Statistical Models in Epidemiology, the Environment, and Clinical Trials (eBook, PDF)

Redaktion: Halloran, M. Elizabeth; Berry, Donald

• Format: PDF

Produktbeschreibung

This IMA Volume in Mathematics and its Applications STATISTICAL MODELS IN EPIDEMIOLOGY, THE ENVIRONMENT,AND CLINICAL TRIALS is a combined proceedings on "Design and Analysis of Clinical Trials" and "Statistics and Epidemiology: Environment and Health. " This volume is the third series based on the proceedings of a very successful 1997 IMA Summer Program on "Statistics in the Health Sciences. " I would like to thank the organizers: M. Elizabeth Halloran of Emory University (Biostatistics) and Donald A. Berry of Duke University (Insti tute of Statistics and Decision Sciences and Cancer Center Biostatistics) for their excellent work as organizers of the meeting and for editing the proceedings. I am grateful to Seymour Geisser of University of Minnesota (Statistics), Patricia Grambsch, University of Minnesota (Biostatistics); Joel Greenhouse, Carnegie Mellon University (Statistics); Nicholas Lange, Harvard Medical School (Brain Imaging Center, McLean Hospital); Barry Margolin, University of North Carolina-Chapel Hill (Biostatistics); Sandy Weisberg, University of Minnesota (Statistics); Scott Zeger, Johns Hop kins University (Biostatistics); and Marvin Zelen, Harvard School of Public Health (Biostatistics) for organizing the six weeks summer program. I also take this opportunity to thank the National Science Foundation (NSF) and the Army Research Office (ARO), whose financial support made the workshop possible. Willard Miller, Jr.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: Springer US
• Seitenzahl: 283
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781461212843
• Artikelnr.: 44175944

Inhaltsangabe

Sensitivity analysis for selection bias and unmeasured confounding in missing data and causal inference models.- Marginal structural models versus structural nested models as tools for causal inference.- Nonparametric locally efficient estimation of the treatment specific survival distribution with right censored data and covariates in observational studies.- Estimation of disease rates in small areas: A new mixed model for spatial dependence.- Markov chain Monte Carlo methods for clustering in case event and count data in spatial epidemiology.- A simulation study of the epidemiological impact of air pollution: Diagnostics of the confounding effects for generalized linear models.- The use of reference priors and Bayes factors in the analysis of clinical trials.- Surrogate endpoints in cancer clinical trials.- List of Participants.