It generates simulated dataset to test multiple stage learning algorithms. The outcomes are generated based on a pattern mixture model using a latent variable with 4 categories. For each category, X has a multivariate normal distribution and each category is assigned a vector of optimal treatments V. Specifically, we generate centroids of the classes from a multivariate normal distribution mean 0 and std 5. We add the centroids to the first pinfo dimension of the vectors of feature variables X simulated from multivariate normal distribution with pinfo+pnoise dimensions.
● Data Source:
CranContrib
● Keywords:
● Alias: make_2classification
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Olearning
(Package: DTRlearn) :
Multiple stage Improved Olearning
This function implements multiple stage O-learning (with improved single stage O-learing) to find optimal DTR by backward induction.
● Data Source:
CranContrib
● Keywords: models
● Alias: Olearning
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Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by time-varying subject-specific features and intermediate outcomes observed in previous stages. For many complex chronic disorders, DTRs operationalize the sequential process of medical decision-making. Sequential Multiple Assignment Randomized Trials (SMARTs) are proposed to best construct DTRs which offer a causal interpretation of their comparisons through randomization at each critical decision point. Machine learning methods such as O-learning (Zhao et. al. 2012,2014), Q-learning (Murphy et. al. 2007, Zhao et.al. 2009) and P-learning (Liu et. al. 2014, 2015) have been proposed to estimate the optimal individualized treatment from a SMART.
● Data Source:
CranContrib
● Keywords:
● Alias: DTRlearn, DTRlearn-package
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Improved outcome weighted learning, first take residuals; and then use cross validation to choose best tuning parameter for wsvm. Return the O-learning models with best tuning parameters. Improving from Zhao 2012, the improved outcome weighted learning first take main effect out by regression; the weights are absolute value of the residual; more details can be found in Liu et al. (2015).
● Data Source:
CranContrib
● Keywords:
● Alias: Olearning_Single
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plot.qlearn
(Package: DTRlearn) :
Plot the linear coefficients of interaction
The function plots the standardized coefficients from linear regression, i.e. it divides the coefficients for a selected set of variables by the L2 norm.
● Data Source:
CranContrib
● Keywords:
● Alias: plot.qlearn
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It generates simulated datasets to test single stage DTR learning algorithms. The outcomes are generated based on a pattern mixture model using a latent variable with 2 categories. Category 1 has the optimal treatment y=1, and category 2 has y=-1. The feature variables X has a multivariate normal distribution. Specifically, we generate centroids of the classes from a multivariate normal distribution mean 0 and std 5. We add the centroids to the first pinfo dimension of the vectors of feature variables X simulated from multivariate normal distribution with pinfo+pnoise dimensions. The observed treatment assignments A are completely random to be 1 and -1 with probability 0.5, and the outcomes are generated as: R_1=0, R_2= 1.5A*y+N(0,1).
● Data Source:
CranContrib
● Keywords:
● Alias: make_classification
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plot.linearcl
(Package: DTRlearn) :
Plot coefficients for SVM with linear kernel
The function visualize the contribution of each feature variable by plotting a selected subset of standardized coefficients from SVM with linear kernel, the coefficients are standardized dividing by the L2 norm of the subvector.
● Data Source:
CranContrib
● Keywords:
● Alias: plot.linearcl
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This funciton impletment multiple stage Q-learning.
● Data Source:
CranContrib
● Keywords: models
● Alias: Qlearning
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This is the Plearning to estimate optimal multistage DTR. It implements improved Olearning to estimate optimal treatment rules for each stage backwardly. And it also borrows idea from Q-learning to utilize the estimated optimal outcomes for later stages.
● Data Source:
CranContrib
● Keywords:
● Alias: Plearning
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It implements single stage Q-learning. Q-learning estimates optimal treatment option by fitting a regression model with treatment, feature variable and their interactions. The optimal treatment option is the the sign of the interaction term which maximize the predicted value from the regression model.
● Data Source:
CranContrib
● Keywords:
● Alias: Qlearning_Single
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