Evidence map›Paper›PMID 33716361›Full record

ArticleJournal of the American Statistical Association2020

Optimal Designs of Two-Phase Studies.

Ran Tao, Donglin Zeng, Dan-Yu Lin

Abstract read
In one paragraph

Article in Journal of the American Statistical Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Optimal multiwave validation of secondary use data with outcome and exposure misclassification.The Canadian journal of statistics = Revue canadienne de statistique · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Two-Phase Sampling Designs for Data Validation in Settings with Covariate Measurement Error and Continuous Outcome.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2021
    Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ran TaoDepartment of Biostatistics and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232.
Donglin ZengDepartment of Biostatistics and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232.
Dan-Yu LinDepartment of Biostatistics and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232.

Funding

Statistical Methods for Cancer Clinical TrialsP01CA142538 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAVIDIAN, MARIE, KOSOROK, MICHAEL R · 2010 to 2019
$21.3M
STATISTICAL ISSUES IN AIDS RESEARCHR37AI029168 · NIAID · UNIVERSITY OF WASHINGTON · PI JAMES P HUGHES · 1999 to 2026
$9.8M
Outcome Dependent Sampling Studies of Longitudinal Data: Design and AnalysisR01HL094786 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Jonathan Scott Schildcrout · 2009 to 2026
$6.1M
STATISTICAL ISSUES IN AIDS RESEARCHR01AI029168 · NIAID · UNIVERSITY OF WASHINGTON · PI HUGHES, JAMES P · 1989 to 2018
$5.3M
Analysis, Validation and Resource Creation for Genome Sequencing of Complex DiseasesU01HG009086 · NHGRI · VANDERBILT UNIVERSITY · PI COX, NANCY J, LI, BINGSHAN · 2016 to 2020
$4.3M
Semiparametric Regression Analysis of Censored DataR01GM047845 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIN, DANYU · 1997 to 2018
$2.9M
Semiparametric Analysis of Big Censored DataR01HL149683 · NHLBI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIN, DANYU · 2020 to 2023
$1.9M
Statistical Methods in Trans-Omics Chronic Disease ResearchR01HG009974 · NHGRI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIN, DANYU · 2018 to 2022
$1.5M
NCI NIH HHS P01 CA142538NHGRI NIH HHS R01 HG009974NHGRI NIH HHS U01 HG009086NHLBI NIH HHS R01 HL094786NHLBI NIH HHS R01 HL149683NIAID NIH HHS R01 AI029168NIAID NIH HHS R37 AI029168NIGMS NIH HHS R01 GM047845
6 · The paper itself

Abstract

The two-phase design is a cost-effective sampling strategy to evaluate the effects of covariates on an outcome when certain covariates are too expensive to be measured on all study subjects. Under such a design, the outcome and inexpensive covariates are measured on all subjects in the first phase and the first-phase information is used to select subjects for measurements of expensive covariates in the second phase. Previous research on two-phase studies has focused largely on the inference procedures rather than the design aspects. We investigate the design efficiency of the two-phase study, as measured by the semiparametric efficiency bound for estimating the regression coefficients of expensive covariates. We consider general two-phase studies, where the outcome variable can be continuous, discrete, or censored, and the second-phase sampling can depend on the first-phase data in any manner. We develop optimal or approximately optimal two-phase designs, which can be substantially more efficient than the existing designs. We demonstrate the improvements of the new designs over the existing ones through extensive simulation studies and two large medical studies.

Indexed as

Case-cohort designCase-control studyGeneralized linear modelsOutcome-dependent samplingProportional hazardsSemiparametric efficiency

Identifiers

PMID33716361
PMCPMC7954143

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.