Joint modelling rationale for chained equations

Hughes, Rachael A.; White, Ian R.; Seaman, Shaun; Carpenter, James; Tilling, Kate; Sterne, Jonathan A C

doi:10.1186/1471-2288-14-28

Cited by 78 publications

(120 citation statements)

References 37 publications

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“…If we modify to have a separate fixed effects vector

((), α_{0, s}^{1}, α_{0, s}^{2}, α_{0, s}^{3})

for each study, it is equivalent to imputing separately in each study using the multivariate normal distribution. For multivariate normal data this is known to be equivalent to FCS (, p. 87–88) and practically equivalent in other settings . Therefore, within‐study joint‐model imputation will give the same results as those reported by Burgess et al , .…”

Section: Meta‐analysis and Multiple Imputation Modelsmentioning

confidence: 99%

Multiple imputation for IPD meta‐analysis: allowing for heterogeneity and studies with missing covariates

Quartagno

Carpenter

2015

Statistics in Medicine

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Recently, multiple imputation has been proposed as a tool for individual patient data meta‐analysis with sporadically missing observations, and it has been suggested that within‐study imputation is usually preferable. However, such within study imputation cannot handle variables that are completely missing within studies. Further, if some of the contributing studies are relatively small, it may be appropriate to share information across studies when imputing. In this paper, we develop and evaluate a joint modelling approach to multiple imputation of individual patient data in meta‐analysis, with an across‐study probability distribution for the study specific covariance matrices. This retains the flexibility to allow for between‐study heterogeneity when imputing while allowing (i) sharing information on the covariance matrix across studies when this is appropriate, and (ii) imputing variables that are wholly missing from studies. Simulation results show both equivalent performance to the within‐study imputation approach where this is valid, and good results in more general, practically relevant, scenarios with studies of very different sizes, non‐negligible between‐study heterogeneity and wholly missing variables. We illustrate our approach using data from an individual patient data meta‐analysis of hypertension trials. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

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“…If we modify to have a separate fixed effects vector

((), α_{0, s}^{1}, α_{0, s}^{2}, α_{0, s}^{3})

Section: Meta‐analysis and Multiple Imputation Modelsmentioning

confidence: 99%

Multiple imputation for IPD meta‐analysis: allowing for heterogeneity and studies with missing covariates

Quartagno

Carpenter

2015

Statistics in Medicine

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“…In special cases, FCS corresponds to joint model MI (Hughes et al, 2014). Otherwise, FCS is less theoretically justified, but there is much evidence that it works well in terms of approximate unbiasedness of parameter and variance estimates and coverage of confidence intervals (van Buuren, 2012;Hughes et al, 2014;Lee and Carlin, 2010). An important theoretical result was given by Liu et al (2014).…”

Section: Joint Model MI and Full-conditional Specification (Fcs) Mimentioning

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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“…; Hughes et al . ). When the imputation models do not correspond to a valid joint distribution (called incompatibility), our imputation method is not guaranteed to converge.…”

Section: Identifiability and Convergencementioning

confidence: 97%

Sequential imputation for models with latent variables assuming latent ignorability

Beesley

Taylor

Little

2019

Aus NZ J of Statistics

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Models that involve an outcome variable, covariates, and latent variables are frequently the target for estimation and inference. The presence of missing covariate or outcome data presents a challenge, particularly when missingness depends on the latent variables. This missingness mechanism is called latent ignorable or latent missing at random and is a generalisation of missing at random. Several authors have previously proposed approaches for handling latent ignorable missingness, but these methods rely on prior specification of the joint distribution for the complete data. In practice, specifying the joint distribution can be difficult and/or restrictive. We develop a novel sequential imputation procedure for imputing covariate and outcome data for models with latent variables under latent ignorable missingness. The proposed method does not require a joint model; rather, we use results under a joint model to inform imputation with less restrictive modelling assumptions. We discuss identifiability and convergence-related issues, and simulation results are presented in several modelling settings. The method is motivated and illustrated by a study of head and neck cancer recurrence. Imputing missing data for models with latent variables under latent-dependent missingness without specifying a full joint model.

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Joint modelling rationale for chained equations

Cited by 78 publications

References 37 publications

Multiple imputation for IPD meta‐analysis: allowing for heterogeneity and studies with missing covariates

Multiple imputation for IPD meta‐analysis: allowing for heterogeneity and studies with missing covariates

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

Sequential imputation for models with latent variables assuming latent ignorability

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