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Witnesses of causal nonseparability: an introduction and a few case studies.

ABSTRACT: It was recently realised that quantum theory allows for so-called causally nonseparable processes, which are incompatible with any definite causal order. This was first suggested on a rather abstract level by the formalism of process matrices, an extension of the quantum formalism which only assumes that quantum theory holds locally in some observers' laboratories, but does not impose a global causal structure; it was then shown, on a more practical level, that the quantum switch-a new, already implementable resource for quantum computation that goes beyond causally ordered circuits-provided precisely a physical example of a causally nonseparable process. To demonstrate that a given process is causally nonseparable, the concept of witnesses of causal nonseparability was introduced. Here we present a shorter introduction to this concept, and concentrate on some explicit examples-by considering in particular different noise models for the quantum switch-to show how to construct and use such witnesses in practice.

SUBMITTER: Branciard C

PROVIDER: S-EPMC4870702 | biostudies-other | 2016

REPOSITORIES: biostudies-other

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Witnesses of causal nonseparability: an introduction and a few case studies.

Branciard Cyril C

Scientific reports 20160518

It was recently realised that quantum theory allows for so-called causally nonseparable processes, which are incompatible with any definite causal order. This was first suggested on a rather abstract level by the formalism of process matrices, an extension of the quantum formalism which only assumes that quantum theory holds locally in some observers' laboratories, but does not impose a global causal structure; it was then shown, on a more practical level, that the quantum switch-a new, already ...[more]

PMID: 27188962

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Project description:BACKGROUND:Standard random-effects meta-analysis methods perform poorly when applied to few studies only. Such settings however are commonly encountered in practice. It is unclear, whether or to what extent small-sample-size behaviour can be improved by more sophisticated modeling. METHODS:We consider likelihood-based methods, the DerSimonian-Laird approach, Empirical Bayes, several adjustment methods and a fully Bayesian approach. Confidence intervals are based on a normal approximation, or on adjustments based on the Student-t-distribution. In addition, a linear mixed model and two generalized linear mixed models (GLMMs) assuming binomial or Poisson distributed numbers of events per study arm are considered for pairwise binary meta-analyses. We extract an empirical data set of 40 meta-analyses from recent reviews published by the German Institute for Quality and Efficiency in Health Care (IQWiG). Methods are then compared empirically as well as in a simulation study, based on few studies, imbalanced study sizes, and considering odds-ratio (OR) and risk ratio (RR) effect sizes. Coverage probabilities and interval widths for the combined effect estimate are evaluated to compare the different approaches. RESULTS:Empirically, a majority of the identified meta-analyses include only 2 studies. Variation of methods or effect measures affects the estimation results. In the simulation study, coverage probability is, in the presence of heterogeneity and few studies, mostly below the nominal level for all frequentist methods based on normal approximation, in particular when sizes in meta-analyses are not balanced, but improve when confidence intervals are adjusted. Bayesian methods result in better coverage than the frequentist methods with normal approximation in all scenarios, except for some cases of very large heterogeneity where the coverage is slightly lower. Credible intervals are empirically and in the simulation study wider than unadjusted confidence intervals, but considerably narrower than adjusted ones, with some exceptions when considering RRs and small numbers of patients per trial-arm. Confidence intervals based on the GLMMs are, in general, slightly narrower than those from other frequentist methods. Some methods turned out impractical due to frequent numerical problems. CONCLUSIONS:In the presence of between-study heterogeneity, especially with unbalanced study sizes, caution is needed in applying meta-analytical methods to few studies, as either coverage probabilities might be compromised, or intervals are inconclusively wide. Bayesian estimation with a sensibly chosen prior for between-trial heterogeneity may offer a promising compromise.

Project description:BackgroundPneumococcal conjugate vaccines (PCVs) are used in many low-income countries but their impact on the incidence of pneumonia is unclear. The Gambia introduced PCV7 in August, 2009, and PCV13 in May, 2011. We aimed to measure the impact of the introduction of these vaccines on pneumonia incidence.MethodsWe did population-based surveillance and case-control studies. The primary endpoint was WHO-defined radiological pneumonia with pulmonary consolidation. Population-based surveillance was for suspected pneumonia in children aged 2-59 months (minimum age 3 months in the case-control study) between May 12, 2008, and Dec 31, 2015. Surveillance for the impact study was limited to the Basse Health and Demographic Surveillance System (BHDSS), whereas surveillance for the case-control study included both the BHDSS and Fuladu West Health and Demographic Surveillance System. Nurses screened all outpatients and inpatients at all health facilities in the surveillance area using standardised criteria for referral to clinicians in Basse and Bansang. These clinicians recorded clinical findings and applied standardised criteria to identify patients with suspected pneumonia. We compared the incidence of pneumonia during the baseline period (May 12, 2008, to May 11, 2010) and the PCV13 period (Jan 1, 2014, to Dec 31, 2015). We also investigated the effectiveness of PCV13 using case-control methods between Sept 12, 2011, and Sept 31, 2014. Controls were aged 90 days or older, and were eligible to have received at least one dose of PCV13; cases had the same eligibility criteria with the addition of having WHO-defined radiological pneumonia.FindingsWe investigated 18 833 children with clinical pneumonia and identified 2156 cases of radiological pneumonia. Among children aged 2-11 months, the incidence of radiological pneumonia fell from 21·0 cases per 1000 person-years in the baseline period to 16·2 cases per 1000 person-years (23% decline, 95% CI 7-36) in 2014-15. In the 12-23 month age group, radiological pneumonia decreased from 15·3 to 10·9 cases per 1000 person-years (29% decline, 12-42). In children aged 2-4 years, incidence fell from 5·2 to 4·1 cases per 1000 person-years (22% decline, 1-39). Incidence of all clinical pneumonia increased by 4% (-1 to 8), but hospitalised cases declined by 8% (3-13). Pneumococcal pneumonia declined from 2·9 to 1·2 cases per 1000 person-years (58% decline, 22-77) in children aged 2-11 months and from 2·6 to 0·7 cases per 1000 person-years (75% decline, 47-88) in children aged 12-23 months. Hypoxic pneumonia fell from 13·1 to 5·7 cases per 1000 person-years (57% decline, 42-67) in children aged 2-11 months and from 6·8 to 1·9 cases per 1000 person-years (72% decline, 58-82) in children aged 12-23 months. In the case-control study, the best estimate of the effectiveness of three doses of PCV13 against radiological pneumonia was an adjusted odds ratio of 0·57 (0·30-1·08) in children aged 3-11 months and vaccine effectiveness increased with greater numbers of doses (p=0·026). The analysis in children aged 12 months and older was underpowered because there were few unvaccinated cases and controls.InterpretationThe introduction of PCV in The Gambia was associated with a moderate impact on the incidence of radiological pneumonia, a small reduction in cases of hospitalised pneumonia, and substantial reductions of pneumococcal and hypoxic pneumonia in young children. Low-income countries that introduce PCV13 with reasonable coverage can expect modest reductions in hospitalised cases of pneumonia and a marked impact on the incidence of severe childhood pneumonia.FundingGAVI's Pneumococcal vaccines Accelerated Development and Introduction Plan, Bill & Melinda Gates Foundation, and UK Medical Research Council.

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Witnesses of causal nonseparability: an introduction and a few case studies.

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Witnesses of causal nonseparability: an introduction and a few case studies.

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