emagnification: A tool for estimating effect-size magnification and performing design calculations in epidemiological studies

Abstract

Artificial effect-size magnification (ESM) may occur in underpowered studies, where effects are reported only because they or their associated p-values have passed some threshold. Ioannidis (2008, Epidemiology 19: 640–648) and Gelman and Carlin (2014, Perspectives on Psychological Science 9: 641–651) have suggested that the plausibility of findings for a specific study can be evaluated by computation of ESM, which requires statistical simulation. In this article, we present a new command called emagnification that allows straightforward implementation of such simulations in Stata. The commands automate these simulations for epidemiological studies and enable the user to assess ESM routinely for published studies using user-selected, study-specific inputs that are commonly reported in published literature. The intention of the command is to allow a wider community to use ESMs as a tool for evaluating the reliability of reported effect sizes and to put an observed statistically significant effect size into a fuller context with respect to potential implications for study conclusions.

An update of clinical value of circulating tumor DNA in esophageal cancer: a systematic review and meta-analysis

“…The following information was collected: first author, year of publication, region, characteristics of the study population (number, sex and age), sequencing platform, detection gene, ctDNA-positive rate, treatment therapy, follow-up period, survival data and its associated standard errors on prognostic outcomes. If the hazard ratio (HR) and their 95% CI were not directly provided in the original articles, the extracted survival information and the published risk table were used to reconstruct the survival curve for each included study using the method of David [ 23 ]. The extraction of information was repeated if two reviewers can’t achieve consensus.…”

Adjustment for sparse data bias in odds ratios: Significance to appraisal of risk of diabetes due to occupational trichlorfon insecticide exposure

“…As a first step in any evaluation of a 2-by-2 epidemiological table, an ESM analysis can be useful to determine the extent to which OR for “discovered” associations may be inflated due to low power. The concepts behind ESM analyses are more thoroughly explained elsewhere [ 13 , [24] , [25] , [26] , [27] , [28] , [29] ]. Briefly, the analysis begins by assuming a true OR for an association and estimates the proportion of exposed individuals among n 0 in a non-diseased group (P o ).…”

Comparison of neoadjuvant immunotherapy versus routine neoadjuvant therapy for patients with locally advanced esophageal cancer: A systematic review and meta-analysis

“…The following information was collected: first author, year of publication, region, characteristics of the study population (number, sex and age), TNM stage, treatment therapy, adverse events of neoadjuvant therapies, postoperative complications, and pathological response. If the HR and its 95% CI were not directly provided in the original articles, the extracted survival information and the published risk table were used to reconstruct the survival curve for each included study using the method of David ( 20 ). The extraction of information was repeated if there were apparent discrepancies.…”