pairwiseHR

Log hazard ratio estimate and its standard error estimate are calculated from dichotomized dataset simultaneously.

Network meta-analysis for survival outcome data often involves several studies only involve dichotomized outcomes (e.g., the numbers of event and sample sizes of individual arms). To combine these different outcome data, Woods et al. (2010) <doi:10.1186/1471-2288-10-54> proposed a Bayesian approach using complicated hierarchical models. Besides, frequentist approaches have been alternative standard methods for the statistical analyses of network meta-analysis, and the methodology has been well established. We proposed an easy-to-implement method for the network meta-analysis based on the frequentist framework in Noma and Maruo (2025) <doi:10.1101/2025.01.23.25321051>. This package involves some convenient functions to implement the simple synthesis method.

Hisashi Noma

survNMA

Network Meta-Analysis Combining Survival and Count Outcomes

pairwiseHR function

<dl><dt>treat</dt>
<dd>The treatments of individual arms.</dd><dt>studlab</dt>
<dd>ID variable of studies.</dd><dt>event</dt>
<dd>The number of events of individual arms.</dd><dt>n</dt>
<dd>The number of sample size of individual arms.</dd><dt>data</dt>
<dd>The dataset object.</dd></dl>

Arguments

Computation of log hazard ratio estimate and its standard error estimate from dichotomized dataset — pairwiseHR

<dl>

<dt>treat</dt>
<dd>The treatments of individual arms.</dd>

<dt>studlab</dt>
<dd>ID variable of studies.</dd>

<dt>event</dt>
<dd>The number of events of individual arms.</dd>

<dt>n</dt>
<dd>The number of sample size of individual arms.</dd>

<dt>data</dt>
<dd>The dataset object.</dd>

</dl>

pairwiseHR: Computation of log hazard ratio estimate and its standard error estimate from dichotomized dataset

Description

Usage

Value

Arguments

References

Examples