penetrance: A Package for Penetrance Estimation

Description

A comprehensive package for penetrance estimation in family-based studies. This package implements Bayesian methods using Metropolis-Hastings algorithm for estimating age-specific penetrance of genetic variants. It supports both sex-specific and non-sex-specific analyses, and provides various visualization tools for examining MCMC results.

This function implements the Independent Metropolis-Hastings algorithm for Bayesian penetrance estimation of cancer risk. It utilizes parallel computing to run multiple chains and provides various options for analyzing and visualizing the results.

Usage

penetrance(
  pedigree,
  twins = NULL,
  n_chains = 1,
  n_iter_per_chain = 10000,
  ncores = 6,
  max_age = 94,
  baseline_data = baseline_data_default,
  remove_proband = FALSE,
  age_imputation = FALSE,
  median_max = TRUE,
  BaselineNC = TRUE,
  var = c(0.1, 0.1, 2, 2, 5, 5, 5, 5),
  burn_in = 0,
  thinning_factor = 1,
  imp_interval = 100,
  distribution_data = distribution_data_default,
  prev = 1e-04,
  sample_size = NULL,
  ratio = NULL,
  prior_params = prior_params_default,
  risk_proportion = risk_proportion_default,
  summary_stats = TRUE,
  rejection_rates = TRUE,
  density_plots = TRUE,
  plot_trace = TRUE,
  penetrance_plot = TRUE,
  penetrance_plot_pdf = TRUE,
  plot_loglikelihood = TRUE,
  plot_acf = TRUE,
  probCI = 0.95,
  sex_specific = TRUE
)

Arguments

Details

Key features:

• Bayesian estimation of penetrance using family-based data

• Support for sex-specific and non-sex-specific analyses

• Age imputation for missing data

• Visualization tools for MCMC diagnostics

• Integration with the clipp package for likelihood calculations

Value

A list containing combined results from all chains, including optional statistics and plots.

Author(s)

Maintainer: Nicolas Kubista bmendel@jimmy.harvard.edu

Authors:

• BayesMendel Lab

See Also

Useful links:

• https://github.com/nicokubi/penetrance

• https://nicokubi.github.io/penetrance/

Examples

# Create example baseline data (simplified for demonstration)
baseline_data_default <- data.frame(
  Age = 1:94,
  Female = rep(0.01, 94),
  Male = rep(0.01, 94)
)

# Create example distribution data
distribution_data_default <- data.frame(
  Age = 1:94,
  Risk = rep(0.01, 94)
)

# Create example prior parameters
prior_params_default <- list(
  shape = 2,
  scale = 50
)

# Create example risk proportion
risk_proportion_default <- 0.5

# Create a simple example pedigree
example_pedigree <- data.frame(
  PedigreeID = rep(1, 4),
  ID = 1:4,
  Sex = c(1, 0, 1, 0),  # 1 for male, 0 for female
  MotherID = c(NA, NA, 2, 2),
  FatherID = c(NA, NA, 1, 1),
  isProband = c(0, 0, 1, 0),
  CurAge = c(70, 68, 45, 42),
  isAff = c(0, 0, 1, 0),
  Age = c(NA, NA, 40, NA),
  Geno = c(NA, NA, 1, NA)
)

# Basic usage with minimal iterations
result <- penetrance(
  pedigree = list(example_pedigree),
  n_chains = 1,
  n_iter_per_chain = 10,  # Very small number for example
  ncores = 1,             # Single core for example
  summary_stats = TRUE,
  plot_trace = FALSE,     # Disable plots for quick example
  density_plots = FALSE,
  penetrance_plot = FALSE,
  penetrance_plot_pdf = FALSE,
  plot_loglikelihood = FALSE,
  plot_acf = FALSE
)

# View basic results
head(result$summary_stats)

Function to return absolute values

Description

Function to return absolute values

Usage

absValue(x)

Arguments

Value

Numeric, the absolute value of the input.

Apply Burn-In

Description

Apply Burn-In

Usage

apply_burn_in(results, burn_in)

Arguments

Value

A list of results with burn-in applied.

Apply Thinning

Description

Apply Thinning

Usage

apply_thinning(results, thinning_factor)

Arguments

Value

A list of results with thinning applied.

Default Baseline Data

Description

This dataset contains age-specific cancer penetrance rates for both females and males. As an example, the data is derived from the SEER program for colorectal cancer females and males.

Usage

baseline_data_default

baseline_data_default

Format

A data frame with 94 rows and 3 variables:

Age

Age in years

Female

Female baseline risk

Male

Male baseline risk

A data frame with 94 rows and 3 variables:

Age

Age in years (1 to 94)

Female

Penetrance rate for females

Male

Penetrance rate for males

Calculate Baseline Risk

Description

This function extracts the penetrance data for a specified cancer type, gene, race, and penetrance type from the provided database.

Usage

calculateBaseline(cancer_type, gene, race, type, db)

Arguments

Value

A matrix of penetrance data for the specified parameters.

Calculate Empirical Age Density

Description

Calculates empirical age density distributions for different subgroups in the data, separated by sex and genetic testing status.

Usage

calculateEmpiricalDensity(
  data,
  aff_column = "aff",
  age_column = "age",
  sex_column = "sex",
  geno_column = "geno",
  n_points = 10000,
  sex_specific = TRUE
)

Arguments

Value

A list of density objects for different subgroups (tested/untested, male/female)

Calculate Age-Specific Non-Carrier Penetrance

Description

This function calculates the age-specific non-carrier penetrance based on SEER baseline data, penetrances for carriers, and allele frequencies. It adjusts penetrance estimates for genetic testing by incorporating the genetic risk attributable to specified alleles.

Usage

calculateNCPen(SEER_baseline, alpha, beta, delta, gamma, prev, max_age)

Arguments

Value

A list containing:

Calculate Weibull Parameters

Description

This function calculates the shape (alpha) and scale (beta) parameters of a Weibull distribution given the median, first quartile, and delta values.

Usage

calculate_weibull_parameters(given_median, given_first_quartile, delta)

Arguments

Value

A list containing the calculated Weibull parameters:

Examples

# Calculate Weibull parameters
params <- calculate_weibull_parameters(
  given_median = 50,
  given_first_quartile = 30,
  delta = 15
)
print(params)

Combine Chains Function to combine the posterior samples from the multiple chains.

Description

Combine Chains Function to combine the posterior samples from the multiple chains.

Usage

combine_chains(results)

Arguments

Value

A list with combined results, including median, threshold, first quartile, and asymptote values.

Combine Chains for Non-Sex-Specific Estimation

Description

Combines the posterior samples from multiple MCMC chains for non-sex-specific estimations.

Usage

combine_chains_noSex(results)

Arguments

Value

A list with combined results, including samples for median, threshold, first quartile, asymptote values, log-likelihoods, and log-acceptance ratios.

Default Distribution Data

Description

Default data frame structure with row names for use in the makePriors function.

Usage

distribution_data_default

distribution_data_default

Format

A data frame for prior distribution parameters

A data frame with the following columns:

age

Age values (NA for default).

at_risk

Proportion of people at risk (NA for default).

Draw Ages Using the Inverse CDF Method from the baseline data

Description

This function draws ages using the inverse CDF method from baseline data.

Usage

drawBaseline(baseline_data)

Arguments

Value

A single age value drawn from the baseline data.

Draw Ages Using the Inverse CDF Method from Empirical Density

Description

This function draws ages using the inverse CDF method from empirical density data, based on sex and whether the individual was tested.

Usage

drawEmpirical(empirical_density, sex, tested, sex_specific = TRUE)

Arguments

Value

A single age value drawn from the appropriate empirical density data.

Generate Posterior Density Plots

Description

Generates histograms of the posterior samples for the different parameters

Usage

generate_density_plots(data)

Arguments

Value

No return value, called for side effects. Creates density plots for each parameter.

Examples

# Create example data
data <- list(
  median_male_results = rnorm(1000, 50, 5),
  median_female_results = rnorm(1000, 45, 5),
  threshold_male_results = runif(1000, 20, 30),
  threshold_female_results = runif(1000, 25, 35),
  asymptote_male_results = rbeta(1000, 2, 2),
  asymptote_female_results = rbeta(1000, 2, 2)
)

# Generate density plots
old_par <- par(no.readonly = TRUE)  # Save old par settings
generate_density_plots(data)
par(old_par)  # Restore old par settings

Generate Summary

Description

Function to generate summary statistics

Usage

generate_summary(data, verbose = FALSE)

Arguments

Value

A data.frame containing summary statistics (min, 1st quartile, median, mean, 3rd quartile, max) for each parameter.

Generate Summary for Non-Sex-Specific Estimation

Description

Generates summary statistics for the combined MCMC results for non-sex-specific estimations.

Usage

generate_summary_noSex(data, verbose = FALSE)

Arguments

Value

A data.frame containing summary statistics (min, 1st quartile, median, mean, 3rd quartile, max) for median, threshold, first quartile, and asymptote values.

Impute Missing Ages in Family-Based Data

Description

Imputes missing ages in family-based data using a combination of Weibull distributions for affected individuals and empirical distributions for unaffected individuals. The function can perform both sex-specific and non-sex-specific imputations.

Usage

imputeAges(
  data,
  na_indices,
  baseline_male = NULL,
  baseline_female = NULL,
  alpha_male = NULL,
  beta_male = NULL,
  delta_male = NULL,
  alpha_female = NULL,
  beta_female = NULL,
  delta_female = NULL,
  baseline = NULL,
  alpha = NULL,
  beta = NULL,
  delta = NULL,
  max_age,
  sex_specific = TRUE,
  max_attempts = 100,
  geno_freq,
  trans,
  lik
)

Arguments

Value

A data frame with the following modifications:

The rest of the data frame remains unchanged.

Examples

# Create sample data with the same structure as used in mhChain
data <- data.frame(
  family = rep(1:2, each=5),
  individual = rep(1:5, 2),
  father = c(NA,1,1,1,1, NA,6,6,6,6),
  mother = c(NA,2,2,2,2, NA,7,7,7,7),
  sex = c(1,2,1,2,1, 1,2,1,2,1),
  aff = c(1,0,1,0,NA, 1,0,1,0,NA),
  age = c(45,NA,25,NA,20, 50,NA,30,NA,22),
  geno = c("1/2",NA,"1/2",NA,NA, "1/2",NA,"1/2",NA,NA),
  isProband = c(1,0,0,0,0, 1,0,0,0,0)
)

# Initialize parameters
na_indices <- which(is.na(data$age))
geno_freq <- c(0.999, 0.001)  # Frequency of normal and risk alleles
trans <- matrix(c(1,0,0.5,0.5), nrow=2)  # Transmission matrix
lik <- matrix(1, nrow=nrow(data), ncol=2)  # Likelihood matrix

# Create baseline data for both sex-specific and non-sex-specific cases
age_range <- 20:94
n_ages <- length(age_range)

# Sex-specific baseline data
baseline_male <- data.frame(
  age = age_range,
  cum_prob = (1:n_ages)/n_ages * 0.8  # Male cumulative probabilities
)

baseline_female <- data.frame(
  age = age_range,
  cum_prob = (1:n_ages)/n_ages * 0.9  # Female cumulative probabilities
)

# Non-sex-specific baseline data
baseline <- data.frame(
  age = age_range,
  cum_prob = (1:n_ages)/n_ages * 0.85  # Overall cumulative probabilities
)

# Example with sex-specific imputation
imputed_data_sex <- imputeAges(
  data = data,
  na_indices = na_indices,
  baseline_male = baseline_male,
  baseline_female = baseline_female,
  alpha_male = 3.5,
  beta_male = 20,
  delta_male = 20,
  alpha_female = 3.2,
  beta_female = 18,
  delta_female = 18,
  max_age = 94,
  sex_specific = TRUE,
  geno_freq = geno_freq,
  trans = trans,
  lik = lik
)

# Example with non-sex-specific imputation
imputed_data_nosex <- imputeAges(
  data = data,
  na_indices = na_indices,
  baseline = baseline,
  alpha = 3.3,
  beta = 19,
  delta = 19,
  max_age = 94,
  sex_specific = FALSE,
  geno_freq = geno_freq,
  trans = trans,
  lik = lik
)

Initialize Age Imputation

Description

Initializes the age imputation process by filling missing ages with random values between a threshold and maximum age.

Usage

imputeAgesInit(data, threshold, max_age)

Arguments

Value

A list containing:

Examples

# Create sample data
data <- data.frame(
  family = c(1, 1),
  individual = c(1, 2),
  father = c(NA, 1),
  mother = c(NA, NA),
  sex = c(1, 2),
  aff = c(1, 0),
  age = c(NA, NA),
  geno = c("1/2", NA),
  isProband = c(1, 0)
)

# Initialize ages with random values between 20 and 94
result <- imputeAgesInit(data, threshold = 20, max_age = 94)

# Access the results
imputed_data <- result$data
missing_indices <- result$na_indices

Impute Ages for Unaffected Individuals

Description

This function imputes ages for unaffected individuals in a dataset based on their sex and whether they were tested, using empirical age distributions.

Usage

imputeUnaffectedAges(data, na_indices, empirical_density, max_age)

Arguments

Value

The data frame with imputed ages for unaffected individuals.

Penetrance Function

Description

Calculates the penetrance for an individual based on Weibull distribution parameters. This function estimates the probability of developing cancer given the individual's genetic and demographic information.

Usage

lik.fn(
  i,
  data,
  alpha_male,
  alpha_female,
  beta_male,
  beta_female,
  delta_male,
  delta_female,
  gamma_male,
  gamma_female,
  max_age,
  baselineRisk,
  BaselineNC,
  prev
)

Arguments

Value

Numeric vector, containing penetrance values for unaffected and affected individuals.

Likelihood Calculation without Sex Differentiation

Description

This function calculates the likelihood for an individual based on Weibull distribution parameters without considering sex differentiation.

Usage

lik_noSex(
  i,
  data,
  alpha,
  beta,
  delta,
  gamma,
  max_age,
  baselineRisk,
  BaselineNC,
  prev
)

Arguments

Value

Numeric vector, containing likelihood values for unaffected and affected individuals.

Make Priors

Description

This function generates prior distributions based on user input or default parameters. It is designed to aid in the statistical analysis of risk proportions in populations, particularly in the context of cancer research. The distributions are calculated for various statistical metrics such as asymptote, threshold, median, and first quartile.

Usage

makePriors(
  data,
  sample_size,
  ratio,
  prior_params,
  risk_proportion,
  baseline_data
)

Arguments

Details

The function includes internal helper functions for normalizing median and first quartile values, and for computing beta distribution parameters. The function handles various settings: using default parameters, applying user inputs, and calculating parameters based on sample size and risk proportions.

If the OR/RR ratio is provided, the asymptote parameters are computed based on this ratio, overriding other inputs for the asymptote.

The function returns a list of distribution functions for the asymptote, threshold, median, and first quartile, which can be used for further statistical analysis.

Value

A list of functions representing the prior distributions for asymptote, threshold, median, and first quartile.

See Also

qbeta, runif

Execution of a Single Chain in Metropolis-Hastings for Cancer Risk Estimation

Description

Performs a single chain execution in the Metropolis-Hastings algorithm for Bayesian inference, specifically tailored for cancer risk estimation. This function can handle both sex-specific and non-sex-specific scenarios.

Usage

mhChain(
  seed,
  n_iter,
  burn_in,
  chain_id,
  ncores,
  data,
  twins,
  max_age,
  baseline_data,
  prior_distributions,
  prev,
  median_max,
  BaselineNC,
  var,
  age_imputation,
  imp_interval,
  remove_proband,
  sex_specific
)

Arguments

Value

A list containing samples, log likelihoods, log-acceptance ratio, and rejection rate for each iteration.

Examples

# Create sample data in PanelPRO format
data <- data.frame(
  ID = 1:10,
  PedigreeID = rep(1, 10),
  Sex = c(0, 1, 0, 1, 0, 1, 0, 1, 0, 1), # 0=female, 1=male
  MotherID = c(NA, NA, 1, 1, 3, 3, 5, 5, 7, 7),
  FatherID = c(NA, NA, 2, 2, 4, 4, 6, 6, 8, 8),
  isProband = c(1, rep(0, 9)),
  CurAge = c(45, 35, 55, 40, 50, 45, 60, 38, 52, 42),
  isAff = c(1, 0, 1, 0, 1, 0, 1, 0, 1, 0),
  Age = c(40, NA, 50, NA, 45, NA, 55, NA, 48, NA),
  Geno = c(1, NA, 1, 0, 1, 0, NA, NA, 1, NA)
)

# Transform data into required format
data <- transformDF(data)

# Set parameters for the chain
seed <- 123
n_iter <- 10
burn_in <- 0.1 # 10% burn-in
chain_id <- 1
ncores <- 1
max_age <- 100

# Create baseline data (simplified example)
baseline_data <- matrix(
  c(rep(0.005, max_age), rep(0.008, max_age)), # Increased baseline risks
  ncol = 2,
  dimnames = list(NULL, c("Male", "Female"))
)

# Set prior distributions with carefully chosen bounds
prior_distributions <- list(
  prior_params = list(
    asymptote = list(g1 = 2, g2 = 3), # Mode around 0.4
    threshold = list(min = 20, max = 30), # Narrower range for threshold
    median = list(m1 = 3, m2 = 2), # Mode around 0.6
    first_quartile = list(q1 = 2, q2 = 3) # Mode around 0.4
  )
)

# Create variance vector for all 8 parameters in sex-specific case
# Using very small variances for initial stability
var <- c(
  0.005, 0.005, # asymptotes (smaller variance since between 0-1)
  1, 1, # thresholds
  1, 1, # medians
  1, 1
) # first quartiles

# Run the chain
results <- mhChain(
  seed = seed,
  n_iter = n_iter,
  burn_in = burn_in,
  chain_id = chain_id,
  ncores = ncores,
  data = data,
  twins = NULL,
  max_age = max_age,
  baseline_data = baseline_data,
  prior_distributions = prior_distributions,
  prev = 0.05, # Increased prevalence
  median_max = FALSE, # Changed to FALSE for simpler median constraints
  BaselineNC = TRUE,
  var = var,
  age_imputation = FALSE,
  imp_interval = 10,
  remove_proband = TRUE,
  sex_specific = TRUE
)

Calculate Log Likelihood using clipp Package

Description

Calculate Log Likelihood using clipp Package

Usage

mhLogLikelihood_clipp(
  paras,
  families,
  twins,
  max_age,
  baseline_data,
  prev,
  geno_freq,
  trans,
  BaselineNC,
  ncores
)

Arguments

Value

Numeric value representing the calculated log likelihood.

Examples

# Create example parameters and data
paras <- c(0.8, 0.7, 20, 25, 50, 45, 30, 35)  # Example parameters

# Create sample data in PanelPRO format
families <- data.frame(
  ID = 1:10,
  PedigreeID = rep(1, 10),
  Sex = c(0, 1, 0, 1, 0, 1, 0, 1, 0, 1),  # 0=female, 1=male
  MotherID = c(NA, NA, 1, 1, 3, 3, 5, 5, 7, 7),
  FatherID = c(NA, NA, 2, 2, 4, 4, 6, 6, 8, 8),
  isProband = c(1, rep(0, 9)),
  CurAge = c(45, 35, 55, 40, 50, 45, 60, 38, 52, 42),
  isAff = c(1, 0, 1, 0, 1, 0, 1, 0, 1, 0),
  Age = c(40, NA, 50, NA, 45, NA, 55, NA, 48, NA),
  Geno = c(1, NA, 1, 0, 1, 0, NA, NA, 1, NA)
)

# Transform data into required format
families <- transformDF(families)

trans <- matrix(
  c(
    1, 0, # both parents are wild type
    0.5, 0.5, # mother is wildtype and father is a heterozygous carrier
    0.5, 0.5, # father is wildtype and mother is a heterozygous carrier
    1 / 3, 2 / 3 # both parents are heterozygous carriers
  ),
 nrow = 4, ncol = 2, byrow = TRUE
)

# Calculate log likelihood
loglik <- mhLogLikelihood_clipp(
  paras = paras,
  families = families,
  twins = NULL,
  max_age = 94,
  baseline_data = baseline_data_default,
  prev = 0.001,
  geno_freq = c(0.999, 0.001),
  trans = trans,
  BaselineNC = TRUE,
  ncores = 1
)

Calculate Log Likelihood without Sex Differentiation

Description

This function calculates the log likelihood for a set of parameters and data without considering sex differentiation using the clipp package.

Usage

mhLogLikelihood_clipp_noSex(
  paras,
  families,
  twins,
  max_age,
  baseline_data,
  prev,
  geno_freq,
  trans,
  BaselineNC,
  ncores
)

Arguments

Value

Numeric, the calculated log likelihood.

References

Details about the clipp package and methods can be found in the package documentation.

Simulated Output Data

Description

This dataset contains the simulated output data for the penetrance package.

Usage

data(out_sim)

Format

A list with the following components:

summary_stats

A data frame with 18000 observations of 8 variables:

Median_Male

numeric, Median value for males

Median_Female

numeric, Median value for females

Threshold_Male

numeric, Threshold value for males

Threshold_Female

numeric, Threshold value for females

First_Quartile_Male

numeric, First quartile value for males

First_Quartile_Female

numeric, First quartile value for females

Asymptote_Male

numeric, Asymptote value for males

Asymptote_Female

numeric, Asymptote value for females

density_plots

A list of 1 element, mfrow: integer vector of length 2

trace_plots

A list of 1 element, mfrow: integer vector of length 2

penetrance_plot

A list of 2 elements: rect and text

pdf_plots

A list of 2 elements: rect and text

combined_chains

A list of 19 numeric vectors with 18000 elements each

results

A list of 1 element which is a list of 24 elements, each with 18000 elements

data

A data frame with 4727 observations of 9 variables:

individual

integer, Individual ID

isProband

numeric, Indicator if the individual is a proband

family

integer, Family ID

mother

numeric, Mother's ID

father

numeric, Father's ID

aff

numeric, Affected status

sex

numeric, Sex of the individual

age

numeric, Age of the individual

geno

character, Genotype

Examples

data(out_sim)
head(out_sim$summary_stats)

Plot Autocorrelation for Multiple MCMC Chains (Posterior Samples)

Description

This function plots the autocorrelation for sex-specific or non-sex-specific posterior samples across multiple MCMC chains. It defaults to key parameters like asymptote_male_samples, asymptote_female_samples, etc.

Usage

plot_acf(results, n_chains, max_lag = 50)

Arguments

Value

A series of autocorrelation plots for each chain.

Plot Log-Likelihood for Multiple MCMC Chains

Description

This function plots the log-likelihood values across iterations for multiple MCMC chains. It helps visualize the convergence of the chains based on the log-likelihood values.

Usage

plot_loglikelihood(results, n_chains)

Arguments

Value

A series of log-likelihood plots for each chain.

Plot Weibull Probability Density Function with Credible Intervals

Description

This function plots the Weibull PDF with credible intervals for the given MCMC results. It allows for visualization of density curves based on the posterior samples.

Usage

plot_pdf(combined_chains, prob, max_age, sex = "NA")

Arguments

Value

A plot showing the Weibull PDF with credible intervals.

Plot Weibull Distribution with Credible Intervals

Description

This function plots the Weibull distribution with credible intervals for the given MCMC results. It allows for visualization of penetrance curves based on the posterior samples.

Usage

plot_penetrance(combined_chains, prob, max_age, sex = "NA")

Arguments

Value

A plot showing the Weibull distribution with credible intervals.

Plot MCMC Trace Plots

Description

Plot MCMC Trace Plots

Usage

plot_trace(results, n_chains, verbose = FALSE)

Arguments

Value

No return value, called for side effects. Creates trace plots for each parameter.

Examples

# Create example results list
results <- list(
  list(
    median_samples = rnorm(100),
    threshold_samples = runif(100),
    first_quartile_samples = rgamma(100, 2, 2),
    asymptote_samples = rbeta(100, 2, 2)
  )
)

# Generate trace plots
plot_trace(results, n_chains = 1)

Print MCMC Rejection Rates

Description

Print MCMC Rejection Rates

Usage

printRejectionRates(results, verbose = TRUE)

Arguments

Details

Extracts and prints the rejection rates from MCMC chain results.

Value

A named numeric vector containing the rejection rate (between 0 and 1) for each MCMC chain. Names are of the form "Chain X" where X is the chain number.

Examples

# Create example results list with two chains
results <- list(
  list(rejection_rate = 0.3),
  list(rejection_rate = 0.4)
)

# Get rejection rates without printing
rates <- printRejectionRates(results, verbose = FALSE)

# Print rejection rates
rates <- printRejectionRates(results)

Default Prior Parameters

Description

Default parameters for the prior distributions used in the makePriors function.

Usage

prior_params_default

prior_params_default

Format

A list of prior distribution parameters

A list with the following components:

asymptote

A list with components g1 and g2, default values for the asymptote parameters.

threshold

A list with components min and max, default values for the threshold parameters.

median

A list with components m1 and m2, default values for the median parameters.

first_quartile

A list with components q1 and q2, default values for the first quartile parameters.

Default Risk Proportions

Description

Default proportions of people at risk used in the makePriors function.

Usage

risk_proportion_default

risk_proportion_default

Format

A data frame of risk proportions

A data frame with the following columns:

median

Proportion of people at risk at the median age.

first_quartile

Proportion of people at risk at the first quartile age.

max_age

Proportion of people at risk at the maximum age.

Processed Family Data

Description

A dataset containing processed information about the first simulated 130 families. These families are referenced in the vigniette simulation_study.Rmd and using_penetrance.Rmd The user must specify the pedigree argument as a data frame which contains the family data (see test_fam). The family data must be in the correct format with the following columns:

Usage

simulated_families

Format

A list of processed family data.

Details

ID

A numeric value representing the unique identifier for each individual. There should be no duplicated entries.

Sex

A numeric value where 0 indicates female and 1 indicates male. Missing entries are not currently supported.

MotherID

A numeric value representing the unique identifier for an individual's mother.

FatherID

A numeric value representing the unique identifier for an individual's father.

isProband

A numeric value where 1 indicates the individual is a proband and 0 otherwise.

CurAge

A numeric value indicating the age of censoring (current age if the person is alive or age at death if the person is deceased). Allowed ages range from 1 to 94.

isAff

A numeric value indicating the affection status of cancer, with 1 for diagnosed individuals and 0 otherwise. Missing entries are not supported.

Age

A numeric value indicating the age of cancer diagnosis, encoded as NA if the individual was not diagnosed. Allowed ages range from 1 to 94.

Geno

A column for germline testing or tumor marker testing results. Positive results should be coded as 1, negative results as 0, and unknown results as NA or left empty.

Source

Generated for package example

Processed Family Data

Description

A dataset containing processed information about the first simulated 130 families. These families are referenced in the vigniette simulation_study_real.Rmd The user must specify the pedigree argument as a data frame which contains the family data (see test_fam). The family data must be in the correct format with the following columns:

Usage

test_fam2

Format

A list of processed family data.

Details

ID

A numeric value representing the unique identifier for each individual. There should be no duplicated entries.

Sex

A numeric value where 0 indicates female and 1 indicates male. Missing entries are not currently supported.

MotherID

A numeric value representing the unique identifier for an individual's mother.

FatherID

A numeric value representing the unique identifier for an individual's father.

isProband

A numeric value where 1 indicates the individual is a proband and 0 otherwise.

CurAge

A numeric value indicating the age of censoring (current age if the person is alive or age at death if the person is deceased). Allowed ages range from 1 to 94.

isAff

A numeric value indicating the affection status of cancer, with 1 for diagnosed individuals and 0 otherwise. Missing entries are not supported.

Age

A numeric value indicating the age of cancer diagnosis, encoded as NA if the individual was not diagnosed. Allowed ages range from 1 to 94.

Geno

A column for germline testing or tumor marker testing results. Positive results should be coded as 1, negative results as 0, and unknown results as NA or left empty.

Source

Generated for package example

Transform Data Frame

Description

This function transforms a data frame from the standard format used in PanelPRO into the required format which conforms to the requirements of penetrance (and clipp).

Usage

transformDF(df)

Arguments

Value

A data frame in the format required for clipp with the following columns:

Examples

# Create example data frame
df <- data.frame(
  ID = 1:2,
  PedigreeID = c(1,1),
  Sex = c(0,1),
  MotherID = c(NA,1),
  FatherID = c(NA,NA),
  isProband = c(1,0),
  CurAge = c(45,20),
  isAff = c(1,0),
  Age = c(40,NA),
  Geno = c(1,0)
)

# Transform the data frame
transformed_df <- transformDF(df)

Validate Weibull Parameters

Description

This function validates the given parameters for calculating Weibull distribution.

Usage

validate_weibull_parameters(
  given_first_quartile,
  given_median,
  threshold,
  asymptote
)

Arguments

Value

Logical value indicating whether the parameters are valid (TRUE) or not (FALSE)

Examples

# Validate parameters
is_valid <- validate_weibull_parameters(
  given_first_quartile = 30,
  given_median = 50,
  threshold = 15,
  asymptote = 0.8
)
print(is_valid)