Quantitative bias analysis allows to estimate nonrandom errors in epidemiologic studies, assessing the magnitude and direction of biases, and quantifying their uncertainties. Every study has some random error due to its limited sample size, and is susceptible to systematic errors as well, from selection bias to the presence of (un)known confounders or information bias (measurement error, including misclassification). Bias analysis methods were compiled by Lash et al. in their book "Applying Quantitative Bias Analysis to Epidemiologic Data". This package implements the various bias analyses from that book, which are also available (for some) as a spreadsheet or a SAS macro, as well as some additional approaches. This vignette provides some examples on how to use the package.

Functions available in `episensr`

are:

`selection`

: Selection bias`mbias`

: Selection bias caused by M bias`confounders`

: Unmeasured or unknown confounders`confounders.poly`

: Polytomous confounders`confounders.emm`

: Unmeasured or unknown confounders in the presence of effect modification`confounders.limit`

: Bounding the bias limits of unmeasured confounding`confounders.array`

: Bias due to unmeasured confounders based on confounding imbalance among exposed and unexposed`confounders.ext`

: Unmeasured confounders based on external measurement`confounders.evalue`

: E-value due to unmeasured confounder`misclassification`

: Disease or exposure misclassification`misclassification_cov`

: Covariate misclassification`bootstrap`

: Bootstrap resampling for selection and misclassification bias`multidimBias`

: Multidimensional bias analysis`probsens.sel`

: Probabilistic analysis for selection bias`probsens.conf`

: Probabilistic analysis for unmeasured confounding`probsens`

: Probabilistic analysis for misclassification`probsens.irr.conf`

: Probabilistic analysis for unmeasured confounding of person-time data`probsens.irr`

: Probabilistic analysis for exposure misclassification of person-time data`multiple bias`

: Multiple bias modeling

We will use a case-control study by Stang et al. on the relation between mobile phone use and uveal melanoma. The observed odds ratio for the association between regular mobile phone use vs. no mobile phone use with uveal melanoma incidence is 0.71 [95% CI 0.51-0.97]. But there was a substantial difference in participation rates between cases and controls (94% vs 55%, respectively) and so selection bias could have an impact on the association estimate. The 2X2 table for this study is the following:

| | Regular use | No use | |---------:|:-----------:|:------:| | Cases | 136 | 107 | | Controls | 297 | 165 |

We use the function `selection`

as shown below.

library(episensr) stang <- selection(matrix(c(136, 107, 297, 165), dimnames = list(c("UM+", "UM-"), c("Mobile+", "Mobile-")), nrow = 2, byrow = TRUE), bias_parms = c(.94, .85, .64, .25)) stang

The various `episensr`

functions return an object which is a list containing the
input and output variables.
You can check it out with `str()`

.

The 2X2 table is provided as a matrix and selection probabilities given with the
argument `bias_parms`

, a vector with the 4 probabilities (guided by the
participation rates in cases and controls) in the following order: among cases
exposed, among cases unexposed, among noncases exposed, and among noncases
unexposed.
The output shows the observed 2X2 table and the observed odds ratio (and relative
risk), followed by the corrected ones.

Misclassification bias can be assessed with the function `misclassification`

.
Confidence intervals for corrected association due to exposure misclassification
are also directly available, or the estimates can also be bootstrapped (see below).
The confidence intervals from the variance of the corrected odds ratio estimator in the
`misclassification`

function are computed as in Greenland et
al. and Chu et
al., when adjusting for exposure misclassification
using sensitivity and specificity.
Using the example in Chu et al. of a case-control study of cigarette smoking and
invasive pneumococcal disease, the unadjusted odds ratio is 4.32, with a 95%
confidence interval of 2.96 to 6.31.
Let's say the sensitivity of self-reported smoking is 94% and specificity is
97%, for both the case and control groups:

misclassification(matrix(c(126, 92, 71, 224), dimnames = list(c("Case", "Control"), c("Smoking +", "Smoking - ")), nrow = 2, byrow = TRUE), type = "exposure", bias_parms = c(0.94, 0.94, 0.97, 0.97))

The corrected odds ratio is now 5.02, with a widened 95% confidence interval (3.28 to 7.69). Note the bias despite the large sensitivity and specificity.

You can even reproduce the contour plots in Chu et al. paper!

dat <- expand.grid(Se = seq(0.582, 1, 0.02), Sp = seq(0.762, 1, 0.02)) dat$OR_c <- apply(dat, 1, function(x) misclassification(matrix(c(126, 92, 71, 224), nrow = 2, byrow = TRUE), type = "exposure", bias_parms = c(x[1], x[1], x[2], x[2]))$adj.measures[2, 1]) dat$OR_c <- round(dat$OR_c, 2) library(ggplot2) library(directlabels) p1 <- ggplot(dat, aes(x = Se, y = Sp, z = OR_c)) + geom_contour(aes(colour = ..level..), breaks = c(4.32, 6.96, 8.56, 12.79, 23.41, 432.1)) + annotate("text", x = 1, y = 1, label = "4.32", size = 3) + scale_fill_gradient(limits = range(dat$OR_c), high = 'red', low = 'green') + scale_x_continuous(breaks = seq(0.5, 1, .1), expand = c(0,0)) + scale_y_continuous(breaks = seq(0.5, 1, .1), expand = c(0,0)) + coord_fixed(ylim = c(0.5, 1.025), xlim = c(0.5, 1.025)) + scale_colour_gradient(guide = 'none') + xlab("Sensitivity") + ylab("Specificity") + ggtitle("Estimates of Corrected OR") direct.label(p1, list("far.from.others.borders", "calc.boxes", "enlarge.box", hjust = 1, vjust = -.5, box.color = NA, cex = .6, fill = "transparent", "draw.rects"))

dat$ORc_lci <- apply(dat, 1, function(x) misclassification(matrix(c(126, 92, 71, 224), nrow = 2, byrow = TRUE), type = "exposure", bias_parms = c(x[1], x[1], x[2], x[2]))$adj.measures[2, 2]) dat$ORc_lci <- round(dat$ORc_lci, 2) p3 <- ggplot(dat, aes(x = Se, y = Sp, z = ORc_lci)) + geom_contour(aes(colour = ..level..), breaks = c(4.05, 4.64, 5.68, 7.00, 9.60)) + annotate("text", x = 1, y = 1, label = "2.96", size = 3) + scale_fill_gradient(limits = range(dat$ORc_lci), high = 'red', low = 'green') + scale_x_continuous(breaks = seq(0.5, 1, .1), expand = c(0,0)) + scale_y_continuous(breaks = seq(0.5, 1, .1), expand = c(0,0)) + coord_fixed(ylim = c(0.5, 1.025), xlim = c(0.5, 1.025)) + scale_colour_gradient(guide = 'none') + xlab("Sensitivity") + ylab("Specificity") + ggtitle("95% LCI of Corrected OR") direct.label(p3, list("far.from.others.borders", "calc.boxes", "enlarge.box", hjust = 1, vjust = -.5, box.color = NA, cex = .6, fill = "transparent", "draw.rects"))

The bias analysis for exposure misclassification can also use positive and negative predictive values.
Bodnar et al. evaluated the accuracy of maternal prepregnancy BMI
and gestational weight gain (GWG) data derived from the Pennsylvania state birth certiļ¬cates against
information collected from the medical record. To estimate positive and negative predictive values, a
validation study was conducted by randomly sampling women conditional on their reported BMI category
and term/preterm status. BMI was obtained from medical records for these sampled women and used as a
gold standard for BMI category, allowing to determine positive and negative predictive values:
PPV_{D1} = 65%, PPV_{D0} = 74%, NPV_{D1} = 100%, NPV_{D0} = 98%. The
analysis is the following.

misclassification(matrix(c(599, 4978, 31175, 391851), dimnames = list(c("Preterm", "Term"), c("Underweight", "Normal weight")), nrow = 2, byrow = TRUE), type = "exposure_pv", bias_parms = c(0.65, 0.74, 1, 0.98))

Note that using predictive values relates to the prevalence of the true exposure status, which might not be the same in every populations.

Covariate misclassification is available via the function `misclassification.cov`

.
For example, the paper by Berry et
al. looked if misclassification of
the confounding variable *in vitro fertilization* (IVF), a confounder, resulted
wrongly on an association between increase folic acid and having twins.
IVF increases the risk of twins, and women undergoing IVF might be more likely
to take folic acid supplements, i.e. IVF would be a confounder between vitamins
and twins.
Data on IVF were not available and a proxy for it was used, *period of
involuntary childlessness*.
However, it was a poor proxy for IVF, with a sensitivity of 60% and a
specificity of 95%.
These bias parameters were assumed to be nondifferential.
Here's the code with `episensr`

:

misclassification.cov(array(c(1319, 38054, 5641, 405546, 565, 3583, 781, 21958, 754, 34471, 4860, 383588), dimnames = list(c("Twins+", "Twins-"), c("Folic acid+", "Folic acid-"), c("Total", "IVF+", "IVF-")), dim = c(2, 2, 3)), bias_parms = c(.6, .6, .95, .95))

While the non-adjusted analysis showed that women taking folic acid were 2.44 times more likely to have twins, after correcting for the misclassification of IVF, risk ratio is now null (= 1.0).

We will use data from a cross-sectional study by Tyndall et al. on the association between male circumcision and the risk of acquiring HIV, which might be confounded by religion. The code to account for unmeasured or unknown confounders is the following, where the 2X2 table is given as a matrix. We choose a risk ratio implementation, provide a vector defining the risk ratio associating the confounder with the disease, and the prevalence of the confounder, religion, among the exposed and the unexposed.

tyndall <- confounders(matrix(c(105, 85, 527, 93), dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")), nrow = 2, byrow = TRUE), type = "RR", bias_parms = c(.63, .8, .05)) tyndall

The output gives the crude 2X2 table, the crude relative risk and confounder specific measures of association between exposure and outcome, and the relationship adjusted for the unknown confounder, using a standardized morbidity ratio (SMR) or a Mantel-Haenszel (MH) estimate of the risk ratio.

Additional information are also available, like the 2X2 tables by levels of the confounder.

## Confounder + tyndall$cfder.data ## Confounder - tyndall$nocfder.data

As described in this function's help file, the bias correction used is the "relative risk due to confounding" as proposed by Miettinen (Components of the crude risk ratio, Am J Epidemiol 1972, 96(2):168-172). The two examples shown in that paper are the following.

The first one is about drug surveillance data analyzed as a cohort study, with the frequency of drug-attributed rash in relation to allopurinol exposure among recipients of ampicillin, with sex treated as a potential confounding factor.

rash <- matrix(c(15, 94, 52, 1163), dimnames = list(c("Rash +", "Rash -"), c("Allopurinol +", "Allopurinol -")), nrow = 2, byrow = TRUE)

knitr::kable(rash)

We can decompose these numbers by confounders:

## Outcome by confounders rash_conf <- matrix(c(36, 58, 645, 518), dimnames = list(c("Rash +", "Rash -"), c("Males", "Females")), nrow = 2, byrow = TRUE) ## By confounders: among males rash_males <- matrix(c(5, 36, 33, 645), dimnames = list(c("Rash +", "Rash -"), c("Allopurinol +", "Allopurinol -")), nrow = 2, byrow = TRUE) ## By confounders: among females rash_females <- matrix(c(10, 58, 19, 518), dimnames = list(c("Rash +", "Rash -"), c("Allopurinol +", "Allopurinol -")), nrow = 2, byrow = TRUE)

knitr::kable(rash_conf, caption = "Outcome by confounders") knitr::kable(rash_males, caption = "Among males") knitr::kable(rash_females, caption = "Among females")

And then get the bias parameters:

(RR_no <- (36/(36+645))/(58/(58+518))) # RR between confounder and outcome among non-exposed (p1 <- (5+33)/(15+52)) # prevalence of confounder among exposed (p2 <- (36+645)/(94+1163)) # prevalence of confounder among unexposed

With the following results:

rash %>% confounders(., type = "RR", bias_parms = c(RR_no, p1, p2))

For the second example, data are from a case-control study of the relationship of oral contraceptive use to venous thrombosis with age as a confounding factor.

thromb <- matrix(c(12, 30, 53, 347), dimnames = list(c("Thrombosis +", "Thrombosis -"), c("OC +", "OC -")), nrow = 2, byrow = TRUE)

knitr::kable(thromb)

## Outcome by confounders thromb_conf <- matrix(c(18, 12, 158, 189), dimnames = list(c("Thrombosis +", "Thrombosis -"), c("20-29 years old", "30-39 years old")), nrow = 2, byrow = TRUE) ## By confounders: among 20-29 years old thromb_younger <- matrix(c(10, 18, 39, 158), dimnames = list(c("Thrombosis +", "Thrombosis -"), c("OC +", "OC -")), nrow = 2, byrow = TRUE) ## By confounders: among 30-39 years old thromb_older <- matrix(c(2, 12, 14, 189), dimnames = list(c("Thrombosis +", "Thrombosis -"), c("OC +", "OC -")), nrow = 2, byrow = TRUE)

knitr::kable(thromb_conf, caption = "Outcome by confounders") knitr::kable(thromb_younger, caption = "Among 20-29 years old") knitr::kable(thromb_older, caption = "Among 30-39 years old")

And then the bias parameters:

(OR_no <- (18/12)/(158/189)) # OR between confounder and outcome among non-exposed (p1 <- (10+39)/(12+53)) # prevalence of confounder among exposed (p2 <- (18+158)/(30+347)) # prevalence of confounder among unexposed

With the following results:

thromb %>% confounders(., type = "OR", bias_parms = c(OR_no, p1, p2))

Selection and misclassification bias models can be bootstrapped in order to get confidence interval on the adjusted association parameter. We can use the ICU dataset from Hosmer and Lemeshow Applied Logistic Regression textbook as an example. The replicates that give negative cells in the adjusted 2X2 table are silently ignored and the number of effective bootstrap replicates is provided in the output. Ten thousands bootstrap samples are a good number for testing everything runs smoothly. But again, don't be afraid of running more, like 100,000 bootstrap samples.

library(aplore3) # to get ICU data data(icu) ## First run the model misclass_eval <- misclassification(icu$sta, icu$inf, type = "exposure", bias_parms = c(.75, .85, .9, .95)) misclass_eval ## Then bootstrap it set.seed(123) misclass_boot <- boot.bias(misclass_eval, R = 10000) misclass_boot

Bootstrap replicates can also be plotted, with the confidence interval shown as dotted lines.

plot(misclass_boot, "rr")

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