Author: Ndoh Penn
Date: 2026-05-29

# Robust, Sceptical, and Power Priors

#### Ndoh Penn

#### 2026-05-29

# 1 Overview

Beyond the elicited informative prior, regulatory submissions typically require one or more alternative prior specifications to demonstrate robustness. bayprior provides three well-established alternatives:

Prior type | Function | Method | Reference
--- | --- | --- | ---
Robust mixture | `robust_prior()` | Mixes informative + vague | Schmidli et al. (2014)
Sceptical | `sceptical_prior()` | Centred at null effect | Spiegelhalter et al. (1994)
Power prior | `calibrate_power_prior()` | Down-weights historical data | Ibrahim & Chen (2000)

# 2 Robust Mixture Prior

## 2.1 Concept

A robust prior protects against prior misspecification by mixing the informative elicited prior with a vague (diffuse) component:

\[\pi_{\text{robust}}(\theta) = (1 - w) \cdot \pi_{\text{informative}}(\theta) + w \cdot \pi_{\text{vague}}(\theta)\]

The vague component — a wide Normal centred at the informative prior mean — ensures the posterior is never completely dominated by a conflicting prior. The default weight is \(w = 0.20\) (80% informative, 20% vague).

## 2.2 Usage

    informative <- elicit_beta(
      mean      = 0.30,
      sd        = 0.08,
      method    = "moments",
      label     = "Response rate"
    )

    rob <- robust_prior(
      informative  = informative,
      vague_weight = 0.20,
      label        = "Robust mixture prior"
    )
    plot(rob)

    cat("Informative component weight:", 1 - rob$vague_weight, "\n")
    #> Informative component weight: 0.8
    cat("Vague component weight:      ", rob$vague_weight, "\n")
    #> Vague component weight:       0.2
    cat("Mixture mean:", round(rob$fit_summary$mean, 4), "\n")
    #> Mixture mean: 0.3
    cat("Mixture SD:  ", round(rob$fit_summary$sd,   4), "\n")
    #> Mixture SD:   0.3649

## 2.3 Varying the Vague Weight

A higher vague weight makes the prior more diffuse and reduces its influence on the posterior:

    oldpar <- par(mfrow = c(1, 1))
    weights <- c(0.10, 0.20, 0.30, 0.50)
    cols    <- c("#185FA5", "#1D9E75", "#D85A30", "#888780")

    x <- seq(0, 0.8, length.out = 300)
    plot(x, bayprior:::.eval_density_vec(informative, x),
         type = "l", lwd = 2, col = "#185FA5",
         xlab = "Response rate", ylab = "Density",
         main = "Effect of vague weight on robust prior",
         ylim = c(0, 6))
    for (i in seq_along(weights)) {
      r <- robust_prior(informative, vague_weight = weights[i])
      lines(x, bayprior:::.eval_density_vec(r, x),
            col = cols[i], lwd = 1.5, lty = i + 1)
    }
    legend("topright",
           legend = c("Informative",
                      paste0("w = ", weights)),
           col    = c("#185FA5", cols),
           lwd    = 2,
           lty    = c(1, 2, 3, 4, 5),
           bty    = "n", cex = 0.8)

    par(oldpar)

## 2.4 Vague SD Multiplier

The vague component’s SD defaults to 10× the informative prior’s SD. Adjust with `vague_sd`:

    rob_narrow <- robust_prior(informative, vague_weight = 0.20,
                               vague_sd = 2 * informative$fit_summary$sd)
    rob_wide   <- robust_prior(informative, vague_weight = 0.20,
                               vague_sd = 20 * informative$fit_summary$sd)

    cat("Narrow vague SD: ", round(rob_narrow$components$vague$fit_summary$sd, 3), "\n")
    #> Narrow vague SD:  0.16
    cat("Default vague SD:", round(rob$components$vague$fit_summary$sd, 3), "\n")
    #> Default vague SD: 0.8
    cat("Wide vague SD:   ", round(rob_wide$components$vague$fit_summary$sd, 3), "\n")
    #> Wide vague SD:    1.6

# 3 Sceptical Prior

## 3.1 Concept

The sceptical prior (Spiegelhalter & Freedman, 1994) represents the view of a conservative regulator who is sceptical of a treatment effect. It is centred at the null value of the treatment effect with width calibrated to a chosen strength of scepticism.

This is the FDA’s recommended sensitivity prior for trials using informative priors: the trial conclusions should hold even under a prior that places most mass at “no effect.”

## 3.2 Normal Family (Mean Differences, Log Odds Ratios)

For continuous or log-scale quantities where the null is typically 0:

    sc_weak <- sceptical_prior(
      null_value = 0, family = "normal", strength = "weak",
      label = "Log OR (weak sceptic)"
    )
    sc_moderate <- sceptical_prior(
      null_value = 0, family = "normal", strength = "moderate",
      label = "Log OR (moderate sceptic)"
    )
    sc_strong <- sceptical_prior(
      null_value = 0, family = "normal", strength = "strong",
      label = "Log OR (strong sceptic)"
    )

    cat("Weak SD:    ", sc_weak$fit_summary$sd, "\n")
    #> Weak SD:     1
    cat("Moderate SD:", sc_moderate$fit_summary$sd, "\n")
    #> Moderate SD: 0.5
    cat("Strong SD:  ", sc_strong$fit_summary$sd, "\n")
    #> Strong SD:   0.25

    plot(sc_moderate)

The SD mapping by strength:

Strength | SD | Interpretation
--- | --- | ---
`weak` | 1.0 | Vague scepticism — wide prior around null
`moderate` | 0.5 | 2-SD departure from null has ~5% prior probability
`strong` | 0.25 | Very concentrated at null — very sceptical

## 3.3 Beta Family (Response Rates)

For binary endpoints, `null_value` must be in \((0, 1)\) — it represents the null response rate, not a difference:

    # Null response rate of 20%: sceptic believes treatment is no better than 20%
    sc_beta <- sceptical_prior(
      null_value = 0.20,
      family     = "beta",
      strength   = "moderate",
      label      = "Response rate (sceptical)"
    )
    plot(sc_beta)

## 3.4 Log-Normal Family (Hazard Ratios)

For hazard ratios, the null is HR = 1, which corresponds to `null_value = 0` on the log scale:

    sc_hr <- sceptical_prior(
      null_value = 0,         # log(1) = 0, i.e. HR = 1
      family     = "lognormal",
      strength   = "moderate",
      label      = "Hazard ratio (sceptical)"
    )
    plot(sc_hr)

## 3.5 Enthusiastic vs Sceptical Pair

The FDA recommends presenting conclusions under both an enthusiastic prior (favouring treatment benefit) and a sceptical prior:

    enthusiastic <- elicit_beta(
      mean = 0.45, sd = 0.08,
      method = "moments", label = "Response rate (enthusiastic)"
    )
    sceptical <- sceptical_prior(
      null_value = 0.20, family = "beta", strength = "moderate",
      label = "Response rate (sceptical)"
    )

    data_obs <- list(type = "binary", x = 18, n = 40)

    post_enth <- bayprior:::.conjugate_update(enthusiastic, data_obs)
    post_scep <- bayprior:::.conjugate_update(sceptical,    data_obs)

    cat("Posterior mean (enthusiastic):", round(post_enth$fit_summary$mean, 3), "\n")
    #> Posterior mean (enthusiastic): 0.45
    cat("Posterior mean (sceptical):   ", round(post_scep$fit_summary$mean, 3), "\n")
    #> Posterior mean (sceptical):    0.356
    cat("Posterior SD (enthusiastic):  ", round(post_enth$fit_summary$sd,   3), "\n")
    #> Posterior SD (enthusiastic):   0.056
    cat("Posterior SD (sceptical):     ", round(post_scep$fit_summary$sd,   3), "\n")
    #> Posterior SD (sceptical):      0.059

# 4 Power Prior

## 4.1 Concept

The power prior (Ibrahim & Chen, 2000) provides a principled method for incorporating historical data by down-weighting it by a factor \(\delta \in (0, 1]\):

\[\pi(\theta | D_0, \delta) \propto L(\theta | D_0)^\delta \cdot \pi_0(\theta)\]

where \(D_0\) is the historical data and \(\delta\) controls how much weight it receives. \(\delta = 1\) fully incorporates the historical data (standard Bayesian updating); \(\delta \to 0\) ignores it entirely.

## 4.2 Calibrating \(\delta\)

`calibrate_power_prior()` selects \(\delta\) to achieve a target Bayes Factor between the historical-data-informed prior and the current likelihood — ensuring the historical data is incorporated only to the extent it is compatible with current data:

    base <- elicit_beta(
      mean   = 0.50,
      sd     = 0.20,
      method = "moments",
      label  = "Response rate"
    )

    calib <- calibrate_power_prior(
      historical_data = list(type = "binary", x = 12, n = 40),
      current_data    = list(type = "binary", x = 18, n = 50),
      base_prior      = base,
      target_bf       = 3,
      delta_grid      = seq(0.05, 1.0, by = 0.05),
      method          = "bayes_factor"
    )
    print(calib)

    plot(calib)

The calibration curves show:

- Top panel: Bayes Factor vs \(\delta\). The dashed red line is the target BF; the dotted green line marks the optimal \(\delta\).
- Bottom panel: Box p-value vs \(\delta\). Values below 0.05 indicate conflict at that weight.

## 4.3 Compatibility Method

Alternatively, select \(\delta\) to be the largest value for which the historical-data-informed prior shows no conflict with the current data:

    calib_compat <- calibrate_power_prior(
      historical_data = list(type = "binary", x = 12, n = 40),
      current_data    = list(type = "binary", x = 18, n = 50),
      base_prior      = base,
      method          = "compatibility",
      delta_grid      = seq(0.05, 1.0, by = 0.05)
    )
    cat("Optimal delta (BF method):           ", calib$delta_opt, "\n")
    #> Optimal delta (BF method):            0.05
    cat("Optimal delta (compatibility method):", calib_compat$delta_opt, "\n")
    #> Optimal delta (compatibility method): 1

## 4.4 Power Prior for Other Families

Power prior updating is supported for Beta, Normal, Gamma, Log-Normal, and Mixture priors. For a Normal prior with continuous historical data:

    base_norm <- elicit_normal(
      mean = 0.0, sd = 0.5,
      method = "moments", label = "Mean difference"
    )

    calib_norm <- calibrate_power_prior(
      historical_data = list(type = "continuous", x = 0.35, sd = 0.3, n = 60),
      current_data    = list(type = "continuous", x = 0.42, sd = 0.3, n = 80),
      base_prior      = base_norm,
      target_bf       = 3,
      delta_grid      = seq(0.05, 1.0, by = 0.10),
      method          = "bayes_factor"
    )
    print(calib_norm)

# 5 Choosing the Right Alternative Prior

    library(knitr)
    kable(data.frame(
      Situation = c(
        "No conflict, regulatory requirement",
        "Mild conflict detected",
        "Severe conflict detected",
        "Historical data available",
        "FDA enthusiastic/sceptical pair required"
      ),
      `Recommended prior` = c(
        "Robust mixture (w = 0.20)",
        "Robust mixture (w = 0.30-0.40)",
        "Sceptical prior (moderate-strong)",
        "Power prior (calibrated)",
        "Sceptical prior as second arm"
      ),
      check.names = FALSE
    ), align = "ll")

Situation | Recommended prior
--- | ---
No conflict, regulatory requirement | Robust mixture (w = 0.20)
Mild conflict detected | Robust mixture (w = 0.30-0.40)
Severe conflict detected | Sceptical prior (moderate-strong)
Historical data available | Power prior (calibrated)
FDA enthusiastic/sceptical pair required | Sceptical prior as second arm

# 6 Including Robust Priors in the Regulatory Report

All three robust prior types — robust mixture, sceptical, and power prior — are automatically included in the downloaded prior justification report when they have been computed in the session. Pass them directly to `prior_report()`:

    # After running the analyses above...
    prior_report(
      prior           = prior,
      conflict        = cd,
      sensitivity     = sa,
      robust_prior    = rob,        # adds "Robust Mixture" section to report
      sceptical_prior = scep,       # adds "Sceptical Prior" section to report
      power_prior     = calib,      # adds "Power Prior" section with calibration table
      output_format   = "html",
      output_file     = "prior_justification_report",
      trial_name      = "TRIAL-001",
      sponsor         = "BioPharma Ltd",
      author          = "J. Smith, Biostatistician"
    )

Each section in the report includes a parameter summary table and the corresponding density or calibration plot. The compliance checklist in the report automatically marks “Robust / sceptical prior computed” as Complete when any of the three types is supplied.

When using the Shiny app, the robust priors flow into the report automatically — simply run the analyses in the Robust Priors panel before clicking Download Report.

> Note: `prior_report()` requires `devtools::install()`, not just `devtools::load_all()`. Quarto spawns a fresh R session that requires the package to be properly installed.

# 7 References

Ibrahim, J. G. & Chen, M.-H. (2000). Power prior distributions for regression models. Statistical Science, 15, 46–60.

Schmidli, H., Gsteiger, S., Roychoudhury, S., O’Hagan, A., Spiegelhalter, D., & Neuenschwander, B. (2014). Robust meta-analytic-predictive priors in clinical trials with historical control information. Biometrics, 70, 1023–1032.

Spiegelhalter, D. J., Freedman, L. S., & Parmar, M. K. B. (1994). Bayesian approaches to randomized trials. Journal of the Royal Statistical Society A, 157, 357–416.

Gravestock, I. & Held, L. (2017). Adaptive power priors with empirical Bayes for clinical trials. Pharmaceutical Statistics, 16, 349–360.