Consulta: creatorFacets:"Greenland,   Sander"
Registros recuperados: 2
Data/hora: 08/06/2024 13:50:14

Generalized least squares for trend estimation of summarized dose–response data
Provedor de dados: 31
Autores: Orsini,   Nicola; Bellocco,   Rino; Greenland,   Sander.
This paper presents a command, glst, for trend estimation across different exposure levels for either single or multiple summarized case–control, incidence-rate, and cumulative incidence data. This approach is based on constructing an approximate covariance estimate for the log relative risks and estimating a corrected linear trend using generalized least squares. For trend analysis of multiple studies, glst can estimate fixed- and random-effects metaregression models.
Tipo: Journal Article
Palavras-chave: Glst; Dose–response data; Generalized least squares; Trend; Meta-analysis; Metaregression; Research Methods/ Statistical Methods.
Ano: 2006
URL: http://purl.umn.edu/117556

A tool for deterministic and probabilistic sensitivity analysis of epidemiologic studies
Provedor de dados: 31
Autores: Orsini,   Nicola; Bellocco,   Rino; Bottai,   Matteo; Wolk,   Alicja; Greenland,   Sander.
Classification errors, selection bias, and uncontrolled confounders are likely to be present in most epidemiologic studies, but the uncertainty introduced by these types of biases is seldom quantified. The authors present a simple yet easy-to-use Stata command to adjust the relative risk for exposure misclassification, selection bias, and an unmeasured confounder. This command implements both deterministic and probabilistic sensitivity analysis. It allows the user to specify a variety of probability distributions for the bias parameters, which are used to simulate distributions for the bias-adjusted exposure–disease relative risk. We illustrate the command by applying it to a case–control study of occupational resin exposure and lung-cancer deaths. By...
Tipo: Article
Palavras-chave: Episens; Episensi; Sensitivity analysis; Unmeasured confounder; Misclassification; Bias; Epidemiology; Research Methods/ Statistical Methods.
Ano: 2008
URL: http://purl.umn.edu/120927