Introduction to the Elja package

Determine the type of model you want to use

According to the class of the outcome, one model will be preferred to another. It is therefore necessary to choose the right model for the type of variable chosen as the health event.

We have seen previously that our health event is binary categorical: Diabetes (Yes/No).

str(PimaIndiansDiabetes$diabetes)
#>  Factor w/ 2 levels "neg","pos": 2 1 2 1 2 1 2 1 2 2 ...

We can therefore use a logistic regression model.

Use of the ELJAlogistic function

The approach for the logistic regression is similar for the models linear models with ELJAlinear function and for Generalized Linear Models with ELJAglm function.

The dataset being prepared and the type of model chosen, we can proceed to the analysis.

To do so, the following information are needed:

• var: Outcome / health event; it must be categorical for a logistic regression model

• data: Dataframe that contains the outcome and all the exposures to be tested

Other information can be added to the output of the function:

• manplot: Indicates if it is desired to display a Manhattan plot of the results in the output of the function

• nbvalmanplot: Indicates the number of values to display in the Manhattan plot (in order not to overload the graphs)

• Bonferroni: Indicates if we want to display the Bonferroni threshold on the Manhattan plot

• FDR : Indicates if you want to display the False Discovery Rate threshold according to the Benjamini-Hochberg method on the Manhattan plot

• manplotsign : Indicates if you want to display a Manhattan plot containing only significant only the significant values with a p-value > 0.05.

ELJAlogistic(var = 'diabetes',data = PimaIndiansDiabetes,manplot = TRUE,
             Bonferroni = TRUE,FDR = TRUE, nbvalmanplot = 30, manplotsign = FALSE)

results
#>                      level odd_ratio    ci_low  ci_high      p_value   n
#> pregnant_pregnant pregnant  1.147008 1.0970869 1.200315 2.147445e-09 768
#> glucose_glucose    glucose  1.038599 1.0321816 1.045439 2.378098e-31 768
#> pressure_pressure pressure  1.007452 0.9994922 1.015902 7.299362e-02 768
#> triceps_triceps    triceps  1.009911 1.0005344 1.019455 3.881576e-02 768
#> insulin_insulin    insulin  1.002301 1.0010311 1.003607 4.353455e-04 768
#> mass_mass             mass  1.098044 1.0730012 1.124942 8.449577e-15 768
#> pedigree_pedigree pedigree  2.953073 1.8799770 4.713627 3.702926e-06 768
#> age_age                age  1.042922 1.0296867 1.056659 1.773155e-10 768
#>                        AIC
#> pregnant_pregnant 960.2099
#> glucose_glucose   812.7196
#> pressure_pressure 994.1276
#> triceps_triceps   993.1890
#> insulin_insulin   984.8104
#> mass_mass         924.7142
#> pedigree_pedigree 974.8609
#> age_age           954.7203

We observe a Manhattan plot showing the results of the EWAS analysis and a dataframe showing the more detailed results.

References

• Dunn OJ. Multiple Comparisons Among Means. Journal of the American Statistical Association. 1961;56(293):52‑64.
• Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57(1):289‑300.
• MLBench · Distributed Machine Learning Benchmark. Available from: https://mlbench.github.io/
• Smith JW, Everhart JE, Dickson WC, Knowler WC, Johannes RS. Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus. Proc Annu Symp Comput Appl Med Care. 1988 Nov 9;261–5.