Rethinking time-lag bias in meta-analysis: Broader definitions and conceptual frameworks

Meta-analysis is often viewed as a gold standard for synthesizing available evidence, but it faces challenges as evidence becomes available gradually and studies may be disseminated selectively and at different times. When publication timing is systematically related to study results, meta-analytic conclusions at any given time can be biased; for example, studies with statistically significant or larger effects may be published earlier than those with null or smaller effects. This phenomenon, commonly referred to as time-lag bias, is a key reason why early meta-analytic conclusions may later weaken, reverse, or become more heterogeneous as additional evidence accrues. To address the limitations of existing definitions and detection approaches, which are largely tied to monotone, effect-size-driven dissemination, we broaden the concept of time-lag bias beyond the traditional yet simple notion that "positive" studies are published earlier. Specifically, we view time-lag bias more generally as a manifestation of time-dependent evidence availability, whereby the timing of study dissemination can distort meta-analytic conclusions at different stages of evidence accrual. We illustrate this broader perspective through several real-world case studies, including classical time-lag bias, inverse time-lag bias, non-monotone temporal patterns, and anomalous evidence localized within specific time windows. These examples show that time-lag bias can arise through multiple dissemination mechanisms and may exaggerate, attenuate, or even reverse pooled estimates over time. They highlight the importance of explicitly accounting for the temporal dimension of evidence accumulation and motivate the routine use of diagnostic and sensitivity strategies to assess robustness to selective dissemination over time.