Semiparametric Bayesian Analysis of Matched Case-Control Studies With Missing Exposure

Sinha, Samiran; Mukherjee, Bhramar; Ghosh, Malay; Mallick, Bani K.; Carroll, Raymond J.

doi:10.1198/016214504000001411

Cited by 19 publications

(22 citation statements)

References 33 publications

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“…Even when matching could, in principle, be modeled parametrically, this is only possible if the analyst has data on matching variables, which is not always so, and some analysts may prefer to avoid modeling effects of matching variables, since CLR makes no assumptions about the association between disease and matching variables. One solution, adopted by Sinha et al (2005), is to allow each matched set to have its own parameter in the covariate model but treat these as random effects. They assume a single partially observed covariate and that the random effects are generated by a Dirichlet process.…”

Section: Introductionmentioning

confidence: 99%

Handling Missing Data in Matched Case-Control Studies Using Multiple Imputation

Seaman

Keogh

2015

Biometrics

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Summary. Analysis of matched case-control studies is often complicated by missing data on covariates. Analysis can be restricted to individuals with complete data, but this is inefficient and may be biased. Multiple imputation (MI) is an efficient and flexible alternative. We describe two MI approaches. The first uses a model for the data on an individual and includes matching variables; the second uses a model for the data on a whole matched set and avoids the need to model the matching variables. Within each approach, we consider three methods: full-conditional specification (FCS), joint model MI using a normal model, and joint model MI using a latent normal model. We show that FCS MI is asymptotically equivalent to joint model MI using a restricted general location model that is compatible with the conditional logistic regression analysis model. The normal and latent normal imputation models are not compatible with this analysis model. All methods allow for multiple partially-observed covariates, non-monotone missingness, and multiple controls per case. They can be easily applied in standard statistical software and valid variance estimates obtained using Rubin's Rules. We compare the methods in a simulation study. The approach of including the matching variables is most efficient. Within each approach, the FCS MI method generally yields the least-biased odds ratio estimates, but normal or latent normal joint model MI is sometimes more efficient. All methods have good confidence interval coverage. Data on colorectal cancer and fibre intake from the EPIC-Norfolk study are used to illustrate the methods, in particular showing how efficiency is gained relative to just using individuals with complete data.

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Section: Introductionmentioning

confidence: 99%

Handling Missing Data in Matched Case-Control Studies Using Multiple Imputation

Seaman

Keogh

2015

Biometrics

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“…These lemmas follow by repeating essentially the proofs of Lemmas 1-3 of Sinha et al (2003). The details are omitted.…”

Section: Likelihood Priors and Posteriorsmentioning

confidence: 95%

“…In such situations, a typical conditional frequentist matched analysis loses the entire information on a subject with a single missing exposure. Modeling the exposure distribution in such situations leads to more efficient estimation of parameters of interest as compared to completely ignoring the partial information that is still available (Satten and Kupper, 1993a,b;Satten and Carroll, 2000;Sinha et al, 2003).…”

Section: Likelihood Priors and Posteriorsmentioning

confidence: 99%

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Bayesian Semiparametric Modeling for Matched Case–Control Studies with Multiple Disease States

2004

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We present a Bayesian approach to analyze matched "case-control" data with multiple disease states. The probability of disease development is described by a multinomial logistic regression model. The exposure distribution depends on the disease state and could vary across strata. In such a model, the number of stratum effect parameters grows in direct proportion to the sample size leading to inconsistent MLEs for the parameters of interest even when one uses a retrospective conditional likelihood. We adopt a semiparametric Bayesian framework instead, assuming a Dirichlet process prior with a mixing normal distribution on the distribution of the stratum effects. We also account for possible missingness in the exposure variable in our model. The actual estimation is carried out through a Markov chain Monte Carlo numerical integration scheme. The proposed methodology is illustrated through simulation and an example of a matched study on low birth weight of newborns (Hosmer, D. A. and Lemeshow, S., 2000, Applied Logistic Regression) with two possible disease groups matched with a control group.

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“…A good discussion can be found in [11] regarding the optimality of different procedures for estimating parameters in this context. Sinha et al [12] proposed a nonparametric Bayesian procedure to capture unobserved stratum heterogeneity in the distribution of the missing covariate. Importantly, in the inference procedure they also used a likelihood function similar to that proposed in Satten and Carroll's paper that is quite different from the conditional likelihood function in our current approach.…”

Section: Introductionmentioning

confidence: 99%

A new semiparametric procedure for matched case-control studies with missing covariates

Sinha

Wang

2009

Journal of Nonparametric Statistics

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In this paper, we propose an easy-to-use semiparametric method for analysing matched case-control data when one of the covariates of interest is partially missing. Missing covariate information in matched case-control studies may create bias and reduce efficiency of the parameter estimates. In order to cope with this situation we consider a robust approach which is comprised of estimating some functionals of the distribution of the partially missing covariate using a kernel regression technique in a conditional likelihood framework. The large sample theory of the proposed estimator is investigated and the asymptotic normality is obtained. A simulation study is conducted to assess the performance of the proposed method in terms of robustness and efficiency. The proposed method is also applied to a real dataset which motivates this work.

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Semiparametric Bayesian Analysis of Matched Case-Control Studies With Missing Exposure

Cited by 19 publications

References 33 publications

Handling Missing Data in Matched Case-Control Studies Using Multiple Imputation

Handling Missing Data in Matched Case-Control Studies Using Multiple Imputation

Bayesian Semiparametric Modeling for Matched Case–Control Studies with Multiple Disease States

A new semiparametric procedure for matched case-control studies with missing covariates

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