Over the last few years, I have been trying to tease relationship out of data. I have tried a wide variety of methods and finally found one that been producing good results.

The method is conceptually straight forward:

• Take the actual reading and apply a monotonic increasing function to it. Thus if Value_a < value_b then func(Value_a) < func(value_b)
• With the resulting data, transform it to be a rectangular distribution for all samples
• Hypothesis test the values from people who recorded symptoms using P=0.01 as a threshold

Once the candidate association are done then we can also test if a sample’s item satisfies the hypothesis.

This approach has some nice characteristics, because it will detect patterns that:

• are not linear on the values
• does not assume a normal distribution
• does not not assume items are caused by end associations (i.e. too high or too low)
  • In some cases, we see a shift into a middle range that is statistically significant

Adjusting “Middle Peak” patterns

Both of the above above are typical beliefs that people will attempt to apply to the data.