Before updating a prior with trial data it is essential to check whether the two are compatible. Substantial prior-data conflict indicates one of three things: the prior was mis-specified, the data are anomalous, or the model is wrong. Regulators increasingly require evidence that conflict was assessed and addressed.
bayprior provides four complementary univariate diagnostics and one multivariate diagnostic, following Box (1980) and related work. The conflict module supports four data types: binary, continuous, Poisson/count, and survival.
| Data type | type = argument |
Conjugate update | Typical endpoint |
|---|---|---|---|
| Binary | "binary" |
Beta–Binomial | Response rate, ORR |
| Continuous | "continuous" |
Normal–Normal | Mean difference, HbA1c |
| Poisson / count | "poisson" |
Gamma–Poisson | Adverse event rate |
| Survival | "survival" |
Gamma–Exponential | Hazard rate, OS, PFS |
prior_conflict()# No-conflict: data consistent with prior
cd_none <- prior_conflict(
prior = prior,
data_summary = list(type = "binary", x = 13, n = 40),
alpha = 0.05
)
print(cd_none)# Mild conflict
cd_mild <- prior_conflict(
prior = prior,
data_summary = list(type = "binary", x = 20, n = 40)
)
print(cd_mild)# Severe conflict
cd_severe <- prior_conflict(
prior = prior,
data_summary = list(type = "binary", x = 35, n = 40)
)
print(cd_severe)\[p_{\text{Box}} = 2\Phi\!\left(-\left|\frac{\hat\theta - \mu_\pi} {\sqrt{\sigma_\pi^2 + \text{SE}^2}}\right|\right)\]
Interpretation: \(p < 0.05\) flags conflict at the 5% level.
\[S = \left|\frac{\hat\theta - \mu_\pi} {\sqrt{\sigma_\pi^2 + \text{SE}^2}}\right|\]
Interpretation: \(S > 2\) moderate; \(S > 3\) high surprise.
\[\text{KL}(P \| Q) = \log\frac{\sigma_Q}{\sigma_P} + \frac{\sigma_P^2 + (\mu_P - \mu_Q)^2}{2\sigma_Q^2} - \frac12\]
Interpretation: \(> 1\) indicates large information distance.
\[\text{BC} = \exp\!\left( -\frac{(\mu_P - \mu_Q)^2}{4(\sigma_P^2 + \sigma_Q^2)}\right) \cdot \left(\frac{2\sigma_P\sigma_Q} {\sigma_P^2 + \sigma_Q^2}\right)^{1/2}\]
Interpretation: 1 = identical distributions; 0 = no overlap.
| Diagnostic | No conflict | Mild conflict | Severe conflict |
|---|---|---|---|
| Box p-value | >= 0.05 | 0.01-0.05 | < 0.01 |
| Surprise index | < 2 | 2-3 | > 3 |
| KL divergence | < 0.5 | 0.5-1 | > 1 |
| Bhattacharyya overlap | > 0.6 | 0.3-0.6 | < 0.3 |
plot_prior_likelihood(
prior,
data_summary = list(type = "binary", x = 13, n = 40),
show_posterior = TRUE
)
No conflict: prior and likelihood overlap substantially.
plot_prior_likelihood(
prior,
data_summary = list(type = "binary", x = 35, n = 40),
show_posterior = TRUE
)
Severe conflict: prior (blue) and likelihood (orange) widely separated.
prior_norm <- elicit_normal(
mean = 0.0, sd = 0.3, method = "moments",
label = "Log odds ratio"
)
# No conflict: observed log OR consistent with prior
prior_conflict(
prior_norm,
data_summary = list(type = "continuous", x = 0.15, sd = 0.20, n = 80)
)Poisson/count data arises for endpoints such as adverse event rates, where the observed data consists of a count of events over an exposure period (person-time). The conjugate update uses a Gamma-Poisson model:
\[\text{Prior: Gamma}(\alpha, \beta) \quad\Rightarrow\quad \text{Posterior: Gamma}(\alpha + x,\; \beta + n)\]
where \(x\) = observed events and \(n\) = total person-time.
# Gamma prior on adverse event rate
# Expert believes the AE rate is ~0.15 events per person-year (SD 0.06)
prior_ae <- elicit_gamma(
mean = 0.15,
sd = 0.06,
method = "moments",
label = "Adverse event rate (per person-year)",
expert_id = "Safety Expert"
)
print(prior_ae)# Observed: 12 AEs over 100 person-years => rate 0.12 — consistent with prior
cd_pois_none <- prior_conflict(
prior = prior_ae,
data_summary = list(type = "poisson", x = 12, n = 100),
alpha = 0.05
)
print(cd_pois_none)# Observed: 40 AEs over 100 person-years => rate 0.40 — much higher than prior
cd_pois_conf <- prior_conflict(
prior = prior_ae,
data_summary = list(type = "poisson", x = 40, n = 100)
)
print(cd_pois_conf)plot_prior_likelihood(
prior_ae,
data_summary = list(type = "poisson", x = 40, n = 100),
show_posterior = TRUE
)
Survival data conflict arises for endpoints like OS and PFS hazard rates. The observed data consists of event counts over a total follow-up time (person-months). The conjugate update uses a Gamma-Exponential model:
\[\text{Prior: Gamma}(\alpha, \beta) \quad\Rightarrow\quad \text{Posterior: Gamma}(\alpha + d,\; \beta + T)\]
where \(d\) = observed events and \(T\) = total follow-up time.
An Exponential prior (constant hazard) is equivalent to \(\text{Gamma}(1, \lambda)\).
# Exponential prior on hazard rate
# Expert believes median OS ≈ 20 months => hazard ≈ 0.05
prior_hz <- elicit_exponential(
mean = 0.05,
method = "moments",
label = "OS hazard rate",
expert_id = "Clinical Expert"
)
print(prior_hz)# Observed: 20 deaths over 400 person-months => hazard 0.05 — consistent
cd_surv_none <- prior_conflict(
prior = prior_hz,
data_summary = list(type = "survival", x = 20, n = 400),
alpha = 0.05
)
print(cd_surv_none)# Observed: 60 deaths over 400 person-months => hazard 0.15 — much higher
cd_surv_conf <- prior_conflict(
prior = prior_hz,
data_summary = list(type = "survival", x = 60, n = 400)
)
print(cd_surv_conf)plot_prior_likelihood(
prior_hz,
data_summary = list(type = "survival", x = 60, n = 400),
show_posterior = TRUE
)
Also works with a Gamma prior on the hazard rate:
prior_hz_gamma <- elicit_gamma(
mean = 0.05, sd = 0.02, method = "moments",
label = "OS hazard rate (Gamma prior)"
)
prior_conflict(
prior_hz_gamma,
data_summary = list(type = "survival", x = 20, n = 400)
)bayprior warns when the prior family is atypical for the chosen data type, helping users avoid inadvertent misspecification:
# Warning: Beta prior with Poisson data is unusual
prior_beta <- elicit_beta(mean = 0.15, sd = 0.06, method = "moments",
label = "Rate")
# The app shows a warning notification; the function still runs with
# a Normal approximation
cd_warn <- prior_conflict(
prior_beta,
data_summary = list(type = "poisson", x = 15, n = 100)
)All diagnostics work transparently with mixture priors via a Normal
approximation from the mixture’s fit_summary:
e1 <- elicit_beta(mean = 0.25, sd = 0.08, method = "moments",
expert_id = "E1", label = "ORR")
e2 <- elicit_beta(mean = 0.40, sd = 0.12, method = "moments",
expert_id = "E2", label = "ORR")
mix <- aggregate_experts(list(E1 = e1, E2 = e2), weights = c(0.5, 0.5))
prior_conflict(mix, list(type = "binary", x = 18, n = 40))conflict_mahalanobis()For trials with co-primary endpoints, the Mahalanobis distance provides an omnibus multivariate conflict test:
conflict_mahalanobis(
prior_means = c(0.35, 0.60),
prior_cov = matrix(c(0.010, 0.003, 0.003, 0.015), 2, 2),
obs_means = c(0.55, 0.58),
obs_cov = matrix(c(0.008, 0.002, 0.002, 0.010), 2, 2) / 50,
labels = c("Response rate", "OS rate"),
alpha = 0.05
)##
## Marginal z-scores per parameter:
## Response rate OS rate
## 1.984 -0.162| Severity | Recommended action |
|---|---|
| None | Proceed — prior is consistent with data |
| Mild | Consider reporting both prior-weighted and likelihood-only estimates |
| Severe | Revise prior or use a robust/power prior; report sensitivity |