Design and relative efficiency in two-phase studies

Zhao, Yong; Lawless, J. F.; McLeish, D. L.

doi:10.1016/j.jspi.2012.04.013

Cited by 6 publications

(4 citation statements)

References 11 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typically, in the design of case-control studies, optimization is performed to determine sampling fractions across predefined strata subject to a budget constraint on the phase 2 sample size [6;7;8] . Another approach, described in Zhao et al, seeks to optimize the sampling fraction (ρ) under simple random sampling considering asymptotic relative efficiency of the maximum likelihood estimators from the one-versus two-phase designs [9] . More recently, Tao et al derived general optimal designs of two-phase studies paying special attention to continuous, binary, and time-toevent outcomes [10] .…”

Section: Introductionmentioning

confidence: 99%

Two-phase sample selection strategies for design and analysis in post-genome wide association fine-mapping studies

Espin‐Garcia

Craiu

Bull

2021

Preprint

View full text Add to dashboard Cite

Post-GWAS analysis, in many cases, focuses on fine-mapping targeted genetic regions discovered at GWAS-stage; that is, the aim is to pinpoint potential causal variants and susceptibility genes for complex traits and disease outcomes using next-generation sequencing (NGS) technologies. Large-scale GWAS cohorts are necessary to identify target regions given the typically modest genetic effect sizes. In this context, two-phase sampling design and analysis is a cost-reduction technique that utilizes data collected during phase 1 GWAS to select an informative subsample for phase 2 sequencing. The main goal is to make inference for genetic variants measured via NGS by efficiently combining data from phases 1 and 2. We propose two approaches for selecting a phase 2 design under a budget constraint. The first method identifies sampling fractions that select a phase 2 design yielding an asymptotic variance covariance matrix with certain optimal characteristics, e.g. smallest trace, via Lagrange multipliers (LM). The second relies on a genetic algorithm (GA) with a defined fitness function to identify exactly a phase 2 subsample. We perform comprehensive simulation studies to evaluate the empirical properties of the proposed designs for a genetic association study of a quantitative trait. We compare our methods against two ranked designs: residual-dependent sampling and a recently identified optimal design. Our findings demonstrate that the proposed designs, GA in particular, can render competitive power in combined phase 1 and 2 analysis compared to alternative designs while preserving type 1 error control. These results are especially apparent under the more practical scenario where design values need to be defined a priori and are subject to mispecification. We illustrate the proposed methods in a study of triglyceride levels in the North Finland Birth Cohort of 1966. R code to reproduce our results is available at github.com/egosv/TwoPhase_postGWAS.

show abstract

Section: Introductionmentioning

confidence: 99%

Two-phase sample selection strategies for design and analysis in post-genome wide association fine-mapping studies

Espin‐Garcia

Craiu

Bull

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Typically, in the design of case‐control studies, optimization is performed to determine sampling fractions across predefined strata subject to a budget constraint on the phase 2 sample size 6‐8 . Another approach, described in Zhao et al, seeks to optimize the sampling fraction (

ρ

) under simple random sampling considering asymptotic relative efficiency of the maximum likelihood estimators from the one‐ vs two‐phase designs 9 . More recently, Tao et al derived general optimal designs of two‐phase studies paying special attention to continuous, binary, and time‐to‐event outcomes 10 .…”

Section: Introductionmentioning

confidence: 99%

“… 6 , 7 , 8 Another approach, described in Zhao et al, seeks to optimize the sampling fraction (

) under simple random sampling considering asymptotic relative efficiency of the maximum likelihood estimators from the one‐ vs two‐phase designs. 9 More recently, Tao et al derived general optimal designs of two‐phase studies paying special attention to continuous, binary, and time‐to‐event outcomes. 10 Specifically, Tao et al demonstrate the relationship between their optimal design (hereafter referred to as TZL) and previously proposed (ranked) designs such as outcome‐dependent and residual‐dependent sampling (ODS and RDS, respectively).…”

Section: Introductionmentioning

confidence: 99%

Two‐phase sample selection strategies for design and analysis in post‐genome‐wide association fine‐mapping studies

Espin‐Garcia

Craiu

Bull

2021

Statistics in Medicine

View full text Add to dashboard Cite

Post‐GWAS analysis, in many cases, focuses on fine‐mapping targeted genetic regions discovered at GWAS‐stage; that is, the aim is to pinpoint potential causal variants and susceptibility genes for complex traits and disease outcomes using next‐generation sequencing (NGS) technologies. Large‐scale GWAS cohorts are necessary to identify target regions given the typically modest genetic effect sizes. In this context, two‐phase sampling design and analysis is a cost‐reduction technique that utilizes data collected during phase 1 GWAS to select an informative subsample for phase 2 sequencing. The main goal is to make inference for genetic variants measured via NGS by efficiently combining data from phases 1 and 2. We propose two approaches for selecting a phase 2 design under a budget constraint. The first method identifies sampling fractions that select a phase 2 design yielding an asymptotic variance covariance matrix with certain optimal characteristics, for example, smallest trace, via Lagrange multipliers (LM). The second relies on a genetic algorithm (GA) with a defined fitness function to identify exactly a phase 2 subsample. We perform comprehensive simulation studies to evaluate the empirical properties of the proposed designs for a genetic association study of a quantitative trait. We compare our methods against two ranked designs: residual‐dependent sampling and a recently identified optimal design. Our findings demonstrate that the proposed designs, GA in particular, can render competitive power in combined phase 1 and 2 analysis compared with alternative designs while preserving type 1 error control. These results are especially evident under the more practical scenario where design values need to be defined a priori and are subject to misspecification. We illustrate the proposed methods in a study of triglyceride levels in the North Finland Birth Cohort of 1966. R code to reproduce our results is available at https://github.com/egosv/TwoPhase_postGWAS.

show abstract

“…Sampling designs such as the case-control, where expensive data ascertainment is based on strata defined by values of a cheaper auxiliary variable, may be viewed as special cases of the general two-phase sampling design (Neyman [1934], Chatterjee et al [2003]). In the context of effect estimation, targeted sampling through two-phase has been shown to provide efficiency over simple random sampling (Zhao et al [2009], McIsaac and Cook [2014]), especially when using sampling variables that are highly correlated and informative for the outcome (Zhao et al [2012]). However, the effect of selectively sampled training data on ultimate machine-learning prediction accuracy has not been thoroughly investigated.…”

Section: Introductionmentioning

confidence: 99%

Surrogate-guided sampling designs for classification of rare outcomes from electronic medical records data

Tan,

Heagerty

2019

Preprint

View full text Add to dashboard Cite

Scalable and accurate identification of specific clinical outcomes has been enabled by machine-learning applied to electronic medical record (EMR) systems. The development of automatic classification requires the collection of a complete labeled data set, where true clinical outcomes are obtained by human expert manual review. For example, the development of natural language processing algorithms requires the abstraction of clinical text data to obtain outcome information necessary for training models. However, if the outcome is rare then simple random sampling results in very few cases and insufficient information to develop accurate classifiers. Since large scale detailed abstraction is often expensive, time-consuming, and not feasible, more efficient strategies are needed. Under such resource constrained settings, we propose a class of enrichment sampling designs, where selection for abstraction is stratified by auxiliary variables related to the true outcome of interest. Stratified sampling on highly specific variables results in targeted samples that are more enriched with cases, which we show translates to increased model discrimination and better statistical learning performance. We provide mathematical details, and simulation evidence that links sampling designs to their resulting prediction model performance. We discuss the impact of our proposed sampling on both model development and validation. Finally, we illustrate the proposed 0 To whom correspondence should be addressed at wlktan [at] uw [dot] edu.

show abstract

Design and relative efficiency in two-phase studies

Cited by 6 publications

References 11 publications

Two-phase sample selection strategies for design and analysis in post-genome wide association fine-mapping studies

Two-phase sample selection strategies for design and analysis in post-genome wide association fine-mapping studies

Two‐phase sample selection strategies for design and analysis in post‐genome‐wide association fine‐mapping studies

Surrogate-guided sampling designs for classification of rare outcomes from electronic medical records data

Contact Info

Product

Resources

About