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Power priors (Ibrahim and Chen; 2000 ) are a useful family of informative priors when relevant historical data is available. You can use power priors when you want to take historical data into account while analyzing similar, current data. This example illustrates fitting a Bayesian binomial model with a power prior and also demonstrates two useful features of PROC MCMC: using nonstandard distributions and assigning different likelihood functions to different subsets of the data.

Suppose that, in addition to data from a current study, you have similar data from a historical data set or pilot study. A power prior enables you to capture the information collected in the pilot study and incorporate it into analysis and inference in the current study. The historical data gets integrated via an informative prior and provides a convenient way of updating past knowledge.

A power prior can be expressed as a product of the weighted likelihood of conditional on the historical data and a prior distribution on the parameters before any data is observed. More formally, the power prior distribution of is written as

where are the historical data (from the pilot study), is the likelihood of conditional on historical data, is a discounting or scale parameter with that controls the amount of weight you want to put on the historical data, and is a prior for before the historical data are observed.

The posterior distribution of is

( 1 ) |

where represents the data from the current study, for , for represents the historical data, and represents the density function for a single observation in either the historical or the current data set.

You can combine the two data sets to form a new data set and rewrite the posterior distribution in Equation 1 as the following:

( 2 ) | |||||

The posterior distribution Equation 2 is used to model the hypothetical clinical trial setting described in this example. The following SAS statements create the PTRIALS data set that represents binomial data collected in a pilot study of a clinical trial.

data ptrials; input y n; datalines; 5 163 ;

In the INPUT statement, represents the number of successes out of independent Bernoulli trials. Similarly, for the current study, suppose you have the following hypothetical clinical trials data set BTRIALS .

data btrials; input y n @@; datalines; 2 86 2 69 1 71 1 113 1 103 ;

In order to fit a model with a power prior, first combine both the current and pilot study data sets. The indicator
GROUP
variable takes on the values '
`
CURRENT
`
' and '
`
PILOT
`
' for the current and pilot study data set, respectively. The following SAS statements demonstrate how to create a new combined data set
ALLDATA
.

data alldata; set btrials(in=i) ptrials; if i then group='current'; else group = 'pilot'; run;

Suppose you want to fit a Bayesian binomial model with a power prior for a hypothetical clinical trial setting with density

( 3 ) |

where is the success probability for observations.

The likelihood function for each of the observations is

( 4 ) |

where denotes a conditional probability mass function. The binomial density is evaluated at the specified value of and corresponding success probability . Then, a power prior is used to assign each observation in the combined data set a binomial or weighted binomial likelihood function depending whether the observation was from the current or pilot study, respectively.

Suppose the following power prior is placed on the success probability parameters, where represents the binomial likelihood:

( 5 ) |

The variables and represent the historical successes and sample size, respectively, and indicates a prior distribution. Suppose for this example that the discounting factor is set to . The diffuse prior expresses your lack of knowledge about the success probability.

According to Bayes’ theorem, the likelihood function and prior distribution determine the posterior distribution of as given in Equation 2 . PROC MCMC obtains samples from the desired posterior distribution, which is determined by the specified prior and likelihood. It does not require the exact form of the posterior distribution.

The following SAS statements use the likelihood and power prior distribution to fit the Bayesian binomial model with the historical data. The PROC MCMC statement invokes the procedure and specifies the input data set. The SEED= option specifies a seed for the random number generator, which guarantees the reproducibility of the random stream. The NMC= option specifies the number of posterior simulation iterations.

ods graphics on; proc mcmc data=alldata seed=1181 nmc=10000; parms p 0.2; begincnst; a0 = 0.5; endcnst; prior p ~ uniform(0,1); llike = logpdf('binomial',y,p,n); if (group='pilot') then llike = a0*llike; model general(llike); run; ods graphics off;

The PARMS statement designates as a parameter in the model and assigns an initial value of 0.2 to it. The programming statements enclosed by the BEGINCNST and ENDCNST statements are executed once per procedure call and designate the discount factor for the power prior.

The PRIOR statement specifies a prior as given in Equation 5 . The LLIKE= assignment statement assigns the log-likelihood function for as given in Equation 3 ; the LOGPDF function calculates the logarithm of the binomial function. The IF/THEN statement uses the GROUP indicator variable to weight the binomial likelihood when the observation is from the pilot study.

The MODEL statement uses the GENERAL function, which indicates that you are using a SAS statement to construct a nonstandard density or distribution. The argument is an expression that takes the value of the logarithm function of the prior or likelihood distribution. In this case, the nonstandard density is the binomial or weighted binomial likelihood, and the argument is the logarithm of the density.

Figure 1 displays convergence diagnostic plots for to assess whether the Markov chains have converged. Inferences should not be made if the Markov chain has not converged.

The trace plot shows that the mean of the Markov chain is constant over the graph and is stabilized. The chain is able to traverse the support of the target distribution, and the mixing is good. The trace plot shows that the Markov chain appears to have reached a stationary distribution.

The autocorrelation plot indicates low autocorrelation and efficient sampling. The kernel density plot shows a smooth, unimodal posterior marginal distribution for the parameter.

PROC MCMC produces formal diagnostic tests by default, but they are omitted here because informal checks on the chains, autocorrelation, and posterior density plots show desired stabilization and convergence.

Figure 2 reports summary and interval statistics for the parameter’s posterior distribution.

The MCMC Procedure

Posterior Summaries | ||||||
---|---|---|---|---|---|---|

Parameter | N | Mean |
Standard
Deviation |
Percentiles | ||

25% | 50% | 75% | ||||

p | 10000 | 0.0200 | 0.00602 | 0.0156 | 0.0194 | 0.0237 |

Posterior Intervals | |||||
---|---|---|---|---|---|

Parameter | Alpha | Equal-Tail Interval | HPD Interval | ||

p | 0.050 | 0.0100 | 0.0333 | 0.00900 | 0.0316 |

Using the power prior, the posterior mean of the success probability is 2.00% with a 95% credible interval of , providing evidence of a treatment effect.

Modeling the clinical trial data set in a Bayesian binomial model with a power prior is a good way to make use of the pilot study. It enables you to use a different class of informative priors and incorporates relevant historical data and information.