help remr

Title

remr -- Robust error meta-regression method for dose–response meta-analysis

Syntax

remr varlist [if] [in], id(varname) category(varname) [options]

varlist should contain either three or four variables in the form of:

Three variables:

ES seES dose effect size, standard error, and dose

Ratio based effect estimates can be entered in this format after log transform.

Four variables:

cases no_cases total dose cell counts, total of cell counts, and dose from a contingency table row

Results will be presented with log transform for ratio based effect estimates unless eform is specified.

In addition to varlist, id and category variables need to be specified.

- id(varname) specifies the variable for study-specific indicator.

- category(varname) specifies in ascending order (0, 1, 2 to k) the dose levels within a study. The lowest category level within a study has to be 0.
This is required in lieu of the dose to enable adequate handling of the same doses repeated within study and missing data.

Description

remr performs dose-response meta-analysis using inverse variance weighted least squares (WLS) regression with cluster robust error variances.
This approach is a special case of the one-stage generalised least squares for trend approach where the covariance need not be imputed from the data.
This method allows the model to include a non-zero intercept to absorb any bias that is not accounted for by the terms in the model but notably the intercept now becomes a coefficient rather than a constant given the use of WLS.
Weights for the reference doses are imputed, these are equal to the maximum of the within study weights based on the inverse of the variance of the effect sizes in the study.
This minimises the possible deviation of the regression intercept from the origin without having to force the regression model through the origin.
This method does not require knowledge of the correlation structure of the data within a study,
because it stacks included effects as a cluster by study and uses the cluster-robust analysis to obtain a robust standard error,
thus treating observations as independent across clusters but correlated within each cluster.

Modules required

Users need to install xblc

Options

center(#) specifies that data should be centered. The center starting value to be used for the data is defined by #.

startdose(#1 #2) is used to make starting doses comparable across studies. It will drop studies with starting values outside the interval #1 to #2.
It is not recommended to use a wide range to maintain homogeneity. The range #1 - #2 must contain the # in center(#). Requires center(#).

rcs(#) specifies the number of knots to be used for the restricted cubic spline. The minimum number of knots is 3. Knot values are automatically placed; see help mkspline.

knots(numlist) specifies the exact location and number of the knots to be used for a restricted cubic spline. The values of these knots must be entered in ascending order.

reference(#) specifies the reference dose. When specified the graph and table are recomputed to display differences in predictions from the specified reference value. Requires center(#).

or specifies the effect size of interest as odds ratio when data is entered using the four variables syntax (default).

rr specifies the effect size of interest as risk ratio when data is entered using the four variables syntax.

rd specifies the effect size of interest as risk difference when data is entered using the four variables syntax.

eform reports the exponential form of predictions. Must be used with the four variable input for ratio based estimates of effect if results on the natural scale are required.

table provides the regression model output and values of doses.

nograph suppresses the plot of point and interval estimates of the predictions against the doses.

bplot plots the point and interval estimates against the doses and the study data points (bubble plot). bplot cannot be used when a reference(#) has been specified.

Saved results

remr stores the following in r()

Matrices

r(output) coordinates of point estimates and doses

r(model) output table of the regression model

r(doses) output table of the doses

Examples

The example is taken from Qin Liu et al. Comput Stat Data Anal 2009; 53, 4157–4167. https://doi.org/10.1016/j.csda.2009.05.001

use http://nicolaorsini.altervista.org/data/bmi_rc, clear

Generate the category variable

sort id bmi

bysort id: gen cat = _n-1

Three variables entry - restricted cubic spline with three knots, center at 20, include studies with start doses between 18 to 22

remr logor selogor bmi, id(id) cat(cat) rcs(3) center(20) start(18 22) eform

Four variables entry - restricted cubic spline with four knots at 19 24 27 33, center at 20, include studies with start doses between 18 to 22, and bubble plot

remr case control n bmi, id(id) cat(cat) knots(19 24 27 33) center(20) start(18 22) bplot eform

Testing for linearity (syntax is test __doses2 … __doses(k-1) where k is the number of knots

test __doses2 __doses3

Four variables entry - linear dose response analysis

remr case control n bmi, id(id) cat(cat) center(20) start(18 22) bplot eform

Authors

Luis Furuya-Kanamori, UQ Centre for Clinical Research, The University of Queensland, Australia

l.furuya@uq.edu.au

Chang Xu, Department of Population Medicine, College of Medicine, Qatar University, Qatar

Suhail AR Doi, Department of Population Medicine, College of Medicine, Qatar University, Qatar

Reference

Xu C, Doi SAR. 2018. The robust error meta-regression method for dose–response meta-analysis. Int J Evid Based Healthc 16:138-144

Funding

This work was supported by Program Grant #NPRP10-0129-170274 from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work and are solely the responsibility of the authors.

LFK was supported by an Australian National Health and Medical Research Council Fellowship (APP1158469).