{smcl} {* 18sep2018 Orsini N}{...} {hline} {title:Title} {p2colset 6 20 20 2}{...} {p2col :{hi: drmeta_gof} {hline 2}}Goodness-of-fit after dose-response meta-analysis{p_end} {p2colreset}{...} {title:Syntax for predict} {p 8 16 2} {cmd:drmeta_gof} [ {cmd:,} {opt r2s} {opt opvd:plot(dosevar, [xb|xbs|fitted])} {opt drvd:plot(dosevar)} {opt dovp:plot} {it:{help twoway_options:twoway_options}} ] {title:Description} {pstd} The {cmd:drmeta_gof} command provides tools (deviance test, R-squared) to evaluate the goodness-of-fit in dose-response meta-analysis. It is a post-estimation tool of the {helpb drmeta:drmeta} command.{p_end} {title:Options} {phang} {opt r2s} shows study-specific coefficient of determination (R-squared). {phang} {opt opvd:plot(dosevar, [xb|xbs|fitted])} plots the observed and specified predicted values vs the specified dose. The default is to use study-specific predictions using generalized least squares (xbs). See {helpb drmeta_predict:drmeta_predict}. {phang} {opt drvd:plot(dosevar)} plots the decorrelated residuals vs the specified dose. {phang} {opt dovp:plot} plots decorrelated observed contrasts vs predicted contrasts. {title:Examples} * Read data from 7 simulated studies with a common underlying linear dose-response relationship. * The true value of the slope is 0.1 (RR=1.11). {stata "use http://www.stats4life.se/data/table1sim.dta, clear"} * One-stage random-effects dose-response model assuming a linear trend {stata "drmeta logor dose , se(selogor) data(n case) id(id) type(study) reml"} {stata "drmeta_gof"} {stata "drmeta_gof, r2s"} {stata "drmeta_gof, opvd(dose)"} {stata "drmeta_gof, opvd(dose, fitted)"} {stata "drmeta_gof, dovp"} * One-stage random-effects dose-response model using restricted cubic splines {stata "mkspline doses = dose, nk(3) cubic"} {stata "drmeta logor doses1 doses2 , se(selogor) data(n case) id(id) type(study) reml"} {stata "drmeta_gof, r2s"} {stata "drmeta_gof, drvd(dose)"} {stata "drmeta_gof, opvd(dose)"} {stata "drmeta_gof, dovp"} {title:Reference} {p 4 8 2}Discacciati A, Crippa A, Orsini N. Goodness of fit tools for dose-response meta-analysis of binary outcomes. {it:Research Synthesis Methods}. 2017 Jun;8(2):149-160.{p_end} {title:Author} {p 4 8 2}Nicola Orsini, Biostatistics Team, Department of Public Health Sciences, Karolinska Institutet, Sweden{p_end} {title:Support} {p 4 8 2}{browse "http://www.stats4life.se"}{p_end} {p 4 8 2}{browse "mailto:nicola.orsini@ki.se?subject=drmeta_gof":nicola.orsini@ki.se}{p_end} {p 7 14 2}Help: {helpb drmeta}{p_end}