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Too variable.
Inclusion of overly specific temporal variables (e.g., wind direction) for each neighborhood may have contributed to poor direct transfer of the models.
Our data are consistent with literature suggesting that steps and time required to develop and activate multicentre clinical trials are too extensive and the development time is overly long and highly variable [11], [12].
The major drawbacks are: 1) age cannot be included in the model as a continuous variable without overly stratifying the datasets and 2) large sample sizes are needed for each susceptibility combination to ensure stability of the model.
Nonetheless, our findings are not overly surprising given the variable evidence in the literature regarding the differential effects of sex on the responsivity to a substance following exposure in a social situation.
So that outlying values were not overly influential, these three variables were categorised for analysis.
However, response-shift literature warns us not to be overly optimistic, as no variables have been identified that consistently moderate different degrees of response-shift [ 5].
This improved adjustment may still be overly cautious because the variables under test are, to some extent, repeated each time – they are measures of things that are likely to be in approximately the same place in different years.
We chose a significance level of 0.01 (two tailed) as we were considering several variables as potential risk factors in a large dataset and wanted to reduce the risk of having an overly complex model including variables with limited prognostic value.
However, this assumption can be overly restrictive as proportions of variable sites may vary in different lineages [ 22, 54].
Model selection bias often derives from the selection of overly complex models, which include irrelevant variables.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com