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Title:	G-Computation to Estimate Interpretable Epidemiological Effects
Version:	1.0.1
Depends:	R (≥ 3.5)
Imports:	boot, dplyr, ggplot2, ggpubr, magrittr, MASS, methods, purrr,rlang, stats, tidyr, tidyselect
Description:	Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting. It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) <doi:10.1016/0270-0255(86)90088-6> and has been described in detail by Ahern, Hubbard, and Galea (2009) <doi:10.1093/aje/kwp015>; Snowden, Rose, and Mortimer (2011) <doi:10.1093/aje/kwq472>; and Westreich et al. (2012) <doi:10.1002/sim.5316>.
License:	GPL-3
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.2.0
Suggests:	knitr, rmarkdown, testthat, tidyverse, printr, stringr,formatR, sandwich
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2022-05-31 21:06:05 UTC; JGrembi
Author:	Jessica Grembi [aut, cre, cph], Elizabeth Rogawski McQuade [ctb]
Maintainer:	Jessica Grembi <jess.grembi@gmail.com>
Repository:	CRAN
Date/Publication:	2022-05-31 23:20:02 UTC

A subset of theframingham teaching data

Description

A subset of theframingham teaching dataset containing the following changes:

• removal of all observations where PERIOD == 2 or PERIOD == 3 (i.e. keep only PERIOD == 1)

• removal of all observations where PREVCHD == 1 (i.e. all patients with coronary heart disease at baseline)

• created a new variable,cvd_dth signifying an outcome of cardiovascular disease OR death (i.e. if the patient had either CVD or DEATH, this new variable is 1, otherwise 0)

• created a new variable,timeout, which calculates the number of days from the start of the study to cardiovascular disease, death, or loss to follow-up

• created a new variable,logpdays, which is the log oftimeout

• created a new variable,nhosp, which is a simulated number of hospitalizations

Usage

data(cvdd)

Format

A data frame with 4240 rows and 31 variables:

RANDID

Unique identification number for each participant.

SEX

Participant sex. 0 = Male, 1 = Female.

TOTCHOL

Serum Total Cholesterol (mg/dL).

AGE

Age at exam (years).

SYSBP

Systolic Blood Pressure (mean of last two of three measurements) (mmHg).

DIABP

Diastolic Blood Pressure (mean of last two of three measurements) (mmHg).

CURSMOKE

Current cigarette smoking at exam. 0 = Not current smoker, 1 = Current smoker.

CIGPDAY

Number of cigarettes smoked each day. 0 = Not current smoker.

BMI

Body Mass Index, weight in kilograms/height meters squared.

DIABETES

Diabetic according to criteria of first exam treated or first exam with casual glucose of 200 mg/dL or more. 0 = Not a diabetic, 1 = Diabetic.

BPMEDS

Use of Anti-hypertensive medication at exam. 0 = Not currently used, 1 = Current use.

HEARTRTE

Heart rate (Ventricular rate) in beats/min.

GLUCOSE

Casual serum glucose (mg/dL).

educ

Level of completed education. 1 = 0-11 years, 2 = high school or GED, 3 = some college, 4 = college graduate or higher.

PREVSTRK

Prevalent Stroke. 0 = Free of disease, 1 = Prevalent disease.

PREVHYP

Prevalent Hypertensive. Subject was defined as hypertensive if treated or if second exam at which mean systolic was >=140 mmHg or mean Diastolic >=90 mmHg. 0 = Free of disease, 1 = Prevalent disease.

DEATH

Death from any cause. 0 = Did not occur during followup, 1 = Did occur during followup.

ANGINA

Angina Pectoris. 0 = Did not occur during followup, 1 = Did occur during followup.

HOSPMI

Hospitalized Myocardial Infarction. 0 = Did not occur during followup, 1 = Did occur during followup.

MI_FCHD

Hospitalized Myocardial Infarction or Fatal Coronary Heart Disease. 0 = Did not occur during followup, 1 = Did occur during followup.

ANYCHD

Angina Pectoris, Myocardial infarction (Hospitalized and silent or unrecognized), Coronary Insufficiency (Unstable Angina), or Fatal Coronary Heart Disease. 0 = Did not occur during followup, 1 = Did occur during followup.

STROKE

Atherothrombotic infarction, Cerebral Embolism, Intracerebral Hemorrhage, or Subarachnoid Hemorrhage or Fatal Cerebrovascular Disease. 0 = Did not occur during followup, 1 = Did occur during followup.

CVD

Myocardial infarction (Hospitalized and silent or unrecognized), Fatal Coronary Heart Disease, Atherothrombotic infarction, Cerebral Embolism, Intracerebral Hemorrhage, or Subarachnoid Hemorrhage or Fatal Cerebrovascular Disease. 0 = Did not occur during followup, 1 = Did occur during followup.

HYPERTEN

Hypertensive. Defined as the first exam treated for high blood pressure or second exam in which either Systolic is 6 140 mmHg or Diastolic 6 90mmHg. 0 = Did not occur during followup, 1 = Did occur during followup.

cvd_dth

Cardiovascular disease OR death. 0 = Did not occur during followup, 1 = Did occur during followup.

timeout

Number of days from the start of the study to cardiovascular disease, death, or loss to follow-up.

drop

Participant was lost to follow-up before 24 months complete followup. 0 = no, 1 = yes

glucoseyear6

Casual serum glucose (mg/dL) after 6 years of follow-up

logpdays

Natural log oftimeout.

bmicat

BMI category. 0 = Normal, 1 = Underweight, 2 = Overweight, 3 = Obese.

nhosp

Simulated number of hospitalizations over 24 months, associated with age, sex, BMI, and diabetes (not collected in the Framingham study).

Details

The National Heart, Lung, and Blood Institute of the National Institutes of Health developed a longitudinal, epidemiology-focused dataset using the Framingham Heart Study. 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. This dataset contains three clinic examinations and 20 year follow-up data on a large subset of the original Framingham cohort participants.

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.

Source

https://biolincc.nhlbi.nih.gov/teaching/

Theframingham data set

Description

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.The enclosed dataset is a subset of the data collected as part of the Framingham study and includes laboratory, clinic, questionnaire, and adjudicated event data on 4,434 participants. 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.

Usage

data(framingham)

Format

A data frame with 11627 rows and 39 variables:

RANDID

Unique identification number for each participant. Values range from 2448-999312.

SEX

Participant sex. 1 = Male (n = 5022), 2 = Female (n = 6605).

TOTCHOL

Serum Total Cholesterol (mg/dL). Values range from 107-696.

AGE

Age at exam (years). Values range from 32-81.

SYSBP

Systolic Blood Pressure (mean of last two of three measurements) (mmHg). Values range from 83.5-295.

DIABP

Diastolic Blood Pressure (mean of last two of three measurements) (mmHg). Values range from 30-150.

CURSMOKE

Current cigarette smoking at exam. 0 = Not current smoker (n = 6598), 1 = Current smoker (n = 5029).

CIGPDAY

Number of cigarettes smoked each day. 0 = Not current smoker. Values range from 0-90 cigarettes per day.

BMI

Body Mass Index, weight in kilograms/height meters squared. Values range from 14.43-56.8.

DIABETES

Diabetic according to criteria of first exam treated or first exam with casual glucose of 200 mg/dL or more. 0 = Not a diabetic (n = 11097), 1 = Diabetic (n = 530)

BPMEDS

Use of Anti-hypertensive medication at exam. 0 = Not currently used (n = 10090), 1 = Current use (n = 944).

HEARTRTE

Heart rate (Ventricular rate) in beats/min. Values range from 37-220.

GLUCOSE

Casual serum glucose (mg/dL). Values range from 39-478.

educ

PREVCHD

Prevalent Coronary Heart Disease defined as pre-existing Angina Pectoris, Myocardial Infarction (hospitalized, silent or unrecognized), or Coronary Insufficiency (unstable angina). 0 = Free of disease (n = 10785), 1 = Prevalent disease (n = 842).

PREVAP

Prevalent Angina Pectoris at exam. 0 = Free of disease (n = 11000), 1 = Prevalent disease (n = 627).

PREVMI

Prevalent Myocardial Infarction. 0 = Free of disease (n = 11253), 1 = Prevalent disease (n = 374).

PREVSTRK

Prevalent Stroke. 0 = Free of disease (n = 11475), 1 = Prevalent disease (n = 152).

PREVHYP

Prevalent Hypertensive. Subject was defined as hypertensive if treated or if second exam at which mean systolic was >=140 mmHg or mean Diastolic >=90 mmHg. 0 = Free of disease (n = 6283), 1 = Prevalent disease (n = 5344).

TIME

Number of days since baseline exam. Values range from 0-4854

PERIOD

Examination Cycle. 1 = Period 1 (n = 4434), 2 = Period 2 (n = 3930), 3 = Period 3 (n = 3263)

HDLC

High Density Lipoprotein Cholesterol (mg/dL). Available for Period 3 only. Values range from 10-189.

LDLC

Low Density Lipoprotein Cholesterol (mg/dL). Available for Period 3 only. Values range from 20-565.

DEATH

Death from any cause. 0 = Did not occur during followup, 1 = Did occur during followup.

ANGINA

Angina Pectoris. 0 = Did not occur during followup, 1 = Did occur during followup.

HOSPMI

Hospitalized Myocardial Infarction. 0 = Did not occur during followup, 1 = Did occur during followup.

MI_FCHD

Hospitalized Myocardial Infarction or Fatal Coronary Heart Disease. 0 = Did not occur during followup, 1 = Did occur during followup.

ANYCHD

Angina Pectoris, Myocardial infarction (Hospitalized and silent or unrecognized), Coronary Insufficiency (Unstable Angina), or Fatal Coronary Heart Disease. 0 = Did not occur during followup, 1 = Did occur during followup.

STROKE

Atherothrombotic infarction, Cerebral Embolism, Intracerebral Hemorrhage, or Subarachnoid Hemorrhage or Fatal Cerebrovascular Disease. 0 = Did not occur during followup, 1 = Did occur during followup.

CVD

Myocardial infarction (Hospitalized and silent or unrecognized), Fatal Coronary Heart Disease, Atherothrombotic infarction, Cerebral Embolism, Intracerebral Hemorrhage, or Subarachnoid Hemorrhage or Fatal Cerebrovascular Disease. 0 = Did not occur during followup, 1 = Did occur during followup.

HYPERTEN

Hypertensive. Defined as the first exam treated for high blood pressure or second exam in which either Systolic is 6 140 mmHg or Diastolic 6 90mmHg. 0 = Did not occur during followup, 1 = Did occur during followup.

TIMEAP

Number of days from Baseline exam to first Angina during the followup or Number of days from Baseline to censor date. Censor date may be end of followup, death or last known contact date if subject is lost to followup.

TIMEMI

Number of days from Baseline exam to first HOSPMI event during followup or Number of days from Baseline to censor date. Censor date may be end of followup, death or last known contact date if subject is lost to followup.

TIMEMIFC

Number of days from Baseline exam to first MI_FCHD event during followup or Number of days from Baseline to censor date. Censor date may be end of followup, death or last known contact date if subject is lost to followup.

TIMECHD

Number of days from Baseline exam to first ANYCHD event during followup or Number of days from Baseline to censor date. Censor date may be end of followup, death or last known contact date if subject is lost to followup.

TIMESTRK

Number of days from Baseline exam to first STROKE event during followup or Number of days from Baseline to censor date. Censor date may be end of followup, death or last known contact date if subject is lost to followup.

TIMECVD

Number of days from Baseline exam to first CVD event during followup or Number of days from Baseline to censor date. Censor date may be end of followup, death or last known contact date if subject is lost to followup.

TIMEDTH

Number of days from Baseline exam to death if occurring during followup or Number of days from Baseline to censor date. Censor date may be end of followup, or last known contact date if subject is lost to followup.

TIMEHYP

Number of days from Baseline exam to first HYPERTEN event during followup or Number of days from Baseline to censor date. Censor date may be end of followup, death or last known contact date if subject is lost to followup.

Details

This dataset is the teaching dataset from the Framingham Heart Study (No. N01-HC-25195), provided with permission from #' the National Heart, Lung, and Blood Institute (NHLBI). The Framingham Heart Study is conducted and supported by the NHLBI in collaboration with Boston University. This package was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or NHLBI.

Estimate difference and ratio effects with 95% confidence intervals.

Description

Obtain a point estimate and 95% confidence interval fordifference and ratio effects comparing exposed and unexposed (or treatment and non-treatment)groups using g-computation.

Usage

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))

Arguments

Details

ThegComp function executes the following steps:

1. Calls thepointEstimate function on the data to obtainthe appropriate effect estimates (difference, ratio, etc.).

2. GeneratesR bootstrap resamples of the data, with replacement. Ifthe resampling is to be done at the cluster level (set using theclusterID argument), the number of clusters will remain constant butthe total number of observations in each resampled data set might bedifferent if clusters are not balanced.

3. Calls thepointEstimate function on each of the resampled data sets.

4. Calculates the 95% confidence interval of the difference and ratioestimates using the results obtained from theR resampled parameterestimates.

As bootstrap resamples are generated with random sampling, users shouldset a seed (set.seed for reproducibleconfidence intervals.

While offsets are used to account for differences in follow-up time between individuals in theglm model, rate differences are calculated assuming equivalent follow-up of all individuals (i.e. predictions for each exposure are based on all observations having the same offset value). The default is 1 (specifying 1 unit of the original offset variable) or the user can specify an offset to be used in the predictions with the rate.multiplier argument.

Value

An object of classgComp which is a named list with components:

Note

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.

For continuous exposure variables, the default effects are provided for a one unit difference in the exposure at the mean value of the exposure variable. 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 log-odds (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.

Interaction terms are not allowed in the model formula. Thesubgroup argument affords interaction between the exposure variable and a single covariate (that is forced to categorical if supplied as numeric) to estimate effects of the exposure within subgroups defined by the interacting covariate. To include additional interaction terms with variables other than the exposure, we recommend that users create the interaction term as a cross-product of the two interaction variables in a data cleaning step prior to running the model.

The documentation forboot includes details about reproducible seeds when using parallel computing.

References

Ahern J, Hubbard A, Galea S. Estimating the effects of potential public health interventions on population disease burden: a step-by-step illustration of causal inference methods. Am. J. Epidemiol. 2009;169(9):1140–1147.doi:10.1093/aje/kwp015

Altman DG, Deeks JJ, Sackett DL. Odds ratios should be avoided when events are common. BMJ. 1998;317(7168):1318.doi:10.1136/bmj.317.7168.1318

Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.Book link

Hutton JL. Number needed to treat: properties and problems. Journal of the Royal Statistical Society: Series A (Statistics in Society). 2000;163(3):381–402.doi:10.1111/1467-985X.00175

Robins J. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Mathematical Modelling. 1986;7(9):1393–1512.doi:10.1016/0270-0255(86)90088-6

Snowden JM, Rose S, Mortimer KM. Implementation of G-computation on a simulated data set: demonstration of a causal inference technique. Am. J. Epidemiol. 2011;173(7):731–738.doi:10.1093/aje/kwq472

Stang A, Poole C, Bender R. Common problems related to the use of number needed to treat. Journal of Clinical Epidemiology. 2010;63(8):820–825.doi:10.1016/j.jclinepi.2009.08.006

Westreich D, Cole SR, Young JG, et al. The parametric g-formula to estimate the effect of highly active antiretroviral therapy on incident AIDS or death. Stat Med. 2012;31(18):2000–2009.doi:10.1002/sim.5316

See Also

pointEstimateboot

Examples

## Obtain the risk difference and risk ratio for cardiovascular disease or death between## patients with and without diabetes.data(cvdd)set.seed(538)diabetes <- gComp(cvdd, formula = "cvd_dth ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP",outcome.type = "binary", R = 20)

Take predicted dataframe and calculate the outcome (risk difference/ratio,incidence rate difference/ratio, mean difference, and/or number needed totreat)

Description

Take predicted dataframe and calculate the outcome (risk difference/ratio,incidence rate difference/ratio, mean difference, and/or number needed totreat)

Usage

get_results_dataframe(predict.df, outcome.type)

Arguments

Value

A list containing the calculated results for the applicable measures(based on the outcome.type): Risk Difference, Risk Ratio, Odds Ratio,Incidence Risk Difference, Incidence Risk Ratio, Mean Difference, NumberNeeded to Treat, Average Tx (average predicted outcome of all observations with treatment/exposure), and Average noTx (average predicted outcome of all observations without treatment/exposure)

Usingglm results, predict outcomes for each individual at each levelof treatment/exposure

Description

Usingglm results, predict outcomes for each individual at each levelof treatment/exposure

Usage

make_predict_df(  glm.res,  df,  X,  subgroup = NULL,  offset = NULL,  rate.multiplier = 1)

Arguments

Value

A data.frame of predicted outcomes for each level oftreatment/exposure. Additional columns are provided for each subgroup*x*treatment, if specified.

Plot estimates of difference and ratio effects obtained in the bootstrapcomputations of the g-computation

Description

Plot histograms and Q-Q plots for each the difference and ratioestimates

Usage

## S3 method for class 'gComp'plot(x, ...)

Arguments

Value

a plot containing histograms and Q-Q plots of the difference andratio estimates returned from R bootstrap iterations

Examples

## Obtain the risk difference and risk ratio for cardiovascular disease or death## between patients with and without diabetes, while controlling for## age,## sex,## BMI,## whether the individual is currently a smoker, and## if they have a history of hypertension.data(cvdd)set.seed(58)diabetes.result <- gComp(data = cvdd, Y = "cvd_dth", X = "DIABETES",Z = c("AGE", "SEX", "BMI", "CURSMOKE", "PREVHYP"), outcome.type = "binary", R = 60)plot(diabetes.result)

Perform g-computation to estimate difference and ratio effects of an exposure

Description

Generate a point estimate of the outcome difference and ratiousing G-computation

Usage

pointEstimate(  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,  exposure.center = TRUE)

Arguments

Details

ThepointEstimate function executes the following steps onthe data:

1. Fit a regression of the outcome on the exposureand relevant covariates, using the provided data set.

2. Using the model fit in step 1, predict counterfactuals (e.g. calculate predicted outcomes for each observation in the data set under each level of the treatment/exposure).

3. Estimate the marginal difference/ratio of treatment effect by taking the difference or ratio of the average of all observations under the treatment/no treatment regimes.

As counterfactual predictions are generated with random sampling of thedistribution, users should set a seed (set.seed) prior to calling the function for reproducible confidence intervals.

Value

A named list containing the following:

formula = formula,

Note

While offsets are used to account for differences in follow-up time between individuals in theglm model, rate differences are calculated assuming equivalent follow-up of all individuals (i.e. predictions for each exposure are based on all observations having the same offset value). The default is 1 (specifying 1 unit of the original offset variable) or the user can specify an offset to be used in the predictions with the rate.multiplier argument.

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.

For continuous exposure variables, the default effects are provided for a one unit difference in the exposure at the mean value of the exposure variable. 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 log-odds (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.

@note Interaction terms are not allowed in the model formula. Thesubgroup argument affords interaction between the exposure variable and a single covariate (that is forced to categorical if supplied as numeric) to estimate effects of the exposure within subgroups defined by the interacting covariate. To include additional interaction terms with variables other than the exposure, we recommend that users create the interaction term as a cross-product of the two interaction variables in a data cleaning step prior to running the model.

@note For negative binomial models,MASS::glm.nb is used instead of thestandardstats::glm function used for all other models.

References

Ahern J, Hubbard A, Galea S. Estimating the effects of potential public health interventions on population disease burden: a step-by-step illustration of causal inference methods. Am. J. Epidemiol. 2009;169(9):1140–1147.doi:10.1093/aje/kwp015

Altman DG, Deeks JJ, Sackett DL. Odds ratios should be avoided when events are common. BMJ. 1998;317(7168):1318.doi:10.1136/bmj.317.7168.1318

Hernán MA, Robins JM (2020). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC.Book link

Robins J. A new approach to causal inference in mortality studies with a sustained exposure period—application to control of the healthy worker survivor effect. Mathematical Modelling. 1986;7(9):1393–1512.doi:10.1016/0270-0255(86)90088-6

Snowden JM, Rose S, Mortimer KM. Implementation of G-computation on a simulated data set: demonstration of a causal inference technique. Am. J. Epidemiol. 2011;173(7):731–738.doi:10.1093/aje/kwq472

Westreich D, Cole SR, Young JG, et al. The parametric g-formula to estimate the effect of highly active antiretroviral therapy on incident AIDS or death. Stat Med. 2012;31(18):2000–2009.doi:10.1002/sim.5316

See Also

gComp

Examples

## Obtain the risk difference and risk ratio for cardiovascular disease or death## between patients with and without diabetes, while controlling for## age,## sex,## BMI,## whether the individual is currently a smoker, and## if they have a history of hypertension.data(cvdd)ptEstimate <- pointEstimate(data = cvdd, Y = "cvd_dth", X = "DIABETES",Z = c("AGE", "SEX", "BMI", "CURSMOKE", "PREVHYP"), outcome.type = "binary")

Print estimates of difference and ratio effects obtained in the bootstrapcomputations of the g-computation

Description

Print results from bootstrap computations of the g-computation

Usage

## S3 method for class 'gComp'print(x, ...)

Arguments

Value

Returns the formula and resulting point estimate and 95% confidence intervals of the difference and ratio.

Examples

## Obtain the risk difference and risk ratio for cardiovascular disease or ## death between patients with and without diabetes, while controlling for## age, sex, BMI, whether the individual is currently a smoker, and ## if they have a history of hypertension.data(cvdd)set.seed(4832)diabetes.result <- gComp(cvdd,    formula = "cvd_dth ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP",   outcome.type = "binary", R = 20)print(diabetes.result)

riskCommunicator: Obtaining interpretable epidemiological effectestimates

Description

riskCommunicator is a package for estimating flexible epidemiologicaleffect measures including both differences and ratios. The package is basedon the parametric G-formula (g-computation with parametric models) developedby Robbins et. al. in 1986 as an alternative to inverse probability weighting. It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback and is a powerful tool for causal inference, but has seen limited success due to lack of software for the computationally intensive components. This package provides three main functions. The first,pointEstimate, obtains a point estimate of the difference and ratio effect estimates. This function is typically called within thegComp function, but is available for use in special cases for example when the user requires more explicit control over bootstrap resampling (e.g. nested clusters). The second function,gComp, is the workhorse function that obtains point estimates for difference and ratio effects along with their 95/to visualize the bootstrap results. We provide theframingham dataset, which is the teaching dataset from the Framingham Heart Study, as well as a subset of that data,cvdd for users.

References

Robins, James. 1986. “A New Approach To Causal Inference inMortality Studies with a Sustained Exposure Period - Application To Controlof the Healthy Worker Survivor Effect.” Mathematical Modelling 7: 1393–1512.doi:10.1016/0270-0255(86)90088-6.

See Also

gComp

pointEstimate

plot.gComp

Print a summary of a gComp class object.

Description

Takes agComp object produced bygComp() and produces various useful summaries from it.

Usage

## S3 method for class 'gComp'summary(object, ...)## S3 method for class 'summary.gComp'print(x, ...)

Arguments

Value

Returns the formula, family (with link function), contrast evaluated, resulting point estimate and 95% confidence intervals of the parameters estimated, and the underlying glm used for model predictions.

Examples

## Obtain the risk difference and risk ratio for cardiovascular disease or ## death between patients with and without diabetes, while controlling for## age, sex, BMI, whether the individual is currently a smoker, and ## if they have a history of hypertension.data(cvdd)set.seed(4832)diabetes.result <- gComp(cvdd,    formula = "cvd_dth ~ DIABETES + AGE + SEX + BMI + CURSMOKE + PREVHYP",   outcome.type = "binary", R = 20)summary(diabetes.result)