Title:	Biostatistics and Clinical Data Analysis
Version:	1.0.0
Description:	Biostatistical and clinical data analysis, including descriptive statistics, exploratory data analysis, sample size and power calculations, statistical inference, and data visualization. Normality tests are implemented following Mishra et al. (2019) <doi:10.4103/aca.ACA_157_18>, omnibus test procedures are based on Blanca et al. (2017) <doi:10.3758/s13428-017-0918-2> and Field et al. (2012, ISBN:9781446200469), while sample size and power calculation methods follow Chow et al. (2017) <doi:10.1201/9781315183084>.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.3
Depends:	R (≥ 4.1.0)
Suggests:	testthat (≥ 3.0.0), covr
Config/testthat/edition:	3
Imports:	ggplot2, gridExtra, rlang, car, gt
URL:	https://github.com/sebasquirarte/biostats
BugReports:	https://github.com/sebasquirarte/biostats/issues
NeedsCompilation:	no
Packaged:	2025-11-11 22:11:53 UTC; squirartej
Author:	Sebastian Quirarte-Justo [aut, cre], Angela Carolina Montano-Ruiz [aut], Jose Maria Torres-Arellano [aut], Laboratorios Sophia S.A. de C.V. [cph, fnd]
Maintainer:	Sebastian Quirarte-Justo <sebastian.quirarte@sophia.com.mx>
Repository:	CRAN
Date/Publication:	2025-11-13 21:40:02 UTC

Simulate Simple Clinical Trial Data

Description

Creates a simple simulated clinical trial dataset with participant demographics, multiple visits, treatment groups with different effects, numerical and categorical variables, as well as optional missing data and dropout rates.

Usage

clinical_data(
  n = 100,
  visits = 3,
  arms = c("Placebo", "Treatment"),
  dropout = 0,
  missing = 0
)

Arguments

Value

Dataframe with columns: participant_id, visit, sex, treatment, age, weight, biomarker, and response in long format.

Examples

# Basic dataset
clinical_df <- clinical_data()

# Multiple treatment arms with dropout rate and missing data
clinical_df <- clinical_data(arms = c('Placebo', 'A', 'B'), missing = 0.05, dropout = 0.10)

Effect Measures

Description

Calculates measures of effect: Odds Ratio (OR), Risk Ratio (RR), and either Number Needed to Treat (NNT) or Number Needed to Harm (NNH).

Usage

effect_measures(
  exposed_event,
  exposed_no_event,
  unexposed_event,
  unexposed_no_event,
  alpha = 0.05,
  correction = TRUE
)

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

Arguments

Value

An object of class "effect_measures" containing the contingency table, effect size estimates (OR, RR, risk difference, NNT/NNH), and related statistics.

Methods (by generic)

• print(effect_measures): Print method for objects of class "effect_measures".

Examples

effect_measures(exposed_event = 15, 
                exposed_no_event = 85,
                unexposed_event = 5,
                unexposed_no_event = 95)

Descriptive and Visual Missing Value Assessment

Description

Provides descriptive statistics and visualizations of missing values in a dataframe.

Usage

missing_values(data, color = "#79E1BE", all = FALSE)

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

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

Arguments

Value

An object of class "missing_values" containing descriptive statistics and ggplot objects.

Methods (by generic)

• print(missing_values): Print method for objects of class "missing_values".

Functions

• print(outliers): Print method for objects of class "outliers".

Examples

# Clinical dataset with missing values
clinical_df <- clinical_data(dropout = 0.1, missing = 0.05)

# Missing value analysis of only variables with missing values
missing_values(clinical_df)

# Show all variables including those without missing values
missing_values(clinical_df, all = TRUE)

Statistical and Visual Normality Assessment

Description

Tests normality using sample size-appropriate methods: Shapiro-Wilk test (n less than or equal to 50) or Kolmogorov-Smirnov test (n greater than 50) with Q-Q plots and histograms. Evaluates skewness and kurtosis using z-score criteria based on sample size. Automatically detects outliers and provides comprehensive visual and statistical assessment.

Usage

normality(data, x, all = FALSE, color = "#79E1BE")

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

Arguments

Value

Returns an object of class "normality" with normality statistics and ggplot objects.

Methods (by generic)

• print(normality): Print method for objects of class "normality".

References

Mishra P., Pandey C.M., Singh U., Gupta A., Sahu C., and Keshri A. Descriptive statistics and normality tests for statistical data. Ann Card Anaesth. 2019 Jan-Mar;22(1):67-72. doi: 10.4103/aca.ACA_157_18. PMID: 30648682; PMCID: PMC6350423.

Examples

# Simulated clinical data
clinical_df <- clinical_data()

# Normally assesment of numerical variable
normality(clinical_df, "biomarker")

# Normally assesment of numerical variable with points outside 95% CI displayed
normality(clinical_df, "weight", all = TRUE)

Omnibus Tests for Comparing Three or More Groups

Description

Performs omnibus tests to evaluate overall differences between three or more groups. Automatically selects the appropriate statistical test based on data characteristics and assumption testing. Supports both independent groups and repeated measures designs. Tests include one-way ANOVA, repeated measures ANOVA, Kruskal-Wallis test, and Friedman test. Performs comprehensive assumption checking (normality, homogeneity of variance, sphericity) and post-hoc testing when significant results are detected.

Usage

omnibus(
  data,
  y,
  x,
  paired_by = NULL,
  alpha = 0.05,
  p_method = "holm",
  na.action = "na.omit"
)

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

Arguments

Value

An object of class "omnibus" containing the formula, statistic summary, name of the test performed, value of the test statistic, p value, alpha, the results of the post-hoc test and assumptions, the sample size's coefficient of variance, and corresponding degrees of freedom.

Methods (by generic)

• print(omnibus): Print method for objects of class "omnibus".

References

Blanca, M., Alarcón, R., Arnau, J. et al. Effect of variance ratio on ANOVA robustness: Might 1.5 be the limit?. Behav Res. 2017 Jun 22; 50:937–962. https://doi.org/10.3758/s13428-017-0918-2 Field, A., Miles, J., & Field, Z. (2012). Discovering Statistics Using R. London: SAGE Publications.

Examples

# Simulated clinical data with multiple treatment arms and visits
clinical_df <- clinical_data(n = 300, visits = 6, arms = c("A", "B", "C"))

# Compare numerical variable across treatments
omnibus(data = clinical_df, y = "biomarker", x = "treatment")

# Filter simulated data to just one treatment
clinical_df_A <- clinical_df[clinical_df$treatment == "A", ]

# Compare numerical variable changes across visits 
omnibus(y = "biomarker", x = "visit", data = clinical_df_A, paired_by = "participant_id")

Descriptive and Visual Outlier Assessment

Description

Identifies outliers using Tukey's interquartile range (IQR) method and provides descriptive statistics and visualizations for outlier assessment in numeric data.

Usage

outliers(data, x, threshold = 1.5, color = "#79E1BE")

Arguments

Value

An object of class "outliers" containing a list with outlier statistics and ggplot objects.

Examples

# Simulated clinical data
clinical_df <- clinical_data()

# Basic outlier detection
outliers(clinical_df, "biomarker")

# Using custom threshold
outliers(clinical_df, "biomarker", threshold = 1.0)

Create Simple Professional Bar Plots

Description

Generates publication-ready bar plots with minimal code using ggplot2.

Usage

plot_bar(
  data,
  x,
  y = NULL,
  group = NULL,
  facet = NULL,
  position = c("dodge", "stack", "fill"),
  stat = NULL,
  colors = NULL,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  legend_title = NULL,
  flip = FALSE,
  values = FALSE
)

Arguments

Value

A ggplot2 object

Examples

# Simulated clinical data
clinical_df <- clinical_data()

# Proportion of response by treatment
plot_bar(data = clinical_df, x = "treatment", group = "response", position = "fill", 
        title = "Proportion of response by treatment", values = TRUE)
         
# Grouped barplot of categorical variable by treatment with value labels
plot_bar(data = clinical_df, x = "response", group = "visit", facet = "treatment", 
        title = "Response by visit and treatment",values = TRUE)
        

Create Simple Professional Box Plots

Description

Generates publication-ready boxplots with optional jittered points and mean overlay.

Usage

plot_box(
  data,
  x,
  y,
  group = NULL,
  facet = NULL,
  colors = NULL,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  legend_title = NULL,
  points = FALSE,
  point_size = 2,
  y_limits = NULL,
  show_mean = TRUE
)

Arguments

Value

A ggplot2 object

Examples

#Simulated clinical data
clinical_df <- clinical_data(visits = 10)

# Boxplot of biomarker by treatment
plot_box(clinical_df, x = "treatment", y = "biomarker", group = "treatment")

# Barplot of bimarker by study visit and treatment
plot_box(clinical_df, x = "visit", y = "biomarker", group = "treatment")

Create Simple Professional Correlation Matrix Plots

Description

Generates publication-ready correlation matrix heatmaps with minimal code using ggplot2.

Usage

plot_corr(
  data,
  vars = NULL,
  method = c("pearson", "spearman"),
  type = c("full", "upper", "lower"),
  colors = NULL,
  title = NULL,
  show_values = TRUE,
  value_size = 3,
  show_sig = FALSE,
  sig_level = 0.05,
  sig_only = FALSE,
  show_legend = TRUE,
  p_method = "holm"
)

Arguments

Value

A ggplot2 object

Examples

# Correlation matrix for base R dataset 'swiss'
plot_corr(data = swiss)

# Lower triangle with significance indicators and filtering
plot_corr(data = swiss, type = "lower", show_sig = TRUE, sig_only = TRUE)

Create Simple Professional Histogram Plots

Description

Generates publication-ready histogram plots with minimal code using ggplot2.

Usage

plot_hist(
  data,
  x,
  group = NULL,
  facet = NULL,
  bins = 30,
  binwidth = NULL,
  alpha = 0.7,
  colors = NULL,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  legend_title = NULL,
  y_limits = NULL,
  x_limits = NULL,
  stat = NULL
)

Arguments

Value

A ggplot2 object

Examples

# Simulated clinical data
clinical_df <- clinical_data()

# Basic histogram
plot_hist(clinical_df, x = "biomarker")

# Grouped histogram
plot_hist(clinical_df, x = "biomarker", group = "treatment")

# Faceted histogram
plot_hist(clinical_df, x = "biomarker", facet = "treatment")

Create Simple Professional Line Plots

Description

Generates publication-ready line plots with minimal code using ggplot2.

Usage

plot_line(
  data,
  x,
  y,
  group = NULL,
  facet = NULL,
  stat = NULL,
  error = "se",
  error_width = 0.2,
  colors = NULL,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  legend_title = NULL,
  points = TRUE,
  line_size = 1,
  point_size = 3,
  y_limits = NULL,
  x_limits = NULL
)

Arguments

Value

A ggplot2 object

Examples

# Simulated clinical data
clinical_df <- clinical_data(arms = c("A","B","C"), visits = 10)

# Line plot with mean and standard error by treatment
plot_line(clinical_df, x = "visit", y = "biomarker",
          group = "treatment", stat = "mean", error = "se")

# Faceted line plots with median and 95% CI
plot_line(clinical_df, x = "visit", y = "biomarker", group = "treatment", 
          facet = "sex", stat = "median", error = "ci", points = FALSE)   
    

Sample Size Calculation for Clinical Trials

Description

Calculates the sample size needed in a clinical trial based on study design and statistical parameters using standard formulas for hypothesis testing (Chow, S. 2008).

Usage

sample_size(
  sample = c("one-sample", "two-sample"),
  design = NULL,
  outcome = c("mean", "proportion"),
  type = c("equality", "equivalence", "non-inferiority", "superiority"),
  alpha = 0.05,
  beta = 0.2,
  x1 = NULL,
  x2 = NULL,
  SD = NULL,
  delta = NULL,
  dropout = 0,
  k = 1
)

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

Arguments

Value

An object of class "sample_size" containing the calculated sample size and study parameters.

Methods (by generic)

• print(sample_size): Print method for objects of class "sample_size".

References

Chow, S.-C., Shao, J., Wang, H., & Lokhnygina, Y. (2017). Sample Size Calculations in Clinical Research (3rd ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781315183084

Examples

# Two-sample parallel non-inferiority test for means with 10% expected dropout
sample_size(sample = 'two-sample', design = 'parallel', outcome = 'mean',
            type = 'non-inferiority', x1 = 5.0, x2 = 5.0, 
            SD = 0.1, delta = -0.05, k = 1, dropout = 0.1)
            
# One-sample equivalence test for means
sample_size(sample = "one-sample", outcome = "mean", type = "equivalence",
            x1 = 0, x2 = 0, SD = 0.1, delta = 0.05)

Calculate and visualize sample size across a range of treatment effects

Description

Calculates required sample sizes for specified power levels (70%, 80%, 90%) across a range of treatment effect values (x1), while keeping the control group value (x2) fixed. Internally calls sample_size() and generates a plot to visualize how total sample size changes with varying x1.

Usage

sample_size_range(x1_range, x2, step = 0.1, ...)

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

Arguments

Value

An object of class "sample_size_range" containing the dataframe of sample size calculations and the ggplot object. A plot is also generated to visualize the relationship between treatment effects and required sample sizes.

Methods (by generic)

• print(sample_size_range): Print method for objects of class "sample_size_range".

References

Chow, S.-C., Shao, J., Wang, H., & Lokhnygina, Y. (2017). Sample Size Calculations in Clinical Research (3rd ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781315183084

See Also

sample_size

Examples

# Two-sample parallel non-inferiority test for proportions with 10% dropout
sample_size_range(x1_range = c(0.65, 0.75), x2 = 0.65, step = 0.01,
                  sample = "two-sample", design = "parallel", outcome = "proportion",
                  type = "non-inferiority", delta = -0.1, dropout = 0.1)
                  
# One-sample equivalence test for means
sample_size_range(x1_range = c(-0.01, 0.01), x2 = 0, step = 0.005,
                  sample = "one-sample", outcome = "mean", type = "equivalence",
                  SD = 0.1, delta = 0.05, alpha = 0.05)

Summary Table with Optional Group Comparisons

Description

Generates a summary table for biostatistics and clinical data analysis with automatic normality, effect size, and statistical test calculations. Handles both numeric and categorical variables, performing appropriate descriptive statistics and inferential tests for single-group summaries or two-group comparisons.

Usage

summary_table(
  data,
  group_by = NULL,
  normality_test = "S-W",
  all = FALSE,
  effect_size = FALSE,
  exclude = NULL
)

Arguments

Value

A gt table object with formatted summary statistics.

Examples

# Simulated clinical data
clinical_df <- clinical_data()

# Overall summary without considering treatment groups
summary_table(clinical_df,
              exclude = c('participant_id', 'visit'))

# Grouped summary by treatment group
summary_table(clinical_df,
              group_by = 'treatment',
              exclude = c('participant_id', 'visit'))

# Grouped summary by treatment group with all stats and effect size
summary_table(clinical_df,
              group_by = 'treatment',
              all = TRUE,
              effect_size = TRUE,
              exclude = c('participant_id', 'visit'))