lrapproximations

Assess the accuracy of the log rank statistic approximation to the true value, in the case without censoring. Provides plots of statistics, and empirical test level.

Proportional hazards estimation in the presence of a partially monotone likelihood has difficulties, in that finite estimators do not exist.  These difficulties are related to those arising from logistic and multinomial regression.  References for methods are given in the separate function documents.  Supported by grant NSF DMS 1712839.

John E Kolassa

PHInfiniteEstimates

Tools for Inference in the Presence of a Monotone Likelihood

lrapproximations function

<dl><dt>nobs</dt>
<dd>number of observations in each group. This currently supports only equal group size data sets.</dd>
<dt>ratio</dt>
<dd>Ratio of group means; use 1 for null.</dd>
<dt>nsamp</dt>
<dd>Monte Carlo sample size.</dd></dl>

Arguments

Assess the accuracy of the log rank statistic approximation to the true value, in the case without censoring.  Provides plots of statistics, and empirical test level. — lrapproximations

<dl>

<dt>nobs</dt>
<dd>number of observations in each group. This currently supports only equal group size data sets.</dd>


<dt>ratio</dt>
<dd>Ratio of group means; use 1 for null.</dd>


<dt>nsamp</dt>
<dd>Monte Carlo sample size.</dd>

</dl>

Assess the accuracy of the log rank statistic approximation to the true value, in the case without censoring.  Provides plots of statistics, and empirical test level.

lrapproximations: Assess the accuracy of the log rank statistic approximation to the true value, in the case without censoring. Provides plots of statistics, and empirical test level.

Description

Usage

Value

Arguments

Examples