An efficient method of estimation for longitudinal surveys with monotone missing data

Zhou, Ming; Kim, Jae Kwang

doi:10.1093/biomet/ass026

Cited by 13 publications

(15 citation statements)

References 33 publications

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“…To avoid bias, we must apply regression with all incomplete data up to time t . Similar conclusions were obtained by Zhou and Kim () and Han et al () for the case of ignorable dropout. To derive an asymptotically unbiased regression estimator, we have to consider t working regression models at each time point t :

E false(y_{t} false| Z, O_{s} false) = m_{t s} false(ϑ_{t s}, Z, O_{s} false), s = 1, ⋯, t,

where ϑ t s is an unknown parameter vector and m t s is a known continuous function with bounded second‐order derivative.…”

Section: Estimation Of Population Parameterssupporting

confidence: 90%

“…However, this estimator is biased, because data from subjects dropped out prior to t are not used. To avoid bias, we must apply regression with all incomplete data up to time t. Similar conclusions were obtained by Zhou and Kim (2012) and Han et al (2015) for the case of ignorable dropout. To derive an asymptotically unbiased regression estimator, we have to consider t working regression models at each time point t:…”

Section: Estimation Of Population Parametersmentioning

confidence: 69%

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Model‐Assisted Regression Estimators for Longitudinal Data with Nonignorable Dropout

Wang

Shao

2018

Int Statistical Rev

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Summary Estimation with longitudinal Y having nonignorable dropout is considered when the joint distribution of Y and covariate X is nonparametric and the dropout propensity conditional on (Y,X) is parametric. We apply the generalised method of moments to estimate the parameters in the nonignorable dropout propensity based on estimating equations constructed using an instrument Z, which is part of X related to Y but unrelated to the dropout propensity conditioned on Y and other covariates. Population means and other parameters in the nonparametric distribution of Y can be estimated based on inverse propensity weighting with estimated propensity. To improve efficiency, we derive a model‐assisted regression estimator making use of information provided by the covariates and previously observed Y‐values in the longitudinal setting. The model‐assisted regression estimator is protected from model misspecification and is asymptotically normal and more efficient when the working models are correct and some other conditions are satisfied. The finite‐sample performance of the estimators is studied through simulation, and an application to the HIV‐CD4 data set is also presented as illustration.

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E false(y_{t} false| Z, O_{s} false) = m_{t s} false(ϑ_{t s}, Z, O_{s} false), s = 1, ⋯, t,

where ϑ t s is an unknown parameter vector and m t s is a known continuous function with bounded second‐order derivative.…”

Section: Estimation Of Population Parameterssupporting

confidence: 90%

Section: Estimation Of Population Parametersmentioning

confidence: 69%

Model‐Assisted Regression Estimators for Longitudinal Data with Nonignorable Dropout

Wang

Shao

2018

Int Statistical Rev

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“…To study asymptotic properties of the parameter from the main analysis model, it is convenient to treat both types of estimators as parts of an enlarged joint estimator. For the PSA method, the expression for

A V false({\overset{θ}{true}}^{p s a} false)

follows similar arguments in earlier article s; see Rotnitzky & Robins (), Rotnitzky, Robins & Scharfstein () and Zhou & Kim ().…”

Section: Appendixmentioning

confidence: 63%

Validity and efficiency in analyzing ordinal responses with missing observations

She

2019

Can J Statistics

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This article addresses issues in creating public‐use data files in the presence of missing ordinal responses and subsequent statistical analyses of the dataset by users. The authors propose a fully efficient fractional imputation (FI) procedure for ordinal responses with missing observations. The proposed imputation strategy retrieves the missing values through the full conditional distribution of the response given the covariates and results in a single imputed data file that can be analyzed by different data users with different scientific objectives. Two most critical aspects of statistical analyses based on the imputed data set, validity and efficiency, are examined through regression analysis involving the ordinal response and a selected set of covariates. It is shown through both theoretical development and simulation studies that, when the ordinal responses are missing at random, the proposed FI procedure leads to valid and highly efficient inferences as compared to existing methods. Variance estimation using the fractionally imputed data set is also discussed. The Canadian Journal of Statistics 48: 138–151; 2020 © 2019 Statistical Society of Canada

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“…We finally note that instead of plugging in an estimate for η , one could alternatively follow Zhou and Kim (2012) and include the logistic regression score for estimating η in U c as yet another estimating function so that β and η can be estimated simultaneously. However, this would add to the already considerable computational complexity and we prefer to stick with the simpler plug-in approach.…”

Section: Data From Multiple Sources With Potential Selection Bias Amentioning

confidence: 99%

“…In the second step, we combine all the available estimating functions efficiently in order to make full use of information contained in data. Our method of combining several estimating functions is related to the generalized method of moments used to combine various sources of information for microeconomic models and longitudinal surveys in respectively Imbens and Lancaster (1994) and Zhou and Kim (2012). Because our approach is constructed by forming unbiased estimating functions in terms of the risk factors, it avoids the use of the computationally intensive MCMC algorithms.…”

Section: Introductionmentioning

confidence: 99%

A New Estimation Approach for Combining Epidemiological Data From Multiple Sources

Huang¹,

Ma²,

Waagepetersen³

et al. 2014

Journal of the American Statistical Association

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We propose a novel two-step procedure to combine epidemiological data obtained from diverse sources with the aim to quantify risk factors affecting the probability that an individual develops certain disease such as cancer. In the first step we derive all possible unbiased estimating functions based on a group of cases and a group of controls each time. In the second step, we combine these estimating functions efficiently in order to make full use of the information contained in data. Our approach is computationally simple and flexible. We illustrate its efficacy through simulation and apply it to investigate pancreatic cancer risks based on data obtained from the Connecticut Tumor Registry, a population-based case-control study, and the Behavioral Risk Factor Surveillance System which is a state-based system of health surveys.

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An efficient method of estimation for longitudinal surveys with monotone missing data

Cited by 13 publications

References 33 publications

Model‐Assisted Regression Estimators for Longitudinal Data with Nonignorable Dropout

Model‐Assisted Regression Estimators for Longitudinal Data with Nonignorable Dropout

Validity and efficiency in analyzing ordinal responses with missing observations

A New Estimation Approach for Combining Epidemiological Data From Multiple Sources

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