oneStepATT

An internal function called by the <code>tmle</code> function to calculate the additive treatment effect among the treated (ATT) using a universal least favorable submodel (on the transformed scale if outcomes are continuous). The function is called a second time with updated arguments to calculate the additive treatment effect among the controls (ATC). Missingness in the outcome data is allowed.

Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006.  This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC).  The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes.  Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values.  The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.

Susan Gruber

tmle

Targeted Maximum Likelihood Estimation

Mark van der Laan

Chris Kennedy

oneStepATT function

<dl> <dt>Y</dt>
<dd>continuous or binary outcome variable</dd> <dt>A</dt>
<dd>binary treatment indicator, <code>1</code> - treatment, <code>0</code> - control</dd> <dt>Delta</dt>
<dd>indicator of missing outcome. <code>1</code> - observed, <code>0</code> - missing</dd> <dt>Q</dt>
<dd>a 3-column matrix <code>(Q(A,W), Q(1,W), Q(0,W))</code></dd> <dt>g1W</dt>
<dd>treatment mechanism estimates, \(P(A=1|W)\)</dd> <dt>pDelta1</dt>
<dd>censoring mechanism estimates, a 2-column matrix [\(P(Delta=1|A=0,W)\), \(P(Delta=1|A=1,W)\)]</dd> <dt>depsilon</dt>
<dd>step size for delta moves, set to 0.001</dd> <dt>max_iter</dt>
<dd>maximum number of iterations before terminating without convergence</dd> <dt>gbounds</dt>
<dd>bounds on the propensity score for untreated subjects</dd> <dt>Qbounds</dt>
<dd>alpha bounds on the logit scale</dd> <dt>obsWeights</dt>
<dd>sampling weights</dd></dl>

Arguments

Author

Calculate Additive treatment effect among the treated (oneStepATT) — oneStepATT

<dl>

 <dt>Y</dt>
<dd>continuous or binary outcome variable</dd>

 <dt>A</dt>
<dd>binary treatment indicator, <code>1</code> - treatment, <code>0</code> - control</dd>

 <dt>Delta</dt>
<dd>indicator of missing outcome. <code>1</code> - observed, <code>0</code> - missing</dd>

 <dt>Q</dt>
<dd>a 3-column matrix <code>(Q(A,W), Q(1,W), Q(0,W))</code></dd>

 <dt>g1W</dt>
<dd>treatment mechanism estimates, \(P(A=1|W)\)</dd>

 <dt>pDelta1</dt>
<dd>censoring mechanism estimates, a 2-column matrix [\(P(Delta=1|A=0,W)\), \(P(Delta=1|A=1,W)\)]</dd>

 <dt>depsilon</dt>
<dd>step size for delta moves, set to 0.001</dd>

 <dt>max_iter</dt>
<dd>maximum number of iterations before terminating without convergence</dd>

 <dt>gbounds</dt>
<dd>bounds on the propensity score for untreated subjects</dd>

 <dt>Qbounds</dt>
<dd>alpha bounds on the logit scale</dd>

 <dt>obsWeights</dt>
<dd>sampling weights</dd>

</dl>

oneStepATT: Calculate Additive treatment effect among the treated (oneStepATT)

Description

Usage

Value

Arguments

Author

See Also