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As such, they were treated as continuous variables to conduct the tests for normality.
This novel formulation requires less binary variables and continuous variables to formulate a problem, resulting in lower computation times.
Cutoff values were chosen for all continuous variables to simplify predictors, making them easier to enter into models utilized in a busy clinical setting (Table 2).
Multiple linear regression models were used in case of continuous variables to adjust for potential confounding factors such as asexual parasite and gametocyte density at enrolment.
If δτ>δt (no discrete reaction has taken place), set t = t+δt, and update the continuous variables to the values appropriate for this new time.
We performed bivariate analyses using the chi-square test for nominal categories and the t-test for continuous variables to narrow the list of potential predictor variables.
In the final analysis, we created dummy variables for the three continuous variables to compare the results to those from linear and restricted cubic splines PLS regression.
For both cases, we update the continuous variables to the values for the new time by using a numerical solution algorithm for ordinary differential equations (Euler, Runge-Kutta, etc).
We then converted the continuous variables to categorical variables.
Median values were used to convert continuous variables to categorical variables.
Independent sample t-tests were used with continuous variables to compare the mean scores between groups.
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