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There are already some overlapping Q&A in CV that you might wish to look at: Propensity Score Matching in R with Multiple Treatments Software that matches 6 groups by propensity score? … Comparing two or more treatments with inverse probablity of treatment weighting My advice would be to use the twang R package. …

Egger's), which is easily performed in R with the funnel command and with the regtest command. …

I recommend you to simply follow Rubin's rule, as spelled out in page 4 of this paper by Dong and Peng, SpringerPlus 2013: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701793/pdf/40064_2013_Article_ …

Anyway, there are many suitable packages in R, just look at the CRAN Task View on Clinical Trials. For instance, you can peruse the following pacakges: pwr, samplesize, and TrialSize. …

I recommend to perform multiple imputation (eg with mice in R), and then use a mixed model or generalizing estimating equations, explicitly recognizing the clustering features. …

In addition, there is now the possibility to compute the Mantel-Byar test and accompanying Simon-Makuch plot in R, using the Rcmdr and the RcmdrPlugin.EZR packages. … See the related SO post containing a detailed R code: https://stackoverflow.com/questions/40431559/mantel-byar-test-and-simon-makuch-plot-for-survival-analysis-with-time-dependent …

As I previously stated, instead of doing propensity matching it can be reasonable to use inverse probability of treatment weighting after missing data imputation. Suitable Stata examples follow: cle …

You can indeed use the mada R package, by means of the madauni command for univariate meta-analysis and the SummaryPts command for bivariate meta-analysis (the latter being recommended for instance by …

I am conducting an individual patient level data meta-analysis using the survey package in R (I tried Stata 13 but I get stuck with an error). … As per meta-analytic practice, I would prefer to conduct both fixed and random effect analyses, but it appears no such option is available with the survey packages in either Stata or R. …

In my opinion you can perform a meta-analysis for almost everything. The key point is to be able to refrain from using its results inappropriately. Accordingly, you can pool variances, for sure, but w …

On top of mvmeta in R or Stata, and gemtc in R, you can also use netmeta in R. …

There is a detailed publication on this topic from Greco et al, How to impute study-specific standard deviations in meta-analyses of skewed continuous endpoints? World Journal of Meta-Analysis 2015;3( …

outcomes, some non-fatal which occur earlier and some fatal occurring later, I have stumbled upon mediation analysis (eg Zhang et al, Ann Transl Med 2016), which is already available in the mediation R …

I think your point is correct, in the sense that nominal significance is met (p=0.023) for the false positive rate (FPR), which appears positively associated with self-administered questionnaires (SAQ …

Once you have for each study the point estimate and the standard error, it is easy to combine them with a statistical package (eg metan in Stata, meta or metafor in R). … Note indeed that the R meta package offers the metaprop command which will directly suit you, as clarified by this illustrative code: library(meta) studyid <- c(1:10) events <- sample(5:20, 10, replace …