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A dynamic approach for reconstructing missing longitudinal data using the linear increments model.

Aalen OO, Gunnes N - Biostatistics (2010)

Bottom Line: The computational procedures suggested are very simple and easily applicable.They can also be used to estimate causal effects in the presence of time-dependent confounding.There are also connections to methods from survival analysis: The Aalen-Johansen estimator for the transition matrix of a Markov chain turns out to be a special case.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, 0317 Oslo, Norway. o.o.aalen@medisin.uio.no

ABSTRACT
Missing observations are commonplace in longitudinal data. We discuss how to model and analyze such data in a dynamic framework, that is, taking into consideration the time structure of the process and the influence of the past on the present and future responses. An autoregressive model is used as a special case of the linear increments model defined by Farewell (2006. Linear models for censored data, [PhD Thesis]. Lancaster University) and Diggle and others (2007. Analysis of longitudinal data with drop-out: objectives, assumptions and a proposal. Journal of the Royal Statistical Society, Series C (Applied Statistics, 56, 499-550). We wish to reconstruct responses for missing data and discuss the required assumptions needed for both monotone and nonmonotone missingness. The computational procedures suggested are very simple and easily applicable. They can also be used to estimate causal effects in the presence of time-dependent confounding. There are also connections to methods from survival analysis: The Aalen-Johansen estimator for the transition matrix of a Markov chain turns out to be a special case. Analysis of quality of life data from a cancer clinical trial is analyzed and presented. Some simulations are given in the supplementary material available at Biostatistics online.

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The empirical variance of the mean score estimates of item 30 (quality of life),based on 1000 bootstrap samples, as functions of weeks since treatment onset.
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fig3: The empirical variance of the mean score estimates of item 30 (quality of life),based on 1000 bootstrap samples, as functions of weeks since treatment onset.

Mentions: Figure 3 displays the empirical variance of themean score estimates of item 30, based on 1000 bootstrap samples, as functions of weekssince treatment onset. There is not much difference in variability between the estimatedmean score achieved by using the compensator technique and the estimated mean scoreachieved by using the imputation technique, neither in arm A nor in arm B.


A dynamic approach for reconstructing missing longitudinal data using the linear increments model.

Aalen OO, Gunnes N - Biostatistics (2010)

The empirical variance of the mean score estimates of item 30 (quality of life),based on 1000 bootstrap samples, as functions of weeks since treatment onset.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3293429&req=5

fig3: The empirical variance of the mean score estimates of item 30 (quality of life),based on 1000 bootstrap samples, as functions of weeks since treatment onset.
Mentions: Figure 3 displays the empirical variance of themean score estimates of item 30, based on 1000 bootstrap samples, as functions of weekssince treatment onset. There is not much difference in variability between the estimatedmean score achieved by using the compensator technique and the estimated mean scoreachieved by using the imputation technique, neither in arm A nor in arm B.

Bottom Line: The computational procedures suggested are very simple and easily applicable.They can also be used to estimate causal effects in the presence of time-dependent confounding.There are also connections to methods from survival analysis: The Aalen-Johansen estimator for the transition matrix of a Markov chain turns out to be a special case.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, 0317 Oslo, Norway. o.o.aalen@medisin.uio.no

ABSTRACT
Missing observations are commonplace in longitudinal data. We discuss how to model and analyze such data in a dynamic framework, that is, taking into consideration the time structure of the process and the influence of the past on the present and future responses. An autoregressive model is used as a special case of the linear increments model defined by Farewell (2006. Linear models for censored data, [PhD Thesis]. Lancaster University) and Diggle and others (2007. Analysis of longitudinal data with drop-out: objectives, assumptions and a proposal. Journal of the Royal Statistical Society, Series C (Applied Statistics, 56, 499-550). We wish to reconstruct responses for missing data and discuss the required assumptions needed for both monotone and nonmonotone missingness. The computational procedures suggested are very simple and easily applicable. They can also be used to estimate causal effects in the presence of time-dependent confounding. There are also connections to methods from survival analysis: The Aalen-Johansen estimator for the transition matrix of a Markov chain turns out to be a special case. Analysis of quality of life data from a cancer clinical trial is analyzed and presented. Some simulations are given in the supplementary material available at Biostatistics online.

Show MeSH
Related in: MedlinePlus