Using the Whole Cohort in the Analysis of Countermatched Samples

Self Cite

Countermatching designs can provide more efficient estimates than simple matching or case-cohort designs in certain situations such as when good surrogate variables for an exposure of interest are available. We extend pseudolikelihood estimation for the Cox model under countermatching designs to models where time-varying covariates are considered. We also implement pseudolikelihood with calibrated weights to improve efficiency in nested case-control designs in the presence of time-varying variables. A simulation study is carried out, which considers four different scenarios including a binary time-dependent variable, a continuous time-dependent variable, and the case including interactions in each. Simulation results show that pseudolikelihood with calibrated weights under countermatching offers large gains in efficiency if compared to case-cohort. Pseudolikelihood with calibrated weights yielded more efficient estimators than pseudolikelihood estimators. Additionally, estimators were more efficient under countermatching than under case-cohort for the situations considered. The methods are illustrated using the Colorado Plateau uranium miners cohort. Furthermore, we present a general method to generate survival times with time-varying covariates. Copyright © 2016 John Wiley & Sons, Ltd.

Section: Discussionsupporting

confidence: 86%

“…They were first introduced by Samuelsen for simple matching. Rivera et al , showed that, for countermatching designs, the probabilities are

π_{k} = 1 - \prod_{b_{k} \leq X_{i} \leq X_{k}} (1 - p_{k} (X_{i})),

where b i is the time at which the subject enters the study,

p_{k} (t) = P (k \in \overset{scriptR}{true} (t)) = ({, \begin{matrix} \frac{m_{A_{k} (t)}}{n_{A_{k} (t)}} & if A_{k} (t) \neq A_{t} (t) \\ \frac{m_{A_{k} (t)} - 1}{n_{A_{k} (t)} - 1} & if A_{k} (t) = A_{t} (t) \end{matrix})

is the inclusion probability for k at time t , A k ( t ) denotes the stratum to which subject k belongs at time t , and n l ( t ) is the stratum size at time t .…”

Section: Model and Parameter Estimationmentioning

confidence: 99%

\overset{boldU}{true}

w_{c} = w_{0} \exp (λ^{'} \overset{boldU}{true})

, where λ solves the equation

\sum_{i = 1}^{N} ζ_{i} \exp (- λ^{'} {\overset{boldU}{true}}_{i}) w_{i} {\overset{boldU}{true}}_{i} = {boldT}_{\overset{boldU}{true}}

. We carry out pseudolikelihood estimation approach for proportional hazard models using the calibrated weights ( w c ).…”

Section: Model and Parameter Estimationmentioning

confidence: 99%

\overset{I}{false}

, which is obtained in most statistical software.…”

Section: Application To Colorado Plateau Uranium Minersmentioning

confidence: 99%

See 2 more Smart Citations

Using the entire history in the analysis of nested case cohort samples

Rivera

Lumley

2016

Self Cite

“…Furthermore, the recent literature in the independent data setting has sought to use survey sampling techniques, such as calibration, to improve statistical efficiency of standard IPW estimators . Central to the appeal of these techniques is that they facilitate the use of information that is readily available on all participants in the initial cohort but was not used in the design . A second key development of this paper, therefore, is a general framework for the use of calibration as a means to increasing efficiency in the cluster‐correlated data setting, together with practical guidance on how to operationalize the framework in practice.…”

Section: Introductionmentioning

confidence: 99%

On the analysis of two‐phase designs in cluster‐correlated data settings

Rivera-Rodriguez

Spiegelman

Haneuse

2019

In public health research, information that is readily available may be insufficient to address the primary question(s) of interest. One cost‐efficient way forward, especially in resource‐limited settings, is to conduct a two‐phase study in which the population is initially stratified, at phase I, by the outcome and/or some categorical risk factor(s). At phase II detailed covariate data is ascertained on a subsample within each phase I strata. While analysis methods for two‐phase designs are well established, they have focused exclusively on settings in which participants are assumed to be independent. As such, when participants are naturally clustered (eg, patients within clinics) these methods may yield invalid inference. To address this, we develop a novel analysis approach based on inverse‐probability weighting that permits researchers to specify some working covariance structure and appropriately accounts for the sampling design and ensures valid inference via a robust sandwich estimator for which a closed‐form expression is provided. To enhance statistical efficiency, we propose a calibrated inverse‐probability weighting estimator that makes use of information available at phase I but not used in the design. In addition to describing the technique, practical guidance is provided for the cluster‐correlated data settings that we consider. A comprehensive simulation study is conducted to evaluate small‐sample operating characteristics, including the impact of using naïve methods that ignore correlation due to clustering, as well as to investigate design considerations. Finally, the methods are illustrated using data from a one‐time survey of the national antiretroviral treatment program in Malawi.

Optimal multiwave sampling for regression modeling in two‐phase designs

Chen

Lumley

2020

Two‐phase designs involve measuring extra variables on a subset of the cohort where some variables are already measured. The goal of two‐phase designs is to choose a subsample of individuals from the cohort and analyse that subsample efficiently. It is of interest to obtain an optimal design that gives the most efficient estimates of regression parameters. In this article, we propose a multiwave sampling design to approximate the optimal design for design‐based estimators. Influence functions are used to compute the optimal sampling allocations. We propose to use informative priors on regression parameters to derive the wave‐1 sampling probabilities because any prespecified sampling probabilities may be far from optimal and decrease the design efficiency. The posterior distributions of the regression parameters derived from the current wave will then be used as priors for the next wave. Generalized raking is used in the final statistical analysis. We show that a two‐wave sampling with reasonable informative priors will end up with a highly efficient estimation for the parameter of interest and be close to the underlying optimal design.