Modelling the rate of change in a longitudinal study with missing data, adjusting for contact attempts

Akacha, Mouna; Hutton, Jane

doi:10.1002/sim.4165

Cited by 2 publications

(3 citation statements)

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“…In other words, the missing data mechanism is non-ignorable, for if it were ignored, the estimation of parameters would be biased and not consistent. There have been several approaches suggested to handle this problem, including jointly modeling drop-out and the response variable [22], imposing a missing data indicator [23], creating a logit covariate-based model for non-response [24], classifying partial and complete responses into a matrix or an array [25], and incorporating missing data to model the repeated outcomes data and missingness process jointly [26]. …”

Section: Introductionmentioning

confidence: 99%

Using group‐based latent class transition models to analyze chronic disability data from the National Long‐Term Care Survey 1984–2004

White

Erosheva

2013

Statistics in Medicine

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Latent class transition models track how individuals move among latent classes through time, traditionally assuming a complete set of observations for each individual. In this paper, we develop group-based latent class transition models that allow for staggered entry and exit, common in surveys with rolling enrollment designs. Such models are conceptually similar to, but structurally distinct from, pattern mixture models of the missing data literature. We employ group-based latent class transition modeling to conduct an in-depth data analysis of recent trends in chronic disability among the U.S. elderly population. Using activities of daily living data from the National Long-Term Care Survey (NLTCS), 1982-2004, we estimate model parameters using the expectation-maximization algorithm, implemented in SAS PROC IML. Our findings indicate that declines in chronic disability prevalence, observed in the 1980s and 1990s, did not continue in the early 2000s as previous NLTCS cross-sectional analyses have indicated.

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Section: Introductionmentioning

confidence: 99%

Using group‐based latent class transition models to analyze chronic disability data from the National Long‐Term Care Survey 1984–2004

White

Erosheva

2013

Statistics in Medicine

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“…The modelling allows for an association between the trial outcome and the missing data indicator but also accommodates less extreme assumptions than “missing=smoking”. We extend this approach in the next section by using data on the repeated attempts to obtain outcome data [12-15]. …”

Section: Which Baseline Variables Are Predictive Of Missingness? a Sementioning

confidence: 99%

“…In order to overcome the problems associated with the estimation of MNAR missing data models using selection models, models for the repeated attempts to obtain outcome data have been proposed [12-15]. This type of modelling is possible where a number of attempts to obtain outcome data are made, as is the case for the iQuit trial: as explained above, participants in the iQuit trial receive between one and ten telephone calls to obtain outcome data, and if these are unsuccessful they receive where possible a further attempt by email.…”

Section: Is the Primary Trial Outcome Predictive Of Missingness? Modementioning

confidence: 99%

An exploration of the missing data mechanism in an Internet based smoking cessation trial

Jackson

Mason

White

et al. 2012

BMC Med Res Methodol

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BackgroundMissing outcome data are very common in smoking cessation trials. It is often assumed that all such missing data are from participants who have been unsuccessful in giving up smoking (“missing=smoking”). Here we use data from a recent Internet based smoking cessation trial in order to investigate which of a set of a priori chosen baseline variables are predictive of missingness, and the evidence for and against the “missing=smoking” assumption.MethodsWe use a selection model, which models the probability that the outcome is observed given the outcome and other variables. The selection model includes a parameter for which zero indicates that the data are Missing at Random (MAR) and large values indicate “missing=smoking”. We examine the evidence for the predictive power of baseline variables in the context of a sensitivity analysis. We use data on the number and type of attempts made to obtain outcome data in order to estimate the association between smoking status and the missing data indicator.ResultsWe apply our methods to the iQuit smoking cessation trial data. From the sensitivity analysis, we obtain strong evidence that older participants are more likely to provide outcome data. The model for the number and type of attempts to obtain outcome data confirms that age is a good predictor of missing data. There is weak evidence from this model that participants who have successfully given up smoking are more likely to provide outcome data but this evidence does not support the “missing=smoking” assumption. The probability that participants with missing outcome data are not smoking at the end of the trial is estimated to be between 0.14 and 0.19.ConclusionsThose conducting smoking cessation trials, and wishing to perform an analysis that assumes the data are MAR, should collect and incorporate baseline variables into their models that are thought to be good predictors of missing data in order to make this assumption more plausible. However they should also consider the possibility of Missing Not at Random (MNAR) models that make or allow for less extreme assumptions than “missing=smoking”.

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Modelling the rate of change in a longitudinal study with missing data, adjusting for contact attempts

Cited by 2 publications

References 34 publications

Using group‐based latent class transition models to analyze chronic disability data from the National Long‐Term Care Survey 1984–2004

Using group‐based latent class transition models to analyze chronic disability data from the National Long‐Term Care Survey 1984–2004

An exploration of the missing data mechanism in an Internet based smoking cessation trial

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