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While difference-in-differences models have been proposed as a way to tease out policy impacts, in this case, data limitations limit interpretation to association rather than causality.
These limitations limit us from making any causal conclusion, and these limitations can be improved in future studies by utilizing a longitudinal design with multiple data sources.
As a consequence of these data limitations, current approaches are largely limited to applying default uncertainty factors to account for uncertainty associated with within-species variability (Stedeford et al. 2007).
Data limitations preclude quantitative, and limit accurate qualitative, estimation of the human risk for monkeypox in the United States (Table 3).
Data limitations have generally restricted cross-country comparative analysis, limiting the focus to national case studies.
Data limitations preclude assessing objective stressors and also result in imprecise estimates, limiting our ability to draw firm inferences.
Data limitations directly affect the outcome of statistical analyses and may at times limit the level of analyses that might be performed with a given data set.
Limitations: Clinical trial data have limited applicability to clinical practice.
There are also data limitations.
Both studies were hampered by data limitations.
Despite data limitations, certain generalizations are possible.
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