Related Experiment Video
Updated: Oct 8, 2025
06:55
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
14.7K
Handling Missing Data and Drop Out in Hospice/Palliative Care Trials Through the Estimand Framework
Anneke C Grobler1, Katherine J Lee1, Aaron Wong2
1Department of Paediatrics , University of Melbourne (A.C.G., K.J.L.); Murdoch Children's Research Institute (A.C.G., K.J.L.), Melbourne, Australia.
Journal of Pain and Symptom Management
|December 26, 2021
Summary
The estimand framework, from the International Council for Harmonization (ICH), offers a structured approach to managing missing data in palliative care trials. This method ensures clearer trial objectives and analysis, reducing bias in treatment effect estimates.
Area of Science:
- Clinical Trials
- Biostatistics
- Palliative Care Research
Background:
- Missing data are prevalent in hospice and palliative care randomized trials, often due to patient dropout.
- This missingness can introduce bias into treatment effect estimates and reduce precision.
Purpose of the Study:
- To demonstrate the application of the estimand framework for handling missing data in palliative care trials.
- To align trial objectives, conduct, analysis, and interpretation using the estimand approach.
Main Methods:
- Outlined the five elements of the estimand framework: treatment, population, variable, summary measure, and intercurrent event handling.
- Identified common intercurrent events in palliative care and presented five ICH-guided strategies for their management.
Main Results:
- Discussed and justified analytical strategies for common intercurrent events in palliative care.
- Provided a practical example using a palliative care trial comparing opioid treatments for cancer pain.
Conclusions:
- Explicitly stating the estimand, including intercurrent event handling, is crucial during palliative care trial planning.
- The estimand framework guides missing data management throughout the trial and requires a multidisciplinary approach.
Keywords:
EstimandICH E9 addendumMissing dataintercurrent eventrandomised controlled clinical trial designwithdrawalMore Related Videos
Related Concept Videos
01:09Censoring Survival Data
274
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
274
01:24Kaplan-Meier Approach
296
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,...
296
01:09Truncation in Survival Analysis
341
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
341
01:15Assumptions of Survival Analysis
215
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.
215
01:20Comparing the Survival Analysis of Two or More Groups
336
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...
336
01:14Parametric Survival Analysis: Weibull and Exponential Methods
691
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
691