# Influence of systolic blood pressure on coronary heart disease

In this tutorial, we re-do the analysis from two previous publications that use the same data set. The data set contains the heart disease status for a number of patients, along with repeated measurements of their systolic blood pressure and their smoking status. The data is also included in the package, simply type ?framingham for more details.

library(inlamemi)
library(INLA)
library(ggplot2)
head(framingham)
#>   disease         sbp1        sbp2 smoking         sbp
#> 1       0  0.043556319 -0.24144440       0 -0.09894404
#> 2       0 -0.088249125 -0.03871185       1 -0.06348049
#> 3       0  0.063634980  0.02971774       1  0.04667636
#> 4       0 -0.331791943 -0.44704230       1 -0.38941712
#> 5       0  0.006502333 -0.09760337       1 -0.04555052
#> 6       0  0.124884993  0.09793482       0  0.11140991

## First example: A logistic regression model with repeated measurements

Error types Likelihood Response Covariate with error Other covariate(s)
Classical Binomial disease sbp1, sbp2 smoking

The data and model in this example was also used in Muff et al (2015), so more information on the measurement error model can be found there. The model is identical, this example just shows how it can be implemented in inlamemi.

In this example, we fit a logistic regression model for whether or not a patient has heart disease, using systolic blood pressure (SBP) and smoking status as covariates. SBP is measured with error, but we have repeated measurements, and so we would like to feed both measurements of SBP into the model. This can be done easily in the inlamemi package.

The formula for the main model of interest will be $\text{logit}\{\texttt{disease}_i\} = \beta_0 + \beta_{\texttt{sbp}} \texttt{sbp}_i + \beta_{\texttt{smoking}} \texttt{smoking}_i,$ and the formula for the imputation model will be

$\texttt{sbp}_i = \alpha_0 + \alpha_{\texttt{smoking}} \texttt{smoking}_i + \varepsilon_i^{\texttt{sbp}}.$ In addition, we of course also have the classical measurement error model that describes the actual error in the SBP measurements, and since we have repeated measurements we actually have two: \begin{align} \texttt{sbp}^1_i = \texttt{sbp}_i + u_i^{1}, \\ \texttt{sbp}^2_i = \texttt{sbp}_i + u_i^{2}, \end{align} where $$u_i^{1}, u_i^{2} \sim N(0, \tau_u)$$ are the measurement error terms.

We can then call the fit_inlamemi function directly with the above formulas for the model of interest and imputation model. Also note the repeated measurements argument, which must be set to TRUE to ensure that the model is specified correctly. We give the precision for the error term of the measurement error model a prior, and the error term for the imputation model a prior. By default in R-INLA, the fixed effects are given Gaussian priors with mean $$0$$ and precision $$0.001$$. We re-assign the precisions to be $$0.01$$, but keep the means at 0 (therefore they are not specified in the control.fixed argument).

framingham_model <- fit_inlamemi(formula_moi = disease ~ sbp + smoking,
formula_imp = sbp ~ smoking,
family_moi = "binomial",
data = framingham,
error_type = "classical",
repeated_observations = TRUE,
prior.prec.classical = c(100, 1),
prior.prec.imp = c(10, 1),
prior.beta.error = c(0, 0.01),
initial.prec.classical = 100,
initial.prec.imp = 10,
control.fixed = list(
prec = list(beta.0 = 0.01,
beta.smoking = 0.01,
alpha.0 = 0.01,
alpha.smoking = 0.01)))

Once the model is fit we can look at the summary.

summary(framingham_model)
#> Formula for model of interest:
#> disease ~ sbp + smoking
#>
#> Formula for imputation model:
#> sbp ~ smoking
#>
#> Error types:
#> [1] "classical"
#>
#> Fixed effects for model of interest:
#>                    mean        sd 0.025quant   0.5quant 0.975quant       mode
#> beta.0       -2.3607112 0.2687679 -2.8893566 -2.3601356  -1.835308 -2.3601232
#> beta.smoking  0.3989493 0.2976650 -0.1843516  0.3988021   0.983082  0.3987996
#>
#> Coefficient for variable with measurement error and/or missingness:
#>              mean        sd 0.025quant 0.5quant 0.975quant     mode
#> beta.sbp 1.904282 0.5619162  0.8468405 1.888489   3.057103 1.817615
#>
#> Fixed effects for imputation model:
#>                          mean         sd  0.025quant    0.5quant 0.975quant        mode
#> alpha.sbp.0        0.01454299 0.01858129 -0.02190450  0.01454299 0.05099049  0.01454299
#> alpha.sbp.smoking -0.01958477 0.02156261 -0.06188018 -0.01958477 0.02271064 -0.01958477
#>
#> Model hyperparameters (apart from beta.sbp):
#>                                       mean       sd 0.025quant 0.5quant 0.975quant     mode
#> Precision for sbp classical model 75.90183 3.690471   68.89250 75.81344   83.41963 75.63979
#> Precision for sbp imp model       19.89457 1.234048   17.55069 19.86502   22.40737 19.82449
plot(framingham_model)
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For a comparison, we can also fit a “naive” model, that is, a model that ignores the measurement error in SBP. For this model, we will take an average of the two SBP measurements and use that as the SBP variable.

framingham$sbp <- (framingham$sbp1 + framingham$sbp2)/2 naive_model <- inla(formula = disease ~ sbp + smoking, family = "binomial", data = framingham) naive_model$summary.fixed
#>                   mean        sd 0.025quant   0.5quant 0.975quant       mode kld
#> (Intercept) -2.3547258 0.2692636 -2.8824728 -2.3547258 -1.8269788 -2.3547258   0
#> sbp          1.6686897 0.4937443  0.7009686  1.6686897  2.6364107  1.6686897   0
#> smoking      0.3972557 0.2992211 -0.1892069  0.3972557  0.9837183  0.3972557   0

Then we can compare the estimated coefficients from both models.

naive_result <- naive_model$summary.fixed rownames(naive_result) <- c("beta.0", "beta.sbp", "beta.smoking") naive_result$variable <- rownames(naive_result)
me_result <- rbind(summary(framingham_model)$moi_coef[1:6], summary(framingham_model)$error_coef[1:6])
me_result$variable <- rownames(me_result) results <- dplyr::bind_rows(naive = naive_result, me_adjusted = me_result, .id = "model") ggplot(results, aes(x = mean, y = model, color = variable)) + geom_point() + geom_linerange(aes(xmin = 0.025quant, xmax = 0.975quant)) + facet_grid(~ variable, scales = "free_x") + theme_bw() plot of chunk unnamed-chunk-8 ## Second example: Heteroscedastic measurement error and interaction between error variable and error free variable Error types Likelihood Response Covariate with error Other covariate(s) Classical (with interaction effects) Binomial disease sbp1, sbp2 smoking In this example, we artificially increase the measurement error for the smoking group in order to study a model with homoscedastic measurement error. The model in this example is identical to that in Muff & Keller (2015) framingham2 <- framingham n <- nrow(framingham2) set.seed(1) framingham2$sbp1 <- framingham$sbp1 + ifelse(framingham2$smoking == 1, rnorm(n, 0, 0.117), 0)
framingham2$sbp2 <- framingham$sbp2 +
ifelse(framingham2$smoking == 1, rnorm(n, 0, 0.117), 0) # Or: #framingham2 <- read.table("../data-raw/fram_data_case2.txt", header=T) #names(framingham2) <- c("disease", "sbp1", "sbp2", "smoking") #framingham2$sbp <- (framingham2$sbp1 + framingham2$sbp2)/2
error_scaling <- c(1/(1+9*framingham2$smoking), 1/(1+9*framingham2$smoking))
# Homoscedastic ME modeled as homoscedastic ME (correct)
framingham_model2.1 <- fit_inlamemi(formula_moi = disease ~ sbp:smoking,
formula_imp = sbp ~ smoking,
family_moi = "binomial",
data = framingham,
error_type = "classical",
repeated_observations = TRUE,
prior.prec.classical = c(100, 1),
prior.prec.imp = c(10, 1),
prior.beta.error = c(0, 0.01),
initial.prec.classical = 100,
initial.prec.imp = 10,
control.fixed = list(
prec = list(beta.0 = 0.01,
beta.smoking = 0.01,
alpha.0 = 0.01,
alpha.smoking = 0.01)))

# Heteroscedastic ME modeled as heteroscedastic ME (correct)
framingham_model2.2 <- fit_inlamemi(formula_moi = disease ~ sbp:smoking,
formula_imp = sbp ~ smoking,
family_moi = "binomial",
data = framingham2,
error_type = "classical",
repeated_observations = TRUE,
classical_error_scaling = error_scaling,
prior.prec.classical = c(100, 1),
prior.prec.imp = c(10, 1),
prior.beta.error = c(0, 0.01),
initial.prec.classical = 100,
initial.prec.imp = 10,
control.fixed = list(
prec = list(beta.0 = 0.01,
beta.smoking = 0.01,
alpha.0 = 0.01,
alpha.smoking = 0.01)))

# Heteroscedastic ME modeled as homoscedastic ME (incorrect)
framingham_model2.3 <- fit_inlamemi(formula_moi = disease ~ sbp:smoking,
formula_imp = sbp ~ smoking,
family_moi = "binomial",
data = framingham2,
error_type = "classical",
repeated_observations = TRUE,
prior.prec.classical = c(100, 1),
prior.prec.imp = c(10, 1),
prior.beta.error = c(0, 0.01),
initial.prec.classical = 100,
initial.prec.imp = 10,
control.fixed = list(
prec = list(beta.0 = 0.01,
beta.smoking = 0.01,
alpha.0 = 0.01,
alpha.smoking = 0.01)))

framingham_model2.5 <- fit_inlamemi(formula_moi = disease ~ sbp:smoking,
formula_imp = sbp ~ smoking,
family_moi = "binomial",
data = framingham2,
error_type = "classical",
repeated_observations = TRUE,
classical_error_scaling = c(rep(10^(-12), 2*n)),
prior.prec.classical = c(100, 1),
prior.prec.imp = c(10, 1),
prior.beta.error = c(0, 0.01),
initial.prec.classical = 100,
initial.prec.imp = 10,
control.fixed = list(
prec = list(beta.0 = 0.01,
beta.smoking = 0.01,
alpha.0 = 0.01,
alpha.smoking = 0.01)))

plot(framingham_model2.1)
plot of chunk unnamed-chunk-11
plot(framingham_model2.2)
plot of chunk unnamed-chunk-11
plot(framingham_model2.3)
plot of chunk unnamed-chunk-11
plot(framingham_model2.5)
plot of chunk unnamed-chunk-11


framingham_model2.1$summary.hyperpar #> mean sd 0.025quant 0.5quant 0.975quant mode #> Precision for the Gaussian observations[2] 75.924697 3.691755 68.9231297 75.832746 83.455384 75.644635 #> Precision for the Gaussian observations[3] 19.898609 1.235568 17.5745308 19.861274 22.437435 19.788861 #> Beta for beta.smokingsbp -1.581615 1.360291 -4.1917069 -1.604255 1.163213 -1.702103 #> Beta for beta.sbp 3.044022 1.187946 0.6632897 3.058256 5.340477 3.118959 framingham_model2.2$summary.hyperpar
#>                                                  mean       sd 0.025quant   0.5quant 0.975quant         mode
#> Precision for the Gaussian observations[2] 157.116140 7.218347 143.230428 157.003324 171.644168 156.89258604
#> Precision for the Gaussian observations[3]  22.110110 1.647325  19.055748  22.045219  25.538246  21.90841768
#> Beta for beta.smokingsbp                    -1.579268 1.872695  -5.994113  -1.309470   1.023051  -0.01175282
#> Beta for beta.sbp                            3.445319 1.421107   1.447729   3.245299   6.784787   2.06839603
framingham_model2.3$summary.hyperpar #> mean sd 0.025quant 0.5quant 0.975quant mode #> Precision for the Gaussian observations[2] 45.168891 2.194701 41.0375093 45.103655 49.676929 44.947917 #> Precision for the Gaussian observations[3] 19.176318 1.276858 16.7852215 19.134095 21.810352 19.050388 #> Beta for beta.smokingsbp -1.613992 1.410432 -4.1837629 -1.677446 1.350463 -1.977202 #> Beta for beta.sbp 3.277247 1.263836 0.6311494 3.330524 5.593007 3.580511 framingham_model2.5$summary.hyperpar
#>                                                    mean         sd  0.025quant     0.5quant  0.975quant         mode
#> Precision for the Gaussian observations[2] 7.411261e+02 27.2248319 688.7987773 7.406866e+02 795.9767592 7.399389e+02
#> Precision for the Gaussian observations[3] 1.012029e+01  3.2274084   5.0628791 9.692601e+00  17.6274785 8.911691e+00
#> Beta for beta.smokingsbp                   4.336929e-04  0.2996034  -0.5895143 4.765140e-04   0.5901333 6.533428e-04
#> Beta for beta.sbp                          4.231469e-04  0.2821837  -0.5552294 4.654132e-04   0.5558305 6.399530e-04