61 research outputs found
Estimating the average treatment effects of nutritional label use using subclassification with regression adjustment
Propensity score methods are common for estimating a binary treatment effect
when treatment assignment is not randomized. When exposure is measured on an
ordinal scale (i.e., low - medium - high), however, propensity score inference
requires extensions which have received limited attention. Estimands of
possible interest with an ordinal exposure are the average treatment effects
between each pair of exposure levels. Using these estimands, it is possible to
determine an optimal exposure level. Traditional methods, including
dichotomization of the exposure or a series of binary propensity score
comparisons across exposure pairs, are generally inadequate for identification
of optimal levels.We combine subclassification with regression adjustment to
estimate transitive, unbiased average causal effects across an ordered
exposure, and apply our method on the 2005-06 National Health and Nutrition
Examination Survey to estimate the effects of nutritional label use on body
mass index.Comment: Statistical Methods in Medical Research (online first, November 2014
Estimation of Causal Effects with Multiple Treatments: A Review and New Ideas
The propensity score is a common tool for estimating the causal effect of a binary treatment in observational data. In this setting, matching, subclassification, imputation, or inverse probability weighting on the propensity score can reduce the initial covariate bias between the treatment and control groups. With more than two treatment options, however, estimation of causal effects requires additional assumptions and techniques, the implementations of which have varied across disciplines. This paper reviews current methods, and it identifies and contrasts the treatment effects that each one estimates. Additionally, we propose possible matching techniques for use with multiple, nominal categorical treatments, and use simulations to show how such algorithms can yield improved covariate similarity between those in the matched sets, relative the pre-matched cohort. To sum, this manuscript provides a synopsis of how to notate and use causal methods for categorical treatments
Estimation of Causal Effects with Multiple Treatments: A Review and New Ideas
The propensity score is a common tool for estimating the causal effect of a binary treatment in observational data. In this setting, matching, subclassification, imputation, or inverse probability weighting on the propensity score can reduce the initial covariate bias between the treatment and control groups. With more than two treatment options, however, estimation of causal effects requires additional assumptions and techniques, the implementations of which have varied across disciplines. This paper reviews current methods, and it identifies and contrasts the treatment effects that each one estimates. Additionally, we propose possible matching techniques for use with multiple, nominal categorical treatments, and use simulations to show how such algorithms can yield improved covariate similarity between those in the matched sets, relative the pre-matched cohort. To sum, this manuscript provides a synopsis of how to notate and use causal methods for categorical treatments
Bayesian Record Linkage with Variables in One File
In many healthcare and social science applications, information about units
is dispersed across multiple data files. Linking records across files is
necessary to estimate the associations of interest. Common record linkage
algorithms only rely on similarities between linking variables that appear in
all the files. Moreover, analysis of linked files often ignores errors that may
arise from incorrect or missed links. Bayesian record linking methods allow for
natural propagation of linkage error, by jointly sampling the linkage structure
and the model parameters. We extend an existing Bayesian record linkage method
to integrate associations between variables exclusive to each file being
linked. We show analytically, and using simulations, that this method can
improve the linking process, and can yield accurate inferences. We apply the
method to link Meals on Wheels recipients to Medicare Enrollment records
Phase Coupled Meta-analysis: sensitive detection of oscillations in cell cycle gene expression, as applied to fission yeast
Background: Many genes oscillate in their level of expression through the cell division cycle. Previous studies have identified such genes by applying Fourier analysis to cell cycle time course experiments. Typically, such analyses generate p-values; i.e., an oscillating gene has a small p-value, and the observed oscillation is unlikely due to chance. When multiple time course experiments are integrated, p-values from the individual experiments are combined using classical meta-analysis techniques. However, this approach sacrifices information inherent in the individual experiments, because the hypothesis that a gene is regulated according to the time in the cell cycle makes two independent predictions: first, that an oscillation in expression will be observed; and second, that gene expression will always peak in the same phase of the cell cycle, such as S-phase. Approaches that simply combine p-values ignore the second prediction. Results: Here, we improve the detection of cell cycle oscillating genes by systematically taking into account the phase of peak gene expression. We design a novel meta-analysis measure based on vector addition: when a gene peaks or troughs in all experiments in the same phase of the cell cycle, the representative vectors add to produce a large final vector. Conversely, when the peaks in different experiments are in various phases of the cycle, vector addition produces a small final vector. We apply the measure to ten genome-wide cell cycle time course experiments from the fission yeast Schizosaccharomyces pombe, and detect many new, weakly oscillating genes. Conclusion: A very large fraction of all genes in S. pombe, perhaps one-quarter to one-half, show some cell cycle oscillation, although in many cases these oscillations may be incidental rather than adaptive.Statistic
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