NAPrior (version 0.2.0)

NAP_prior: NAP_prior: Derive NAP/mNAP/eNAP priors

Description

Builds the informative NAP component (mean/variance from the indirect path) and the vague component, and reports the mixing weight depending on the mode:

• weight_mtd = "fixed": use the supplied fixed weight w in [0,1].

• weight_mtd = "adaptive" (eNAP): if y_EC2 is provided, compute the data-dependent weight via the elastic link; otherwise, print a formula note.

Derive NAP-based prior (s) based on indirect evidence

Derive the NAP-based posteriors with provided summary statistics on indirect evidence edges By default, the function assumes a vague component is desired, as a result, to obtain NAP/mNAP/eNAP:

• NAP Set weight_mtd="fixed" and w=1, use the NAP (informative component) column results

• mNAP Set weight_mtd="fixed" and w as pre-specified fixed weight. The resulting mNAP is \(w\pi_{NAP}+(1-w)\pi_0\)

• eNAP Set weight_mtd="adaptive" and provide calibrated a and b as from tune_param_eNAP function, then either: 1). Provide assumed value for y_EC2 and s_EC2 (i.e., as for sample size calculation): return a calculated dynamic weight \(w(Z)\), the resulting eNAP is then \(w(Z)\pi_{NAP}+(1-w(Z))\pi_0\); OR 2). Leave y_EC2 and s_EC2 as NULL, return the NAP (informative component) and Vague component, with description for protocol reference

Usage

NAP_prior(
  weight_mtd = c("adaptive", "fixed"),
  w = NULL,
  a = NULL,
  b = NULL,
  y_EC2 = NULL,
  s_EC2 = NULL,
  y_EC1,
  s_EC1,
  y_C2C1,
  s_C2C1,
  mu0 = 0,
  tau0 = 1000,
  lambda = 1,
  sigma2_hat = NULL
)

Value

Displays the NAP prior as a mixture of an informative prior (constructed based on the indirect evidence path) and a vague prior.

An object of class "NAPrior" (data.frame + attributes).

Arguments

weight_mtd

Either "adaptive" (eNAP) or "fixed" (NP/NAP/mNAP).

w

Fixed prior weight in [0,1]; required only if weight_mtd="fixed". Ignored otherwise. 0<w<1 infers mixture NAP; w=0 infers NP; w=1 infers NAP.

a, b

eNAP tuning parameters; required only if weight_mtd="adaptive" (a<0 and b>0). Ignored in fixed mode.

y_EC2, s_EC2

Log-HR and SE for \(E:C2\) (Current trial post-SoC change).

y_EC1, s_EC1

Log-HR and SE for \(E:C1\) (Current trial pre-SoC change).

y_C2C1, s_C2C1

Historical C2 vs. C1 trial Log-HRs and SEs.

mu0, tau0

mean and variance of the vague component (default sqrt(1000)).

lambda

Randomization ratio (default 1).

sigma2_hat

Positive scalar, required only for multiple external trials setting, leave blank if use default REML estimate, otherwise provide user-specified value

Details

This function automatically selects one external trial vs multiple external trials setting:

• One external trial if length(y_C2C1) == 1 & length(s_C2C1)==1 (one external trial).

• Multiple external trials if length(y_C2C1) > 1 & length(s_C2C1)==length(y_C2C1). By default uses metafor::rma.uni(..., method="REML") to obtain REML estimate; Otherwise please provide sigma2_hat

Examples

## ------------------------------------------------------------
## Example 1: One external trial setting with fixed mixing weight of 0.5 (mNAP)
## ------------------------------------------------------------
mNAP_test1 <- NAP_prior(
  weight_mtd = "fixed", w = 0.50,                  # fixed mixture weight
  y_EC1  = -0.36, s_EC1  = 0.16^2,
  y_C2C1 = -0.30, s_C2C1 = 0.14^2,                  # single external trial
  tau0   = 1000
)
print(mNAP_test1)  
plot(mNAP_test1)   

## ------------------------------------------------------------
## Example 2: RE case (multiple historical), ADAPTIVE weight
## ------------------------------------------------------------
eNAP_test1 <- NAP_prior(
  weight_mtd = "adaptive",
  a = -2, b = 10,            # from calibration
  y_EC1  = -0.36, s_EC1  = 0.16^2,                 # E:C1 (current, pre-change)
  y_C2C1 = c(-0.28, -0.35, -0.31),                 # C2:C1 (external trials)
  s_C2C1 = c(0.12^2, 0.11^2, 0.15^2),
  tau0   = 1000                                     # vague variance
)
print(eNAP_test1)