Knowledge Discovery by Accuracy Maximization

Introduction

The clinical package is designed to facilitate exploratory data analysis and statistical testing on clinical datasets. This vignette presents the full usage of all core functions included in the package.

2 Installation

2.1 Installation via CRAN

The R package clinical is part of the Comprehensive R Archive Network (CRAN)1. The simplest way to install the package is to enter the following command into your R session: install.packages("clinical").

2.3 Compatibility issues

All versions downloadable from CRAN have been built using R version, R.3.2.3. The package should work without major issues on R versions > 3.5.0.

3 Getting Started

To load the package, enter the following instruction in your R session:

If this command terminates without any error messages, you can be sure that the package has been installed successfully. The clinical package is now ready for use.

The package includes both a user manual (this document) and a reference manual (help pages for each function). To view the user manual, enter vignette("clinical"). Help pages can be viewed using the help command help(package="clinical").

Prostate Data

The clinical package includes a simulated dataset representing clinical information from patients diagnosed with prostate cancer. This dataset is provided as a data.frame and is intended for demonstration and instructional purposes.

The dataset includes the following variables:

  • Hospital: Factor indicating the hospital where the patient was treated.
  • Gender: Factor indicating the patient’s gender.
  • Gleason: Ordered factor representing the Gleason score assigned to the tumor.
  • BMI: Numeric value for the patient’s Body Mass Index.
  • Age: Numeric value for the patient’s age (in years).
  • Hypertension: Factor indicating whether the patient has hypertension.

To load the dataset:

data(prostate)
head(prostate)
##     Hospital Gender Gleason score BMI Age hypertension
## 1 Hospital A   Male           3+3  35  71           no
## 2 Hospital A   Male           3+3  36  75           no
## 3 Hospital A   Male           3+4  29  78           no
## 4 Hospital A   Male           4+3  28  76           no
## 5 Hospital A   Male           4+4  35  73           no
## 6 Hospital A   Male           3+4  36  74           no

Function: txtsummary

The txtsummary() function provides a concise textual summary of a numeric variable using either the mean or median, along with a measure of variability such as the interquartile range (IQR), 95% confidence interval, standard deviation, or full range.

This is particularly useful when preparing reports or markdown documents where inline descriptive statistics are needed in a clean format.

Example with the prostate dataset

# Summarize Age using mean and IQR
txtsummary(prostate$Age, f = "mean", digits = 2, range = "IQR")
## [1] "67.52 [56 75]"

Function: continuous.test

Comparing Continuous Variables Across Groups

The continuous.test() function allows comparison of a continuous variable across groups. It returns a formatted summary of the data (e.g., mean and standard deviation or median and IQR) for each group, along with a p-value from a statistical test. This is useful for generating clean, publication-ready result tables.

Function Parameters

  • feature: A string indicating the name of the variable.
  • values: A numeric vector containing the continuous data.
  • group: A factor or character vector indicating group membership.
  • center: The measure of central tendency, either "mean" or "median".
  • range: A measure of variability: "sd", "IQR", "range", or "95%CI".
  • method: Statistical method to use: "parametric" or "non-parametric". The function uses:
    • t-test (2 groups) or ANOVA (>2 groups) for parametric
    • Wilcoxon (2 groups) or Kruskal-Wallis (>2 groups) for non-parametric

Example 1: Wilcoxon Rank-Sum Test (2 Groups, Non-Parametric)

# Non-parametric comparison using Wilcoxon test
result_wilcox <- continuous.test(
  name = "Age",
  x = prostate$Age,
  y = prostate$Hospital,
  center = "median",
  range = "IQR",
  method = "non-parametric"
)

print(result_wilcox)
##             Feature Hospital A     Hospital B  p-value
## 1 Age, median [IQR] 63 [56 74] 74 [57.5 79.5] 3.07e-01

Example 1: Wilcoxon Rank-Sum Test (2 Groups, Non-Parametric)

# Non-parametric comparison using Wilcoxon test
result_wilcox <- continuous.test(
  name = "Age",
  x = prostate$Age,
  y = prostate$Hospital,
  center = "median",
  range = "IQR",
  method = "non-parametric"
)

print(result_wilcox)
##             Feature Hospital A     Hospital B  p-value
## 1 Age, median [IQR] 63 [56 74] 74 [57.5 79.5] 3.07e-01

Function: categorical.test

Comparing Categorical Variables Across Groups

The categorical.test() function compares categorical variables across groups and returns a formatted summary table with the test result. It automatically selects the appropriate statistical test depending on whether the categorical variable is ordered or not:

  • Unordered factor: Uses Fisher’s exact test (2 groups) or Chi-squared test (>2 groups).
  • Ordered factor: Uses the Jonckheere–Terpstra test to detect monotonic trends across ordered categories.

The output is suitable for inclusion in summary tables or reports.

Function Parameters

  • feature: A string indicating the name of the categorical variable.
  • values: A factor or ordered factor representing the categorical data.
  • group: A factor or character vector indicating group membership.

Example 1: Unordered Categorical Variable (Fisher’s Exact Test)

# Compare Gender (unordered factor) across hospitals
categorical_test_result <- categorical.test(
  name = "Gender",
  x = prostate$Gender,
  y = prostate$Hospital
)

print(categorical_test_result)
##   Feature          Hospital A   Hospital B   p-value
## v "Gender"         ""           ""           ""     
##   "   Male, n (%)" "81 (100.0)" "24 (100.0)" ""     
## attr(,"p-value")
## [1] NA
## attr(,"shapiro test")
## [1] NA

Example 2: Ordered Categorical Variable (Jonckheere–Terpstra Test)

# Compare Gleason score (ordered factor) across hospitals
categorical_test_result <- categorical.test(
  name = "Gleason",
  x = prostate$Gleason,
  y = prostate$Hospital
)

print(categorical_test_result)
##   Feature         Hospital A  Hospital B p-value   
## v "Gleason"       ""          ""         "1.29e-01"
##   "   3+3, n (%)" "30 (37.0)" "7 (29.2)" ""        
##   "   3+4, n (%)" "30 (37.0)" "5 (20.8)" ""        
##   "   4+3, n (%)" "8 (9.9)"   "6 (25.0)" ""        
##   "   4+4, n (%)" "13 (16.0)" "6 (25.0)" ""        
## attr(,"p-value")
## [1] 0.1286
## attr(,"shapiro test")
## [1] NA

Function: correlation.test

Computes Pearson, Spearman, or MINE correlation between two numeric vectors.

correlation_result <- correlation.test(prostate$Age, prostate$BMI, method = "spearman", name = "Age vs BMI")
## Warning in cor.test.default(x, y, method = "spearman"): cannot compute exact
## p-value with ties
print(correlation_result)
##      Feature  rho  p-value
## 1 Age vs BMI 0.05 6.15e-01

Function: multi_analysis

Applies a test (continuous or correlation) across multiple features of a dataset.

multi_cont <- multi_analysis(prostate[, c("Age", "BMI")], prostate$Hospital, FUN = "continuous.test")
print(multi_cont)
##             Feature Hospital A     Hospital B  p-value      FDR
## 1 Age, median [IQR] 63 [56 74] 74 [57.5 79.5] 3.07e-01 3.07e-01
## 2 BMI, median [IQR] 32 [25 35]  26 [25 30.25] 9.66e-03 1.93e-02
multi_corr <- multi_analysis(prostate[, c("Age", "BMI")], prostate$BMI, FUN = "correlation.test")
print(multi_corr)
##   Feature    r p-value     FDR
## 1     Age 0.03 7.5e-01 7.5e-01
## 2     BMI 1.00   0e+00 0.0e+00

Function: intersect

Finds the intersection of multiple vectors.

v1 <- c("A", "B", "C")
v2 <- c("B", "C", "D")
v3 <- c("C", "B", "E")

intersect(v1, v2, v3)
## [1] "B" "C"

Function: frequency_matching

Matches samples across classes (e.g., control vs case) by discretizing numeric features into bins and stratifying selection.

hosp=prostate[,"Hospital"]
gender=prostate[,"Gender"]
GS=prostate[,"Gleason score"]
BMI=prostate[,"BMI"]
age=prostate[,"Age"]

A=categorical.test("Gender",gender,hosp)
B=categorical.test("Gleason score",GS,hosp)

C=continuous.test("BMI",BMI,hosp,digits=2)
D=continuous.test("Age",age,hosp,digits=1)

# Analysis without matching
rbind(A,B,C,D)
##               Feature Hospital A     Hospital B  p-value
## v              Gender                                   
##           Male, n (%) 81 (100.0)     24 (100.0)         
## v1      Gleason score                           1.35e-01
## X          3+3, n (%)  30 (37.0)       7 (29.2)         
## X.1        3+4, n (%)  30 (37.0)       5 (20.8)         
## X.2        4+3, n (%)    8 (9.9)       6 (25.0)         
## X.3        4+4, n (%)  13 (16.0)       6 (25.0)         
## 1   BMI, median [IQR] 32 [25 35]  26 [25 30.25] 9.66e-03
## 11  Age, median [IQR] 63 [56 74] 74 [57.5 79.5] 3.07e-01
# The order is important. Right is more important than left in the vector
# So, Ethnicity will be more important than Age
var=c("Age","BMI","Gleason score")
data.categorized=prostate[,var]

# Extract the Age vector
x <- data.categorized[["Age"]]

# Compute quantiles (0%, 25%, 50%, 75%, 100%) with NA handling
breaks <- quantile(x, probs = c(0, 0.25, 0.5, 0.75, 1), na.rm = TRUE)

# Apply the cut and update the Age column with labeled bins
data.categorized[["Age"]] <- cut(x, breaks = breaks, include.lowest = TRUE)

# Extract the Age vector
x <- data.categorized[["BMI"]]

# Compute quantiles (0%, 25%, 50%, 75%, 100%) with NA handling
breaks <- quantile(x, probs = c(0, 0.25, 0.5, 0.75, 1), na.rm = TRUE)

# Apply the cut and update the Age column with labeled bins
data.categorized[["BMI"]] <- cut(x, breaks = breaks, include.lowest = TRUE)

times=c(1,1)
names(times)=c("Hospital A","Hospital B")
t=frequency_matching(data.categorized,prostate[,"Hospital"],times=times)



newdata=prostate[t$selection,]

hosp.new=newdata[,"Hospital"]
gender.new=newdata[,"Gender"]
GS.new=newdata[,"Gleason score"]
BMI.new=newdata[,"BMI"]
age.new=newdata[,"Age"]

A=categorical.test("Gender",gender.new,hosp.new)
B=categorical.test("Gleason score",GS.new,hosp.new)

C=continuous.test("BMI",BMI.new,hosp.new,digits=2)
D=continuous.test("Age",age.new,hosp.new,digits=1)

# Analysis with matching
rbind(A,B,C,D)
##               Feature    Hospital A     Hospital B  p-value
## v              Gender                                      
##           Male, n (%)    24 (100.0)     24 (100.0)         
## v1      Gleason score                                 1e+00
## X          3+3, n (%)      7 (29.2)       7 (29.2)         
## X.1        3+4, n (%)      5 (20.8)       5 (20.8)         
## X.2        4+3, n (%)      6 (25.0)       6 (25.0)         
## X.3        4+4, n (%)      6 (25.0)       6 (25.0)         
## 1   BMI, median [IQR] 29 [25 32.75]  26 [25 30.25]    3e-01
## 11  Age, median [IQR]  61 [56 73.2] 74 [57.5 79.5] 2.51e-01

Conclusion

The clinical package provides an extensive toolkit for evaluating clinical datasets, from statistical comparisons to frequency matching and summarization. This vignette serves as a comprehensive guide for using each function effectively.

  1. https://cran.r-project.org/↩︎