of such models, we take a look at credal classification. We show that they are very natural and potentially promising candidates for describing the attributes of a credal classifier, also in the case of continuous attributes