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This is more of a statistical finding and points to the fact that the nature of variables (continuous vs. categorical) will affect the sensitivity of analysis.
Variables measured over time (continuous and categorical) will be entered into survival models as time-dependent covariates as these measurements will be repeatedly obtained from individual participants.
In these models the gene and environment variables (both categorical) will be used in the model as predictor variables in addition to a variable that represents the interaction between the gene and environment variables.
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Categorical variables will be presented as number and percentage; χ-test will be used to compare groups of categorical variables.
So, for example, categorical variables will be modelled using logistic regression and continuous variables will be modelled using linear regression.
Secondary outcome measures of categorical data will be analysed with χ tests and continuous data will be analysed with t-tests (for normally distributed data).
Measures of categorical data will be analysed with chi-squared tests and continuous data will be analysed with t-tests.
Categorical variables will be summarized by frequencies and proportions; and continuous variables will be summarized by means and standard deviations.
Categorical data will be presented as frequency and percentage, and continuous variables will be summarised using descriptive statistics (mean, SD, median, 1st and 3rd quartiles, minimum and maximum).
To avoid instability in the regression analyses, frequency distributions for categorical predictors will be examined and categories with small numbers of respondents will be combined.
Categorical outcomes will be described through raw frequencies or percentages, and continuous outcomes will be described through mean scores and standard deviations.
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