gbayes {Hmisc} | R Documentation

## Gaussian Bayesian Posterior and Predictive Distributions

### Description

`gbayes` derives the (Gaussian) posterior and optionally the predictive distribution when both the prior and the likelihood are Gaussian, and when the statistic of interest comes from a 2-sample problem. This function is especially useful in obtaining the expected power of a statistical test, averaging over the distribution of the population effect parameter (e.g., log hazard ratio) that is obtained using pilot data. `gbayes` is also useful for summarizing studies for which the statistic of interest is approximately Gaussian with known variance. An example is given for comparing two proportions using the angular transformation, for which the variance is independent of unknown parameters except for very extreme probabilities. A `plot` method is also given. This plots the prior, posterior, and predictive distributions on a single graph using a nice default for the x-axis limits and using the `labcurve` function for automatic labeling of the curves.

`gbayes2` uses the method of Spiegelhalter and Freedman (1986) to compute the probability of correctly concluding that a new treatment is superior to a control. By this we mean that a 1-`alpha` normal theory-based confidence interval for the new minus old treatment effect lies wholly to the right of `delta.w`, where `delta.w` is the minimally worthwhile treatment effect (which can be zero to be consistent with ordinary null hypothesis testing, a method not always making sense). This kind of power function is averaged over a prior distribution for the unknown treatment effect. This procedure is applicable to the situation where a prior distribution is not to be used in constructing the test statistic or confidence interval, but is only used for specifying the distribution of `delta`, the parameter of interest.

Even though `gbayes2` assumes that the test statistic has a normal distribution with known variance (which is strongly a function of the sample size in the two treatment groups), the prior distribution function can be completely general. Instead of using a step-function for the prior distribution as Spiegelhalter and Freedman used in their appendix, `gbayes2` uses the built-in `integrate` function for numerical integration. `gbayes2` also allows the variance of the test statistic to be general as long as it is evaluated by the user. The conditional power given the parameter of interest `delta` is `1 - pnorm((delta.w - delta)/sd + z)`, where z is the normal critical value corresponding to 1 - `alpha`/2.

`gbayesMixPredNoData` derives the predictive distribution of a statistic that is Gaussian given `delta` when no data have yet been observed and when the prior is a mixture of two Gaussians.

`gbayesMixPost` derives the posterior density or cdf of `delta` given the statistic `x`, when the prior for `delta` is a mixture of two Gaussians and when `x` is Gaussian given `delta`.

`gbayesMixPowerNP` computes the power for a test for `delta` > `delta.w` for the case where (1) a Gaussian prior or mixture of two Gaussian priors is used as the prior distribution, (2) this prior is used in forming the statistical test or credible interval, (3) no prior is used for the distribution of `delta` for computing power but instead a fixed single `delta` is given (as in traditional frequentist hypothesis tests), and (4) the test statistic has a Gaussian likelihood with known variance (and mean equal to the specified `delta`). `gbayesMixPowerNP` is handy where you want to use an earlier study in testing for treatment effects in a new study, but you want to mix with this prior a non-informative prior. The mixing probability `mix` can be thought of as the "applicability" of the previous study. As with `gbayes2`, power here means the probability that the new study will yield a left credible interval that is to the right of `delta.w`. `gbayes1PowerNP` is a special case of `gbayesMixPowerNP` when the prior is a single Gaussian.

### Usage

    gbayes(mean.prior, var.prior, m1, m2, stat, var.stat,
           n1, n2, cut.prior, cut.prob.prior=0.025)

    ## S3 method for class 'gbayes'
    plot(x, xlim, ylim, name.stat='z', ...)

    gbayes2(sd, prior, delta.w=0, alpha=0.05, upper=Inf, prior.aux)

    gbayesMixPredNoData(mix=NA, d0=NA, v0=NA, d1=NA, v1=NA,
                        what=c('density','cdf'))

    gbayesMixPost(x=NA, v=NA, mix=1, d0=NA, v0=NA, d1=NA, v1=NA,
                  what=c('density','cdf'))

    gbayesMixPowerNP(pcdf, delta, v, delta.w=0, mix, interval,
                     nsim=0, alpha=0.05)

    gbayes1PowerNP(d0, v0, delta, v, delta.w=0, alpha=0.05)

### Arguments

`mean.prior` | mean of the prior distribution

`cut.prior,cut.prob.prior,var.prior`

variance of the prior. Use a large number such as 10000 to effectively use a flat (noninformative) prior. Sometimes it is useful to compute the variance so that the prior probability that `stat` is greater than some impressive value `u` is only `alpha`. The correct `var.prior` to use is then `((u-mean.prior)/qnorm(1-alpha))^2`. You can specify `cut.prior=u` and `cut.prob.prior=alpha` (whose default is 0.025) in place of `var.prior` to have `gbayes` compute the prior variance in this manner.

`m1` | sample size in group 1
`m2` | sample size in group 2
`stat` | statistic comparing groups 1 and 2, e.g., log hazard ratio, difference in means, difference in angular transformations of proportions
`var.stat` | variance of `stat`, assumed to be known. `var.stat` should either be a constant (allowed if `n1` is not specified), or a function of two arguments which specify the sample sizes in groups 1 and 2. Calculations will be approximate when the variance is estimated from the data.
`x` | an object returned by `gbayes` or the value of the statistic which is an estimator of delta, the parameter of interest
`sd` | the standard deviation of the treatment effect
`prior` | a function of possibly a vector of unknown treatment effects, returning the prior density at those values
`pcdf` | a function computing the posterior CDF of the treatment effect `delta`, such as a function created by `gbayesMixPost` with `what="cdf"`.
`delta` | a true unknown single treatment effect to detect
`v` | the variance of the statistic `x`, e.g., `s^2 * (1/n1 + 1/n2)`. Neither `x` nor `v` need to be defined to `gbayesMixPost`, as they can be defined at run time to the function created by `gbayesMixPost`.
`n1` | number of future observations in group 1, for obtaining a predictive distribution
`n2` | number of future observations in group 2
`xlim` | vector of 2 x-axis limits. Default is the mean of the posterior plus or minus 6 standard deviations of the posterior.
`ylim` | vector of 2 y-axis limits. Default is the range over combined prior and posterior densities.
`name.stat` | label for x-axis. Default is `"z"`.
`...` | optional arguments passed to `labcurve` from `plot.gbayes`
`delta.w` | the minimum worthwhile treatment difference to detech. The default is zero for a plain uninteristing null hypothesis.
`alpha` | type I error, or more accurately one minus the confidence level for a two-sided confidence limit for the treatment effect
`upper` | upper limit of integration over the prior distribution multiplied by the normal likelihood for the treatment effect statistic. Default is infinity.
`prior.aux` | argument to pass to `prior` from `integrate` through `gbayes2`. Inside of `power` the argument must be named `prior.aux` if it exists. You can pass multiple parameters by passing `prior.aux` as a list and pulling off elements of the list inside `prior`. This setup was used because of difficulties in passing `...` arguments through `integrate` for some situations.
`mix` | mixing probability or weight for the Gaussian prior having mean `d0` and variance `v0`. `mix` must be between 0 and 1, inclusive.
`d0` | mean of the first Gaussian distribution (only Gaussian for `gbayes1PowerNP` and is a required argument)
`v0` | variance of the first Gaussian (only Gaussian for `gbayes1PowerNP` and is a required argument)
`d1` | mean of the second Gaussian (if `mix` < 1)
`v1` | variance of the second Gaussian (if `mix` < 1). Any of these last 5 arguments can be omitted to `gbayesMixPredNoData` as they can be provided at run time to the function created by `gbayesMixPredNoData`.
`what` | specifies whether the predictive density or the CDF is to be computed. Default is `"density"`.
`interval` | a 2-vector containing the lower and upper limit for possible values of the test statistic `x` that would result in a left credible interval exceeding `delta.w` with probability 1-`alpha`/2

`nsim`

defaults to zero, causing `gbayesMixPowerNP` to solve numerically for the critical value of `x`, then to compute the power accordingly. Specify a nonzero number such as 20000 for `nsim` to instead have the function estimate power by simulation. In this case 0.95 confidence limits on the estimated power are also computed. This approach is sometimes necessary if `uniroot` can't solve the equation for the critical value.

### Value

`gbayes` returns a list of class `"gbayes"` containing the following names elements: `mean.prior`,`var.prior`,`mean.post`, `var.post`, and if `n1` is specified, `mean.pred` and `var.pred`. Note that `mean.pred` is identical to `mean.post`. `gbayes2` returns a single number which is the probability of correctly rejecting the null hypothesis in favor of the new treatment. `gbayesMixPredNoData` returns a function that can be used to evaluate the predictive density or cumulative distribution. `gbayesMixPost` returns a function that can be used to evaluate the posterior density or cdf. `gbayesMixPowerNP` returns a vector containing two values if `nsim` = 0. The first value is the critical value for the test statistic that will make the left credible interval > `delta.w`, and the second value is the power. If `nsim` > 0, it returns the power estimate and confidence limits for it if `nsim` > 0. The examples show how to use these functions.

### Author(s)

Frank Harrell
Department of Biostatistics
Vanderbilt University School of Medicine
f.harrell@vanderbilt.edu

### References

Spiegelhalter DJ, Freedman LS, Parmar MKB (1994): Bayesian approaches to randomized trials. JRSS A 157:357–416. Results for `gbayes` are derived from Equations 1, 2, 3, and 6.

Spiegelhalter DJ, Freedman LS (1986): A predictive approach to selecting the size of a clinical trial, based on subjective clinical opinion. Stat in Med 5:1–13.

Joseph, Lawrence and Belisle, Patrick (1997): Bayesian sample size determination for normal means and differences between normal means. The Statistician 46:209–226.

Grouin, JM, Coste M, Bunouf P, Lecoutre B (2007): Bayesian sample size determination in non-sequential clinical trials: Statistical aspects and some regulatory considerations. Stat in Med 26:4914–4924.

### Examples

    # Compare 2 proportions using the var stabilizing transformation
    # arcsin(sqrt((x+3/8)/(n+3/4))) (Anscombe), which has variance
    # 1/[4(n+.5)]

    m1 <- 100;     m2 <- 150
    deaths1 <- 10; deaths2 <- 30

    f <- function(events,n) asin(sqrt((events+3/8)/(n+3/4)))
    stat <- f(deaths1,m1) - f(deaths2,m2)
    var.stat <- function(m1, m2) 1/4/(m1+.5) + 1/4/(m2+.5)
    cat("Test statistic:",format(stat),"  s.d.:",
        format(sqrt(var.stat(m1,m2))), "\n")
    #Use unbiased prior with variance 1000 (almost flat)
    b <- gbayes(0, 1000, m1, m2, stat, var.stat, 2*m1, 2*m2)
    print(b)
    plot(b)
    #To get posterior Prob[parameter > w] use
    # 1-pnorm(w, b$mean.post, sqrt(b$var.post))

    #If g(effect, n1, n2) is the power function to
    #detect an effect of 'effect' with samples size for groups 1 and 2
    #of n1,n2, estimate the expected power by getting 1000 random
    #draws from the posterior distribution, computing power for
    #each value of the population effect, and averaging the 1000 powers
    #This code assumes that g will accept vector-valued 'effect'
    #For the 2-sample proportion problem just addressed, 'effect'
    #could be taken approximately as the change in the arcsin of
    #the square root of the probability of the event

    g <- function(effect, n1, n2, alpha=.05) {
      sd <- sqrt(var.stat(n1,n2))
      z <- qnorm(1 - alpha/2)
      effect <- abs(effect)
      1 - pnorm(z - effect/sd) + pnorm(-z - effect/sd)
    }

    effects <- rnorm(1000, b$mean.post, sqrt(b$var.post))
    powers <- g(effects, 500, 500)
    hist(powers, nclass=35, xlab='Power')
    describe(powers)

    # gbayes2 examples
    # First consider a study with a binary response where the
    # sample size is n1=500 in the new treatment arm and n2=300
    # in the control arm.  The parameter of interest is the
    # treated:control log odds ratio, which has variance
    # 1/[n1 p1 (1-p1)] + 1/[n2 p2 (1-p2)].  This is not
    # really constant so we average the variance over plausible
    # values of the probabilities of response p1 and p2.  We
    # think that these are between .4 and .6 and we take a
    # further short cut

    v <- function(n1, n2, p1, p2) 1/(n1*p1*(1-p1)) + 1/(n2*p2*(1-p2))
    n1 <- 500; n2 <- 300
    ps <- seq(.4, .6, length=100)
    vguess <- quantile(v(n1, n2, ps, ps), .75)
    vguess
    #        75%
    # 0.02183459

    # The minimally interesting treatment effect is an odds ratio
    # of 1.1.  The prior distribution on the log odds ratio is
    # a 50:50 mixture of a vague Gaussian (mean 0, sd 100) and
    # an informative prior from a previous study (mean 1, sd 1)

    prior <- function(delta)
      0.5*dnorm(delta, 0, 100)+0.5*dnorm(delta, 1, 1)
    deltas <- seq(-5, 5, length=150)
    plot(deltas, prior(deltas), type='l')

    # Now compute the power, averaged over this prior
    gbayes2(sqrt(vguess), prior, log(1.1))
    # [1] 0.6133338

    # See how much power is lost by ignoring the previous
    # study completely

    gbayes2(sqrt(vguess), function(delta)dnorm(delta, 0, 100), log(1.1))
    # [1] 0.4984588

    # What happens to the power if we really don't believe the treatment
    # is very effective?  Let's use a prior distribution for the log
    # odds ratio that is uniform between log(1.2) and log(1.3).
    # Also check the power against a true null hypothesis

    prior2 <- function(delta) dunif(delta, log(1.2), log(1.3))
    gbayes2(sqrt(vguess), prior2, log(1.1))
    # [1] 0.1385113

    gbayes2(sqrt(vguess), prior2, 0)
    # [1] 0.3264065

    # Compare this with the power of a two-sample binomial test to
    # detect an odds ratio of 1.25
    bpower(.5, odds.ratio=1.25, n1=500, n2=300)
    #     Power
    # 0.3307486

    # For the original prior, consider a new study with equal
    # sample sizes n in the two arms.  Solve for n to get a
    # power of 0.9.  For the variance of the log odds ratio
    # assume a common p in the center of a range of suspected
    # probabilities of response, 0.3.  For this example we
    # use a zero null value and the uniform prior above

    v   <- function(n) 2/(n*.3*.7)
    pow <- function(n) gbayes2(sqrt(v(n)), prior2)
    uniroot(function(n) pow(n)-0.9, c(50,10000))$root
    # [1] 2119.675
    # Check this value
    pow(2119.675)
    # [1] 0.9

    # Get the posterior density when there is a mixture of two priors,
    # with mixing probability 0.5.  The first prior is almost
    # non-informative (normal with mean 0 and variance 10000) and the
    # second has mean 2 and variance 0.3.  The test statistic has a value
    # of 3 with variance 0.4.
    f <- gbayesMixPost(3, 4, mix=0.5, d0=0, v0=10000, d1=2, v1=0.3)

    args(f)

    # Plot this density
    delta <- seq(-2, 6, length=150)
    plot(delta, f(delta), type='l')

    # Add to the plot the posterior density that used only
    # the almost non-informative prior
    lines(delta, f(delta, mix=1), lty=2)

    # The same but for an observed statistic of zero
    lines(delta, f(delta, mix=1, x=0), lty=3)

    # Derive the CDF instead of the density
    g <- gbayesMixPost(3, 4, mix=0.5, d0=0, v0=10000, d1=2, v1=0.3,
                       what='cdf')
    # Had mix=0 or 1, gbayes1PowerNP could have been used instead
    # of gbayesMixPowerNP below

    # Compute the power to detect an effect of delta=1 if the variance
    # of the test statistic is 0.2
    gbayesMixPowerNP(g, 1, 0.2, interval=c(-10,12))

    # Do the same thing by simulation
    gbayesMixPowerNP(g, 1, 0.2, interval=c(-10,12), nsim=20000)

    # Compute by what factor the sample size needs to be larger
    # (the variance needs to be smaller) so that the power is 0.9
    ratios <- seq(1, 4, length=50)
    pow <- single(50)
    for(i in 1:50)
      pow[i] <- gbayesMixPowerNP(g, 1, 0.2/ratios[i], interval=c(-10,12))[2]

    # Solve for ratio using reverse linear interpolation
    approx(pow, ratios, xout=0.9)$y

    # Check this by computing power
    gbayesMixPowerNP(g, 1, 0.2/2.1, interval=c(-10,12))
    # So the study will have to be 2.1 times as large as earlier thought

[Package Hmisc version 3.17-3 Index]