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Related Concept Videos

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...
Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
Pharmacokinetics in Pediatric Patients: Drug Excretion01:26

Pharmacokinetics in Pediatric Patients: Drug Excretion

In pediatric medicine, understanding the renal function and drug elimination nuances is crucial for administering safe and effective treatments. Newborns, in particular, display markedly slower renal functions than adults, profoundly affecting how drugs are cleared from their bodies. This slower drug clearance requires clinicians to extend the dosing intervals for many medications to prevent drug accumulation and toxicity while ensuring therapeutic efficacy.One key area where these adjustments...
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...

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Related Experiment Video

Updated: Jul 2, 2026

Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia
08:31

Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia

Published on: October 17, 2025

Relapse Thresholds (12/24 Mo) Define Survival Disparity in Pediatric B-ALL.

Binjun Xiong1,2, Jianwen Zhou3, Xuhan Zhang4

  • 1Precision Oncology and Intelligent Theranostics Laboratory, Children's Hospital of Chongqing Medical University, Chongqing, China.

American Journal of Hematology
|July 1, 2026
PubMed
Summary

Progression of disease within 12 months (POD12) and 24 months (POD24) are critical indicators for poor survival in pediatric B-cell acute lymphoblastic leukemia (B-ALL). These data-driven landmarks help identify high-risk patients early.

Keywords:
B‐cell acute lymphoblastic leukemia (B‐ALL)pediatricprogression of disease (POD)relapse prognosisthresholds

Related Experiment Videos

Last Updated: Jul 2, 2026

Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia
08:31

Murine Model of Leukemia Relapse to Induction Chemotherapy for Acute Lymphoblastic Leukemia

Published on: October 17, 2025

Area of Science:

  • Pediatric Oncology
  • Hematologic Malignancies
  • Clinical Trial Design

Background:

  • Pediatric B-cell acute lymphoblastic leukemia (B-ALL) relapse significantly impacts patient survival.
  • Accurate prognostic markers are crucial for risk stratification and treatment optimization in B-ALL.
  • Existing endpoints may not adequately capture early disease progression and its survival implications.

Purpose of the Study:

  • To systematically analyze relapse patterns in pediatric B-ALL to define clinically relevant prognostic thresholds.
  • To establish practical, data-driven landmarks for identifying patients with adverse survival outcomes.
  • To evaluate the potential of early progression landmarks as candidate endpoints for clinical trials.

Main Methods:

  • Analysis of relapse patterns in over 5,800 pediatric B-ALL patients from TARGET, MP2PRT, and four external validation cohorts.
  • Monthly landmark-based Cox proportional hazards analyses to identify peak hazard ratios and statistical significance.
  • Selection and validation of Progression of Disease within 12 months (POD12) and 24 months (POD24) as key prognostic landmarks.

Main Results:

  • POD12 and POD24 were identified as clinically practical landmarks, with POD12 occurring in 2.56% and POD24 in 8.67% of patients in the primary cohorts.
  • Patients experiencing POD12 had a 5-year overall survival (OS) of 11.13%, versus 90.89% for non-POD12 patients.
  • Patients experiencing POD24 had a 5-year OS of 36.19%, versus 93.62% for non-POD24 patients, consistent across validation cohorts.
  • E2A-PBX1 and MLL rearrangements were associated with an increased risk of POD12 and POD24.

Conclusions:

  • POD12 and POD24 serve as robust, clinically practical landmarks for identifying pediatric B-ALL patients with significantly inferior survival.
  • These landmarks provide valuable prognostic information, aiding in risk stratification.
  • POD12 and POD24 may serve as hypothesis-generating candidate early trial endpoints for progression-free survival in future pediatric B-ALL studies.