Wrapper function for simulations performing inference on given treatment arms using given models

This function analyzes given data using different models as indicated by the user. It performs inference for indicated experimental treatment arms.

Usage

all_models(
  data,
  arms,
  models = c("fixmodel", "sepmodel", "poolmodel"),
  endpoint,
  alpha = 0.025,
  unit_size = 250,
  ncc = TRUE,
  opt = 2,
  prior_prec_tau = 4,
  prior_prec_eta = 0.001,
  n_samples = 1000,
  n_chains = 4,
  n_iter = 4000,
  n_adapt = 1000,
  robustify = TRUE,
  weight = 0.1,
  ci = FALSE,
  prec_theta = 0.001,
  prec_eta = 0.001,
  tau_a = 0.1,
  tau_b = 0.01,
  prec_a = 0.001,
  prec_b = 0.001,
  bucket_size = 25,
  smoothing_basis = "tp",
  basis_dim = -1,
  gam_method = "GCV.Cp",
  bs_degree = 3,
  poly_degree = 3
)

Arguments

data

Data frame with trial data, e.g. result from the datasim_bin() or datasim_cont() function.

arms

Integer vector with treatment arms to perform inference on. These arms are compared to the control group. Default - all arms except the first one.

models

Character vector with models that should be used for the analysis. Default=c("fixmodel", "sepmodel", "poolmodel"). Available models for continuous endpoints are: 'fixmodel', 'fixmodel_cal', 'gam', 'MAPprior', 'mixmodel', 'mixmodel_cal', 'mixmodel_AR1', 'mixmodel_AR1_cal', 'piecewise', 'piecewise_cal', 'poolmodel', 'sepmodel', 'sepmodel_adj', 'splines', 'splines_cal', 'timemachine'. Available models for binary endpoints are: 'fixmodel', 'fixmodel_cal', 'MAPprior', 'poolmodel', 'sepmodel', 'sepmodel_adj', 'timemachine'.

endpoint

Endpoint indicator. "cont" for continuous endpoints, "bin" for binary endpoints.

alpha

Double. Significance level (one-sided). Default=0.025.

unit_size

Integer. Number of patients per calendar time unit for frequentist models adjusting for calendar time. Default=25.

ncc

Logical. Whether to include NCC data into the analysis using frequentist models. Default=TRUE.

opt

Integer (1 or 2). In the MAP Prior approach, if opt==1, all former periods are used as one source; if opt==2, periods get separately included into the final analysis. Default=2.

prior_prec_tau

Double. Dispersion parameter of the half normal prior, the prior for the between study heterogeneity in the MAP Prior approach. Default=4.

prior_prec_eta

Double. Dispersion parameter of the normal prior, the prior for the control response (log-odds or mean) in the MAP Prior approach. Default=0.001.

n_samples

Integer. Number of how many random samples will get drawn for the calculation of the posterior mean, the p-value and the CI's in the MAP Prior approach. Default=1000.

n_chains

Integer. Number of parallel chains for the rjags model in the MAP Prior approach. Default=4.

n_iter

Integer. Number of iterations to monitor of the jags.model. Needed for coda.samples in the MAP Prior approach. Default=4000.

n_adapt

Integer. Number of iterations for adaptation, an initial sampling phase during which the samplers adapt their behavior to maximize their efficiency. Needed for jags.model in the MAP Prior approach. Default=1000.

robustify

Logical. Indicates whether a robust prior is to be used. If TRUE, a mixture prior is considered combining a MAP prior and a weakly non-informative component prior. Default=TRUE.

weight

Double. Weight given to the non-informative component (0 < weight < 1) for the robustification of the MAP Prior according to Schmidli (2014). Default=0.1.

ci

Logical. Whether confidence intervals for the mixed models should be computed. Default=FALSE.

prec_theta

Double. Precision (\(1/\sigma^2_{\theta}\)) of the prior regarding the treatment effect \(\theta\). I.e. \(\theta \sim N(0, \sigma^2_{\theta})\) . Default=0.001.

prec_eta

Double. Precision (\(1/\sigma^2_{\eta_0}\)) of the prior regarding the control response \(\eta_0\). I.e. \(\eta_0 \sim N(0, \sigma^2_{\eta_0})\). Default=0.001.

tau_a

Double. Parameter \(a\) of the Gamma distribution for the precision parameter \(\tau\) in the model for the time trend in the Time Machine approach. I.e., \(\tau \sim Gamma(a,b)\). Default=0.1.

tau_b

Double. Parameter \(b\) of the Gamma distribution for the precision parameter \(\tau\) in the model for the time trend in the Time Machine approach. I.e., \(\tau \sim Gamma(a,b)\). Default=0.01.

prec_a

Double. Parameter \(a\) of the Gamma distribution regarding the precision of the responses for continuous endpoints in the Time Machine approach. I.e., \(\sigma \sim Gamma(a,b)\). Default=0.001.

prec_b

Double. Parameter \(b\) of the Gamma distribution regarding the precision of the responses for continuous endpoints in the Time Machine approach. I.e., \(\sigma \sim Gamma(a,b)\). Default=0.001.

bucket_size

Integer. Number of patients per time bucket in the Time Machine approach. Default=25.

smoothing_basis

String indicating the (penalized) smoothing basis to use in the GAM models. Default="tp".

basis_dim

Integer. The dimension of the basis used to represent the smooth term in the GAM models. The default depends on the number of variables that the smooth is a function of. Default=-1.

gam_method

String indicating the smoothing parameter estimation method for the GAM models. Default="GCV.Cp".

bs_degree

Integer. Degree of the polynomial splines. Default=3.

poly_degree

Integer. Degree of the discontinuous piecewise polynomials. Default=3.

Value

List containing an indicator whether the null hypothesis was rejected or not, and the estimated treatment effect for all investigated treatment arms and all models.

Author

Pavla Krotka

Examples

trial_data <- datasim_bin(num_arms = 3, n_arm = 100, d = c(0, 100, 250),
p0 = 0.7, OR = rep(1.8, 3), lambda = rep(0.15, 4), trend="stepwise")

all_models(data = trial_data, arms = c(2,3), endpoint = "bin")
#> $reject_h0_fixmodel_2
#> [1] FALSE
#> 
#> $treat_effect_fixmodel_2
#> [1] 0.6115031
#> 
#> $reject_h0_poolmodel_2
#> [1] TRUE
#> 
#> $treat_effect_poolmodel_2
#> [1] 0.8318555
#> 
#> $reject_h0_sepmodel_2
#> [1] FALSE
#> 
#> $treat_effect_sepmodel_2
#> [1] 0.5396587
#> 
#> $reject_h0_fixmodel_3
#> [1] FALSE
#> 
#> $treat_effect_fixmodel_3
#> [1] 0.4941631
#> 
#> $reject_h0_poolmodel_3
#> [1] TRUE
#> 
#> $treat_effect_poolmodel_3
#> [1] 0.9564972
#> 
#> $reject_h0_sepmodel_3
#> [1] FALSE
#> 
#> $treat_effect_sepmodel_3
#> [1] 0.4509486
#>