df_netKmax2


Simulated dataset containing measured i.i.d. baseline covariate (<code>W1</code>), dependent binary exposure (<code>A</code>)
 and binary binary outcome (<code>Y</code>), along with a known network of friends encoded by strings on space separated
 friend IDs in <code>Net_str</code>.
 The 1,000 baseline covariates <code>W1</code> were sampled as i.i.d.,
 while the exposure value of <code>A</code> for each observation <code>i</code> was sampled
 conditionally on the value of <code>i</code>'s baseline covariate <code>W1[i]</code>,
 as well as the baseline covariate values of <code>i</code>'s friends in <code>Net_str</code>.
 Similarly, the binary outcome <code>Y</code> for each observation was generated conditionally on <code>i</code>'s
 exposure and baseline covariates values in (<code>W1[i]</code>,<code>A[i]</code>),
 as well as the values of exposures and baseline covariates of <code>i</code>'s friends in <code>Net_str</code>.
 Individual variables are described below.


datasets

Estimation of average causal effects for single time point
interventions in network-dependent data (e.g., in the presence of spillover
and/or interference). Supports arbitrary interventions (static or
stochastic). Implemented estimation algorithms are the targeted maximum
likelihood estimation (TMLE), the inverse-probability-of-treatment (IPTW)
estimator and the parametric G-computation formula estimator. Asymptotically
correct influence-curve-based confidence intervals are constructed for the
TMLE and IPTW. The data are assumed to consist of rows of unit-specific
observations, each row i represented by variables (F.i,W.i,A.i,Y.i), where
F.i is a vector of friend IDs of unit i (i's network), W.i is a vector of
i's baseline covariates, A.i is i's exposure (can be binary, categorical or
continuous) and Y.i is i's binary outcome. Exposure A.i depends on
(multivariate) user-specified baseline summary measure(s) sW.i, where sW.i
is any function of i's baseline covariates W.i and the baseline covariates
of i's friends in F.i. Outcome Y.i depends on sW.i and (multivariate)
user-specified summary measure(s) sA.i, where sA.i is any function of i's
baseline covariates and exposure (W.i,A.i) and the baseline covariates and
exposures of i's friends. The summary measures are defined with functions
def.sW and def.sA. See ?'tmlenet-package' for a general overview.

Oleg Sofrygin

tmlenet

Targeted Maximum Likelihood Estimation for Network Data

df_netKmax2 function

A data frame with 1,000 dependent observations (rows) and 6 variables:
<dl>
  <dt>IDs</dt><dd>unique observation identifier</dd> <dt>Y</dt><dd>binary outcome that depends on unit's baseline covariate value and exposure in <code>W1</code>, <code>A</code>, as well as the
     baseline covariate values and exposures <code>W1</code>, <code>A</code> of observations in the friend network <code>Net_str</code></dd> <dt>nFriends</dt><dd>number of friends for each observation (row), range 0-2</dd> <dt>W1</dt><dd>binary baseline covariate (independent)</dd> <dt>A</dt><dd>binary exposure status that depends on unit's baseline covariate value in <code>W1</code>, as well as the
     baseline covariate values <code>W1</code> of observations in the friend network <code>Net_str</code></dd> <dt>Net_str</dt><dd>each observation is a string of space separated friend IDs (this can be either observation IDs or
     just space separated friend row numbers)</dd> </dl>

df_netKmax2: An example of a row-dependent dataset with known network of at most 2 friends.

Description

Usage

Arguments

Format