Description Details The estimation of the propensity score Propensity score methods Balance checks Effect estimation Author(s) Examples

nonrandom offers a comprehensive data analysis if stratification, matching and covariate adjustment by the propensity score should be applied. Several functions are implemented, starting from the selection of the propensity score model up to estimating propensity score based treatment effects. All functions can be applied separately as well as combined.

Package: | nonrandom |

Type: | Package |

Version: | 1.3 |

Date: | 2012-10-05 |

License: | What license is it under? |

The propensity score is
the conditional probability of receiving a certain treatment given
patient's covariates. It is generally unknown and has to be estimated,
e.g. using logistic regression. The selection of an appropriate
propensity score model is mostly difficult. A measure describing the
extent to which a covariate is confounding the treatment effect on
outcome is implemented in `relative.effect()`

. `pscore`

estimates the propensity score.

Propensity score methods aims to
balance covariate distributions between treatment groups and allow for
estimating marginal effects. Stratification (`ps.makestrata()`

)
and matching (`ps.match`

)) by the estimated propensity score are
the most popular methods to eliminate imbalances in covariate
distributions. `ps.estimate`

also offers covariate adjustment by
the propensity score.

An important, but often neglected issue in
propensity score based data analyses is the check of covariate
distributions between treatment groups after stratification and
matching. Both graphical checks (`dist.plot`

and
`plot.stdf`

) as well as classical statistical tests and
standardized differences (`ps.balance`

) can be used to examine
covariate distributions.

After stratification and matching by the
propensity score, treatment effects are estimated in the stratified an
matched data, respectively. `ps.estimate`

estimates those effects
depending on the data structure. It offers an additional adjustment
for residual imbalances in the stratified or matched data as well as
the estimation of effect using traditional regression models including
covariate adjustment by the propensity score.

Susanne Stampf

Maintainer: Susanne Stampf <susanne.stampf@usb.ch>

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | ```
## data on quality of life
data(stu1)
## estimate relative effects for covariates 'tgr' and 'age' regarding
## the effect of treatment 'therapie' on response 'pst'
rel.eff <- relative.effect(data = stu1,
formula = pst~therapie+tgr+age)
## estimate the propensity score
ps <- pscore(data = stu1,
formula = therapie~tgr+age)
## stratify data
strata <- ps.makestrata(object = ps)
## match data in a ratio of 1:1 with a caliper size of 0.5
match <- ps.match(object = ps,
ratio = 1,
caliper = 0.5,
givenTmatchingC = FALSE)
## graphical check of distribution of both covariates between
## treatment groups in the matched data
bal.plot1 <- dist.plot(object = strata,
sel = c("tmass"))
bal.plot2 <- dist.plot(object = match,
sel = c("alter"),
plot.type = 2,
compare = TRUE)
## calculate standardized differences of both covariates
## in case of matched data
bal.table <- ps.balance(object = match,
sel = c("tgr","age"),
method = "stand.diff",
alpha = 20)
## estimate propensity score based effects and in comparion the
## regression based treatment effect on response
ps.est <- ps.estimate(object = strata,
resp = "pst",
regr = pst~therapie+tgr+age)
``` |

```
Loading required package: lme4
Loading required package: Matrix
Argument 'givenTmatchingC'=FALSE: Treated elements were matched to each untreated element.
Loading required package: colorspace
```

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