Related Experiment Video
Updated: Dec 30, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Adjudication rather than experience of data abstraction matters more in reducing errors in abstracting data in
Jian-Yu E1, Ian J Saldanha2, Joseph Canner3
1Center for Clinical Trials and Evidence Synthesis, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland.
Background:
During systematic reviews, "data abstraction" refers to the process of collecting data from reports of studies. The data abstractors' level of experience may affect the accuracy of data abstracted. Using data from a randomized crossover trial in which different data abstraction approaches were compared, we examined the association between abstractors' level of experience and accuracy of data abstraction.
Methods:
We classified abstractors as "more experienced" if they had authored three or more published systematic reviews, and "less experienced" otherwise. Each abstractor abstracted data related to study design, baseline characteristics, and outcomes/results from six articles. We considered two types of errors: incorrect abstraction and errors of omission. We estimated the proportion of errors by level of experience using a binomial generalized linear mixed model.
Results:
We used data from 25 less experienced and 25 more experienced data abstractors. Overall error proportions were similar for less experienced abstractors (21%) and more experienced abstractors (19%). Compared with less experienced abstractors, more experienced abstractors had a lower odds of errors for data items related to outcomes/results (adjusted odds ratio [OR] = 0.53; 95% CI, 0.34-0.82) and potentially for data items related to study design (adjusted OR = 0.83; 95% CI, 0.64-1.09) but a potentially higher odds of errors for items related to baseline characteristics (adjusted OR = 1.42; 95% CI, 0.97-2.06).
Conclusion:
Experience of data abstraction matters little. Errors are reduced by adjudication but still remain high for data items related to outcomes/results.
More Related Videos
Related Concept Videos
Systematic Error: Methodological and Sampling Errors
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Bias in Epidemiological Studies
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Data Validation
Nursing assessment guides are generally based on holistic models rather than medical...
Data Validation
Key parameters for method validation include:

