Adaptive response‐dependent two‐phase designs: Some results on robustness and efficiency

Abstract

Large cohort studies now routinely involve biobanks in which biospecimens are stored for use in future biomarker studies. In such settings, two-phase response-dependent sampling designs involve subsampling individuals in the cohort, assaying their biospecimen to measure an expensive biomarker, and using this data to estimate key parameters of interest under budgetary constraints. When analyses are based on inverse probability weighted estimating functions, recent work has described adaptive two-phase designs in which a preliminary phase of subsampling based on a standard design facilitates approximation of an optimal selection model for a second subsampling phase. In this article, we refine the definition of an optimal subsampling scheme within the framework of adaptive two-phase designs, describe how adaptive two-phase designs can be used when analyses are based on likelihood or conditional likelihood, and consider the setting of a continuous biomarker where the nuisance covariate distribution is estimated nonparametrically at the design stage and analysis stage as required; efficiency and robustness issues are investigated. We also explore these methods for the surrogate variable problem and describe a generalization to accommodate multiple stages of phase II subsampling. A study involving individuals with psoriatic arthritis is considered for illustration, where the aim is to assess the association between the biomarker MMP-3 and the development of joint damage.

Incorporating Covariates into Measures of Surrogate Paradox Risk

“…Surrogate outcomes are commonly used in clinical trials, and their prevalence has led to the development of innovative trial designs that aim to efficiently use the additional information provided by surrogate outcomes [18][19][20]. Despite the valuable additional information that surrogate outcomes provide, their use also comes with risk.…”

Microfluidic concurrent assessment of red blood cell adhesion and microcapillary occlusion: potential hemorheological biomarkers in sickle cell disease

“…Data were initially analyzed for normality, followed by appropriate two-group analyses: the unpaired t-test if data in both groups are normally distributed, and the Mann-Whitney U test if data in one of the two groups are non-normal. [52][53][54] Simple linear regression was used to assess the correlation between measurements and clinical variables, and the coefficient of determination R 2 was reported. Statistical significance was defined with P-values less than 0.05 (P < 0.05).…”

A Novel Methodology Based on a Deep Neural Network and Data Mining for Predicting the Segmental Voltage Drop in Automated Guided Vehicle Battery Cells

“…It is also planned to apply the method of multivariate imputation using chain equations for MAR missingness because this is the best approach for dealing with missing data [59]. When the analysis is based on probability or conditional probability, it is advisable to use adaptive two-phase plans [60].…”