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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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,...

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Time-Scale Target Parameters and Two-Step Estimation in Longitudinal Trials for Progressive Diseases.

Florian Stijven1, Craig Mallinckrodt2, Geert Molenberghs1,3

  • 1I-BioStat, KU Leuven, Leuven, Belgium.

Statistics in Medicine
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

New methods quantify Alzheimer's treatment effects on a time scale, showing benefits like "time saved." This helps interpret clinical trial results for progressive diseases, aiding early intervention strategies.

Keywords:
Alzheimer's diseaselongitudinal dataprogressive diseasestwo‐step estimator

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Neurodegenerative Disease Research

Background:

  • Progressive diseases like Alzheimer's necessitate early treatment intervention to maintain patient function.
  • Current clinical trial analyses often use mean differences at fixed time points, which may obscure early treatment benefits.
  • Interpreting the clinical relevance of small mean differences in early disease stages is challenging.

Purpose of the Study:

  • To introduce novel target parameters for quantifying treatment effects on a time scale in longitudinal studies.
  • To provide methods for estimating these parameters, applicable to both randomized and non-randomized treatment scenarios.
  • To enhance the interpretation of treatment efficacy in progressive neurological disorders.

Main Methods:

  • Development of target parameters measuring treatment effects in terms of time saved or percentage slowing of disease progression.
  • Proposal of general two-step estimators for these parameters, utilizing standard longitudinal data analysis methods in the first step.
  • Implementation of the second step for inference in the TCT R package, followed by asymptotic property analysis and simulation studies.

Main Results:

  • The proposed two-step estimators provide a robust framework for analyzing time-scale treatment effects.
  • The methodology was successfully applied to a phase 2/3 clinical trial for Alzheimer's disease.
  • The analysis yielded significant additional insights into the treatment effect beyond traditional metrics.

Conclusions:

  • The novel time-scale parameters offer a more clinically relevant measure of treatment efficacy in progressive diseases.
  • The proposed estimation methods are versatile and can be implemented using existing statistical software.
  • This approach improves the understanding and communication of treatment benefits, particularly for early-stage interventions in Alzheimer's disease.