Statistics in medicine
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Statistics in medicine · Feb 2015
Accounting for uncertainty due to 'last observation carried forward' outcome imputation in a meta-analysis model.
Missing outcome data are a problem commonly observed in randomized control trials that occurs as a result of participants leaving the study before its end. Missing such important information can bias the study estimates of the relative treatment effect and consequently affect the meta-analytic results. Therefore, methods on manipulating data sets with missing participants, with regard to incorporating the missing information in the analysis so as to avoid the loss of power and minimize the bias, are of interest. ⋯ Allowing for uncertainty in the imputation process, precision is decreased depending on the priors used for sensitivity and specificity. Results on the significance of amisulpride versus conventional drugs differ between the standard LOCF approach and our model depending on prior beliefs on the imputation process. Our method can be regarded as a useful sensitivity analysis that can be used in the presence of concerns about the LOCF process.
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Statistics in medicine · Feb 2015
Bayesian dose-finding designs for combination of molecularly targeted agents assuming partial stochastic ordering.
Molecularly targeted agent (MTA) combination therapy is in the early stages of development. When using a fixed dose of one agent in combinations of MTAs, toxicity and efficacy do not necessarily increase with an increasing dose of the other agent. Thus, in dose-finding trials for combinations of MTAs, interest may lie in identifying the optimal biological dose combinations (OBDCs), defined as the lowest dose combinations (in a certain sense) that are safe and have the highest efficacy level meeting a prespecified target. ⋯ We demonstrate that our proposed method appropriately accounts for the partial ordering constraints, including potential plateaus on the dose-response surfaces, and is computationally efficient. We develop a dose-combination-finding algorithm to identify the OBDCs. We use simulations to compare the proposed designs with an alternative design based on Bayesian isotonic regression transformation and a design based on parametric change-point dose-toxicity and dose-efficacy models and demonstrate desirable operating characteristics of the proposed designs.
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Statistics in medicine · Feb 2015
Randomization, matching, and propensity scores in the design and analysis of experimental studies with measured baseline covariates.
In many experimental situations, researchers have information on a number of covariates prior to randomization. This information can be used to balance treatment assignment with respect to these covariates as well as in the analysis of the outcome data. In this paper, we investigate the use of propensity scores in both of these roles. ⋯ This procedure is compared with recently proposed methods in terms of resulting covariate balance and estimation efficiency. Properties of the estimators resulting from each procedure are compared with estimates which incorporate the propensity score in the analysis stage. Simulation results show that analytical adjustment for the propensity score yields results on par with those obtained through restricted randomization procedures and can be used in conjunction with such procedures to further improve inferential efficiency.
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Statistics in medicine · Dec 2014
What to expect from net reclassification improvement with three categories.
The net reclassification improvement (NRI) has become a popular measure of incremental usefulness of markers added to risk prediction models. However, the expected magnitude of the three-category NRI is not well understood, leading researchers to rely on statistical significance. In this paper, we describe a slight modification to the original definition of the NRI, which weighs each reclassification by the number of categories by which a given individual is reclassified. ⋯ We observe that the size of the intermediate risk category and the event rate have limited impact on the magnitude of the NRI. As expected, the NRI increases with the strength of the added marker, and this relationship appears fairly proportional for markers with non-weak net effect size (above 0.25). Furthermore, we conclude that using the NRI as a metric, it is harder to improve models that already perform well.
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Statistics in medicine · Nov 2014
Covariance adjustment on propensity parameters for continuous treatment in linear models.
Propensity scores are widely used to control for confounding when estimating the effect of a binary treatment in observational studies. They have been generalized to ordinal and continuous treatments in the recent literature. Following the definition of propensity function and its parameterizations (called the propensity parameter in this paper) proposed by Imai and van Dyk, we explore sufficient conditions for selecting propensity parameters to control for confounding for continuous treatments in the context of regression-based adjustment in linear models. ⋯ When the treatment is the only predictor in the structural, that is, causal model, it is sufficient to adjust only for the propensity parameters that characterize the expectation of the treatment variable or its functional form. When the structural model includes selected baseline covariates other than the treatment variable, those baseline covariates, in addition to the propensity parameters, must also be adjusted in the model. We demonstrate these points with an example estimating the dose-response relationship for the effect of erythropoietin on hematocrit level in patients with end-stage renal disease.