Performing meta-analysis with incomplete statistical information in clinical trials

Background: Results from clinical trials are usually summarized in the form of sampling distributions. When full information (mean, SEM) about these distributions is given, performing meta-analysis is straightforward. However, when some of the sampling distributions only have mean values, a challenging issue is to decide how to use such distributions in meta-analysis. Currently, the most common approaches are either ignoring such trials or for each trial with a missing SEM, finding a similar trial and taking its SEM value as the missing SEM. Both approaches have drawbacks. As an alternative, this paper develops and tests two new methods, the first being the prognostic method and the second being the interval method, to estimate any missing SEMs from a set of sampling distributions with full information. A merging method is also proposed to handle clinical trials with partial information to simulate meta-analysis.

Temporal and statistical information in causal structure learning

Three experiments examined children’s and adults’ abilities to use statistical and temporal information to distinguish between common cause and causal chain structures. In Experiment 1, participants were provided with conditional probability information and/or temporal information and asked to infer the causal structure of a three-variable mechanical system that operated probabilistically. Participants of all ages preferentially relied on the temporal pattern of events in their inferences, even if this conflicted with statistical information. In Experiments 2 and 3, participants observed a series of interventions on the system, which in these experiments operated deterministically. In Experiment 2, participants found it easier to use temporal pattern information than statistical information provided as a result of interventions. In Experiment 3, in which no temporal pattern information was provided, children from 6-7 years, but not younger children, were able to use intervention information to make causal chain judgments, although they had difficulty when the structure was a common cause. The findings suggest that participants, and children in particular, may find it more difficult to use statistical information than temporal pattern information because of its demands on information processing resources. However, there may also be an inherent preference for temporal information.

Competencies in understanding statistical information in primary and secondary school levels : an inter-cultural empirical study in Germany and Colombian students

Resumen en espa??ol, portugu??s y franc??s

Distribución de la información estadística = Distribution of statistical information

Describe los canales de distribucion de informacion estadistica disponibles en la actualidad y presenta un nuevo enfoque que permitiria vincular distribucion y produccion con el uso alternativo de esos posibles canales y que se denomina "distribucion multimedia"; examina la organizacion institucional de la distribucion; y plantea algunas reflexiones e interrogantes con el objetivo de cooperar al debate de la reunion.

Programa de trabajo estadístico internacional para América Latina y el Caribe, 1995-1996: Subprograma 4: difusión de la información estadística = Programme of international statistical work for Latin America and the Caribbean, 1955-1996: Subprogramme 4: dissemination of statistical information

Incluye Bibliografía

The ECLA Programme of Population research and the improvement of statistical information

Includes bibliography

Statistical Information: A Bayesian Perspective

We explore the meaning of information about quantities of interest. Our approach is divided in two scenarios: the analysis of observations and the planning of an experiment. First, we review the Sufficiency, Conditionality and Likelihood principles and how they relate to trivial experiments. Next, we review Blackwell Sufficiency and show that sampling without replacement is Blackwell Sufficient for sampling with replacement. Finally, we unify the two scenarios presenting an extension of the relationship between Blackwell Equivalence and the Likelihood Principle.