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Third, there were important baseline differences between the HIV-positive and HIV-negative women in our study, a potential source of selection bias, which cannot be fully adjusted for in the analyses.
However, it may remain a substantial selection bias, which cannot be fully eliminated even by using a multivariate analysis.
A possible software error would result in a constant bias, which cannot be excluded in this study.
Second, the low number of included studies limited our assessment of a potential publication bias which cannot be excluded due to unpublished negative research results.
This could have resulted in a certain reading bias, which cannot be avoided, however, and is a limitation for all intra-individual magnetic field strength comparisons.
One possible explanation is the fact that GPI were more frequently used in high-risk scenarios and it may remain a substantial selection bias, which cannot be covered by the routine parameters in the multivariate analysis.
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However, the positioning accuracies are still limited by other factors including the satellite geometry and ambiguity fractional biases which cannot be corrected by the local troposphere model.
Of course, there is always the possibility of a placebo effect or an observer bias component, which cannot be ruled out in an open trial.
However, some caution should be applied to the interpretation of the mapping results, since bias may remain which cannot be overcome by the analysis of the data.
Third, our results might be biased due to the confounding variables which cannot be controlled by fixed effects: we cannot completely control the bias arising from the change in the population or population structure which is associated with subsidy policy.
However, the results are biased because of the incomplete observation of patient survival, which cannot be properly accounted for with the multivariable regression model.
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CEO of Professional Science Editing for Scientists @ prosciediting.com