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Related Concept Videos

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:
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...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical 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:
Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
Regression Toward the Mean01:52

Regression 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...
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.

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Related Experiment Video

Updated: Jun 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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

Health dynamics and reporting bias at retirement: An analysis using high-frequency data.

Jiayi Wen1, Zixi Ye2, Xuan Zhang3

  • 1Center for Macroeconomic Research, Xiamen University, Fujian, China; School of Economics, Xiamen University, Fujian, China; Wang Yanan Institute of Studies in Economics (WISE), Xiamen University, Fujian, China.

Journal of Health Economics
|June 10, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to detect reporting bias in self-reported health (SRH) after retirement. Findings show no evidence of bias when analyzing short-term health changes, suggesting health is a stock, not flow.

Keywords:
Health dynamicsRetirementSelf-reported healthState-dependent reporting bias

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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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Published on: July 27, 2018

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Last Updated: Jun 12, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Area of Science:

  • Health Economics
  • Biostatistics
  • Sociology of Health

Background:

  • Subjective health measures are crucial for policy but susceptible to reporting bias.
  • Retirement is a significant life event that can trigger changes in self-reported health (SRH).
  • Understanding whether SRH changes reflect true health or reporting bias is vital for accurate policy evaluation.

Purpose of the Study:

  • To develop and apply a novel approach for identifying state-dependent reporting bias in SRH.
  • To differentiate between stock and flow outcomes in health to better interpret SRH changes.
  • To investigate the impact of retirement on SRH, distinguishing genuine health shifts from reporting biases.

Main Methods:

  • Differentiating health as a stock (enduring) versus flow (transient) based on established health theory.
  • Employing a regression discontinuity design-inspired strategy for robust identification.
  • Utilizing a unique high-frequency dataset capturing monthly health and retirement information.

Main Results:

  • Traditional long-term analyses suggest a decline in SRH post-retirement.
  • This apparent decline diminishes significantly when using a narrow observation window.
  • No evidence of state-dependent reporting bias was found when examining short-term health dynamics.

Conclusions:

  • Health should be conceptualized as a stock, implying that abrupt SRH shifts post-retirement are unlikely to reflect true health changes.
  • The distinction between stock and flow health outcomes is critical for accurate interpretation of SRH data in policy contexts.
  • The study highlights the importance of high-frequency data and specific analytical strategies to avoid misinterpreting reporting bias as genuine health dynamics.