Methodological overview demonstrates the utility of differential weighting across observations, highlighting how adjusting precision improves meta-analyses and clustered models.
Statistical analysis usually treats all observations as equally important. In some circumstances, however, it is appropriate to vary the weight given to different observations. Well known examples are in meta-analysis, where the inverse variance (precision) weight given to each contributing study varies, and in the analysis of clustered data.
No takes yet. Share an insight, caveat, or question.
Mansournia et al. (2016) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: