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Jackson et al); hierarchical arm-based Bayesian modeling in WinBUGS (eg Zhao et al); hierarchical contrast-based (i.e. node-splitting) Bayesian modeling, either with WinBUGS or through gemtc and rjags in R … (eg White et al); network meta-analysis with lme and netmeta in R (eg Lumley, which is however limited to two-arm trials, or Rucker et al). …

My packages of choice would be R or Stata. References Dafni U. Landmark analysis at the 25-year landmark point. Circulation: Cardiovascular Quality and Outcomes 2011;4:363-71. Ying Z, Wei LJ. … Simon R, Makuch RW. A non-parametric graphical representation of the relationship between survival and the occurrence of an event: Application to responder versus non-responder bias. …

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 …

R; frequentist bivariate model using metamisc in R; frequentist bivariate model using metandi in Stata; frequentist copula mixed model using CopulaREMADA in R; frequentist hierarchical summary receiver … in R. …

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 …

My preference would be to conduct the analysis first using 1:1 propensity score matching, for instance using twang or MatchIt in R, or psmatch2 in Stata. … Then, confirm the main analysis without excluding any case by means of inverse probability of treatment weighting, for instance using twang in R, or meglm in Stata. …

In particular, I am giving below an example of the dataset and the possible analysis in R (disregard the overfitting, it's just to make an example, my actual dataset has at least 10,000 cases): dep <- …

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. …

I have been struggling with flat file databases and corresponding statistical packages for almost 20 years now (from Excel to SPSS, then Stata, and currently R). …

I am conducting a meta-analysis of diagnostic test accuracy studies comparing myocardial perfusion scintigraphy vs coronary angiography using the mada R package. …

I am working on an individual patient data meta-analysis, using a Cox proportional hazard model, with and without taking into account study identification, in Stata and 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( …

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 …

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. …