Trial Sequential Boundaries for Cumulative Meta-Analyses

Branko Miladinović · Iztok Hozo · Benjamin Djulbegović

76 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 Probability and Statistical Research Statistical Methods in Clinical Trials

Abstract

We present a new command, metacumbounds, for the estimation of trial sequential monitoring boundaries in cumulative meta-analyses. The approach is based on the Lan–DeMets method for estimating group sequential boundaries in individual randomized controlled trials by using the package ldbounds in R statistical software. Through Stata's metan command, metacumbounds plots the Lan–DeMets bounds, z-values, and p-values obtained from both fixed and random-effects cumulative meta-analyses. The analysis can be performed with count data or on the hazard scale for time-to-event data.

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 — 76 citations exist upstream.

References

2007Practical methods for incorporating summary time-to-event data into meta-analysis 2011Cochrane Database of Systematic Reviews 2007Trial sequential analysis may establish when firm evidence is reached in cumulative meta-analysis 2002The Cochrane Database of Systematic Reviews 2008Trial sequential analysis reveals insufficient information size and potentially false positive results in many meta-analyses 2008Metan: Fixed- and Random-Effects Meta-Analysis 1998Overcoming the limitations of current meta-analysis of randomised controlled trials 1997Cumulating evidence from randomized trials: Utilizing sequential monitoring boundaries for cumulative meta-analysis

Cited by

2020Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention 2013Optimal information size in trial sequential analysis of time-to-event outcomes reveals potentially inconclusive results because of the risk of random error 2012Trial sequential analysis may be insufficient to draw firm conclusions regarding statistically significant treatment differences using observed intervention effects: A case study of meta-analyses of multiple myeloma trials

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.