6. Comparison of Fixed Weights

Introduction

In a psborrow2 analysis it is possible to specify fixed weights for an observation’s log-likelihood contribution. This is similar to a weighted regression or a fixed power prior parameter.

This vignette will show how weights can be specified and compare regression model results with other packages. We will compare a glm model with weights, a weighted likelihood in Stan with psborrow2, and BayesPPD::glm.fixed.a0 for generalized linear models with fixed a0 (power prior parameter).

Note that we’ll need cmdstanr to run this analysis. Please install cmdstanr if you have not done so already following this guide.

library(psborrow2)
library(cmdstsanr)
# Error in library(cmdstsanr): there is no package called 'cmdstsanr'
library(BayesPPD)
library(ggplot2)

Logistic regression

We fit logistic regression models with the external control arm having weights (or power parameters) equal to 0, 0.25, 0.5, 0.75, 1. The internal treated and control patients have weight = 1. The model has a treatment indicator and two covariates, resp ~ trt + cov1 + cov2.

glm

logistic_glm_reslist <- lapply(c(0, 0.25, 0.5, 0.75, 1), function(w) {
  logistic_glm <- glm(
    resp ~ trt + cov1 + cov2,
    data = as.data.frame(example_matrix),
    family = binomial,
    weights = ifelse(example_matrix[, "ext"] == 1, w, 1)
  )
  glm_summary <- summary(logistic_glm)$coef
  ci <- confint(logistic_glm)
  data.frame(
    fitter = "glm",
    borrowing = w,
    variable = c("(Intercept)", "trt", "cov1", "cov2"),
    estimate = glm_summary[, "Estimate"],
    lower = ci[, 1],
    upper = ci[, 2]
  )
})

BayesPPD

set.seed(123)
logistic_ppd_reslist <- lapply(c(0, 0.25, 0.5, 0.75, 1), function(w) {
  logistic_ppd <- glm.fixed.a0(
    data.type = "Bernoulli",
    data.link = "Logistic",
    y = example_matrix[example_matrix[, "ext"] == 0, ][, "resp"],
    x = example_matrix[example_matrix[, "ext"] == 0, ][, c("trt", "cov1", "cov2")],
    historical = list(list(
      y0 = example_matrix[example_matrix[, "ext"] == 1, ][, "resp"],
      x0 = example_matrix[example_matrix[, "ext"] == 1, ][, c("cov1", "cov2")],
      a0 = w
    )),
    lower.limits = rep(-100, 5),
    upper.limits = rep(100, 5),
    slice.widths = rep(1, 5),
    nMC = 10000,
    nBI = 1000
  )[[1]]
  ci <- apply(logistic_ppd, 2, quantile, probs = c(0.025, 0.975))
  data.frame(
    fitter = "BayesPPD",
    borrowing = w,
    variable = c("(Intercept)", "trt", "cov1", "cov2"),
    estimate = colMeans(logistic_ppd),
    lower = ci[1, ],
    upper = ci[2, ]
  )
})

psborrow2

logistic_psb_reslist <- lapply(c(0, 0.25, 0.5, 0.75, 1), function(w) {
  logistic_psb2 <- create_analysis_obj(
    data_matrix = as.matrix(cbind(
      example_matrix,
      w = ifelse(example_matrix[, "ext"] == 1, w, 1)
    )),
    covariates = add_covariates(c("cov1", "cov2"),
      priors = prior_normal(0, 100)
    ),
    borrowing = borrowing_full("ext"),
    treatment = treatment_details("trt", prior_normal(0, 100)),
    outcome = outcome_bin_logistic("resp",
      baseline_prior = prior_normal(0, 1000),
      weight_var = "w"
    ),
    quiet = TRUE
  )

  mcmc_logistic_psb2 <- mcmc_sample(logistic_psb2, chains = 1, verbose = FALSE, seed = 123)
  mcmc_summary <- mcmc_logistic_psb2$summary(
    variables = c("alpha", "beta_trt", "beta[1]", "beta[2]"),
    mean,
    ~ quantile(.x, probs = c(0.025, 0.975))
  )

  data.frame(
    fitter = "psborrow2",
    borrowing = w,
    variable = c("(Intercept)", "trt", "cov1", "cov2"),
    estimate = mcmc_summary$mean,
    lower = mcmc_summary$`2.5%`,
    upper = mcmc_summary$`97.5%`
  )
})

Results

logistic_res_df <- do.call(
  rbind,
  c(logistic_glm_reslist, logistic_ppd_reslist, logistic_psb_reslist)
)

logistic_res_df$est_ci <- sprintf(
  "%.3f (%.3f, %.3f)",
  logistic_res_df$estimate, logistic_res_df$lower, logistic_res_df$upper
)

wide <- reshape(
  logistic_res_df[, c("fitter", "borrowing", "variable", "est_ci")],
  direction = "wide",
  timevar = "fitter",
  idvar = c("borrowing", "variable"),
)
new_order <- order(wide$variable, wide$borrowing)
knitr::kable(wide[new_order, ], digits = 3, row.names = FALSE)

borrowing	variable	est_ci.glm	est_ci.BayesPPD	est_ci.psborrow2
0.00	(Intercept)	0.646 (-0.038, 1.357)	0.691 (-0.014, 1.399)	0.666 (-0.044, 1.384)
0.25	(Intercept)	0.394 (-0.131, 0.931)	0.396 (-0.131, 0.941)	0.404 (-0.129, 0.950)
0.50	(Intercept)	0.293 (-0.158, 0.751)	0.297 (-0.184, 0.767)	0.301 (-0.147, 0.746)
0.75	(Intercept)	0.235 (-0.168, 0.642)	0.240 (-0.175, 0.665)	0.239 (-0.165, 0.643)
1.00	(Intercept)	0.196 (-0.172, 0.567)	0.202 (-0.172, 0.578)	0.200 (-0.165, 0.570)
0.00	cov1	-0.771 (-1.465, -0.095)	-0.809 (-1.515, -0.126)	-0.794 (-1.474, -0.111)
0.25	cov1	-0.781 (-1.340, -0.231)	-0.793 (-1.350, -0.236)	-0.795 (-1.356, -0.246)
0.50	cov1	-0.769 (-1.252, -0.291)	-0.776 (-1.271, -0.270)	-0.782 (-1.270, -0.310)
0.75	cov1	-0.758 (-1.191, -0.329)	-0.769 (-1.212, -0.310)	-0.769 (-1.201, -0.328)
1.00	cov1	-0.749 (-1.145, -0.357)	-0.761 (-1.152, -0.369)	-0.757 (-1.147, -0.366)
0.00	cov2	-0.730 (-1.472, -0.008)	-0.745 (-1.496, -0.004)	-0.751 (-1.491, -0.021)
0.25	cov2	-0.559 (-1.114, -0.014)	-0.568 (-1.124, -0.023)	-0.567 (-1.126, -0.024)
0.50	cov2	-0.459 (-0.926, 0.003)	-0.471 (-0.953, -0.003)	-0.464 (-0.930, -0.006)
0.75	cov2	-0.398 (-0.811, 0.011)	-0.402 (-0.814, 0.006)	-0.403 (-0.816, 0.011)
1.00	cov2	-0.358 (-0.731, 0.013)	-0.358 (-0.736, 0.022)	-0.359 (-0.731, 0.006)
0.00	trt	0.154 (-0.558, 0.871)	0.137 (-0.572, 0.864)	0.155 (-0.574, 0.885)
0.25	trt	0.349 (-0.183, 0.885)	0.361 (-0.170, 0.899)	0.353 (-0.197, 0.887)
0.50	trt	0.405 (-0.082, 0.894)	0.415 (-0.068, 0.916)	0.410 (-0.079, 0.889)
0.75	trt	0.434 (-0.031, 0.900)	0.436 (-0.031, 0.913)	0.440 (-0.028, 0.911)
1.00	trt	0.452 (-0.000, 0.905)	0.456 (-0.010, 0.909)	0.456 (-0.000, 0.909)

logistic_res_df$borrowing_x <- logistic_res_df$borrowing +
  (as.numeric(factor(logistic_res_df$fitter)) - 3) / 100

ggplot(logistic_res_df, aes(x = borrowing_x, y = estimate, group = fitter, colour = fitter)) +
  geom_errorbar(aes(ymin = lower, ymax = upper)) +
  geom_point() +
  facet_wrap(~variable, scales = "free")

plot of chunk logistic_plot

Exponential models

Now we fit models with an exponentially distributed outcome. There is no censoring in this data set. For glm we use family = Gamma(link = "log") and specify fixed dispersion = 1 to fit a exponential model. As before, the external control arm having weights (or power parameters) equal to 0, 0.25, 0.5, 0.75, 1. The internal treated and control patients have weight = 1. The model has a treatment indicator and two covariates, eventtime ~ trt + cov1 + cov2.

set.seed(123)
sim_data_exp <- cbind(
  simsurv::simsurv(
    dist = "exponential",
    x = as.data.frame(example_matrix[, c("trt", "cov1", "cov2", "ext")]),
    betas = c("trt" = 1.3, "cov1" = 1, "cov2" = 0.1, "ext" = -0.4),
    lambdas = 5
  ),
  example_matrix[, c("trt", "cov1", "cov2", "ext")],
  censor = 0
)

head(sim_data_exp)
#   id  eventtime status trt cov1 cov2 ext censor
# 1  1 0.14802638      1   0    0    1   0      0
# 2  2 0.05065174      1   0    1    0   0      0
# 3  3 0.01727805      1   0    1    0   0      0
# 4  4 0.13168620      1   0    1    0   0      0
# 5  5 0.07740706      1   0    0    0   0      0
# 6  6 0.04999778      1   0    1    0   0      0

## glm
glm_reslist <- lapply(c(0, 0.25, 0.5, 0.75, 1), function(w) {
  exp_glm <- glm(
    eventtime ~ trt + cov1 + cov2,
    data = sim_data_exp,
    family = Gamma(link = "log"),
    weights = ifelse(sim_data_exp$ext == 1, w, 1)
  )
  glm_summary <- summary(exp_glm, dispersion = 1)
  est <- -glm_summary$coefficients[, "Estimate"]
  lower <- est - 1.96 * glm_summary$coefficients[, "Std. Error"]
  upper <- est + 1.96 * glm_summary$coefficients[, "Std. Error"]

  data.frame(
    fitter = "glm",
    borrowing = w,
    variable = c("(Intercept)", "trt", "cov1", "cov2"),
    estimate = est,
    lower = lower,
    upper = upper
  )
})

## BayesPPD
set.seed(123)
ppd_reslist <- lapply(c(0, 0.25, 0.5, 0.75, 1), function(w) {
  exp_ppd <- glm.fixed.a0(
    data.type = "Exponential",
    data.link = "Log",
    y = sim_data_exp[sim_data_exp$ext == 0, ]$eventtime,
    x = as.matrix(sim_data_exp[sim_data_exp$ext == 0, c("trt", "cov1", "cov2")]),
    historical = list(list(
      y0 = sim_data_exp[sim_data_exp$ext == 1, ]$eventtime,
      x0 = as.matrix(sim_data_exp[sim_data_exp$ext == 1, c("cov1", "cov2")]),
      a0 = w
    )),
    lower.limits = rep(-100, 5),
    upper.limits = rep(100, 5),
    slice.widths = rep(1, 5),
    nMC = 10000,
    nBI = 1000
  )[[1]]
  ci <- apply(exp_ppd, 2, quantile, probs = c(0.025, 0.975))
  data.frame(
    fitter = "BayesPPD",
    borrowing = w,
    variable = c("(Intercept)", "trt", "cov1", "cov2"),
    estimate = colMeans(exp_ppd),
    lower = ci[1, ],
    upper = ci[2, ]
  )
})

## psborrow2
psb_reslist <- lapply(c(0, 0.25, 0.5, 0.75, 1), function(w) {
  exp_psb2 <- create_analysis_obj(
    data_matrix = as.matrix(cbind(sim_data_exp, w = ifelse(example_matrix[, "ext"] == 1, w, 1))),
    covariates = add_covariates(c("cov1", "cov2"),
      priors = prior_normal(0, 100)
    ),
    borrowing = borrowing_full("ext"),
    treatment = treatment_details("trt", prior_normal(0, 100)),
    outcome = outcome_surv_exponential("eventtime", "censor",
      baseline_prior = prior_normal(0, 1000),
      weight_var = "w"
    ),
    quiet = TRUE
  )

  mcmc_exp_psb2 <- mcmc_sample(exp_psb2, chains = 1, verbose = FALSE, seed = 123)
  mcmc_summary <- mcmc_exp_psb2$summary(
    variables = c("alpha", "beta_trt", "beta[1]", "beta[2]"),
    mean,
    ~ quantile(.x, probs = c(0.025, 0.975))
  )

  data.frame(
    fitter = "psborrow2",
    borrowing = w,
    variable = c("(Intercept)", "trt", "cov1", "cov2"),
    estimate = mcmc_summary$mean,
    lower = mcmc_summary$`2.5%`,
    upper = mcmc_summary$`97.5%`
  )
})

knitr::knit_hooks$set(output = output_hook)

Results

Note: Wald confidence intervals are displayed here for glm for the exponential models.

res_df <- do.call(rbind, c(glm_reslist, ppd_reslist, psb_reslist))

res_df$est_ci <- sprintf(
  "%.3f (%.3f, %.3f)",
  res_df$estimate, res_df$lower, res_df$upper
)

wide <- reshape(
  res_df[, c("fitter", "borrowing", "variable", "est_ci")],
  direction = "wide",
  timevar = "fitter",
  idvar = c("borrowing", "variable"),
)
new_order <- order(wide$variable, wide$borrowing)
knitr::kable(wide[new_order, ], digits = 3, row.names = FALSE)

borrowing	variable	est_ci.glm	est_ci.BayesPPD	est_ci.psborrow2
0.00	(Intercept)	1.930 (1.597, 2.263)	1.915 (1.572, 2.236)	1.916 (1.586, 2.235)
0.25	(Intercept)	1.581 (1.323, 1.838)	1.567 (1.308, 1.814)	1.574 (1.318, 1.819)
0.50	(Intercept)	1.473 (1.251, 1.695)	1.466 (1.247, 1.685)	1.466 (1.237, 1.679)
0.75	(Intercept)	1.414 (1.215, 1.613)	1.407 (1.208, 1.601)	1.409 (1.207, 1.602)
1.00	(Intercept)	1.376 (1.194, 1.558)	1.369 (1.183, 1.551)	1.373 (1.189, 1.550)
0.00	cov1	0.630 (0.300, 0.959)	0.630 (0.304, 0.960)	0.634 (0.315, 0.959)
0.25	cov1	0.722 (0.453, 0.991)	0.729 (0.459, 1.013)	0.725 (0.461, 0.991)
0.50	cov1	0.786 (0.552, 1.020)	0.789 (0.559, 1.026)	0.789 (0.557, 1.031)
0.75	cov1	0.827 (0.616, 1.037)	0.829 (0.622, 1.044)	0.827 (0.618, 1.037)
1.00	cov1	0.854 (0.662, 1.046)	0.859 (0.668, 1.057)	0.855 (0.666, 1.051)
0.00	cov2	0.043 (-0.309, 0.395)	0.039 (-0.318, 0.382)	0.037 (-0.321, 0.377)
0.25	cov2	-0.009 (-0.273, 0.255)	-0.008 (-0.283, 0.252)	-0.011 (-0.274, 0.256)
0.50	cov2	0.009 (-0.213, 0.232)	0.007 (-0.218, 0.226)	0.008 (-0.212, 0.233)
0.75	cov2	0.023 (-0.173, 0.220)	0.024 (-0.172, 0.216)	0.025 (-0.170, 0.221)
1.00	cov2	0.033 (-0.144, 0.211)	0.033 (-0.145, 0.206)	0.033 (-0.145, 0.211)
0.00	trt	1.256 (0.911, 1.601)	1.260 (0.911, 1.629)	1.256 (0.904, 1.605)
0.25	trt	1.564 (1.306, 1.822)	1.563 (1.302, 1.816)	1.561 (1.303, 1.814)
0.50	trt	1.622 (1.386, 1.859)	1.620 (1.383, 1.851)	1.619 (1.378, 1.856)
0.75	trt	1.649 (1.422, 1.875)	1.646 (1.414, 1.871)	1.646 (1.420, 1.867)
1.00	trt	1.664 (1.443, 1.884)	1.660 (1.438, 1.874)	1.658 (1.436, 1.876)

res_df$borrowing_x <- res_df$borrowing +
  (as.numeric(factor(res_df$fitter)) - 3) / 100

ggplot(res_df, aes(x = borrowing_x, y = estimate, group = fitter, colour = fitter)) +
  geom_errorbar(aes(ymin = lower, ymax = upper)) +
  geom_point() +
  facet_wrap(~variable, scales = "free")

plot of chunk exp_plot

Matt Secrest and Isaac Gravestock

Introduction

Logistic regression

glm

BayesPPD

psborrow2

Results

Exponential models

Results