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Different combinations of cases and controls result in seven study designs.
These four problems, each with two objectives, represent all combinations of cases in which there are (1) conflicting or concordant objectives and (2) two pattern objectives or one pattern objective and one non-pattern objective, representing objectives with similar or different degrees of difficulty.
Four combinations of cases and controls were analyzed: PLCO cases vs. PLCO controls (PLCOca-PLCOco), NHS cases vs. NHS controls (NHSca-NHSco), PLCO cases vs. NHS controls (PLCOca-NHSco), and NHS cases vs. PLCO controls (NHSca-PLCOco).
For each of the four combinations of cases and controls, assuming an additive genetic model we performed a 1-df Wald test (without adjusting for PS) using the standard logistic regression for each testing autosomal SNP.
Although we see evidence of at least three axes with large genetic variations in each of the four combinations of cases and controls considered here, confounding by PS would occur only when cases and controls distribute differently along one or more main axes of variations.
We used the C statistic (the proportion of all possible pairwise combinations of cases and controls in which the case has a higher predicted probability of failure than the control) to assess predictive ability.
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We first calculated the difference in day of symptom onset for all combinations of case pairs.
This is a fairly simple system of notation used to summarise the combinations of case features.
Step 6. Use the software to consider all missing combinations of case conditions.
That is, the QCA software automatically generates all the possible combinations of case conditions for the outcomes of interest as if every single possible missing case existed.
In this step, all the possible combinations of case conditions (all possible missing cases) are considered and added to the observed dataset.
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