meeting_probs

Probabilities of meeting positivity threshold under target HR

These guidelines are meant to provide a pragmatic, yet rigorous, help to drug developers and decision makers, since they are shaped by three fundamental ingredients: the clinically determined margin of detriment on OS that is unacceptably high (delta null); the benefit on OS that is plausible given the mechanism of action of the novel intervention (delta alt); and the quantity of information (i.e. survival events) it is feasible to accrue given the clinical and drug development setting. The proposed guidelines facilitate transparent discussions between stakeholders focusing on the risks of erroneous decisions and what might be an acceptable trade-off between power and the false positive error rate.

Thibaud Coroller

monitOS

Monitoring Overall Survival in Pivotal Trials in Indolent
Cancers

Thomas Fleming

Lisa Hampson

Bharani Bharani-Dharan

Frank Bretz

Arunava Chakravartty

Evanthia Koukouli

Janet Wittes

Nigel Yateman

Emmanuel Zuber

Novartis Pharma AG

meeting_probs function

<dl><dt>summary</dt>
<dd>DataFrame. Summary dataframe from bounds.R</dd>
<dt>lhr_pos</dt>
<dd>List. Log HRs for positive threshold</dd>
<dt>lhr_target</dt>
<dd>Scalar. Target log HR to calculate the probability of meeting positivity thresholds</dd>
<dt>rand_ratio</dt>
<dd>Integer. If patients are randomized k:1 between experimental intervention and control,
rand_ratio should be inputted as k.
Example: if patients are randomized 1:1 between experimental and control, k=1. If patients are randomized 2:1
between experimental and control, k=2.</dd></dl>

Arguments

Probabilities of meeting positivity threshold under target HR — meeting_probs

<dl>

<dt>summary</dt>
<dd>DataFrame. Summary dataframe from bounds.R</dd>


<dt>lhr_pos</dt>
<dd>List. Log HRs for positive threshold</dd>


<dt>lhr_target</dt>
<dd>Scalar. Target log HR to calculate the probability of meeting positivity thresholds</dd>


<dt>rand_ratio</dt>
<dd>Integer. If patients are randomized k:1 between experimental intervention and control,
rand_ratio should be inputted as k.
Example: if patients are randomized 1:1 between experimental and control, k=1. If patients are randomized 2:1
between experimental and control, k=2.</dd>

</dl>

Probabilities of meeting positivity threshold under target HR

meeting_probs: Probabilities of meeting positivity threshold under target HR

Description

Usage

Value

Arguments