Two-phase outcome-dependent studies for failure times and testing for effects of expensive covariates

Lawless, J. F.

doi:10.1007/s10985-016-9386-8

Cited by 22 publications

(25 citation statements)

References 33 publications

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“…We report the empirical standard errors in Table for 500 simulated samples with n =1000 individuals each. We show only settings (i) and (ii) in the Table; results for (ii) and (iii) were very similar, contrary to expectations, but consistent with some other two‐phase studies involving failure times and expensive covariates . We see that, as is plausible, tracing mainly improves the estimation of upper quantiles when censoring is fairly heavy.…”

Section: Illustrative Calculations and Numerical Studiessupporting

confidence: 64%

A new perspective on loss to follow‐up in failure time and life history studies

Lawless

Cook

2019

Statistics in Medicine

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A framework is proposed for the joint modeling of life history and loss to follow‐up (LTF) processes in cohort studies. This framework provides a basis for discussing independence conditions for LTF and censoring and examining the implications of dependent LTF. We consider failure time and more general life history processes. The joint models are based on multistate processes with expanded state spaces encompassing both the life history and LTF processes. Tracing studies are discussed as a means of investigating the presence of dependent censoring and providing valid estimates of transition intensities and state occupancy probabilities. Simulation studies and an illustration based on a cohort of individuals with systemic lupus erythematosus demonstrate the usefulness and properties of the proposed methods.

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Section: Illustrative Calculations and Numerical Studiessupporting

confidence: 64%

A new perspective on loss to follow‐up in failure time and life history studies

Lawless

Cook

2019

Statistics in Medicine

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“…In principle, these extensions can be covered by a general framework (Derkach et al, 2015). However, further considerations of sampling designs for these models will be required (see Lawless, 2016).…”

Section: Discussionmentioning

confidence: 99%

Two‐phase designs for joint quantitative‐trait‐dependent and genotype‐dependent sampling in post‐GWAS regional sequencing

Espin‐Garcia

Craiu

Bull

2017

Genetic Epidemiology

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We evaluate two‐phase designs to follow‐up findings from genome‐wide association study (GWAS) when the cost of regional sequencing in the entire cohort is prohibitive. We develop novel expectation‐maximization‐based inference under a semiparametric maximum likelihood formulation tailored for post‐GWAS inference. A GWAS‐SNP (where SNP is single nucleotide polymorphism) serves as a surrogate covariate in inferring association between a sequence variant and a normally distributed quantitative trait (QT). We assess test validity and quantify efficiency and power of joint QT‐SNP‐dependent sampling and analysis under alternative sample allocations by simulations. Joint allocation balanced on SNP genotype and extreme‐QT strata yields significant power improvements compared to marginal QT‐ or SNP‐based allocations. We illustrate the proposed method and evaluate the sensitivity of sample allocation to sampling variation using data from a sequencing study of systolic blood pressure.

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“…The method requires that the imputation is unbiased and the genotype is independent of the other variables in the phenotype model. Derkach et al (2015) and Lawless (2016) proposed to model the variable with missing values under outcome-dependent sampling and studied the score test based on the full likelihood. Derkach et al (2015) assumed a nonparametric model for the variable with missing values and restricted covariates to only a few possible values.…”

Section: Introductionmentioning

confidence: 99%

“…Derkach et al (2015) assumed a nonparametric model for the variable with missing values and restricted covariates to only a few possible values. Lawless (2016) assumed a full parametric missing-data model. All existing methods require unbiased imputation or correct modeling of the variable with missing values.…”

Section: Introductionmentioning

confidence: 99%

Robust Score Tests With Missing Data in Genomics Studies

Wong

Zeng

Lin

2019

Journal of the American Statistical Association

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Analysis of genomic data is often complicated by the presence of missing values, which may arise due to cost or other reasons. The prevailing approach of single imputation is generally invalid if the imputation model is misspecified. In this paper, we propose a robust score statistic based on imputed data for testing the association between a phenotype and a genomic variable with (partially) missing values. We fit a semiparametric regression model for the genomic variable against an arbitrary function of the linear predictor in the phenotype model and impute each missing value by its estimated posterior expectation. We show that the score statistic with such imputed values is asymptotically unbiased under general missing-data mechanisms, even when the imputation model is misspecified. We develop a spline-based method to estimate the semiparametric imputation model and derive the asymptotic distribution of the corresponding score statistic with a consistent variance estimator using sieve approximation theory and empirical process theory. The proposed test is computationally feasible regardless of the number of independent variables in the imputation model. We demonstrate the advantages of the proposed method over existing methods through extensive simulation studies and provide an application to a major cancer genomics study.

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Two-phase outcome-dependent studies for failure times and testing for effects of expensive covariates

Cited by 22 publications

References 33 publications

A new perspective on loss to follow‐up in failure time and life history studies

A new perspective on loss to follow‐up in failure time and life history studies

Two‐phase designs for joint quantitative‐trait‐dependent and genotype‐dependent sampling in post‐GWAS regional sequencing

Robust Score Tests With Missing Data in Genomics Studies

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