The Annals of Applied Statistics

Confident inference for SNP effects on treatment efficacy

Ying Ding, Ying Grace Li, Yushi Liu, Stephen J. Ruberg, and Jason C. Hsu

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Our research is for finding SNPs that are predictive of treatment efficacy, to decide which subgroup (with enhanced treatment efficacy) to target in drug development. Testing SNPs for lack of association with treatment outcome is inherently challenging, because any linkage disequilibrium between a noncausal SNP with a causal SNP, however small, makes the zero-null (no association) hypothesis technically false. Control of Type I error rate in testing such null hypotheses are therefore difficult to interpret. We propose a completely different formulation to address this problem. For each SNP, we provide simultaneous confidence intervals directed toward detecting possible dominant, recessive, or additive effects. Across the SNPs, we control the expected number of SNPs with at least one false confidence interval coverage. Since our confidence intervals are constructed based on pivotal statistics, the false coverage control is guaranteed to be exact and unaffected by the true values of test quantities (whether zero or nonzero). Our method is applicable to the therapeutic areas of Diabetes and Alzheimer’s diseases, and perhaps more, as a step toward confidently targeting a patient subgroup in a tailored drug development process.

Article information

Ann. Appl. Stat., Volume 12, Number 3 (2018), 1727-1748.

Received: May 2016
Revised: August 2017
First available in Project Euclid: 11 September 2018

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Multiple testing simultaneous confidence intervals SNP tailored drug development treatment efficacy


Ding, Ying; Li, Ying Grace; Liu, Yushi; Ruberg, Stephen J.; Hsu, Jason C. Confident inference for SNP effects on treatment efficacy. Ann. Appl. Stat. 12 (2018), no. 3, 1727--1748. doi:10.1214/17-AOAS1128.

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