Related Experiment Video
Updated: Jun 19, 2026
05:10
Multidisciplinary Approach to Obesity Management: A Case Report
Published on: May 30, 2025
Interpreting weight losses from lifestyle modification trials: using categorical data.
J G Christian1, A G Tsai, D H Bessesen
1PHCC LP, 421 N Main Street, Suite 217, Pueblo, CO 81003, USA. jim@phcclp.com
International Journal of Obesity (2005)
|October 14, 2009
Summary
Many individuals achieve clinically significant weight loss through lifestyle modification interventions, even when average weight loss is modest. This highlights the effectiveness of these programs for obesity management.
Area of Science:
- Obesity research
- Lifestyle interventions
- Weight management
Background:
- Studies on weight loss medications often report mean and categorical data.
- Diet and exercise intervention trials typically report only mean weight change and significance.
- Current data reporting practices hinder the assessment of individual weight loss success probabilities.
Purpose of the Study:
- To determine the fraction of subjects achieving clinically significant weight loss in lifestyle modification interventions.
- To provide data for clinicians and administrators to better select participants for interventions.
Main Methods:
- Requested categorical weight loss data from published high- and low-intensity lifestyle modification intervention studies.
- Analyzed the distribution of weight loss outcomes beyond simple mean change.
Main Results:
- A substantial fraction of subjects achieved clinically meaningful weight loss across different lifestyle modification interventions.
- Categorical data revealed significant individual success rates, even with modest average weight reduction.
Conclusions:
- Lifestyle modification interventions yield clinically meaningful weight loss in a significant proportion of participants.
- Categorical outcome data is crucial for understanding the real-world effectiveness of weight loss programs.
More Related Videos
06:55Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
03:05Influence of Emotional Factors on the Efficacy of Acupuncture Treatment for Overweight Complicated with Hyperlipidemia: A Retrospective Cohort Study
Published on: November 21, 2025
Related Concept Videos
01:09Truncation in Survival Analysis
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 observed.
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 observed.
01:20Comparing 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...
01:52Regression Toward the Mean
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
01:09Censoring Survival Data
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 reasons...
01:25Statistical Methods for Analyzing Epidemiological Data
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
01:19The Mantel-Cox Log-Rank Test
The Mantel-Cox log-rank test is a widely used statistical method for comparing the survival distributions of two groups. It tests whether a statistically significant difference exists in survival times between the groups without assuming a specific distribution for the survival data, making it a non-parametric test. This flexibility makes the log-rank test particularly valuable in medical research and other fields where the timing of an event, such as death or disease recurrence, is of interest.