Regenstrief Conference. Marshall, IN, USA, 1996/09/04.
The aim of many analyses of large databases is to draw causal inferences about the effects of actions, treatments, or interventions.
Examples include the effects of various options available to a physician for treating a particular patient, the relative efficacies of various health care providers, and the consequences of implementing a new national health care policy.
A complication of using large databases to achieve such aims is that their data are almost always observational rather than experimental.
That is, the data in most large data sets are not based on the results of carefully conducted randomized clinical trials, but rather represent data collected through the observation of systems as they operate in normal practice without any interventions implemented by randomized assignment rules.
Such data are relatively inexpensive to obtain, however, and often do represent the spectrum of medical practice better than the settings of randomized experiments.
Consequently, it is sensible to try to estimate the effects of treatments from such large data sets, even if only to help design a new randomized experiment or shed light on the generalizability of results from existing randomized experiments.
However, standard methods of analysis using available statistical software (such as linear or logistic regression) can be deceptive for these objectives because they provide no warnings about their propriety. (...)
Mots-clés Pascal : Base donnée, Facteur efficacité, Etude comparative, Test score, Evaluation, Estimation moyenne, Classification automatique, Homme, Informatique, Organisation santé
Mots-clés Pascal anglais : Database, Effectiveness factor, Comparative study, Score test, Evaluation, Mean estimation, Automatic classification, Human, Computer science, Public health organization
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 98-0051839
Code Inist : 002B28D. Création : 14/05/1998.