Predictive probability of success
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Predictive probability of success (PPOS) is a statistics concept commonly used in the pharmaceutical industry including by health authorities to support decision making. In clinical trials, PPOS is the probability of observing a success in the future based on existing data. It is one type of probability of success. A Bayesian means by which the PPOS can be determined is through integrating the data's likelihood over possible future responses (posterior distribution).[1]
- Classification based on type of end point: Normal, binary, time to event.
- Classification based on the relationship between the trial providing data and the trial to be predicted
- Cross trial PPOS: using data from one trial to predict the other trial
- Within trial PPOS: using data at interim analysis to predict the same trial at final analysis
- Classification based on the relationship between the end point(s) with data and the end point to be predicted
- 1 to 1 PPOS: using one end point to predict the same end point
- 1 to 1* PPOS: using one end point to predict another different but correlated end point
Relationship with conditional power and predictive power
Conditional power is the probability of observing a statistically significance assuming the parameter equals to a specific value.[2] More specifically, these parameters could be treatment and placebo event rates that could be fixed in future observations.[3] This is a frequentist statistical power. Conditional power is often criticized for assuming the parameter equals to a specific value which is not known to be true. If the true value of the parameter is known, there is no need to do an experiment.
Predictive power addresses this issue assuming the parameter has a specific distribution. Predictive power is a Bayesian power. A parameter in Bayesian setting is a random variable. Predictive power is a function of a parameter(s), therefore predictive power is also a variable.
Both conditional power and predictive power use statistical significance as success criteria. However statistical significance is often not enough to define success. For example, health authorities often require the magnitude of treatment effect to be bigger than statistical significance to support a registration decision.
To address this issue, predictive power can be extended to the concept of PPOS. The success criteria for PPOS is not restricted to statistical significance. It can be something else such as clinical meaningful results. PPOS is conditional probability conditioned on a random variable, therefore it is also a random variable. The observed value is just a realization of the random variable.[4]
Relationship with posterior probability of success
Posterior probability of success is calculated from posterior distribution. PPOS is calculated from predictive distribution. Posterior distribution is the summary of uncertainties about the parameter. Predictive distribution has not only the uncertainty about parameter but also the uncertainty about estimating parameter using data. Posterior distribution and predictive distribution have same mean, but former has smaller variance.
Common issues in current practice of PPOS
PPOS is a conditional probability conditioned on randomly observed data and hence is a random variable itself. Currently common practice of PPOS uses only its point estimate in applications. This can be misleading. For a variable, the amount of uncertainty is an important part of the story. To address this issue, Tang[5] introduced PPOS credible interval to quantify the amount of its uncertainty. Tang advocates to use both PPOS point estimate and credible interval in applications such as decision making and clinical trial designs. Another common issue is the mixed use of posterior probability of success and PPOS. As described in the previous section, the 2 statistics are measured in 2 different metrics, comparing them is like comparing apples and oranges.