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Published on: October 6, 2023
Attrition rate in infant fNIRS research: A meta-analysis
Sori Baek1, Sabrina Marques1, Kennedy Casey1
1Psychology Department, Princeton University, Princeton, New Jersey, USA.
Insights
Infant functional near-infrared spectroscopy (fNIRS) research sees high attrition rates (34.23%). Modifying study designs and recruitment can reduce data loss in infant neuroscience research.
Area of Science:
- Developmental cognitive neuroscience
- Neuroimaging techniques
- Infant research methodologies
Background:
- Attrition, the exclusion of data, impacts research planning, data integrity, and generalizability in infant neuroscience.
- High attrition rates in infant functional near-infrared spectroscopy (fNIRS) research necessitate investigation into trends and predictors.
- Understanding attrition is crucial for enhancing the rigor and representativeness of developmental cognitive neuroscience studies.
Approach:
- A pre-registered meta-analysis synthesized data from 182 publications on infant fNIRS research (1998-2020).
- Examined 272 experiments to determine average attrition rates and identify drivers of data exclusion.
- Analyzed 136 experiments reporting specific reasons for attrition, categorizing them as infant-driven or signal-driven.
Key Points:
- The average attrition rate in infant fNIRS studies was 34.23%.
- Infant-driven factors accounted for 21.50% of attrition, while signal-driven factors accounted for 14.21%.
- Subject characteristics (e.g., age) and study design elements (e.g., cap configuration, design type, stimulus) significantly predicted attrition.
Conclusions:
- Modifications to recruitment strategies and study designs can substantially decrease attrition rates in infant fNIRS research.
- Established guidelines for reporting attrition rates to promote scientific transparency.
- Recommendations are provided to minimize attrition, aiding developmental cognitive neuroscientists in conducting more robust and generalizable research.
Abstract:
Understanding the trends and predictors of attrition rate, or the proportion of collected data that is excluded from the final analyses, is important for accurate research planning, assessing data integrity, and ensuring generalizability. In this pre-registered meta-analysis, we reviewed 182 publications in infant (0-24 months) functional near-infrared spectroscopy (fNIRS) research published from 1998 to April 9, 2020, and investigated the trends and predictors of attrition. The average attrition rate was 34.23% among 272 experiments across all 182 publications. Among a subset of 136 experiments that reported the specific reasons for subject exclusion, 21.50% of the attrition was infant-driven, while 14.21% was signal-driven. Subject characteristics (e.g., age) and study design (e.g., fNIRS cap configuration, block/trial design, and stimulus type) predicted the total and subject-driven attrition rates, suggesting that modifying the recruitment pool or the study design can meaningfully reduce the attrition rate in infant fNIRS research. Based on the findings, we established guidelines for reporting the attrition rate for scientific transparency and made recommendations to minimize the attrition rates. This research can facilitate developmental cognitive neuroscientists in their quest toward increasingly rigorous and representative research.
