3

If you represent the wheezing status with a value that can be ordered (such as a number) whereby Yes is represented as the larger number, and order the data by age (or date), then the correct status is the cumulative maximum of these values. The errors occur where there are discrepancies between the recorded value and this cumulative maximum. For instance, ...


2

It's a good idea to apply your knowledge of the subject matter intelligently first, before you focus on statistical analysis issues. First, consider whether you really want to examine all mutations in all genes as predictors. Many genes are mutated only rarely. They are "passenger" mutations that don't really drive cancer but just happen to get ...


2

The biggest problem that you face with your approach is multiple comparisons. For the Wilcoxon tests you have 10 pairwise comparisons among the blood-sample types and 100 lipid types, for 1000 comparisons. Even if there are no real differences you would expect 50 "significant" differences at the usual p = 0.05 cutoff. For the Friedman tests you ...


1

If all you care about is the proportion of binary "true positives" estimated by 4 different methods, then you have a pretty simply logistic regression model.* The binary outcome for the regression would be success/failure, with a "true positive" being a success and all other results being failures. The 4 methods would be treated as 4 ...


1

It is very difficult to understand plant growth conditional on the plant not dying. I would opt for an unconditional analysis of an ordinal outcome Y where the lowest level of Y codes for death and all other levels are actual plant heights. That way you don't need to use a hard-to-interpret "missing" data imputation. Consider a longitudinal ...


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