Between-litter variation in developmental studies of hormones and behavior: Inflated false positives and diminished power

摘要

Highlights ? The natural variation in maternal care and prenatal stress literatures neglect statistical dependencies among littermates. ? Commonly used statistical methods may have seriously inflated false positive findings in the literature. ? Alternative statistical approaches maintain nominal type I error rates, while optimizing power. ? Implications for the replicability of research findings in the field of behavioral neuroendocrinology are discussed. Abstract Developmental studies of hormones and behavior often include littermates—rodent siblings that share early-life experiences and genes. Due to between-litter variation (i.e., litter effects), the statistical assumption of independent observations is untenable. In two literatures—natural variation in maternal care and prenatal stress—entire litters are categorized based on maternal behavior or experimental condition. Here, we (1) review both literatures; (2) simulate false positive rates for commonly used statistical methods in each literature; and (3) characterize small sample performance of multilevel models (MLM) and generalized estimating equations (GEE). We found that the assumption of independence was routinely violated (>85%), false positives (α=0.05) exceeded nominal levels (up to 0.70), and power (1?β) rarely surpassed 0.80 (even for optimistic sample and effect sizes). Additionally, we show that MLMs and GEEs have adequate performance for common research designs. We discuss implications for the extant literature, the field of behavioral neuroendocrinology, and provide recommendations.