Exploring the Cystfibr Dataset: What Affects Lung Performance?

 

For this analysis, I used the cystfibr dataset from the ISwR package in R to explore what factors influence maximum expiratory pressure (pemax) in cystic fibrosis patients. I focused on four predictors: age, weight, bmp (body mass percentile), and fev1 (lung function).

I ran a multiple linear regression:

model <- lm(pemax ~ age + weight + bmp + fev1, data = cystfibr)
summary(model)

Key Results

Intercept: 179.30 represents the baseline pemax when all predictors are zero.
Age (-3.42, p = 0.31): Older age slightly decreases pemax, but it’s not statistically significant.
Weight (2.69, p = 0.033): Higher weight is associated with higher pemax.
BMP (-2.07, p = 0.020): Surprisingly, a higher body mass percentile slightly lowers pemax in this dataset. 
FEV1 (1.09, p = 0.047): Better lung function increases pemax, as expected.

The model explains about 59% of the variation in pemax (R² = 0.59), and the overall F-test confirms that the model is statistically significant (p < 0.001).

Interpretation

These results suggest that weight, body composition, and lung function all play important roles in predicting respiratory strength for cystic fibrosis patients. Age, in this dataset, does not have a strong effect. This analysis shows how multiple regression in R can help uncover meaningful relationships in clinical data




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