Meta-analysis of test accuracy studies using imputation for partial reporting of multiple thresholds.

ABSTRACT:

Introduction

For tests reporting continuous results, primary studies usually provide test performance at multiple but often different thresholds. This creates missing data when performing a meta-analysis at each threshold. A standard meta-analysis (no imputation [NI]) ignores such missing data. A single imputation (SI) approach was recently proposed to recover missing threshold results. Here, we propose a new method that performs multiple imputation of the missing threshold results using discrete combinations (MIDC).

Methods

The new MIDC method imputes missing threshold results by randomly selecting from the set of all possible discrete combinations which lie between the results for 2 known bounding thresholds. Imputed and observed results are then synthesised at each thresh

SUBMITTER: Ensor J 

PROVIDER: S-EPMC5873416 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature