Fixed and random effects meta-analysis
glst Generalized Least Squares for Trend… para estudios de tendencias de análisis de dosis-respuesta Syntax
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- Description glst
- Options se
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glst Generalized Least Squares for Trend… para estudios de tendencias de análisis de dosis-respuesta Syntax glst depvar dose [indepvars] [if] [in], se(varname) cov(n cases) {cc | ir | ci} [options] options description ------------------------------------------------------------------------- * se(varname) variable containing estimate of standard error * cov(n cases) variables containing the information required to fit the covariances + cc case-control data + ir incidence-rate data + ci cumulative incidence data vwls variance-weighted least-squares estimation crudes crude relative risks and correlations pfirst(id study) pool-first method tstage(f|r) two-stage fixed- or random-effects meta-analysis ssest study-specific linear trend estimates random random-effects for the dose coefficient in an aggregate analysis level(#) set confidence level; default is level(95) eform generic label; exp(b); the default ------------------------------------------------------------------------- * se() and cov() are required. + One of cc, ir, or ci is required for trend estimation. depvar contains log relative-risks; dose is the main covariate of interest and contains the exposure levels; and indepvars may contain other covariates, such as polynomial terms of dose or interaction terms.
generalized least squares for trend estimation of single or multiple summarized dose-response epidemiological studies, namely, case-control, incidence-rate, and cumulative incidence data. It differs from variance-weighted least squares (see [R] vwls) in that glst estimates a variance-covariance matrix of the beta coefficients, as proposed by Greenland and Longnecker (1992).
relative-risks. All values of varname must be > 0. cov(n cases) specifies variables containing the information required to fit the covariances among the beta coefficients. At each exposure level, n is the number of subjects (controls plus cases) for case-control data (cc); or the total person-time for incidence-rate data (ir); or the total number of persons (cases plus noncases) for cumulative incidence data (ci). The cases variable contains the number of cases at each exposure level.
one study unless the pfirst() option is specified. ir specifies incidence-rate data. It is required for trend estimation of one study unless the pfirst() option is specified. ci specifies cumulative incidence data. It is required for trend estimation of one study unless the pfirst() option is specified. vwls specifies variance-weighted least-squares (see [R] vwls) estimation, which assumes zero covariances among a series of log relative-risks; the default is generalized least squares.
variance-covariance and correlation matrices. This option also provides the relative differences (as percentages) between crude and adjusted relative risks and their correlation matrix. pfirst(id study) specifies the pool-first method with multiple summarized studies. The id variable is an indicator variable that assumes the same value across correlated parameters within a study. The study variable must take value 1 for case-control data, 2 for incidence-rate data, and 3 for cumulative incidence data. Within each group of parameters, the first observation is assumed to be the referent. This option allows the estimation either a fixed- or random-effects meta-regression model. tstage(f|r) specifies the two-stage fixed-effects (f) (inverse variance-weighted least squares) or random-effects (r) meta-analysis of dose-response linear trends (using the method of moments to estimate the between-study variance, tau2). This option can be specified only if pfirst() is also specified, and if only one covariate, namely, the dose variable, is included in the linear predictor.
specified only if pfirst() is also specified. random specifies the iterative generalized least-squares method to fit a random-effects meta-regression model (aggregate analysis). Between-study variability of the dose coefficient is estimated with the moment estimator. This option can be specified only if pfirst() is specified.
intervals. The default is level(95) or as set by set level. eform reports coefficient estimates as exp(b) rather than b. Standard errors and confidence intervals are similarly transformed. Example Input data from table 1, page 1302 of Greenland and Longnecker (1992) . use http://nicolaorsini.altervista.org/stata/data/dose.dta, clear Go from 95% CI of odds ratios to 95% CI of log odds-ratios . gen double logor = log(adjor) . gen double logorlb = log(lb) . gen double logorub = log(ub) . gen double se = ((logorub - logorlb)/(2*invnorm(.975))) Trend estimation without correction for covariance of odds ratios . vwls logor dose in 2/4, sd(se) nocons . mat list e(V) Trend estimation with correction for covariance of log odds-ratios . glst logor dose, se(se) cov(N case) cc Check the variance-covariance matrix of log odds-ratios . mat list e(Sigma) Reference Greenland S. and M. P. Longnecker. 1992. Methods for trend estimation from summarized dose-reponse data, with applications to meta-analysis. American Journal of Epidemiology 135: 1301-1309.
Environmental Medicine, Karolinska Institutet, Sweden Rino Bellocco, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden Sander Greenland, Department of Epidemiology, UCLA School of Public Health
Support http://nicolaorsini.altervista.org nicola.orsini@ki.se Also see Article: Stata Journal, volume 9, number 2: st0096_2 Stata Journal, volume 9, number 1: st0096_1 Stata Journal, volume 6, number 1: st0096 Manual: [R] vwls Online: [R] vwls Yüklə 211,89 Kb. Dostları ilə paylaş:
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