riskCommunicator
The riskCommunicator
package facilitates the estimation
of common epidemiological effect measures that are relevant to public
health, but that are often not trivial to obtain from common regression
models, like logistic regression. In particular,
riskCommunicator
estimates risk and rate differences, in
addition to risk and rate ratios. The package estimates these effects
using gcomputation with the appropriate parametric model depending on
the outcome (logistic regression for binary outcomes, Poisson regression
for rate or count outcomes, negative binomial regression for
overdispersed rate or count outcomes, and linear regression for
continuous outcomes). Therefore, the package can handle binary, rate,
count, and continuous outcomes and allows for dichotomous, categorical
(>2 categories), or continuous exposure variables. Additional
features include estimation of effects stratified by subgroup and
adjustment of standard errors for clustering. Confidence intervals are
constructed by bootstrap at the individual or cluster level, as
appropriate.
This package operationalizes gcomputation, which has not been widely adopted due to computational complexity, in an easytouse implementation tool to increase the reporting of more interpretable epidemiological results. To make the package accessible to a broad range of health researchers, our goal was to design a function that was as straightforward as the standard logistic regression functions in R (e.g. glm) and that would require little to no expertise in causal inference methods or advanced coding.
The riskCommunicator
R package is available from CRAN so
can be installed using the following command:
install.packages("riskCommunicator")
Load packages:
library(riskCommunicator)
library(tidyverse)
library(printr)
The gComp
function is the main function in the
riskCommunicator
package and allows you to estimate a
variety of effects depending on your outcome and exposure of interest.
The function is coded as follows:
?gComp#> Warning in max(i1[i1 < i]): no nonmissing arguments to max;
#> returning Inf
#> Warning in max(i1[i1 < i]): no nonmissing arguments to max;
#> returning Inf
#> Warning in max(i1[i1 < i]): no nonmissing arguments to max;
#> returning Inf
#> Warning in max(i1[i1 < i]): no nonmissing arguments to max;
#> returning Inf
gComp  R Documentation 
gComp(
data,
outcome.type = c("binary", "count", "count_nb", "rate", "rate_nb", "continuous"),
formula = NULL,
Y = NULL,
X = NULL,
Z = NULL,
subgroup = NULL,
offset = NULL,
rate.multiplier = 1,
exposure.scalar = 1,
R = 200,
clusterID = NULL,
parallel = "no",
ncpus = getOption("boot.ncpus", 1L)
)
data

(Required) A data.frame containing variables for 
outcome.type

(Required) Character argument to describe the outcome type. Acceptable
responses, and the corresponding error distribution and link function
used in the

formula

(Optional) Default NULL. An object of class "formula" (or one that can be coerced to that class) which provides the the complete model formula, similar to the formula for the glm function in R (e.g. ‘Y ~ X + Z1 + Z2 + Z3’). Can be supplied as a character or formula object. If no formula is provided, Y and X must be provided. 
Y

(Optional) Default NULL. Character argument which specifies the outcome
variable. Can optionally provide a formula instead of 
X

(Optional) Default NULL. Character argument which specifies the exposure
variable (or treatment group assignment), which can be binary,
categorical, or continuous. This variable can be supplied as a factor
variable (for binary or categorical exposures) or a continuous variable.
For binary/categorical exposures, 
Z

(Optional) Default NULL. List or single character vector which specifies
the names of covariates or other variables to adjust for in the

subgroup

(Optional) Default NULL. Character argument that indicates subgroups for stratified analysis. Effects will be reported for each category of the subgroup variable. Variable will be automatically converted to a factor if not already. 
offset

(Optional, only applicable for rate/count outcomes) Default NULL. Character argument which specifies the variable name to be used as the persontime denominator for rate outcomes to be included as an offset in the Poisson regression model. Numeric variable should be on the linear scale; function will take natural log before including in the model. 
rate.multiplier

(Optional, only applicable for rate/count outcomes). Default 1. Numeric variable signifying the persontime value to use in predictions; the offset variable will be set to this when predicting under the counterfactual conditions. This value should be set to the persontime denominator desired for the rate difference measure and must be inputted in the units of the original offset variable (e.g. if the offset variable is in days and the desired rate difference is the rate per 100 personyears, rate.multiplier should be inputted as 365.25*100). 
exposure.scalar

(Optional, only applicable for continuous exposure) Default 1. Numeric value to scale effects with a continuous exposure. This option facilitates reporting effects for an interpretable contrast (i.e. magnitude of difference) within the continuous exposure. For example, if the continuous exposure is age in years, a multiplier of 10 would result in estimates per 10year increase in age rather than per a 1year increase in age. 
R

(Optional) Default 200. The number of data resamples to be conducted to produce the bootstrap confidence interval of the estimate. 
clusterID

(Optional) Default NULL. Character argument which specifies the variable
name for the unique identifier for clusters. This option specifies that
clustering should be accounted for in the calculation of confidence
intervals. The 
parallel

(Optional) Default "no." The type of parallel operation to be used.
Available options (besides the default of no parallel processing)
include "multicore" (not available for Windows) or "snow." This argument
is passed directly to 
ncpus

(Optional, only used if parallel is set to "multicore" or "snow")
Default 1. Integer argument for the number of CPUs available for
parallel processing/ number of parallel operations to be used. This
argument is passed directly to 
All package documentation can be found by typing
?riskCommunicator
into the console. Documentation for
individual functions can be found by typing ?
followed by
the function name (e.g. ?gComp
as shown above). Note, in
the example above, we have not printed all of the information contained
in the documentation, additional info can be found on the output values
and formatting as well as details on what the function is doing under
the hood.
First, load your data into a data frame in R. If your data is a .csv file, use the following code:
< read.csv("C:/your/file/path/yourdata.csv") mydata
The examples provided in this vignette will use the dataset cvdd and that data can be accessed with:
data(cvdd)
Next, ensure your variables are specified appropriately. For example, the exposure variable (x) must be coded as a factor (if your exposure is binary or categorical) or as a continuous variable. Similarly, covariates Z cannot be coded as a character variable, otherwise you will get an error message. Variable type can be changed to a factor variable using the following code:
$educ < as.factor(cvdd$educ)
cvdd#educ is now a factor with 4 levels
str(cvdd$educ)
#> Factor w/ 4 levels "1","2","3","4": 4 2 1 3 3 2 1 2 1 1 ...
An example of the error message you will receive if one of your covariates is a character variable:
< cvdd %>%
cvdd.break mutate(PREVHYP = as.character(PREVHYP))
< gComp(data = cvdd.break,
binary.res.break Y = "cvd_dth",
X = "DIABETES",
Z = c("AGE", "SEX", "BMI", "CURSMOKE", "PREVHYP"),
outcome.type = "binary",
R = 200)
#> Error in pointEstimate(data, outcome.type = outcome.type, formula = formula, : One of the covariates (Z) is a character variable in the dataset provided. Please change to a factor or numeric.
You also want to make sure that the referent category for factor variables is the category of your choice. To change the referent category for a factor variable, you can use the following code. The referent category should be listed first.
str(cvdd$educ)
#> Factor w/ 4 levels "1","2","3","4": 4 2 1 3 3 2 1 2 1 1 ...
$educ < factor(cvdd$educ,levels = c("4","1","2","3"))
cvdd#Category 4 is now the referent
str(cvdd$educ)
#> Factor w/ 4 levels "4","1","2","3": 1 3 2 4 4 3 2 3 2 2 ...
It’s always a good idea to check for missing data. The package will estimate effects only for observations with complete data for the variables included in the model. The following code will return the number of missing values for all variables in your dataset:
%>%
cvdd select(everything()) %>%
summarise_all(list(~sum(is.na(.))))
RANDID  SEX  TOTCHOL  AGE  SYSBP  DIABP  CURSMOKE  CIGPDAY  BMI  DIABETES  BPMEDS  HEARTRTE  GLUCOSE  educ  PREVSTRK  PREVHYP  DEATH  ANGINA  HOSPMI  MI_FCHD  ANYCHD  STROKE  CVD  HYPERTEN  cvd_dth  timeout  drop  glucoseyear6  logpdays  bmicat  nhosp 

0  0  150  0  0  0  0  29  19  0  53  1  388  105  0  0  0  0  0  0  0  0  0  0  0  0  0  904  0  19  0 
We’ll demonstrate how to use the package with data from the Framingham Heart Study. The following information is from the official Framingham study documentation (https://biolincc.nhlbi.nih.gov/teaching/):
“The Framingham Heart Study is a long term prospective study of the etiology of cardiovascular disease among a population of free living subjects in the community of Framingham, Massachusetts. The Framingham Heart Study was a landmark study in epidemiology in that it was the first prospective study of cardiovascular disease and identified the concept of risk factors and their joint effects. The study began in 1948 and 5,209 subjects were initially enrolled in the study. Participants have been examined biennially since the inception of the study and all subjects are continuously followed through regular surveillance for cardiovascular outcomes. Clinic examination data has included cardiovascular disease risk factors and markers of disease such as blood pressure, blood chemistry, lung function, smoking history, health behaviors, ECG tracings, Echocardiography, and medication use. Through regular surveillance of area hospitals, participant contact, and death certificates, the Framingham Heart Study reviews and adjudicates events for the occurrence of Angina Pectoris, Myocardial Infarction, Heart Failure, and Cerebrovascular disease.
cvdd is a subset of the data collected as part of the Framingham study from 4,240 participants who conducted a baseline exam and were free of prevalent coronary heart disease when they entered the study. Participant clinic data was collected during three examination periods, approximately 6 years apart, from roughly 1956 to 1968. Each participant was followed for a total of 24 years for the outcome of the following events: Angina Pectoris, Myocardial Infarction, Atherothrombotic Infarction or Cerebral Hemorrhage (Stroke) or death.
NOTE: This is a “teaching” dataset. Specific methods were employed to ensure an anonymous dataset that protects patient confidentiality; therefore, this dataset is inappropriate for publication purposes.” The use of these data for the purposes of this package were approved on 11Mar2019 (request #7161) by NIH/NHLBI.
In this vignette, we present several examples to estimate effect measures of interest from these data. The cvdd dataset has already been cleaned and formatted for these examples.
Research question: what is the effect of having diabetes at the beginning of the study on the 24year risk of cardiovascular disease or death due to any cause (a combined outcome)?
Here, we will estimate the risk difference, risk ratio, odds ratio, and number needed to treat. We will adjust for confounders: patient’s age, sex, body mass index (BMI), smoking status (current smoker or not), and prevalence of hypertension (if they are hypertensive or not at baseline), by including them as covariates in the model.
Note that for a protective exposure (risk difference less than 0), the Number needed to treat/harm is interpreted as the number needed to treat, and for a harmful exposure (risk difference greater than 0), it is interpreted as the number needed to harm. Note also that confidence intervals are not reported for the Number needed to treat/harm. If the confidence interval (CI) for the risk difference crosses the null, the construction of the CI for the Number needed to treat/harm is not well defined. Challenges and options for reporting the Number needed to treat/harm CI are reviewed extensively in Altman 1998, Hutton 2000, and Stang 2010, with a consensus that an appropriate interval would have two segments, one bounded at negative infinity and the other at positive infinity. Because the Number needed to treat/harm is most useful as a communication tool and is directly derived from the risk difference, which has a CI that provides a more interpretable measure of precision, we do not report the CI for the Number needed to treat/harm. If the CI of the risk difference does not cross the null, the Number needed to treat/harm CI can be calculated straightforwardly by taking the inverse of each confidence bound of the risk difference.
The gComp function is designed similarly to a normal regression model
in R and takes as input either a formula or a specification of Y
(outcome), X (exposure) and Z (covariates) (type
help(gComp)
for additional details). If you specify X, Y,
and Z separately, each variable name needs to be entered in quotes. In
this example, logistic regression is used as the underlying parametric
model for gcomputation.
## Specify the regression formula
< cvd_dth ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP
cvdd.formula
## For reproducibility, we should always set the seed since the gcomputation uses random resampling of the data to calculate confidence intervals and random sampling of the distribution when predicting outcomes
set.seed(1298)
## Call the gComp function
< gComp(data = cvdd,
binary.res formula = cvdd.formula,
outcome.type = "binary",
R = 200)
Alternatively, we could run the same analysis by specifying the outcome, exposure, and covariates separately
set.seed(1298)
< gComp(data = cvdd,
binary.res.alt Y = "cvd_dth",
X = "DIABETES",
Z = c("AGE", "SEX", "BMI", "CURSMOKE", "PREVHYP"),
outcome.type = "binary",
R = 200)
Note that we did not need to specify function arguments that did not apply for our analysis. For example, if we wanted to estimate unadjusted effects, with no Z covariates, we could simply leave out the Z argument in the function above. Arguments that are not included in the function statement are automatically populated with the default values. See below another example of the same analysis, which includes all arguments at their default values. This example is syntactically equivalent to the first example for binary.res (i.e. the code being evaluated is exactly the same), but is functionally equivalent to both of the examples above (i.e. will produce the same results).
set.seed(1298)
< gComp(data = cvdd,
binary.res.alt2 formula = cvdd.formula,
outcome.type = "binary",
R = 200,
Y = NULL,
X = NULL,
Z = NULL,
subgroup = NULL,
offset = NULL,
rate.multiplier = 1,
clusterID = NULL,
parallel = "no",
ncpus = 1)
Let’s look at the results. Typing either of the below will provide the point estimate and the 95% confidence limits
binary.res#> Formula:
#> cvd_dth ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP
#>
#> Parameter estimates:
#> DIABETES1_v._DIABETES0 Estimate (95% CI)
#> Risk Difference 0.287 (0.198, 0.393)
#> Risk Ratio 1.700 (1.488, 1.968)
#> Odds Ratio 4.550 (2.784, 8.863)
#> Number needed to treat/harm 3.484
print(binary.res)
#> Formula:
#> cvd_dth ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP
#>
#> Parameter estimates:
#> DIABETES1_v._DIABETES0 Estimate (95% CI)
#> Risk Difference 0.287 (0.198, 0.393)
#> Risk Ratio 1.700 (1.488, 1.968)
#> Odds Ratio 4.550 (2.784, 8.863)
#> Number needed to treat/harm 3.484
Not surprisingly, there is a large effect of diabetes on cardiovascular disease. Specifically, the absolute 24year risk of cardiovascular disease or death due to any cause is 30.0% (95% CI: 21.5, 37.5) higher among subjects with diabetes at baseline compared to subjects without diabetes at baseline. In relative terms, the 24year risk is 55.2% (95% CI: 38.6, 69.6) higher. Because the outcome is common (41.8%), the odds ratio (4.55) is highly inflated compared to the risk ratio (1.55). This is a clear example where the odds ratio may be misleading since the odds ratio is commonly misinterpreted as a risk ratio.
We also estimated the number needed to treat as 1/Risk difference. In this example, with a harmful exposure, we can interpret the number needed to treat as the number needed to harm: we would expect 3 (95% CI: 3, 5) persons would need to have diabetes at baseline to observe an increase in the number of cases of cardiovascular disease or death by 1 over 24 years of followup.
The result obtained from the gComp
function is an object
of class gComp which is a list containing the summary
results (what is seen when you print), plus 8 additional items:
results.df
, n
, R
,
boot.result
, contrast
, family
,
formula
, predicted.outcome
, and
glm.result
(see ?gComp
or
help(gComp)
for a more detailed explanation of each item in
the list). You can access the different items using the $
operator as shown below.
class(binary.res)
#> [1] "gComp" "list"
# The names of the different items in the list
names(binary.res)
#> [1] "summary" "results.df"
#> [3] "n" "R"
#> [5] "boot.result" "contrast"
#> [7] "family" "formula"
#> [9] "predicted.outcome" "glm.result"
# To see the sample size of the original data:
$n
binary.res#> [1] 4240
# To see the contrast being compared in the analysis:
$contrast
binary.res#> [1] "DIABETES1 v. DIABETES0"
There is also a summary method for objects with class gComp that contains the formula, family and link function, contrast being made, parameter estimates with 95% CIs, and a summary of the underlying glm used for predictions.
summary(binary.res)
#> Formula:
#> cvd_dth ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP
#>
#> Family: binomial
#> Link function: logit
#>
#> Contrast: DIABETES1 v. DIABETES0
#>
#> Parameter estimates:
#> DIABETES1_v._DIABETES0 Estimate (95% CI)
#> Risk Difference 0.287 (0.198, 0.393)
#> Risk Ratio 1.700 (1.488, 1.968)
#> Odds Ratio 4.550 (2.784, 8.863)
#> Number needed to treat/harm 3.484
#>
#> Underlying glm:
#> Call: stats::glm(formula = formula, family = family, data = working.df,
#> na.action = stats::na.omit)
#>
#> Coefficients:
#> (Intercept) DIABETES1 AGE SEX1
#> 6.25839 1.51501 0.10246 0.79405
#> BMI CURSMOKE1 PREVHYP1
#> 0.02512 0.58550 0.77804
#>
#> Degrees of Freedom: 4220 Total (i.e. Null); 4214 Residual
#> (19 observations deleted due to missingness)
#> Null Deviance: 5735
#> Residual Deviance: 4697 AIC: 4711
To check to make sure the package is estimating effects as expected, we suggest comparing the riskCommunicator results to a normal logistic regression model (for example, with the glm function in R) using the same model structure. The odds ratio from riskCommunicator should be very similar to the odds ratio obtained from glm (note: minor differences may occur due to estimating marginal vs. covariate conditional effects). Because any errors that you receive while running a normal logistic regression model will also be an issue when using the riskCommunicator package, it may be helpful to optimize the logistic regression first.
We can also do the same analysis within subgroups. Here we’ll estimate effects stratified by sex, or within subgroups of men and women.
set.seed(1298)
< gComp(data = cvdd,
binary.res.subgroup Y = "cvd_dth",
X = "DIABETES",
Z = c("AGE", "SEX", "BMI", "CURSMOKE", "PREVHYP"),
subgroup = "SEX",
outcome.type = "binary",
R = 200)
binary.res.subgroup#> Formula:
#> cvd_dth ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP + DIABETES:SEX
#>
#> Parameter estimates:
#> DIABETES1_v._DIABETES0_SEX0 Estimate (95% CI)
#> Risk Difference 0.259 (0.153, 0.384)
#> Risk Ratio 1.521 (1.306, 1.780)
#> Odds Ratio 3.999 (2.193, 10.546)
#> Number needed to treat/harm 3.861
#> DIABETES1_v._DIABETES0_SEX1 Estimate (95% CI)
#> Risk Difference 0.316 (0.174, 0.453)
#> Risk Ratio 1.922 (1.509, 2.317)
#> Odds Ratio 5.028 (2.428, 11.402)
#> Number needed to treat/harm 3.169
From these results, we see that females (sex = 1) have a larger increase in the absolute 24year risk of cardiovascular disease or death associated with diabetes than males (sex = 0). They also have a larger relative increase in risk than males.
Question: what is the effect of obesity on the 24year risk of cardiovascular disease or death due to any cause?
You can do a similar analysis when your exposure variable is not
binary (has more than 2 categories). In this example, we specify obesity
as a categorical variable (bmicat
coding: 0 = normal
weight; 1=underweight; 2=overweight; 3=obese) and therefore have an
exposure with more than 2 categories. To ensure that the effects are
estimated with the referent of your choice, you can change the referent
category using code provided above or you could code your categorical
exposure with ‘0’ coded as the referent.
As above, we will estimate the risk difference, risk ratio, odds ratio, and number needed to treat.
#number and percent of subjects in each BMI category
table(cvdd$bmicat)
0  1  2  3 

1870  57  1755  539 
prop.table(table(cvdd$bmicat))*100
0  1  2  3 

44.3023  1.350391  41.57783  12.76949 
set.seed(345)
< gComp(data = cvdd,
catExp.res Y = "cvd_dth",
X = "bmicat",
Z = c("AGE", "SEX", "DIABETES", "CURSMOKE", "PREVHYP"),
outcome.type = "binary",
R = 200)
catExp.res#> Formula:
#> cvd_dth ~ bmicat + AGE + SEX + DIABETES + CURSMOKE + PREVHYP
#>
#> Parameter estimates:
#> bmicat1_v._bmicat0 Estimate (95% CI)
#> Risk Difference 0.042 (0.082, 0.178)
#> Risk Ratio 1.106 (0.792, 1.454)
#> Odds Ratio 1.248 (0.631, 2.506)
#> Number needed to treat/harm 23.846
#> bmicat2_v._bmicat0 Estimate (95% CI)
#> Risk Difference 0.017 (0.005, 0.049)
#> Risk Ratio 1.044 (0.988, 1.126)
#> Odds Ratio 1.097 (0.973, 1.297)
#> Number needed to treat/harm 57.405
#> bmicat3_v._bmicat0 Estimate (95% CI)
#> Risk Difference 0.093 (0.056, 0.136)
#> Risk Ratio 1.235 (1.143, 1.354)
#> Odds Ratio 1.628 (1.348, 2.037)
#> Number needed to treat/harm 10.733
From these results, we see that obese persons have the highest increase in 24year risk of cardiovascular disease or death compared to normal weight persons. Underweight persons also have increased risk, more so than overweight persons. Not surprisingly, the estimate comparing underweight to normal weight persons is imprecise given the few people in the dataset who were underweight.
Question: what is the effect of a 10year difference in age on the 24year risk of cardiovascular disease or death due to any cause?
Estimates can also be produced for continuous exposures. The default effects are produced for a one unit difference in the exposure at the mean value of the exposure variable. Under the hood, your continuous exposure is first centered at the mean, and then effects are estimated for a one unit difference. You can specify the exposure.scalar option to estimate effects for a more interpretable contrast, as desired. For example, if the continuous exposure is age in years, specifying the exposure.scalar option as 10 would result in effects for a 10 year difference in age, rather than a 1 year difference. We will try this below.
As above, we will estimate the risk difference, risk ratio, odds ratio, and number needed to treat.
set.seed(4528)
< gComp(data = cvdd,
contExp.res Y = "cvd_dth",
X = "AGE",
Z = c("BMI", "SEX", "DIABETES", "CURSMOKE", "PREVHYP"),
outcome.type = "binary",
exposure.scalar = 10,
R = 200)
#> Proceeding with X as a continuous variable, if it should be binary/categorical, please reformat so that X is a factor variable
contExp.res#> Formula:
#> cvd_dth ~ AGE + BMI + SEX + DIABETES + CURSMOKE + PREVHYP
#>
#> Parameter estimates:
#> AGE59.58_v._AGE49.58 Estimate (95% CI)
#> Risk Difference 0.226 (0.204, 0.244)
#> Risk Ratio 1.558 (1.493, 1.609)
#> Odds Ratio 2.786 (2.509, 3.057)
#> Number needed to treat/harm 4.416
These results demonstrate that the 24year risk of cardiovascular disease or death increases by an absolute 24.3% (95% CI: 22.5%, 26.2%) for a 10 year increase in age. The risk difference, risk ratio, and NNT estimates specifically apply at the mean observed age, or 50 years. The odds ratio is constant regardless of which 10year difference in age is considered along the observed range.
NOTE: in this example, because the underlying parametric model for a binary outcome is logistic regression, the risks for a continuous exposure will be estimated to be linear on the logodds (logit) scale, such that the odds ratio for any one unit increase in the continuous variable is constant. However, the risks will not be linear on the linear (risk difference) or log (risk ratio) scales, such that these parameters will not be constant across the range of the continuous exposure. Users should be aware that the risk difference, risk ratio, number needed to treat/harm (for a binary outcome) and the incidence rate difference (for a rate/count outcome) reported with a continuous exposure apply specifically at the mean of the continuous exposure. The effects do not necessarily apply across the entire range of the variable. However, variations in the effect are likely small, especially near the mean.
While there was very little drop out in these data (<1%), let’s
say that we are interested in estimating the effect of diabetes on the
rate of cardiovascular disease or death due to any cause. For this
analysis, we will take into account the persondays at risk
(timeout) and use Poisson regression as the underlying
parametric model for gcomputation. (Note: for overdispersed count/rate
outcomes, the negative binomial distribution can be specified by setting
outcome.type
to “count_nb” or
“rate_nb”.) This analysis will estimate the incidence
rate difference and incidence rate ratio.
First, we need to modify the dataset to change the variable cvd_dth from a factor to a numeric variable since the outcome for Poisson regression must be numeric.
< cvdd %>%
cvdd.t ::mutate(cvd_dth = as.numeric(as.character(cvd_dth)),
dplyrtimeout = as.numeric(timeout))
Then, we can run the analysis as above, first setting the seed and
then calling the gComp function. Note that we have specified the
outcome.type
as “rate” and included
timeout as the offset. Because our
timeout variable is in units of persondays, we have
included a rate.multiplier
of 365.25*100 so that the
estimates are returned with units of 100 personyears.
set.seed(6534)
< gComp(data = cvdd.t,
rate.res Y = "cvd_dth",
X = "DIABETES",
Z = c("AGE", "SEX", "BMI", "CURSMOKE", "PREVHYP"),
outcome.type = "rate",
rate.multiplier = 365.25*100,
offset = "timeout",
R = 200)
rate.res#> Formula:
#> cvd_dth ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP + offset(log(timeout_adj))
#>
#> Parameter estimates:
#> DIABETES1_v._DIABETES0 Estimate (95% CI)
#> Incidence Rate Difference 2.189 (1.498, 3.103)
#> Incidence Rate Ratio 1.913 (1.615, 2.324)
Alternatively, we could run the same analysis by first specifying the regression model formula.
## Specify the regression formula
< cvd_dth ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP
cvdd.formula
set.seed(6534)
## Call the gComp function
< gComp(data = cvdd.t,
rate.res.alt formula = cvdd.formula,
outcome.type = "rate",
rate.multiplier = (365.25*100),
offset = "timeout",
R = 200)
rate.res.alt#> Formula:
#> cvd_dth ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP + offset(log(timeout_adj))
#>
#> Parameter estimates:
#> DIABETES1_v._DIABETES0 Estimate (95% CI)
#> Incidence Rate Difference 2.189 (1.498, 3.103)
#> Incidence Rate Ratio 1.913 (1.615, 2.324)
Similarly to the risk analysis above, this analysis suggests that there is a large effect of diabetes on cardiovascular disease. Specifically, the absolute rate of cardiovascular disease or death due to any cause is 2.19 cases/100 personyears (95% CI: 1.38, 3.26) higher among subjects with diabetes at baseline compared to subjects without diabetes at baseline. In relative terms, the rate is 91.3% (95% CI: 56.1, 133.9) higher. You will note that the incidence rate ratio is further from the null than the risk ratio, but closer to the null than the odds ratio. This is expected based on the mathematical properties of these effect measures.
Question: what is the effect of having diabetes at the beginning of the study on casual serum glucose (mg/dL) after 6 years of followup?
This example estimates the marginal mean difference in the continuous outcome associated with the exposure. In this example, linear regression is used as the underlying parametric model for gcomputation.
set.seed(9385)
< gComp(data = cvdd,
cont.res Y = "glucoseyear6",
X = "DIABETES",
Z = c("AGE", "SEX", "BMI", "CURSMOKE", "PREVHYP"),
outcome.type = "continuous",
R = 200)
cont.res#> Formula:
#> glucoseyear6 ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP
#>
#> Parameter estimates:
#> DIABETES1_v._DIABETES0 Estimate (95% CI)
#> Mean Difference 61.626 (46.816, 77.613)
This analysis shows that individuals with diabetes at baseline have a 61.6 mg/dL (95% CI: 49.1, 80.8) higher casual serum glucose level after 6 years compared to individuals without diabetes at baseline.
Question: what is the effect of having diabetes at the beginning of the study on the number of hospitalizations experienced over 24 years of followup?
For this analysis, we will use Poisson regression as the underlying parametric model for gcomputation because we have a count outcome. However, we will not include a persontime offset, since there was a fixed followup time for all individuals (24 years). This analysis will estimate the incidence rate difference, incidence rate ratio, and the number needed to treat.
set.seed(7295)
< "nhosp ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP"
count.formula
< gComp(data = cvdd,
count.res formula = count.formula,
outcome.type = "count",
R = 200)
count.res#> Formula:
#> nhosp ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP
#>
#> Parameter estimates:
#> DIABETES1_v._DIABETES0 Estimate (95% CI)
#> Incidence Rate Difference 0.049 (0.012, 0.110)
#> Incidence Rate Ratio 1.537 (0.874, 2.195)
This analysis shows that individuals with diabetes at baseline have 0.05 (95% CI: 0.01, 0.10) more hospital admissions over the 24 years of followup compared to individuals without diabetes at baseline. In relative terms, individuals with diabetes have 53.7% (95% CI: 7.7, 116.4) more admissions than those without diabetes.
The 95% CIs obtained from the riskCommunicator package represent populationstandardized marginal effects obtained with gcomputation. To ensure that the parameter estimates from each bootstrap iteration are normally distributed, we can also look at the histogram and QQ plots of bootstrapped estimates by calling:
plot(catExp.res)
#> Warning in log(as.numeric(.data$value)): NaNs produced
The histograms show the different effect estimates obtained by each bootstrap resampling of the data and should be normally distributed if the model is correctly specified. QQ plots help to verify that the bootstrap values are normally distributed by comparing the actual distribution of bootstrap values against a theoretical normal distribution of values centered at mean = 0. If the estimates are normally distributed, the plotted estimates (black circles) should overlay the diagonal red dashed line.
In order to facilitate plotting of the results, the
results.df
output contains a data.frame with all of the
info needed to make your own results plot, as shown below:
ggplot(catExp.res$results.df %>%
filter(Parameter %in% c("Risk Difference", "Risk Ratio"))
+
) geom_pointrange(aes(x = Comparison,
y = Estimate,
ymin = `2.5% CL`,
ymax = `97.5% CL`,
color = Comparison),
shape = 2
+
) coord_flip() +
facet_wrap(~Parameter, scale = "free") +
theme_bw() +
theme(legend.position = "none")
You can also obtain the marginal mean predicted outcomes under each
exposure level, i.e. what would be the predicted mean outcome had
everyone been exposed (set exposure to 1) and had everyone been
unexposed (set exposure to 0). These predicted outcomes are “adjusted”
for covariates in that they have been standardized over the observed
values of covariates included in the model. Therefore, this outcome
prediction procedure does not require setting the covariates to specific
values (at the mean values, for example). This is a major advantage over
the usual predict
function in R, or similar functions in
other statistical programs (e.g. lsmeans statement in SAS).
$predicted.outcome catExp.res
Parameter  Outcome  Group  Estimate  2.5% CL  97.5% CL 

Mean outcome without exposure/treatment  cvd_dth  bmicat0  0.3968808  0.3707688  0.4147452 
Mean outcome with exposure/treatment  cvd_dth  bmicat1  0.4388168  0.3079166  0.5740355 
Mean outcome with exposure/treatment  cvd_dth  bmicat2  0.4143010  0.3945179  0.4377184 
Mean outcome with exposure/treatment  cvd_dth  bmicat3  0.4900533  0.4550675  0.5262012 
Additionally, if you are interested in examining the coefficient
estimates and other components of the underlying glm
that
is also provided as an output. The final item in the
gComp class object is ‘glm.result’ which is a
glm class object and can be manipulated like the
results from a regular glm
or lm
object in R.
For example, you can obtain the coefficient estimates of the fitted
glm
by using the summary
function.
summary(catExp.res$glm.result)
#>
#> Call:
#> stats::glm(formula = formula, family = family, data = working.df,
#> na.action = stats::na.omit)
#>
#> Deviance Residuals:
#> Min 1Q Median 3Q Max
#> 2.2289 0.8690 0.5181 0.9419 2.4832
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>z)
#> (Intercept) 5.734453 0.258726 22.164 < 2e16 ***
#> bmicat1 0.221844 0.323038 0.687 0.492
#> bmicat2 0.092877 0.079637 1.166 0.244
#> bmicat3 0.487108 0.116259 4.190 2.79e05 ***
#> AGE 0.103046 0.004781 21.553 < 2e16 ***
#> SEX1 0.805224 0.074963 10.742 < 2e16 ***
#> DIABETES1 1.505036 0.274264 5.488 4.08e08 ***
#> CURSMOKE1 0.590569 0.077393 7.631 2.33e14 ***
#> PREVHYP1 0.761737 0.079357 9.599 < 2e16 ***
#> 
#> Signif. codes:
#> 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 5734.6 on 4220 degrees of freedom
#> Residual deviance: 4685.9 on 4212 degrees of freedom
#> (19 observations deleted due to missingness)
#> AIC: 4703.9
#>
#> Number of Fisher Scoring iterations: 4
To generate appropriate confidence intervals with bootstrapping, we recommend setting R = 1000 or 10000 for the final analysis. However, this can result in potentially long runtimes, depending on the computing power of your computer (>20min). Thus, exploratory analyses can be conducted with a lower number of bootstraps. The default is R = 200, which should compute on datasets of 500010000 observations in ~30s. An even lower R value (e.g. R = 20) can be used for very preliminary analyses. Additionally, reducing the number of variables in your dataset to as few as possible can improve computation times, especially if there are >100 variables (the dataset is copied for each bootstrap resample, and copying excessive numbers of unneeded variables will slow down the bootstrap step).
The gComp
function essentially executes four steps:
Please see the references listed under the gComp
or
pointEstimate
function for a more detailed explanation of
gcomputation.
sessionInfo()
#> R version 4.2.0 (20220422)
#> Platform: x86_64appledarwin17.0 (64bit)
#> Running under: macOS Catalina 10.15.7
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] C/en_US.UTF8/en_US.UTF8/C/en_US.UTF8/en_US.UTF8
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets
#> [6] methods base
#>
#> other attached packages:
#> [1] ggpubr_0.4.0 sandwich_3.01
#> [3] formatR_1.12 printr_0.2
#> [5] forcats_0.5.1 stringr_1.4.0
#> [7] dplyr_1.0.9 purrr_0.3.4
#> [9] readr_2.1.2 tidyr_1.2.0
#> [11] tibble_3.1.7 ggplot2_3.3.6
#> [13] tidyverse_1.3.1 riskCommunicator_1.0.1
#>
#> loaded via a namespace (and not attached):
#> [1] lattice_0.2045 lubridate_1.8.0 zoo_1.810
#> [4] assertthat_0.2.1 digest_0.6.29 utf8_1.2.2
#> [7] R6_2.5.1 cellranger_1.1.0 backports_1.4.1
#> [10] reprex_2.0.1 evaluate_0.15 highr_0.9
#> [13] httr_1.4.3 pillar_1.7.0 rlang_1.0.2
#> [16] readxl_1.4.0 rstudioapi_0.13 car_3.013
#> [19] jquerylib_0.1.4 rmarkdown_2.14 labeling_0.4.2
#> [22] munsell_0.5.0 broom_0.8.0 compiler_4.2.0
#> [25] modelr_0.1.8 xfun_0.31 pkgconfig_2.0.3
#> [28] htmltools_0.5.2 tidyselect_1.1.2 gridExtra_2.3
#> [31] codetools_0.218 fansi_1.0.3 crayon_1.5.1
#> [34] tzdb_0.3.0 dbplyr_2.1.1 withr_2.5.0
#> [37] MASS_7.357 grid_4.2.0 jsonlite_1.8.0
#> [40] gtable_0.3.0 lifecycle_1.0.1 DBI_1.1.2
#> [43] magrittr_2.0.3 scales_1.2.0 cli_3.3.0
#> [46] stringi_1.7.6 carData_3.05 farver_2.1.0
#> [49] ggsignif_0.6.3 fs_1.5.2 xml2_1.3.3
#> [52] bslib_0.3.1 ellipsis_0.3.2 generics_0.1.2
#> [55] vctrs_0.4.1 cowplot_1.1.1 boot_1.328
#> [58] tools_4.2.0 glue_1.6.2 hms_1.1.1
#> [61] abind_1.45 fastmap_1.1.0 yaml_2.3.5
#> [64] colorspace_2.03 rstatix_0.7.0 rvest_1.0.2
#> [67] knitr_1.39 haven_2.5.0 sass_0.4.1