PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Data Interpretation, Statistical”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,045 records · Page 58Linked to original sources
PubMed2006

Interpreting trends in cancer patient survival.

Data on cancer patient survival are an invaluable tool in the evaluation of therapeutic progress against cancer as well as other lethal diseases. As with all quantitative information routinely used in evidence-based clinical management--including diagnostic tests, prognostic markers and comparisons of therapeutic interventions--data on patient survival require evaluation based on an understanding of the underlying statistical methodology, methods of data collection and classification, and, most notably, clinical and biologic insight. This article contains an introduction to the methods used for estimating cancer patient survival, including cause-specific survival, relative survival and period analysis. The methods, and their interpretation, are illustrated through presentation of trends in incidence, mortality and patient survival for a range of different cancers. Our aim was to lay out the strengths and limitations of survival analysis as a tool in the evaluation of progress in the diagnosis and treatment of cancer.

Data Collection↗
PubMed1994

A physician-based architecture for the construction and use of statistical models.

Physicians need specially tailored computer tools to take advantage of published research results. We present a knowledge-based computer framework--the physician-based (PB) architecture--for constructing such tools, and we use the problem of physicians' interpretation of two-arm parallel randomized clinical trials (TAPRCT) as a working example. Statistical models are represented by influence diagrams. The interpretation of influence-diagram elements are mapped into users' language in a domain-specific, physician-based user interface, called a patient-flow diagram. Statistical-model transformations that maintain the semantic relationships of the model and that embody clinical-epidemiological knowledge are encoded in a mediating structure called the cohort-state diagram. The algorithm that coordinates the interactions among the knowledge representations uses modular actions called construction steps. This architecture has been implemented in a Bayesian system, called THOMAS, that supports physician decision making in light of TAPRCT data. This support entails assessing clinical significance, prior beliefs, and methodological concerns. We suggest that the PB architecture applies to a wide range of statistical tools and users.

Algorithms↗