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Why significant variables aren t automatically good predictors
#MMPMID26504198
Lo A
; Chernoff H
; Zheng T
; Lo SH
Proc Natl Acad Sci U S A
2015[Nov]; 112
(45
): 13892-7
PMID26504198
show ga
Thus far, genome-wide association studies (GWAS) have been disappointing in the
inability of investigators to use the results of identified, statistically
significant variants in complex diseases to make predictions useful for
personalized medicine. Why are significant variables not leading to good
prediction of outcomes? We point out that this problem is prevalent in simple as
well as complex data, in the sciences as well as the social sciences. We offer a
brief explanation and some statistical insights on why higher significance cannot
automatically imply stronger predictivity and illustrate through simulations and
a real breast cancer example. We also demonstrate that highly predictive
variables do not necessarily appear as highly significant, thus evading the
researcher using significance-based methods. We point out that what makes
variables good for prediction versus significance depends on different properties
of the underlying distributions. If prediction is the goal, we must lay aside
significance as the only selection standard. We suggest that progress in
prediction requires efforts toward a new research agenda of searching for a novel
criterion to retrieve highly predictive variables rather than highly significant
variables. We offer an alternative approach that was not designed for
significance, the partition retention method, which was very effective predicting
on a long-studied breast cancer data set, by reducing the classification error
rate from 30% to 8%.
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