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Abstract: Background: Numerous studies have investigated the impact of smoke-free laws on health outcomes. Large differences in estimates are in part attributable to how the long-term trend is modelled. However, the choice of appropriate trend is not always straightforward. We explore these complexities in an analysis of myocardial infarction (MI) mortality in England before and after the introduction of smoke-free legislation in July 2007. Methods: Weekly rates of MI mortality among men aged 40+ between July 2002 and December 2010 were analysed using quasi-Poisson generalised additive models. We explore two ways of modelling the long-term trend: (1) a parametric approach, where we fix the shape of the trend, and (2) a penalised spline approach, in which we allow the model to decide on the shape of the trend. Results: While both models have similar measures of fit and near identical fitted values, they have different interpretations of the legislation effect. The parametric approach estimates a significant immediate reduction in mortality rate of 13.7% (95% CI: 7.5, 19.5), whereas the penalised spline approach estimates a non-significant reduction of 2% (95% CI:-0.9, 4.8). After considering the implications of the models, evidence from sensitivity analyses and other studies, we conclude that the second model is to be preferred. Conclusions: When there is a strong long-term trend and the intervention of interest also varies over time, it is difficult for models to separate out the two components. Our recommendations will help further studies determine the best way of modelling their data. Keywords: Smoke-free law, myocardial infarction, mortality, second-hand smoke, passive smoke.Download Full Article |
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Abstract: Statistical approaches for drug effectiveness studies after liver transplant have used a survival model with changes in treatment as a time-dependent covariate. However, the approach requires that changes in the time-dependent covariate be unrelated to survival outcome. Usually this is not the case, as one drug may be discontinued and an alternative chosen due to the declining health status of the patient. Other approaches examine only subjects who remain on the same drug over a time window, which discards valuable data and may lead to biased effects since this excludes data related to early deaths and to individuals who perform poorly on the drug and had to switch treatments. Because of these issues there are conflicting results seen in the evaluation of immunosuppressive drug effectiveness after liver transplant. We propose a joint survival outcome model with a time-to-drug-change event and a terminal event in graft failure that is useful in drug effectiveness studies where subjects are discontinued from an immunosuppressant (in favour of alternative treatment) due to health reasons. We also include a longitudinal biomarker component. The model takes account of the dependencies across out- comes through shared random effects. Using a Markov chain Monte Carlo approach, we fit the joint model to data from liver transplant recipients from the Scientific Registry for Transplant Recipients. Keywords: Joint models, longitudinal, survival, transplant, joint outcome.Download Full Article |
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Abstract: Background: Understanding how cancer incidence evolves during economic growth is useful for forecasting the economic impact of cancerous diseases, and for governing the process of resources allocation in planning health services. We analyse the relationship between economic growth and cancer incidence in order to describe and measure the influence of an increasing real per capita income on the overall rate of cancer incidence. Method:We test the relationship between real per capita income and the overall rate of cancer incidence with a cross-sectional analysis, using data from the World Bank and the World Health Organization databases, for 165 countries in 2008. We measure the elasticity of cancer incidence with respect to per capita income, and we decompose the elasticities coefficients into two components: age-effect and lifestyle-effect. Results: An Engel’s model, in a double-log quadratic specification, explains about half of the variations in the age-standardised rates and nearly two thirds of the variations in the incidence crude rates. All the elasticities of the crude rates are positive, but less than one. The income elasticity of the age-standardised rates are negative in lower income countries, and positive (around 0.25 and 0.32) in upper middle and high income countries, respectively. Conclusions:These results are used to develop a basic framework in order to explain how demand-side economic structural changes may affect the long run evolution of cancer incidence. At theoretical level, a J-Curve is a possible general model to represents, other things being equal, how economic growth influence cancer incidence. Keywords: Cancer Incidence, Economic Growth, Engel’s function, Income elasticity, Structural Change.Download Full Article |
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Abstract: The ‘stepped-wedge cluster randomised trial’ (SW-CRT) harbours promise when for ethical or practical reasons the recruitment of a control group is not possible or when a staggered implementation of an intervention is required. Yet SW-CRT designs can create considerable challenges in terms of methodological integration, implementation, and analysis. While cross-sectional methods in participants recruitment of the SW-CRT have been discussed in the literature the cohort method is a novel feature that has not been considered yet. This paper provides a succinct overview of the methodological, analytical, and practical aspects of cohort SW-CRTs.We discuss five issues that are of special relevance to SW-CRTs. First, issues relating to the design, secondly size of clusters and sample size; thirdly, dealing with missing data in the fourth place analysis; and finally, the advantages and disadvantages of SW-CRTs are considered. An Australian study employing a cohort SW-CRT to evaluate a domiciliary aged care intervention is used as case study. The paper concludes that the main advantage of the cohort SW-CRT is that the intervention rolls out to all participants. There are concerns about missing a whole cluster, and difficulty of completing clusters in a given time frame due to involvement frail older people. Cohort SW-CRT designs can be successfully used within public health and health promotion context. However, careful planning is required to accommodate methodological, analytical, and practical challenges. Keywords: Clinical trials, Stepped wedge design, missing data, sample size, Cluster randomized trial. |
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Abstract: In clinical trials, longitudinal data are often analyzed using T-tests, anovas or ancovas instead of the more powerful linear mixed models. The purpose of this paper is to demonstrate how the more sophisticated linear mixed models according to the approach of Singer and Willett, which allows special insight into the behaviour of the data, can be used in clinical trials. Individual trajectories of PANNS-MNS Scores from a controlled clinical trial were used to demonstrate all the steps needed for an analysis of longitudinal data. The model is built step by step, model assumptions are checked, time-variant and time-invariant factors are included and the results are interpreted. The unique needs of a clinical trial, such as the calculation of effect sizes or of an appropriate sample size, are taken into account. Finally, a flow chart is presented that would serve as an instruction tool for the analysis of longitudinal data in clinical trials. Keywords: longitudinal studies, randomized controlled trial, linear models, sample size.Download Full Article |