diff --git a/src/lib.rs b/src/lib.rs
index 0c6eb20..73933ed 100644
--- a/src/lib.rs
+++ b/src/lib.rs
@@ -1,4 +1,5 @@
pub mod intcox;
+pub mod turnbull;
mod pava;
mod term;
diff --git a/src/main.rs b/src/main.rs
index 4938ac9..40b1f67 100644
--- a/src/main.rs
+++ b/src/main.rs
@@ -17,6 +17,7 @@
use clap::{Parser, Subcommand};
use hpstat::intcox;
+use hpstat::turnbull;
#[derive(Parser)]
#[command(about="High-performance statistics implementations")]
@@ -29,6 +30,9 @@ struct MainArgs {
enum Command {
#[command(name="intcox", about="Interval-censored Cox regression", long_about="Fit a Cox proportional hazards model on time-independent interval-censored observations")]
IntCox(intcox::IntCoxArgs),
+
+ #[command(name="turnbull", about="Interval-censored Turnbull survival estimation", long_about="Fit a Turnbull survival estimator on interval-censored observations")]
+ Turnbull(turnbull::TurnbullArgs),
}
fn main() {
@@ -36,5 +40,6 @@ fn main() {
match args.command {
Command::IntCox(intcox_args) => intcox::main(intcox_args),
+ Command::Turnbull(turnbull_args) => turnbull::main(turnbull_args),
}
}
diff --git a/src/turnbull.rs b/src/turnbull.rs
new file mode 100644
index 0000000..6d6ac67
--- /dev/null
+++ b/src/turnbull.rs
@@ -0,0 +1,307 @@
+// hpstat: High-performance statistics implementations
+// Copyright © 2023 Lee Yingtong Li (RunasSudo)
+//
+// This program is free software: you can redistribute it and/or modify
+// it under the terms of the GNU Affero General Public License as published by
+// the Free Software Foundation, either version 3 of the License, or
+// (at your option) any later version.
+//
+// This program is distributed in the hope that it will be useful,
+// but WITHOUT ANY WARRANTY; without even the implied warranty of
+// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+// GNU Affero General Public License for more details.
+//
+// You should have received a copy of the GNU Affero General Public License
+// along with this program. If not, see .
+
+const Z_97_5: f64 = 1.959964; // This is the limit of resolution for an f64
+
+use core::mem::MaybeUninit;
+use std::io;
+
+use clap::{Args, ValueEnum};
+use csv::{Reader, StringRecord};
+use indicatif::{ProgressBar, ProgressDrawTarget, ProgressIterator, ProgressStyle};
+use nalgebra::{Const, DMatrix, DVector, Dyn, MatrixXx2};
+use prettytable::{Table, format, row};
+use serde::{Serialize, Deserialize};
+
+use crate::term::UnconditionalTermLike;
+
+#[derive(Args)]
+pub struct TurnbullArgs {
+ /// Path to CSV input file containing the observations
+ #[arg()]
+ input: String,
+
+ /// Output format
+ #[arg(long, value_enum, default_value="text")]
+ output: OutputFormat,
+
+ /// Maximum number of iterations to attempt
+ #[arg(long, default_value="1000")]
+ max_iterations: u32,
+
+ /// Terminate algorithm when the absolute change in failure probability in each interval is less than this tolerance
+ #[arg(long, default_value="0.0001")]
+ fail_prob_tolerance: f64,
+}
+
+#[derive(ValueEnum, Clone)]
+enum OutputFormat {
+ Text,
+ Json
+}
+
+pub fn main(args: TurnbullArgs) {
+ // Read data
+ let data_times = read_data(&args.input);
+
+ // Fit regression
+ let progress_bar = ProgressBar::with_draw_target(Some(0), ProgressDrawTarget::term_like(Box::new(UnconditionalTermLike::stderr())));
+ let result = fit_turnbull(data_times, progress_bar, args.max_iterations, args.fail_prob_tolerance);
+
+ // Display output
+ match args.output {
+ OutputFormat::Text => {
+ println!();
+ println!();
+
+ let mut summary = Table::new();
+ let format = format::FormatBuilder::new()
+ .separators(&[format::LinePosition::Top, format::LinePosition::Title, format::LinePosition::Bottom], format::LineSeparator::new('-', '+', '+', '+'))
+ .padding(2, 2)
+ .build();
+ summary.set_format(format);
+
+ summary.set_titles(row!["Time", c->"Surv. Prob.", c->"Std Err.", H2c->"(95% CI)"]);
+ summary.add_row(row![r->"0.000", r->"1.00000", "", "", ""]);
+ for (i, prob) in result.survival_prob.iter().enumerate() {
+ summary.add_row(row![
+ r->format!("{:.3}", result.failure_intervals[i].1),
+ r->format!("{:.5}", prob),
+ r->format!("{:.5}", result.survival_prob_se[i]),
+ r->format!("({:.5},", prob - Z_97_5 * result.survival_prob_se[i]),
+ format!("{:.5})", prob + Z_97_5 * result.survival_prob_se[i]),
+ ]);
+ }
+ summary.add_row(row![r->format!("{:.3}", result.failure_intervals.last().unwrap().1), r->"0.00000", "", "", ""]);
+ summary.printstd();
+ }
+ OutputFormat::Json => {
+ println!("{}", serde_json::to_string(&result).unwrap());
+ }
+ }
+}
+
+pub fn read_data(path: &str) -> MatrixXx2 {
+ // Read CSV into memory
+ let _headers: StringRecord;
+ let records: Vec;
+ if path == "-" {
+ let mut csv_reader = Reader::from_reader(io::stdin());
+ _headers = csv_reader.headers().unwrap().clone();
+ records = csv_reader.records().map(|r| r.unwrap()).collect();
+ } else {
+ let mut csv_reader = Reader::from_path(path).unwrap();
+ _headers = csv_reader.headers().unwrap().clone();
+ records = csv_reader.records().map(|r| r.unwrap()).collect();
+ }
+
+ // Read data into matrices
+
+ let mut data_times: MatrixXx2> = MatrixXx2::uninit(
+ Dyn(records.len()),
+ Const::<2> // Left time, right time
+ );
+
+ // Parse data
+ for (i, row) in records.iter().enumerate() {
+ for (j, item) in row.iter().enumerate() {
+ let value = match item {
+ "inf" => f64::INFINITY,
+ _ => item.parse().expect("Malformed float")
+ };
+
+ data_times[(i, j)].write(value);
+ }
+ }
+
+ // TODO: Fail on left time > right time
+ // TODO: Fail on left time < 0
+
+ // SAFETY: assume_init is OK because we initialised all values above
+ unsafe {
+ return data_times.assume_init();
+ }
+}
+
+struct TurnbullData {
+ data_time_interval_indexes: Vec<(usize, usize)>,
+
+ // Cached intermediate values
+ intervals: Vec<(f64, f64)>,
+}
+
+impl TurnbullData {
+ fn num_obs(&self) -> usize {
+ return self.data_time_interval_indexes.len();
+ }
+
+ fn num_intervals(&self) -> usize {
+ return self.intervals.len();
+ }
+}
+
+pub fn fit_turnbull(data_times: MatrixXx2, progress_bar: ProgressBar, max_iterations: u32, fail_prob_tolerance: f64) -> TurnbullResult {
+ // ----------------------
+ // Prepare for regression
+
+ // Get Turnbull intervals
+ let mut all_time_points: Vec<(f64, bool)> = Vec::new(); // Vec of (time, is_left)
+ all_time_points.extend(data_times.column(1).iter().map(|t| (*t, false))); // So we have right bounds before left bounds when sorted - ensures correct behaviour since intervals are left-open
+ all_time_points.extend(data_times.column(0).iter().map(|t| (*t, true)));
+ all_time_points.dedup();
+ all_time_points.sort_by(|(t1, _), (t2, _)| t1.partial_cmp(t2).unwrap());
+
+ let mut intervals: Vec<(f64, f64)> = Vec::new();
+ for i in 1..all_time_points.len() {
+ if all_time_points[i - 1].1 == true && all_time_points[i].1 == false {
+ intervals.push((all_time_points[i - 1].0, all_time_points[i].0));
+ }
+ }
+
+ // Recode times as indexes
+ let data_time_interval_indexes: Vec<(usize, usize)> = data_times.row_iter().map(|t| {
+ let tleft = t[0];
+ let tright = t[1];
+
+ // Left index is first interval >= observation left bound
+ let left_index = intervals.iter().enumerate().find(|(_i, (ileft, _))| *ileft >= tleft).unwrap().0;
+
+ // Right index is last interval <= observation right bound
+ let right_index = intervals.iter().enumerate().rev().find(|(_i, (_, iright))| *iright <= tright).unwrap().0;
+
+ (left_index, right_index)
+ }).collect();
+
+ // Initialise s
+ let mut s = DVector::repeat(intervals.len(), 1.0 / intervals.len() as f64);
+
+ let data = TurnbullData {
+ data_time_interval_indexes: data_time_interval_indexes,
+ intervals: intervals,
+ };
+
+ // ------------------------------------------
+ // Apply iterative algorithm to fit estimator
+
+ progress_bar.set_style(ProgressStyle::with_template("[{elapsed_precise}] {bar:40} {msg}").unwrap());
+ progress_bar.set_length(u64::MAX);
+ progress_bar.reset();
+ progress_bar.println("Running iterative algorithm to fit Turnbull estimator");
+
+ let mut iteration = 1;
+ loop {
+ // Get total failure probability for each observation (denominator of μ_ij)
+ let sum_fail_prob = DVector::from_iterator(
+ data.num_obs(),
+ data.data_time_interval_indexes
+ .iter()
+ .map(|(idx_left, idx_right)| s.view((*idx_left, 0), (*idx_right - *idx_left + 1, 1)).sum())
+ );
+
+ // Compute π_j
+ let mut pi: DVector = DVector::zeros(data.num_intervals());
+ for (i, (idx_left, idx_right)) in data.data_time_interval_indexes.iter().enumerate() {
+ for j in *idx_left..(*idx_right + 1) {
+ pi[j] += s[j] / sum_fail_prob[i] / data.num_obs() as f64;
+ }
+ }
+
+ let largest_delta_s = s.iter().zip(pi.iter()).map(|(x, y)| (y - x).abs()).max_by(|a, b| a.partial_cmp(b).unwrap()).unwrap();
+
+ let converged = largest_delta_s <= fail_prob_tolerance;
+
+ s = pi;
+
+ // Estimate progress bar according to either the order of magnitude of the largest_delta_s relative to tolerance, or iteration/max_iterations
+ let progress2 = (iteration as f64 / max_iterations as f64 * u64::MAX as f64) as u64;
+ let progress3 = ((-largest_delta_s.log10()).max(0.0) / -fail_prob_tolerance.log10() * u64::MAX as f64) as u64;
+
+ // Update progress bar
+ progress_bar.set_position(progress_bar.position().max(progress3.max(progress2)));
+ progress_bar.set_message(format!("Iteration {} (max Δs = {:.4})", iteration + 1, largest_delta_s));
+
+ if converged {
+ progress_bar.println(format!("Converged in {} iterations", iteration));
+ break;
+ }
+
+ iteration += 1;
+ if iteration > max_iterations {
+ panic!("Exceeded --max-iterations");
+ }
+ }
+
+ // Get survival probabilities (1 - cumulative failure probability), excluding at t=0 (prob=1) and t=inf (prob=0)
+ let mut survival_prob: Vec = Vec::with_capacity(data.num_intervals() - 1);
+ let mut acc = 1.0;
+ for j in 0..(data.num_intervals() - 1) {
+ acc -= s[j];
+ survival_prob.push(acc);
+ }
+
+ // --------------------------------------------------
+ // Compute standard errors for survival probabilities
+
+ progress_bar.set_style(ProgressStyle::with_template("[{elapsed_precise}] {bar:40} Compute Hessian {pos}/{len}").unwrap());
+ progress_bar.set_length(data.num_obs() as u64);
+ progress_bar.reset();
+ progress_bar.println("Computing standard errors for survival probabilities");
+
+ let mut hessian: DMatrix = DMatrix::zeros(data.num_intervals() - 1, data.num_intervals() - 1);
+
+ for (idx_left, idx_right) in data.data_time_interval_indexes.iter().progress_with(progress_bar.clone()) {
+ let mut hessian_denominator = s.view((*idx_left, 0), (*idx_right - *idx_left + 1, 1)).sum();
+ hessian_denominator = hessian_denominator.powi(2);
+
+ let idx_start = if *idx_left > 0 { *idx_left - 1 } else { 0 }; // To cover the h+1 case
+ let idx_end = (*idx_right + 1).min(data.num_intervals() - 1); // Go up to and including idx_right but don't go beyond hessian
+
+ for h in idx_start..idx_end {
+ let i_h = if h >= *idx_left && h <= *idx_right { 1.0 } else { 0.0 };
+ let i_h1 = if h + 1 >= *idx_left && h + 1 <= *idx_right { 1.0 } else { 0.0 };
+
+ hessian[(h, h)] -= (i_h - i_h1) * (i_h - i_h1) / hessian_denominator;
+
+ for k in idx_start..h {
+ let i_k = if k >= *idx_left && k <= *idx_right { 1.0 } else { 0.0 };
+ let i_k1 = if k + 1 >= *idx_left && k + 1 <= *idx_right { 1.0 } else { 0.0 };
+
+ let value = (i_h - i_h1) * (i_k - i_k1) / hessian_denominator;
+ hessian[(h, k)] -= value;
+ hessian[(k, h)] -= value;
+ }
+ }
+ }
+ progress_bar.finish();
+
+ let vcov = (-hessian).try_inverse().expect("Matrix not invertible");
+ let survival_prob_se = vcov.diagonal().apply_into(|x| { *x = x.sqrt(); });
+
+ return TurnbullResult {
+ failure_intervals: data.intervals,
+ failure_prob: s.data.as_vec().clone(),
+ survival_prob: survival_prob,
+ survival_prob_se: survival_prob_se.data.as_vec().clone(),
+ };
+}
+
+#[derive(Serialize, Deserialize)]
+pub struct TurnbullResult {
+ pub failure_intervals: Vec<(f64, f64)>,
+ pub failure_prob: Vec,
+ pub survival_prob: Vec,
+ pub survival_prob_se: Vec,
+}