Statistical methods in medical research
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Stat Methods Med Res · Feb 2012
ReviewA review of causal estimation of effects in mediation analyses.
We describe causal mediation methods for analysing the mechanistic factors through which interventions act on outcomes. A number of different mediation approaches have been presented in the biomedical, social science and statistical literature with an emphasis on different aspects of mediation. We review the different sets of assumptions that allow identification and estimation of effects in the simple case of a single intervention, a temporally subsequent mediator and outcome. ⋯ The understanding of such assumptions is crucial since some can be assessed under certain conditions (e.g. treatment-mediator interactions), whereas others cannot (sequential ignorability). These issues become more complex with multiple mediators and longitudinal outcomes. In addressing these assumptions, we review several causal approaches to mediation analyses.
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We wish to deal with investigator bias in a statistical context. We sketch how a textbook solution to the problem of "outliers" which avoids one sort of investigator bias, creates the temptation for another sort. ⋯ Finally, we offer tentative suggestions to deal with the problem of investigator bias which follow from our account. As we have given a very sparse and stylized account of investigator bias, we ask what might be done to overcome this limitation.
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Stat Methods Med Res · Jun 2008
ReviewRandomized trials for the real world: making as few and as reasonable assumptions as possible.
The strength of the randomized trial to yield conclusions not dependent on assumptions applies only in an ideal setting. In the real world various complications such as loss-to-follow-up, missing outcomes, noncompliance and nonrandom selection into a trial force a reliance on assumptions. To handle real world complications, it is desirable to make as few and as reasonable assumptions as possible. This article reviews four techniques for using a few reasonable assumptions to design or analyse randomized trials in the presence of specific real world complications: 1) a double sampling design for survival data to avoid strong assumptions about informative censoring, 2) sensitivity analysis for partially missing binary outcomes that uses the randomization to reduce the number of parameters specified by the investigator, 3) an estimate of the effect of treatment received in the presence of all-or-none compliance that requires reasonable assumptions, and 4) statistics for binary outcomes that avoid some assumptions for generalizing results to a target population.
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Stat Methods Med Res · Feb 2004
ReviewMixed effects multivariate adaptive splines model for the analysis of longitudinal and growth curve data.
In this article, I review the use of nonparametric methods in the analysis of longitudinal and growth curve data, particularly the multivariate adaptive splines models for the analysis of longitudinal data (MASAL). These methods combine nonparametric techniques (B-splines, kernel smoothing, piecewise polynomials) and models with random effects, and provide fruitful alternatives to mixed effects linear models. ⋯ The analysis of a real example is also presented to illustrate the application and interpretation of MASAL. Open questions are posed for further investigation.
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Stat Methods Med Res · Feb 2004
ReviewFunctional data analysis in longitudinal settings using smoothing splines.
Data in many experiments arise as curves and therefore it is natural to use a curve as a basic unit in the analysis, which is termed functional data analysis (FDA). In longitudinal studies, recent developments in FDA have extended classical linear models and linear mixed effects models to functional linear models (also termed varying-coefficient models) and functional mixed effects models. ⋯ Due to the connection between smoothing splines and linear mixed effects models, functional mixed effects models can be fitted using existing software such as SAS Proc Mixed. A case study is presented as an illustration.