  Determination of posterior probability for go-no-go decision and predictive
power are becoming increasingly common for resource optimization in clinical
investigation. There are vast published literature on these topics; however,
the terminologies are not consistently used across the literature. Further,
there is a lack of consolidated presentation of various concepts of the
probability of success. We attempted to fill this gap. This paper first
provides a detailed derivation of these probability of success measures under
the frequentist and Bayesian paradigms in a general setting. Subsequently, we
have presented the analytical formula for these probability of success measures
for continuous, binary, and time-to-event endpoints separately. This paper can
be used as a single point reference to determine the following measures: (a)
the conditional power (CP) based on interim results, (b) the predictive power
of success (PPoS) based on interim results with or without prior distribution,
and (d) the probability of success (PoS) for a prospective trial at the design
stage. We have discussed both clinical success and trial success. This paper's
discussion is mostly based on the normal approximation for prior distribution
and the estimate of the parameter of interest. Besides, predictive power using
the beta prior for the binomial case is also presented. Some examples are given
for illustration. R functions to calculate CP and PPoS are available through
the LongCART package. An R shiny app is also available at
https://ppos.herokuapp.com/.
