\title{studies on low dosage tricyclic antidepressants for the treatment of depression}
\description{results on depression severity from 17 studies comparing low dosage tricyclic antidepressants (tca) and placebo for the treatment of depression.}
\bold{author}  \tab \code{character} \tab first author with information on dosage in parentheses \cr
\bold{ne}      \tab \code{integer}   \tab number of patients in low tca group \cr
\bold{me}      \tab \code{numeric}   \tab depression severity (low tca) \cr
\bold{se}      \tab \code{numeric}   \tab standard deviation (low tca) \cr
\bold{nc}      \tab \code{integer}   \tab number of patients in placebo group \cr
\bold{mc}      \tab \code{numeric}   \tab depression severity (placebo) \cr
\bold{sc}      \tab \code{numeric}   \tab standard deviation (placebo) \cr
\bold{measure} \tab \code{character} \tab depression scale
furukawa et al. (2003) carried out a systematic review comparing low dosage tricyclic antidepressants (tca) with placebo for the treatment of depression. they reported the effect on presence/absence of depression and on depression severity at various time points. here we focus on depression severity at four weeks. most studies used some version of the hamilton depression rating scale, however, some studies used the montgomery-asberg depression rating scale. accordingly, it is not possible to pool the estimated effects directly.
furukawa, t. a., mcguire, h., & barbui, c. (2003). low dosage tricyclic antidepressants for depression. \emph{cochrane database of systematic reviews}, \bold{3}, cd003197. \verb{https://doi.org/10.1002/14651858.cd003197}
schwarzer, g., carpenter, j. r., & \enc{rücker}{ruecker}, g. (2015). \emph{meta-analysis with r}. cham, switzerland: springer.
oldset <- settings.meta("revman5", digits = 2)
### conduct random effects meta-analysis with hedges' g as effect measure
mc2 <- metacont(ne, me, se, nc, mc, sc, common = false,
psychology, psychiatry, standardized mean differences

\title{studies on the effectiveness of self-management education and regular medical review for adults with asthma}
\description{results from 15 trials examining the effectiveness of self-management education and regular medical review for adults with asthma.}
\bold{author}   \tab \code{character} \tab first author of study \cr
\bold{n1i}      \tab \code{numeric}   \tab number of participants in the intervention group \cr
\bold{m1i}      \tab \code{numeric}   \tab mean number of days off work/school in the intervention group \cr
\bold{sd1i}     \tab \code{numeric}   \tab standard deviation of the number of days off work/school in the intervention group \cr
\bold{n2i}      \tab \code{numeric}   \tab number of participants in the control/comparison group \cr
\bold{m2i}      \tab \code{numeric}   \tab mean number of days off work/school in the control/comparison group \cr
\bold{sd2i}     \tab \code{numeric}   \tab standard deviation of the number of days off work/school in the control/comparison group \cr
\bold{ai}       \tab \code{numeric}   \tab number of participants who had one or more days off work/school in the intervention group \cr
\bold{bi}       \tab \code{numeric}   \tab number of participants who no days off work/school in the intervention group \cr
\bold{ci}       \tab \code{numeric}   \tab number of participants who had one or more days off work/school in the control/comparison group \cr
\bold{di}       \tab \code{numeric}   \tab number of participants who no days off work/school in the control/comparison group \cr
\bold{type}     \tab \code{numeric}   \tab numeric code for the intervention type (see \squote{details})
asthma management guidelines typically recommend for patients to receive education and regular medical review. while self-management programs have been shown to increase patient knowledge, it is less clear to what extent they actually impact health outcomes. the systematic review by gibson et al. (2002) examined the effectiveness of self-management education and regular medical review for adults with asthma. in each study, participants receiving a certain management intervention were compared against those in a control/comparison group with respect to a variety of health outcomes. one of the outcomes examined in a number of studies was the number of days off work/school.
the majority of studies reporting this outcome provided means and standard deviations allowing a meta-analysis of standardized mean differences. seven studies also reported the number of participants who had one or more days off work/school in each group. these studies could be meta-analyzed using, for example, (log) risk ratios. finally, one could also consider a combined analysis based on standardized mean differences computed from the means and standard deviations where available and using probit transformed risk differences (which also provide estimates of the standardized mean difference) for the remaining studies.
some degree of patient education was provided in all studies. in addition, the \code{type} variable indicates what additional intervention components were included in each study:
\item optimal self-management (writing action plan, self-monitoring, regular medical review),
\item self-monitoring and regular medical review,
gibson, p. g., powell, h., wilson, a., abramson, m. j., haywood, p., bauman, a., hensley, m. j., walters, e. h., & roberts, j. j. l. (2002). self-management education and regular practitioner review for adults with asthma. \emph{cochrane database of systematic reviews}, \bold{3}, cd001117. \verb{https://doi.org/10.1002/14651858.cd001117}
dat <- escalc(measure="smd", m1i=m1i, sd1i=sd1i, n1i=n1i, m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat)
### fit an equal-effects model to the standardized mean differences (as in gibson et al., 2002)
res <- rma(yi, vi, data=dat, method="ee")
### compute log risk ratios and corresponding sampling variances
dat <- escalc(measure="rr", ai=ai, bi=bi, ci=ci, di=di, data=dat)
### fit an equal-effects model to the log risk ratios
### note: gibson et al. (2002) used the mantel-haenszel method for their analysis
rma.mh(measure="rr", ai=ai, bi=bi, ci=ci, di=di, data=dat, digits=2)
### compute standardized mean differences where possible and otherwise probit transformed
### risk differences (which also provide estimates of the standardized mean differences)
dat <- escalc(measure="smd", m1i=m1i, sd1i=sd1i, n1i=n1i,
m2i=m2i, sd2i=sd2i, n2i=n2i, data=dat, add.measure=true)
dat <- escalc(measure="pbit", ai=ai, bi=bi, ci=ci, di=di, data=dat, replace=false, add.measure=true)
### fit a random-effects model to these estimates
### meta-regression model examining if there are systematic differences based on the
### type of measure used (there are only 2 studies where measure="pbit", so this isn't
### very conclusive here, but shown for illustration purposes)
res <- rma(yi, vi, mods = ~ measure, data=dat)
medicine, primary care, risk ratios, standardized mean differences