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Summary: Data collected in the context of usual care present a rich source of longitudinal data for research, but often require analyses that simultaneously enable causal inferences from observational data while handling irregular and informative assessment times. An inverse‐weighting approach to this was recently proposed, and handles the case where the assessment times are at random (ie, conditionally independent of the outcome process given the observed history). In this paper, we extend the inverse‐weighting approach to handle a special case of assessment not at random, where assessment and outcome processes are conditionally independent given past observed covariates and random effects. We use multiple outputation to accomplish the same purpose as inverse‐weighting, and apply it to the Liang semi‐parametric joint model. Moreover, we develop an alternative joint model that does not require covariates for the outcome model to be known at times where there is no assessment of the outcome. We examine the performance of these methods through simulation and illustrate them through a study of the causal effect of wheezing on time spent playing outdoors among children aged 2–9 years and enrolled in the TargetKids! study. [ABSTRACT FROM AUTHOR]

Observational cohort studies often feature longitudinal data subject to irregular observation. Moreover, the timings of observations may be associated with the underlying disease process and must thus be accounted for when analysing the data. This paper suggests that multiple outputation, which consists of repeatedly discarding excess observations, may be a helpful way of approaching the problem. Multiple outputation was designed for clustered data where observations within a cluster are exchangeable; an adaptation for longitudinal data subject to irregular observation is proposed. We show how multiple outputation can be used to expand the range of models that can be fitted to irregular longitudinal data. [ABSTRACT FROM AUTHOR]

Background: The use of standard statistical methods in the medical literature has been studied extensively; however, the adoption of new methods has received less attention. We sought to understand (i) whether there is a perception that new methods are underused, (ii) what the barriers to use of new methods are, (iii) what dissemination activities are used, and (iv) user preferences for learning about new methods.Methods: We conducted a cross-sectional survey of members of the Statistical Society of Canada (SSC) and of principal investigators (knowledge-users) funded by the Canadian Institutes of Health Research (CIHR).Results: There were 157 CIHR respondents (14% response rate), and 39 respondents were statisticians from the Statistical Society of Canada. Seventy percent of CIHR respondents and 82% of statisticians felt that new developments were under-used. Barriers to use of new methods included lack of access to the necessary expertise (selected by over 90% of respondents), lack of suitable software (selected by 81% of statisticians), and lack of time to implement new methods (selected by 78% of statisticians). Greater access to statistical colleagues with an interest in collaboration and availability of software to implement new methods were the top-rated preferences among knowledge-users.Conclusions: There was a clear perception among all respondents that new statistical methods are underused. Encouraging statistical methodologists to develop a knowledge translation plan for improved dissemination and uptake, placing greater value on the role of the statistical collaborator in research, and providing software alongside new methods may improve the use of newly developed statistical methods. [ABSTRACT FROM AUTHOR]

Longitudinal data arising from routine follow-up of patients will often have irregular measurement times. Existing methods for analysis include joint modelling of the outcome and measurement processes, and inverse-intensity weighting (IIW). This work extends previously proposed analysis of increments to the case of irregular follow-up, yielding a model for the increments that can be used as a stand-alone method. Furthermore, we propose two ways of combining the increments and IIW estimators. First, we use the increment model to select the truncation point for the inverse-intensity weights that minimises the mean squared error of the IIW estimator. Second, we use the increment model to augment the usual IIW estimating equations to form a doubly robust estimator. We evaluate the methods through simulation and apply these to a recent study of juvenile dermatomyositis. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

It is well known that when a Bayesian meta-analysis includes a small number of studies, inference can be sensitive to the choice of prior for the between-study variance. Choosing a vague prior does not solve the problem, as inferences can be substantially different depending on the degree of vagueness. Moreover, because the data provide little information on between-study heterogeneity, posterior inferences for the between-study variance based on vague priors will tend to be unrealistic. It is thus preferable to adopt a reasonable, informed prior for the between-study variance. However, relatively little is known about what constitutes a realistic distribution. On the basis of data from the Cochrane Database of Systematic Reviews, this paper describes the distribution of between-study variance in published meta-analyses, and proposes some realistic, informed priors for use in meta-analyses of binary outcomes. It is hoped that these priors will improve the calibration of inferences from Bayesian meta-analyses. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Many chronic disease processes feature acute episodic conditions which warrant therapeutic intervention to alleviate symptoms or reduce the risk of further complications. Examples of such disease processes arise in fields such as neurology, where migraineurs experience recurrent attacks of migraine, and respirology, where patients suffering from asthma, cystic fibrosis, or chronic obstructive pulmonary disease may experience recurrent exacerbations. In randomized clinical trials, patients suffering from diseases of this sort are often randomized to one of several treatments and followed over a fixed period of time, during which any episodes are treated with the assigned treatment. When the outcome of interest is a response to treatment at each episode, the data have a similar structure to longitudinal data from studies with prescheduled follow-up assessments, and it is commonplace for analyses to be based on the corresponding methodology. However, this approach ignores the fact that the timing of episodes, and hence the number observed in any given period, is stochastic. In this tutorial we demonstrate the biases that result from naive analyses, discuss analyses that account for the complete stochastic nature, and use a recent migraine trial for illustration. We conclude with some considerations for the design of randomized trials where the unit of analysis is the episode rather than the patient. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In a cost-effectiveness analysis using clinical trial data, estimates of the between-treatment difference in mean cost and mean effectiveness are needed. Several methods for handling censored data have been suggested. One of them is inverse-probability weighting, and has the advantage that it can also be applied to estimate the parameters from a linear regression of the mean. Such regression models can potentially estimate the treatment contrast more precisely, since some of the residual variance can be explained by baseline covariates. The drawback, however, is that inverse-probability weighting may not be efficient. Using existing results on semi-parametric efficiency, this paper derives the semi-parametric efficient parameter estimates for regression of mean cost, mean quality-adjusted survival time and mean survival time. The performance of these estimates is evaluated through a simulation study. Applying both the new estimators and the inverse-probability weighted estimators to the results of the EVALUATE trial showed that the new estimators achieved a halving of the variance of the estimated treatment contrast for cost. Some practical suggestions for choosing an estimator are offered. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]