calcSigma

An internal function called by the <code>tmleMSM</code> function to calculate the variance-covariance matrix of the 
parameter estimates based on the influence curve of the specified MSM.

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

calcSigma function

<dl> <dt>hAV</dt>
<dd>values used in numerator of weights applied to the estimation procedure</dd> <dt>gAVW</dt>
<dd>\(P(A=a | V,W,T)*P(Delta=1 | A,V,W,T)\)</dd> <dt>Y</dt>
<dd>continuous or binary outcome variable</dd> <dt>Q</dt>
<dd>estimated \(P(Y | A, V, W, T, Delta=1)\), typically targeted values <code>Q*</code> are passed in</dd> <dt>mAV</dt>
<dd>predicted values for \(EY1\) from the MSM using the targeted estimates for \(psi\)</dd> <dt>covar.MSM</dt>
<dd>covariate values used as predictors for the MSM when <code>A=a</code></dd> <dt>covar.MSMA0</dt>
<dd>covariate values used as predictors for the MSM when <code>A=0</code></dd> <dt>covar.MSMA1</dt>
<dd>covariate values used as predictors for the MSM when <code>A=1</code></dd> <dt>I.V</dt>
<dd>indicator that observation is in stratum of interest</dd>
 
 <dt>Delta</dt>
<dd>indicator of missing outcome. <code>1</code> - observed, <code>0</code> - missing</dd> <dt>ub</dt>
<dd>upper bound on weights</dd> <dt>id</dt>
<dd>subject identifier</dd> <dt>family</dt>
<dd>&#8216;gaussian&#8217; for continuous outcomes, &#8216;binomial&#8217; for binary outcomes</dd></dl>

Arguments

Author

Calculate Variance-Covariance Matrix for MSM Parameters (calcSigma) — calcSigma

<dl>

 <dt>hAV</dt>
<dd>values used in numerator of weights applied to the estimation procedure</dd>

 <dt>gAVW</dt>
<dd>\(P(A=a | V,W,T)*P(Delta=1 | A,V,W,T)\)</dd>

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

 <dt>Q</dt>
<dd>estimated \(P(Y | A, V, W, T, Delta=1)\), typically targeted values <code>Q*</code> are passed in</dd>

 <dt>mAV</dt>
<dd>predicted values for \(EY1\) from the MSM using the targeted estimates for \(psi\)</dd>

 <dt>covar.MSM</dt>
<dd>covariate values used as predictors for the MSM when <code>A=a</code></dd>

 <dt>covar.MSMA0</dt>
<dd>covariate values used as predictors for the MSM when <code>A=0</code></dd>

 <dt>covar.MSMA1</dt>
<dd>covariate values used as predictors for the MSM when <code>A=1</code></dd>

 <dt>I.V</dt>
<dd>indicator that observation is in stratum of interest</dd>
 
 <dt>Delta</dt>
<dd>indicator of missing outcome. <code>1</code> - observed, <code>0</code> - missing</dd>

 <dt>ub</dt>
<dd>upper bound on weights</dd>

 <dt>id</dt>
<dd>subject identifier</dd>

 <dt>family</dt>
<dd>&#8216;gaussian&#8217; for continuous outcomes, &#8216;binomial&#8217; for binary outcomes</dd>

</dl>

calcSigma: Calculate Variance-Covariance Matrix for MSM Parameters (calcSigma)

Description

Usage

Value

Arguments

Author

See Also