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Joint Modeling and Clustering Paired Generalized Longitudinal Trajectories with Application to Cocaine Abuse Treatment Data
Clustering Functional data analysis Exponential family Joint modeling
2016/1/26
In a cocaine dependence treatment study, we have paired binary longitudinal tra-jectories that record the cocaine use patterns of each patient before and after a treat-ment. To better understand the d...
Mann-Whitney Test with Adjustments to Pre-treatment Variables for Missing Values and Observational Study
Dimension reduction Kernel smoothing Mann-Whitney statistic Missing out- comes Observational studies Selection bias
2016/1/25
The conventional Wilcoxon/Mann-Whitney test can be invalid for comparing treatment effects in the presence of missing values or in observational studies. This is because the missingness of the outcome...
Joint Modeling and Clustering Paired Generalized Longitudinal Trajectories with Application to Cocaine Abuse Treatment Data
Clustering functional data analysis exponential family joint modeling EM algorithm
2016/1/20
In a cocaine dependence treatment study, we have paired binary longitudinal tra-jectories that record the cocaine use patterns of each patient before and after a treat-ment. To better understand the d...
Mann-Whitney Test with Adjustments to Pre-treatment Variables for Missing Values and Observational Study
Dimension reduction Kernel smoothing Mann-Whitney statistic
2016/1/20
The conventional Wilcoxon/Mann-Whitney test can be invalid for comparing treatment effects in the presence of missing values or in observational studies. This is because the missingness of the outcome...
Qualitative implication and equivalence relations between treatment effects on surrogates and endpoints
Causal effect Prentice’s criterion Surrogate endpoint
2016/1/19
By the criterion for surrogates proposed by Prentice (1989), a null treatment effect on a surrogate implies a null treatment effect on a true endpoint.In this paper, we show that there may exist simul...
Adjusting for Treatment Effects in Studies of Quantitative Traits
Quantitative traits Imputation QTL mapping Single-marker analysis Mixed models Kruskal-Wallis test
2013/6/14
A population-based study of a quantitative trait, e.g. Blood Pressure(BP) may be seriously compromised when the trait is subject to the effects of a treatment. Without appropriate corrections this can...
Supplementary Appendix for "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"
Supplementary Appendix "Inference on Treatment Effects After Selection Amongst High-Dimensional Controls"
2013/6/14
In this supplementary appendix we provide additional results, omitted proofs and extensive simulations that complement the analysis of the main text
Estimating treatment effect heterogeneity in randomized program evaluation
Causal inference individualized treatment rules LASSO moderation variable selection
2013/6/14
When evaluating the efficacy of social programs and medical treatments using randomized experiments, the estimated overall average causal effect alone is often of limited value and the researchers mus...
Set-valued dynamic treatment regimes for competing outcomes
Set-valued dynamic treatment regimes competing outcomes
2012/9/19
Dynamic treatment regimes operationalize the clinical decision process as a sequence of functions, one for each clinical decision, where each function takes as input up-to-date patient information and...
Performance guarantees for individualized treatment rules
Decision making l1-penalized least squares value
2011/6/17
Because many illnesses show heterogeneous response to treatment,
there is increasing interest in individualizing treatment to patients
[Arch. Gen. Psychiatry 66 (2009) 128–133]. An individualized
t...
Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview
Observational study Potential response Sequential decision theory
2010/10/19
We consider the problem of learning about and comparing the consequences of dynamic treatment strategies on the basis of observational data. We formulate this within a probabilistic decision-theoreti...
Hidden Markov models for alcoholism treatment trial data
Hidden Markov models alcoholism clinical trial
2010/10/19
In a clinical trial of a treatment for alcoholism, a common response variable of interest is the number of alcoholic drinks consumed by each subject each day, or an ordinal version of this response, ...
A practical illustration of the importance of realistic individualized treatment rules in causal inference
Experimental Treatment Assignment assumption positivity assumption dynamic treatment rules physical activity
2009/9/16
The effect of vigorous physical activity on mortality in the elderly is difficult to estimate using conventional approaches to causal inference that define this effect by comparing the mortality risks...