Creates an inverse covariance matrix for a Gaussian prior

inv.prior.cov constructs a diagonal inverse covariance matrix for the Gaussian prior distribution based on the design matrix of covariates. This construction accounts for the number of regression parameters, especially when dealing with categorical covariates. For a linear model, it also includes an additional row and column to represent the variance of the measurement error. In the case of a survival model, it considers the parameters of the baseline hazard function as well.

Usage

inv.prior.cov(X,
              lambda = 1,
              L = 2,
              family = c("gaussian", "binomial", "survival"),
              treatment = NULL,
              treat_round = NULL,
              intercept = TRUE,
              stratified = FALSE,
              strat_par = NULL,
              center_spec = NULL,
              basehaz = c("weibul", "exp", "gomp", "poly", "pwexp", "unspecified"),
              max_order = 2,
              n_intervals = 4)

Arguments

X: design matrix of dimension \(n \times p\), where \(n\) is the number of samples observed, and \(p\) is the number of predictors/variables so excluding the intercept.
lambda: the vector used as the diagonal of the (inverse covariance) matrix that will be created by inv.prior.cov(). The length of the vector depends on the number of columns of X, type of the covariates (continuous/dichotomous or categorical), family, whether an intercept is included in the model, and whether stratified analysis is desired. When stratified = FALSE, lambda could be a single positive number (if all values in the vector are equal), a vector of two elements (the first is used for regression parameters including “intercept” and the second for the “sigma2” in the gaussian family or for the baseline hazard parameters in the survival case), or a vector of length equal to the number of model parameters. However, the length of lambda is different when stratified = TRUE, see ‘Details’ for more information. Default is lambda = 1, which means all model parameters are set to one.
L: the number of centers. This argument is used only when stratified = TRUE. Default is L = 2. See ‘Details’ and ‘Examples’.
family: a description of the error distribution. This is a character string naming a family of the model. In the current version of the package, the family of model can be "gaussian" (with identity link function), "binomial" (with logit link function), or "survival". Can be abbreviated. By default the gaussian family is used. In case of a linear regression model, family = "gaussian", there is an extra model parameter for the variance of measurement error. While in the case of survival model, family = "survival", the number of the model parameters depend on the choice of baseline hazard functions, see ‘Details’ for more information.
treatment: a character string representing the name or place of the binary covariate, respectively. This covariate indicates whether the patient got the new treatment (\(z_{\ell i}=1\)) or the placebo/standard treatment (\(z_{\ell i}=0\)). For both the first and second rounds, it should not be 'NULL'. See ‘Details’.
treat_round: a character string representing the 'first' or 'second' round of estimating treatment effects. In the first round, treat_round = 'first', the local estimates of the coefficients (\(\gamma_{\ell}\)) is estimated. In the second round, treat_round = 'second', the propensity scores and the statistical summaries (for_ATE) are calculated.
intercept: logical flag for having an intercept. It is not used when family = "survival". By changing the intercept the dimension of the inverse covariance matrix changes. If intercept = TRUE (the default), the output matrix created by inv.prior.cov() has one row and one column related to intercept, while if intercept = FALSE, the resulting matrix does not have the row and column called intercept.
stratified: logical flag for performing the stratified analysis. If stratified = TRUE, the parameter(s) selected in the strat_par argument are allowed to be different across centers to deal with heterogeneity across centers. This argument should only be used when designing the inverse covariance matrix for the (fictive) combined data, i.e., the last matrix for the Lambda argument in bfi(). If inv.prior.cov() is used for the analysis in the local centers (to build the \(L\) first matrices for the Lambda argument in bfi()), this argument should be FALSE, even if the BFI analysis is stratified. Default is stratified = FALSE. See ‘Details’ and ‘Examples’.
strat_par: a integer vector for indicating the stratification parameter(s). It can be used to deal with heterogeneity due to center-specific parameters. For the "binomial" and "gaussian" families it is a one- or two-element integer vector so that the values \(1\) and/or \(2\) are/is used to indicate that the “intercept” and/or “sigma2” are allowed to vary, respectively. For the "binomial" family the length of the vector should be one which refers to “intercept”, and the value of this element should be \(1\) (to handel heterogeneity across outcome means). For "gaussian" this vector can be \(1\) for indicating the “intercept” only (handeling heterogeneity across outcome means), \(2\) for indicating the “sigma2” only (handeling heterogeneity due to nuisance parameter), and c(\(1\), \(2\)) for both “intercept” and “sigma2”. When family = "survival", this vector can contain any combination from 1 to the maximum number of parameters of the baseline function, i.e., \(1\) for "exp", \(2\) for "weibul" and "gomp", max_order + 1 for "poly", and n_intervals for "pwexp". This argument is only used when stratified = TRUE. Default is strat_par = NULL. If stratified = TRUE, strat_par can not be NULL except when center_spec is not NULL for handeling heterogeneity due to clustering and missing covariates. See ‘Details’ and ‘Examples’.
center_spec: a vector of \(L\) elements for representing the center specific variable. This argument is used only when stratified = TRUE and strat_par = NULL. Each element represents a specific feature of the corresponding center. There must be only one specific value or attribute for each center. This vector could be a numeric, characteristic or factor vector. Note that, the order of the centers in the vector center_spec must be the same as in the list of the argument theta_hats in the function bfi(). The used data type in the argument center_spec must be categorical. Default is center_spec = NULL. See also ‘Details’ and ‘Examples’.
basehaz: a character string representing one of the available baseline hazard functions; exponential ("exp"), Weibull ("weibul", the default), Gompertz ("gomp"), exponentiated polynomial ("poly"), piecewise constant exponential ("pwexp"), and unspecified baseline hazard ("unspecified"). Can be abbreviated. It is only used when family = "survival".
max_order: an integer representing the maximum value of q_l, which is the order/degree minus 1 of the exponentiated polynomial baseline hazard function. This argument is only used when family = "survival" and basehaz = "poly". Default is 2.
n_intervals: an integer representing the number of intervals in the piecewise exponential baseline hazard function. This argument is only used when family = "survival" and basehaz = "pwexp". Default is 4.

Details

inv.prior.cov creates a diagonal matrix with the vector lambda as its diagonal. The argument stratified = TRUE should only be used to construct a matrix for the prior density in case of stratification in the fictive combined data. Never be used for the construction of the matrix for analysis in the centers.

When stratified = FALSE, the length of the vector lambda depends on the covariate matrix X, family, basehaz, and whether an “intercept” is included in the model. For example, if the design matrix X has p columns with continuous or dichotomous covariates, family = gaussian, and intercept = TRUE, then lambda should have \(p+2\) elements. In this case, if in X there is a categorical covariate with \(q>2\) categories, then the length of lambda increases with \(q-2\).

All values of lambda should be non-negative as they represent the inverse of the variance of the Gaussian prior. This argument is considered as the inverse of the variance of the prior distribution for: \((\beta_0, \boldsymbol{\beta})\) if family = "binomial" and intercept = TRUE; \((\beta_0, \boldsymbol{\beta},\sigma^2)\) if family = "gaussian" and intercept = TRUE; and \(( \boldsymbol{\beta},\boldsymbol{\omega})\) if family = "survival".

If all values in the vector lambda equal, one value is enough to be given as entry. If lambda is a scalar, the function inv.prior.cov sets each value at the diagonal equal to lambda. When lambda is two dimensional: if family = "binomial", the first and second values are used for the inverse of the variance of the prior distribution for the intercept (\(\beta_0\)) and regression parameters (\(\boldsymbol{\beta}\)), respectively; If family = "gaussian", the first and second values are used for the inverse of the variance of the prior distribution for the regression parameters including the intercept (\(\beta_0, \boldsymbol{\beta}\)) and variance of the measurement error (\( \sigma^2\)), respectively; If family = "survival", the first and second values are used for the inverse of the variance of the prior distribution for the regression parameters (\(\boldsymbol{\beta}\)) and baseline hazard parameters (\( \omega\)), respectively. But if stratified = TRUE the length of the vector lambda must be equal to the number of parameters in the combined model.

If intercept = FALSE, for the binomial family the stratified analysis is not possible therefore stratified can not be TRUE.

If stratified = FALSE, both strat_par and center_spec must be NULL (the defaults), while if stratified = TRUE only one of the two must be NULL.

If stratified = TRUE and family = "survival", strat_par = 1 refers to \(\omega_0\) when basehaz = "poly", and to \(\omega_1\) for other baseline hazards.

The output of inv.prior.cov() can be used in the main functions MAP.estimation() and bfi().

Value

inv.prior.cov returns a diagonal matrix. The dimension of the matrix depends on the number of columns of X, type of the covariates (continuous/dichotomous or categorical), intercept, family, and basehaz.

References

Jonker M.A., Pazira H. and Coolen A.C.C. (2024). Bayesian federated inference for estimating statistical models based on non-shared multicenter data sets, Statistics in Medicine, 43(12): 2421-2438. <https://doi.org/10.1002/sim.10072>

Pazira H., Massa E., Weijers J.A.M., Coolen A.C.C. and Jonker M.A. (2025b). Bayesian Federated Inference for Survival Models, Journal of Applied Statistics (Accepted). <https://arxiv.org/abs/2404.17464>

Jonker M.A., Pazira H. and Coolen A.C.C. (2025a). Bayesian Federated Inference for regression models based on non-shared medical center data, Research Synthesis Methods, 1-41. <https://doi.org/10.1017/rsm.2025.6>

Author

Hassan Pazira and Marianne Jonker
Maintainer: Hassan Pazira hassan.pazira@radboudumc.nl

Examples


#----------------
# Data Simulation
#----------------
X <- data.frame(x1=rnorm(50),                     # standard normal variable
                x2=sample(0:2, 50, replace=TRUE), # categorical variable
                x3=sample(0:1, 50, replace=TRUE)) # dichotomous variable
X$x2 <- as.factor(X$x2)
X$x3 <- as.factor(X$x3)

# The (inverse) variance value (lambda=0.05) is assumed to be
# the same for Gaussian prior of all parameters (for non-stratified)

#-------------------------------------------------
# Inverse Covariance Matrix for the Gaussian prior
#-------------------------------------------------
# y ~ Binomial with 'intercept'
inv.prior.cov(X, lambda = 0.05, family = 'binomial')
#>             (Intercept)   x1  x21  x22  x31
#> (Intercept)        0.05 0.00 0.00 0.00 0.00
#> x1                 0.00 0.05 0.00 0.00 0.00
#> x21                0.00 0.00 0.05 0.00 0.00
#> x22                0.00 0.00 0.00 0.05 0.00
#> x31                0.00 0.00 0.00 0.00 0.05
# returns a 5-by-5 matrix

# y ~ Binomial without 'intercept'
inv.prior.cov(X, lambda = 0.05, family = "binomial", intercept = FALSE)
#>       x1  x21  x22  x31
#> x1  0.05 0.00 0.00 0.00
#> x21 0.00 0.05 0.00 0.00
#> x22 0.00 0.00 0.05 0.00
#> x31 0.00 0.00 0.00 0.05
# a 4-by-4 matrix

# y ~ Gaussian with 'intercept'
inv.prior.cov(X, lambda = 0.05, family = 'gaussian')
#>             (Intercept)   x1  x21  x22  x31 sigma2
#> (Intercept)        0.05 0.00 0.00 0.00 0.00   0.00
#> x1                 0.00 0.05 0.00 0.00 0.00   0.00
#> x21                0.00 0.00 0.05 0.00 0.00   0.00
#> x22                0.00 0.00 0.00 0.05 0.00   0.00
#> x31                0.00 0.00 0.00 0.00 0.05   0.00
#> sigma2             0.00 0.00 0.00 0.00 0.00   0.05
# returns a 6-by-6 matrix

# Survival family with 'weibul' baseline hazard
inv.prior.cov(X, lambda = c(0.05, 0.1), family = 'survival')
#>           x1  x21  x22  x31 omega_1 omega_2
#> x1      0.05 0.00 0.00 0.00     0.0     0.0
#> x21     0.00 0.05 0.00 0.00     0.0     0.0
#> x22     0.00 0.00 0.05 0.00     0.0     0.0
#> x31     0.00 0.00 0.00 0.05     0.0     0.0
#> omega_1 0.00 0.00 0.00 0.00     0.1     0.0
#> omega_2 0.00 0.00 0.00 0.00     0.0     0.1
# returns a 6-by-6 matrix

# Survival family with 'pwexp' baseline hazard (4 intervals)
inv.prior.cov(X, lambda = 0.05, family = 'survival', basehaz = "pwexp")
#>           x1  x21  x22  x31 omega_1 omega_2 omega_3 omega_4
#> x1      0.05 0.00 0.00 0.00    0.00    0.00    0.00    0.00
#> x21     0.00 0.05 0.00 0.00    0.00    0.00    0.00    0.00
#> x22     0.00 0.00 0.05 0.00    0.00    0.00    0.00    0.00
#> x31     0.00 0.00 0.00 0.05    0.00    0.00    0.00    0.00
#> omega_1 0.00 0.00 0.00 0.00    0.05    0.00    0.00    0.00
#> omega_2 0.00 0.00 0.00 0.00    0.00    0.05    0.00    0.00
#> omega_3 0.00 0.00 0.00 0.00    0.00    0.00    0.05    0.00
#> omega_4 0.00 0.00 0.00 0.00    0.00    0.00    0.00    0.05
# returns a 8-by-8 matrix

# Survival family with 'poly' baseline hazard
inv.prior.cov(X, lambda = c(0.05, 0.1), family = 'survival', basehaz = "poly")
#>           x1  x21  x22  x31 omega_0 omega_1 omega_2
#> x1      0.05 0.00 0.00 0.00     0.0     0.0     0.0
#> x21     0.00 0.05 0.00 0.00     0.0     0.0     0.0
#> x22     0.00 0.00 0.05 0.00     0.0     0.0     0.0
#> x31     0.00 0.00 0.00 0.05     0.0     0.0     0.0
#> omega_0 0.00 0.00 0.00 0.00     0.1     0.0     0.0
#> omega_1 0.00 0.00 0.00 0.00     0.0     0.1     0.0
#> omega_2 0.00 0.00 0.00 0.00     0.0     0.0     0.1
# returns a 7-by-7 matrix

#--------------------
# Stratified analysis
#--------------------
# y ~ Binomial when 'intercept' varies across 3 centers:
inv.prior.cov(X, lambda = c(.2, 1), family = 'binomial', stratified = TRUE,
              strat_par = 1, L = 3)
#>                  (Intercept)_loc1 (Intercept)_loc2 (Intercept)_loc3 x1 x21 x22
#> (Intercept)_loc1              0.2              0.0              0.0  0   0   0
#> (Intercept)_loc2              0.0              0.2              0.0  0   0   0
#> (Intercept)_loc3              0.0              0.0              0.2  0   0   0
#> x1                            0.0              0.0              0.0  1   0   0
#> x21                           0.0              0.0              0.0  0   1   0
#> x22                           0.0              0.0              0.0  0   0   1
#> x31                           0.0              0.0              0.0  0   0   0
#>                  x31
#> (Intercept)_loc1   0
#> (Intercept)_loc2   0
#> (Intercept)_loc3   0
#> x1                 0
#> x21                0
#> x22                0
#> x31                1

# y ~ Gaussian when 'intercept' and 'sigma2' vary across 2 centers; y ~ Gaussian
inv.prior.cov(X, lambda = c(1, 2, 3), family = "gaussian", stratified = TRUE,
              strat_par = c(1, 2))
#>                  (Intercept)_loc1 (Intercept)_loc2 x1 x21 x22 x31 sigma2_loc1
#> (Intercept)_loc1                1                0  0   0   0   0           0
#> (Intercept)_loc2                0                1  0   0   0   0           0
#> x1                              0                0  2   0   0   0           0
#> x21                             0                0  0   2   0   0           0
#> x22                             0                0  0   0   2   0           0
#> x31                             0                0  0   0   0   2           0
#> sigma2_loc1                     0                0  0   0   0   0           3
#> sigma2_loc2                     0                0  0   0   0   0           0
#>                  sigma2_loc2
#> (Intercept)_loc1           0
#> (Intercept)_loc2           0
#> x1                         0
#> x21                        0
#> x22                        0
#> x31                        0
#> sigma2_loc1                0
#> sigma2_loc2                3

# y ~ Gaussian when 'sigma2' varies across 2 centers (with 'intercept')
inv.prior.cov(X, lambda = c(1, 2, 3), family='gaussian', stratified = TRUE,
              strat_par = 2)
#>             (Intercept) x1 x21 x22 x31 sigma2_loc1 sigma2_loc2
#> (Intercept)           1  0   0   0   0           0           0
#> x1                    0  2   0   0   0           0           0
#> x21                   0  0   2   0   0           0           0
#> x22                   0  0   0   2   0           0           0
#> x31                   0  0   0   0   2           0           0
#> sigma2_loc1           0  0   0   0   0           3           0
#> sigma2_loc2           0  0   0   0   0           0           3

# y ~ Gaussian when 'sigma2' varies across 2 centers (without 'intercept')
inv.prior.cov(X, lambda = c(2, 3), family = "gaussian", intercept = FALSE,
              stratified=TRUE, strat_par = 2)
#>             x1 x21 x22 x31 sigma2_loc1 sigma2_loc2
#> x1           2   0   0   0           0           0
#> x21          0   2   0   0           0           0
#> x22          0   0   2   0           0           0
#> x31          0   0   0   2           0           0
#> sigma2_loc1  0   0   0   0           3           0
#> sigma2_loc2  0   0   0   0           0           3

#--------------------------
# Center specific covariate
#--------------------------
# center specific covariate has K = 2 categories across 4 centers; y ~ Binomial
inv.prior.cov(X, lambda = c(0.1:2), family = 'binomial', stratified = TRUE,
              center_spec = c("Iran","Netherlands","Netherlands","Iran"), L=4)
#>                         (Intercept)_Iran (Intercept)_Netherlands  x1 x21 x22
#> (Intercept)_Iran                     0.1                     0.0 0.0 0.0 0.0
#> (Intercept)_Netherlands              0.0                     0.1 0.0 0.0 0.0
#> x1                                   0.0                     0.0 1.1 0.0 0.0
#> x21                                  0.0                     0.0 0.0 1.1 0.0
#> x22                                  0.0                     0.0 0.0 0.0 1.1
#> x31                                  0.0                     0.0 0.0 0.0 0.0
#>                         x31
#> (Intercept)_Iran        0.0
#> (Intercept)_Netherlands 0.0
#> x1                      0.0
#> x21                     0.0
#> x22                     0.0
#> x31                     1.1

# center specific covariate has K = 3 categories across 5 centers; y ~ Gaussian
inv.prior.cov(X, lambda = c(0.5:3), family = 'gaussian', stratified = TRUE,
              center_spec = c("Medium","Big","Small","Big","Small"), L = 5)
#>                    (Intercept)_Big (Intercept)_Medium (Intercept)_Small  x1 x21
#> (Intercept)_Big                0.5                0.0               0.0 0.0 0.0
#> (Intercept)_Medium             0.0                0.5               0.0 0.0 0.0
#> (Intercept)_Small              0.0                0.0               0.5 0.0 0.0
#> x1                             0.0                0.0               0.0 1.5 0.0
#> x21                            0.0                0.0               0.0 0.0 1.5
#> x22                            0.0                0.0               0.0 0.0 0.0
#> x31                            0.0                0.0               0.0 0.0 0.0
#> sigma2                         0.0                0.0               0.0 0.0 0.0
#>                    x22 x31 sigma2
#> (Intercept)_Big    0.0 0.0    0.0
#> (Intercept)_Medium 0.0 0.0    0.0
#> (Intercept)_Small  0.0 0.0    0.0
#> x1                 0.0 0.0    0.0
#> x21                0.0 0.0    0.0
#> x22                1.5 0.0    0.0
#> x31                0.0 1.5    0.0
#> sigma2             0.0 0.0    2.5

# center specific covariate has K = 4 categories across 5 centers; y ~ Gaussian
inv.prior.cov(X, lambda = 1, family = 'gaussian', stratified = TRUE,
              center_spec = c(3,1:4), L=5)
#>               (Intercept)_1 (Intercept)_2 (Intercept)_3 (Intercept)_4 x1 x21
#> (Intercept)_1             1             0             0             0  0   0
#> (Intercept)_2             0             1             0             0  0   0
#> (Intercept)_3             0             0             1             0  0   0
#> (Intercept)_4             0             0             0             1  0   0
#> x1                        0             0             0             0  1   0
#> x21                       0             0             0             0  0   1
#> x22                       0             0             0             0  0   0
#> x31                       0             0             0             0  0   0
#> sigma2                    0             0             0             0  0   0
#>               x22 x31 sigma2
#> (Intercept)_1   0   0      0
#> (Intercept)_2   0   0      0
#> (Intercept)_3   0   0      0
#> (Intercept)_4   0   0      0
#> x1              0   0      0
#> x21             0   0      0
#> x22             1   0      0
#> x31             0   1      0
#> sigma2          0   0      1