A Short Guide and a Forest Plot Command (Ipdforest) for One-Stage Meta-Analysis

Evangelos Kontopantelis · David Reeves

42 citations0/0 files storedThe Stata Journal Promoting communications on statistics and Stata

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Meta-analysis and systematic reviews Statistical Methods and Bayesian Inference Statistical Methods in Epidemiology

Abstract

In this article, we describe a new individual patient data meta-analysis postestimation command, ipdforest. The command produces a forest plot following a one-stage meta-analysis with xtmixed or xtmelogit. (These commands have been renamed in Stata 13 to mixed and meqrlogit, respectively; ipdforest is currently not compatible with the new names.) The overall effect is obtained from the preceding mixed-effects regression and the study effects from linear or logistic regressions on each study, which are executed within ipdforest. Individual patient data meta-analysis models with Stata are discussed.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 42 citations exist upstream.

References

2003Measuring inconsistency in meta-analyses 2008Metan: Fixed- and Random-Effects Meta-Analysis 2000Fixed Effects vs. Random Effects Meta‐Analysis Models: Implications for Cumulative Research Knowledge 2002Meta‐Analysis Of Controlled Clinical Trials 1998Detecting and describing heterogeneity in meta-analysis 2000A multilevel model framework for meta-analysis of clinical trials with binary outcomes 2010Metaan: Random-effects Meta-analysis 2006A simulation study comparing properties of heterogeneity measures in meta‐analyses

Cited by

2016Association Between Chronic Physical Conditions and the Effectiveness of Collaborative Care for Depression 2017Deriving percentage study weights in multi-parameter meta-analysis models: with application to meta-regression, network meta-analysis and one-stage individual participant data models 2017Guidance for deriving and presenting percentage study weights in meta‐analysis of test accuracy studies

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.