BioPred Package Tutorial
Introduction
The R package BioPred offers a suite of tools for subgroup and biomarker analysis in precision medicine. Leveraging Extreme Gradient Boosting (XGBoost) along with propensity score weighting and A-learning methods, BioPred facilitates the optimization of individualized treatment rules (ITR) to streamline sub-group identification. BioPred also enables the identification of predictive biomarkers and obtaining their importance rankings. Moreover, the package provides graphical plots tailored for biomarker analysis. This tool enables clinical researchers seeking to enhance their understanding of biomarkers and patient population in drug development.
Install from GitHub
install_github(“deeplearner0731/BioPred”)
Install from CRAN
install.packages(“BioPred”)
Loading the Data
The ‘tutorial_data’ dataset is included with the package. Let’s load and inspect it:
library(BioPred)
kable_styling(kable(x=head(tutorial_data), format="html", caption = "The first 6 subjects"),
bootstrap_options="striped",font_size=16)| x1 | x2 | x3 | x4 | x5 | x6 | x7 | x8 | x9 | x10 | y.con | y.bin | y.time | y.event | treatment | subgroup_label | risk_category | treatment_categorical |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| -2.3744988 | 0.0167914 | -2.5412961 | -2.0901502 | -2.6896868 | -1.3455564 | -2.4931001 | -0.3483110 | -1.5673804 | 0.4836952 | 0.0992838 | 0 | 0.0874949 | 0 | 0 | 1 | High Risk | Placebo |
| -0.1838847 | 1.8222234 | 0.4601912 | 0.5497404 | 1.6449981 | 0.5433581 | 0.2364084 | -2.7868360 | -0.4006381 | 0.6874968 | 0.0711574 | 0 | 0.9534603 | 0 | 0 | 0 | Intermediate Risk | Placebo |
| 0.0539828 | -1.9142628 | 1.3112035 | 0.6543154 | -0.0653439 | 0.5491687 | 1.8201585 | 2.5079087 | -0.4686039 | 1.1696815 | -1.2151804 | 0 | 0.0005331 | 0 | 1 | 1 | High Risk | Treatment |
| 1.1048102 | 0.8346022 | -1.9808589 | -0.5632687 | 0.3344664 | 1.4275303 | 0.1324287 | 2.1463655 | 1.3107915 | 0.4411784 | 0.2773950 | 0 | 0.0435684 | 0 | 1 | 0 | High Risk | Treatment |
| -0.4179985 | -2.2484687 | 3.3396929 | 3.1896045 | -0.5779621 | -1.4963500 | 0.4454772 | 0.1323151 | 1.4211205 | -1.5240744 | -2.0069643 | 0 | 0.1050067 | 0 | 1 | 1 | High Risk | Treatment |
| 1.0177850 | 0.1045029 | 1.4632138 | 1.0294900 | 2.0912418 | 2.2324576 | 0.5073059 | -1.4397678 | 1.5456243 | 0.4183628 | 1.4521578 | 1 | 0.1018182 | 0 | 0 | 0 | High Risk | Placebo |
Data Description
The tutorial_data dataset contains the following columns:
- x1 to x10: Biomarker variables.
- y.con: A continuous outcome variable.
- y.bin: A binary outcome variable (0 or 1).
- y.time: Time in months, used for survival analysis.
- y.event: Event indicator (0 for censoring, 1 for event occurrence).
- subgroup_label: Ground truth of subgroup label. In real-world scenarios, this information is typically unavailable.
- treatment: Treatment indicator (0 for control, 1 for treatment).
- treatment_categorical: Categorical treatment variable with levels “Placebo” and “Treatment”.
- risk_category: Risk category
1 Example Analysis for Continuous Outcomes
1.1 Training the XGBoostSub_con Model
We will begin by training the XGBoostSub_con model for a
continuous outcome. This involves using the A-learning or
Weight-learning loss function. Note that the treatment variable must be
converted to (1, -1). If your treatment variable is (1, 0), you will
need to convert it accordingly.
X_feature=tutorial_data[,c("x1", "x2" ,"x3" ,"x4","x5","x6","x7","x8","x9","x10")]
y_label=tutorial_data$y.con
# Convert treatment variable to (1 -1) format (1 for treatment, -1 for control)
trt=ifelse(tutorial_data$treatment==1, 1, -1)
true_label=tutorial_data$subgroup_label
# Estimate the propensity score using logistic regression
data_logit=tutorial_data[,c("treatment","x1", "x2" ,"x3" ,"x4" , "x5", "x6" , "x7","x8","x9","x10")]
logit_model <- glm(treatment~ ., data = data_logit, family = binomial)
pi <- predict(logit_model, type = "response")
# Train the XGBoostSub_con model using the specified parameters
model <- XGBoostSub_con(X_feature, y_label, trt ,pi,Loss_type = "A_learning", params = list(learning_rate = 0.005, max_depth = 1, lambda = 5, tree_method = 'hist'), nrounds = 200, disable_default_eval_metric = 0, verbose = FALSE)After training the model, you can print the loss value at each epoch using the following code:
cat("Evaluation loss:\n")
#> Evaluation loss:
print(model$evaluation_log)
#> iter train_A_loss
#> <num> <num>
#> 1: 1 1.375787
#> 2: 2 1.375424
#> 3: 3 1.375065
#> 4: 4 1.374708
#> 5: 5 1.374355
#> ---
#> 196: 196 1.339571
#> 197: 197 1.339486
#> 198: 198 1.339402
#> 199: 199 1.339319
#> 200: 200 1.339236You can also evaluate the loss on independent datasets using the
eval_metric_con function. In this example, we’ll use the
training data as test data for illustration purposes. However, you may
use a new dataset to see the loss based on the trained model.
1.2 Evaluating Predictive Biomarker Importance with XGBoostSub_con
Next, we evaluate the importance of predictive biomarkers using the
predictive_biomarker_imp function.
kable_styling(kable(x=biomarker_imp, format="html", caption = "Biomarker importance table"),
bootstrap_options="striped",font_size=16)| Biomarker | Importance |
|---|---|
| x2 | 0.4904407 |
| x1 | 0.4793201 |
| x10 | 0.0302392 |
From the results, we observe that the most important predictive
biomarker is x2.
1.3 Obtaining subgroup results with XGBoostSub_con
You can obtain the subgroup results using the
get_subgroup_results function.
subgroup_results=get_subgroup_results(model, X_feature, subgroup_label=true_label, cutoff = 0.5)
kable_styling(kable(x=head(subgroup_results$assignment), format="html", caption = "The first 6 subjects subgroup assigentment"),
bootstrap_options="striped",font_size=16)| Patient_ID | Treatment_Assignment |
|---|---|
| 1 | 1 |
| 2 | 1 |
| 3 | 1 |
| 4 | 0 |
| 5 | 1 |
| 6 | 1 |
cat("Prediction accuracy of true subgroup label:\n")
#> Prediction accuracy of true subgroup label:
cat(subgroup_results$acc)
#> 0.634Note that when using the get_subgroup_results function
with real data, the true subgroup label is typically unknown. In such
cases, set subgroup_label = NULL. This will return only the subgroup
assignment without the prediction accuracy of the subgroup.
1.4 Commonly used subgroup/biomarker analysis tools
Given that x2 was identified as the most important
predictive biomarker by XGBoostSub_con, we will use the
cdf_plot function to evaluate the distribution of this
biomarker by assessing the percentage of subjects falling within
different cutoff values.
cdf_plot (xvar="x2", data=tutorial_data, y.int=5, xlim=NULL, xvar.display="Biomarker_X2", group=NULL)
We will also evaluate x2 prognostic capability. To
determine if this biomarker is prognostic, we can assess the association
between the response variable y.con and the biomarker
x2 using the scat_cont_plot function.
scat_cont_plot(
yvar='y.con',
xvar='x2',
data=tutorial_data,
ybreaks = NULL,
xbreaks = NULL,
yvar.display = 'y',
xvar.display = "Biomarker_X2"
)
#> $Cor.Pearson
#> [1] -0.12
#>
#> $Cor.Spearman
#> [1] -0.12
#>
#> $fig
#>
#> $slope
#> [1] -0.1013384
#>
#> $intercept
#> [1] 0.6769244Selecting the cutoff value for a predictive biomarker is important in
clinical practice, as it can, for example, help enrich responders. We
select the optimal cutoff for the identified biomarker x2
from a list of candidate cutoff values using the fixcut_con
function.
cutoff_result_con=fixcut_con(yvar='y.con', xvar="x2", dir=">", cutoffs=c(-0.1,-0.3,-0.5,0.1,0.3,0.5), data=tutorial_data, method="t.test", yvar.display='y.con', xvar.display='biomarker x2', vert.x=F)
cutoff_result_con$fig
From the results, the optimal cutoff value is 0.5. Next, we define
the biomarker positive group using a cutoff of 0.5. The biomarker
positive group includes subjects with x2 values less than
or equal to 0.5, while the biomarker negative group includes subjects
with x2 values greater than 0.5. We first use the
cut_perf() function to evaluate the performance between the
biomarker positive and negative groups.
res=cut_perf (yvar="y.con", censorvar=NULL, xvar="x2", cutoff=c(0.5), dir="<=", xvars.adj=NULL, data=tutorial_data, type='c', yvar.display='y.con', xvar.display="biomarker x2")
kable_styling(kable(x=res$comp.res.display[,-2:-4], format="html", caption = "Performace at optimal cutoff"),
bootstrap_options="striped",font_size=16)| Response | Median.Diff | Mean.Diff.CI | t.pvalue | WRS.pvalue |
|---|---|---|---|---|
| y.con | -0.3 | -0.38 (NA , NA) | NA | NA |
Next, we will further assess the predictive model performance by examining the differences between the treatment and control groups within both the biomarker positive and negative groups.
tutorial_data$biogroup=ifelse(tutorial_data$x2<=0.5,'biomarker_positive','biomarker_negative')
res = subgrp_perf_pred(yvar="y.time", censorvar="y.event", grpvar="biogroup", grpname=c("biomarker_positive",'biomarker_negative'),trtvar="treatment_categorical", trtname=c("Placebo" , "Treatment"), xvars.adj=NULL,data=tutorial_data, type="s")
kable_styling(kable(x=res$group.res.display, format="html", caption = "BioSubgroup Stat"),
bootstrap_options="striped",font_size=16)| Response | N | Events | Mean.Surv.CI | Med.Surv.CI | |
|---|---|---|---|---|---|
| treatment_categorical=Placebo,biogroup=biomarker_positive | y.time | 321 | 127 | 3.7 (2.49 , 4.92) | 1.99 (1.65 , 2.7) |
| treatment_categorical=Placebo,biogroup=biomarker_negative | y.time | 192 | 76 | 4.49 (2.37 , 6.61) | 1.83 (1.58 , 2.27) |
| treatment_categorical=Treatment,biogroup=biomarker_positive | y.time | 321 | 133 | 4.84 (3.66 , 6.02) | 2.85 (2.11 , 3.52) |
| treatment_categorical=Treatment,biogroup=biomarker_negative | y.time | 166 | 75 | 2.63 (1.93 , 3.32) | 1.51 (0.84 , 2.14) |
From the Kaplan-Meier (KM) curve, a difference between the treatment and
control groups is observed in the biomarker positive group. This
difference is not observed in the biomarker negative group, validating
that biomarker x2 identified by XGBoostSub_con is
predictive.
2 Example Analysis for Binary Outcomes
2.1 Train the XGBoostSub_bin Model
Similar to training the XGBoostSub_con model for
continuous outcomes, we can also train the XGBoostSub_bin
model for binary outcomes.
2.2 Predictive biomarker importance
kable_styling(kable(x=biomarker_imp, format="html", caption = "Biomarker importance table"),
bootstrap_options="striped",font_size=16)| Biomarker | Importance |
|---|---|
| x10 | 0.6100485 |
| x2 | 0.1428244 |
| x8 | 0.1083907 |
| x5 | 0.0805473 |
| x1 | 0.0581890 |
Based on the results, biomarker x10 emerges as the most
important biomarker.
2.3 Get subgroup results
subgroup_results=get_subgroup_results(model, X_feature, subgroup_label=true_label, cutoff = 0.5)
kable_styling(kable(x=head(subgroup_results$assignment), format="html", caption = "The first 6 subjects subgroup assigentment"),
bootstrap_options="striped",font_size=16)| Patient_ID | Treatment_Assignment |
|---|---|
| 1 | 0 |
| 2 | 0 |
| 3 | 0 |
| 4 | 0 |
| 5 | 1 |
| 6 | 0 |
cat("Prediction accuracy of true subgroup label:\n")
#> Prediction accuracy of true subgroup label:
cat(subgroup_results$acc)
#> 0.535Users can freely tune the parameters in the XGBoostSub-based models to enhance the performance of identifying true subgroups. In this example, we provide a random parameter setting for illustration purposes. However, when applying this model to real data, the ground truth labels are unknown, which means you may not be able to tune the parameters based on prediction accuracy.
2.4 Post-Hoc analysis based on subgroup and biomarker results
Continuing our assessment, we aim to determine whether the identified
biomarker is prognostic or not. To do so, we examine the association
between the binary response variable y.bin and biomarker
x10.
roc_bin_plot (yvar='y.bin', xvars="x10", dirs="auto", data=tutorial_data, yvar.display='y.bin', xvars.display="Biomarker x10")
The association between the binary response variable
y.bin and biomarker x2, identified as the
second most important biomarker by the XGBoostSub_bin
model, is shown below.
roc_bin_plot (yvar='y.bin', xvars="x2", dirs="auto", data=tutorial_data, yvar.display='y.bin', xvars.display="Biomarker x2")
We select the optimal cutoff of identified biomarker x10
from a candidate list of cutoff values This capability is particularly
valuable for companion diagnostics (CDx) development when working with a
limited set of candidate cutoffs (e.g., IHC).
cutoff_result_bin=fixcut_bin (yvar='y.bin', xvar="x10", dir=">", cutoffs=c(-0.1,-0.3,-0.5,0.1,0.3,0.5), data=tutorial_data, method="Fisher", yvar.display='Response_Y', xvar.display='Biomarker_X10', vert.x=F)
cutoff_result_bin$fig
Here, users can select the optimal cutoff based on various metrics such
as sensitivity, specificity, PPV (Positive Predictive Value), and NPV
(Negative Predictive Value). The choice of metric depends on the
specific research context. For instance, if the goal is to enrich
responders, focusing on PPV might be more appropriate. Based on the
highest PPV value, users can then select the optimal cutoff.
If accuracy is our primary concern, the optimal cutoff value is 0.5.
In this case, we define the biomarker positive group using a cutoff of
0.5. This group comprises subjects with x10 values greater
than or equal to 0.5, while the biomarker negative group consists of
subjects with x10 values less than 0.5. To assess the
performance between the biomarker positive and negative groups, we
initially employ the cut_perf function.
res=cut_perf (yvar="y.con", censorvar=NULL, xvar="x10", cutoff=c(0.5), dir=">", xvars.adj=NULL, data=tutorial_data, type='c', yvar.display='y.con', xvar.display="biomarker x2")
kable_styling(kable(x=res$comp.res.display[,-2:-4], format="html", caption = "caption"),
bootstrap_options="striped",font_size=16)| Response | Median.Diff | Mean.Diff.CI | t.pvalue | WRS.pvalue |
|---|---|---|---|---|
| y.con | 0.22 | 0.2 (NA , NA) | NA | NA |
Next, the subgrp_perf_pred function will provide the
predictive model performance based on the defined subgroup.
tutorial_data$biogroup=ifelse(tutorial_data$x10>0.5,'biomarker_positive','biomarker_negative')
res = subgrp_perf_pred(yvar="y.time", censorvar="y.event", grpvar="biogroup", grpname=c("biomarker_positive",'biomarker_negative'),trtvar="treatment_categorical", trtname=c("Placebo" , "Treatment"), xvars.adj=NULL,data=tutorial_data, type="s")
kable_styling(kable(x=res$group.res.display, format="html", caption = "BioSubgroup Stat"),
bootstrap_options="striped",font_size=16)| Response | N | Events | Mean.Surv.CI | Med.Surv.CI | |
|---|---|---|---|---|---|
| treatment_categorical=Placebo,biogroup=biomarker_positive | y.time | 191 | 73 | 3.94 (2.27 , 5.6) | 1.83 (1.46 , 2.47) |
| treatment_categorical=Placebo,biogroup=biomarker_negative | y.time | 322 | 130 | 3.98 (2.52 , 5.43) | 2.03 (1.74 , 2.44) |
| treatment_categorical=Treatment,biogroup=biomarker_positive | y.time | 181 | 79 | 3.19 (2.35 , 4.04) | 1.71 (1.16 , 2.14) |
| treatment_categorical=Treatment,biogroup=biomarker_negative | y.time | 306 | 129 | 4.68 (3.46 , 5.89) | 2.96 (2.12 , 3.73) |
From the KM curve, it appears that 0.5 for X10 may not be
the optimal cutoff. Further evaluation using the fixcut_bin
function with different candidate optimal cutoffs is suggested.
3 Example Analysis for time-to-event outcome
3.1 Train the XGBoostSub_sur model
# Prepare the data for training the XGBoostSub_sur
y_label=tutorial_data$y.time
delta=tutorial_data$y.event
# Train the XGBoostSub_sur model using the specified parameters
model <- XGBoostSub_sur(X_feature, y_label, trt ,pi,delta,Loss_type = "A_learning", params=list(learning_rate = 0.005, max_depth = 1,lambda = 9, gamma=1, min_child_weight=1,
max_bin=128, tree_method = 'exact',subsample=0.8), nrounds = 250, disable_default_eval_metric = 1, verbose = FALSE)3.1 Predictive biomarker importance using XGBoostSub_sur
kable_styling(kable(x=biomarker_imp, format="html", caption = "Biomarker importance table"),
bootstrap_options="striped",font_size=16)| Biomarker | Importance |
|---|---|
| x6 | 0.6007191 |
| x2 | 0.3321002 |
| x1 | 0.0283465 |
| x9 | 0.0120170 |
| x7 | 0.0099025 |
| x5 | 0.0081239 |
| x3 | 0.0048171 |
| x4 | 0.0039738 |
3.2 Get subgroup results based on XGBoostSub_sur
subgroup_results=get_subgroup_results(model, X_feature, subgroup_label=true_label, cutoff = 0.5)
kable_styling(kable(x=head(subgroup_results$assignment), format="html", caption = "The first 6 subjects subgroup assigentment"),
bootstrap_options="striped",font_size=16)| Patient_ID | Treatment_Assignment |
|---|---|
| 1 | 1 |
| 2 | 0 |
| 3 | 0 |
| 4 | 0 |
| 5 | 1 |
| 6 | 0 |
4. Other functions for biomarker analysis in clinical practice
Subgroup analysis often involves dividing subjects into different
groups based on categorical variables, such as baseline characteristics
or genetic mutation variables. In this package, we provide the
cat_summary function to summarize these categorical variables. For
example, in the tutorial dataset, there is a variable called
risk_category, which takes values like High Risk,
Intermediate Risk, and Low Risk. First, we use this function to
determine the distribution of subjects in each subgroup.
res = cat_summary(yvar="risk_category", yname=c("High Risk","Intermediate Risk" ,"Low Risk"), xvars="treatment_categorical", xname.list=list(c("Placebo" , "Treatment")), data=tutorial_data)
kable_styling(kable(x=res$cont.display, format="html", caption = "Contingency Table"),
bootstrap_options="striped",font_size=16)| treatment_categorical | risk_category = High Risk | risk_category = Intermediate Risk | risk_category = Low Risk |
|---|---|---|---|
| Placebo | 128 (25%) | 263 (51.3%) | 122 (23.8%) |
| Treatment | 122 (25.1%) | 237 (48.7%) | 128 (26.3%) |
Then, we can also visualize the KM curves based on predefined risk
groups and treatment/control groups using the
subgrp_perf_pred function.
res = subgrp_perf_pred(yvar="y.time", censorvar="y.event", grpvar="risk_category", grpname=c("High Risk","Intermediate Risk" ,"Low Risk"),trtvar="treatment_categorical", trtname=c("Placebo" , "Treatment"), xvars.adj=NULL,data=tutorial_data, type="s")
kable_styling(kable(x=res$group.res.display, format="html", caption = "Subgroup Stat"),
bootstrap_options="striped",font_size=16)| Response | N | Events | Mean.Surv.CI | Med.Surv.CI | |
|---|---|---|---|---|---|
| treatment_categorical=Placebo,risk_category=High Risk | y.time | 128 | 4 | 22.57 (3.96 , 41.17) | NA (0.13 , NA) |
| treatment_categorical=Placebo,risk_category=Intermediate Risk | y.time | 263 | 110 | 2.95 (-0.03 , 5.92) | 1.12 (0.99 , 1.21) |
| treatment_categorical=Placebo,risk_category=Low Risk | y.time | 122 | 89 | 6.18 (4.38 , 7.98) | 3.31 (2.87 , 4.33) |
| treatment_categorical=Treatment,risk_category=High Risk | y.time | 122 | 4 | 0.12 (0.11 , 0.13) | 0.13 (0.12 , NA) |
| treatment_categorical=Treatment,risk_category=Intermediate Risk | y.time | 237 | 108 | 0.96 (0.89 , 1.04) | 0.99 (0.82 , 1.11) |
| treatment_categorical=Treatment,risk_category=Low Risk | y.time | 128 | 96 | 6.49 (5.19 , 7.79) | 4.41 (3.86 , 5.2) |
