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Longitudinal Research02:20

Longitudinal Research

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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...
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Small population sizes put a species at extreme risk of extinction due to a lack of variation, and a consequent decrease in adaptability. This weakens the chances of survival under pressures such as climate change, competition from other species, or new diseases. Large populations are more likely to survive pressures such as these, as such populations are more likely to harbor individuals that have genetic variants that are adaptive under new stresses. Small populations are much less...
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Longitudinal Studies01:26

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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...
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How to Analyze Longitudinal Patient-Reported Outcomes in Populations With High Mortality Rates.

Joosje C Baltussen1, Nienke A de Glas1,2, Saskia le Cessie3

  • 1Department of Medical Oncology, Leiden University Medical Center, Leiden, the Netherlands.

Journal of the American Geriatrics Society
|February 5, 2026
PubMed
Summary

Analyzing patient-reported outcomes (PROs) in cancer patients requires careful method selection, especially when accounting for mortality. Different analytical approaches yield varied results, impacting clinical interpretations of quality of life (QoL).

Keywords:
geriatric oncologymethodologyolder adultspatient‐reported outcomequality of life

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Area of Science:

  • Geriatric Oncology
  • Health Outcomes Research
  • Biostatistics

Background:

  • Longitudinal patient-reported outcomes (PROs) analysis is challenging in high-mortality populations.
  • Overlooking mortality can lead to biased estimates and reduced clinical interpretability of PRO data.
  • This study addresses methods for analyzing PROs in patients who die during follow-up.

Purpose of the Study:

  • To discuss and compare methods for analyzing longitudinal PROs in cancer patients with high mortality.
  • To examine how different analytical methods affect the interpretation of quality of life (QoL) outcomes.
  • To provide a clinical example using older cancer patients.

Main Methods:

  • Included adults aged ≥70 years with cancer from a prospective cohort.
  • Assessed Quality of Life (QoL) using the EuroQoL 5-Dimension (EQ-5D) tool at baseline, 6, and 12 months.
  • Applied six methods: four for absolute changes in mean scores, two for proportions of patients with changed QoL.

Main Results:

  • Of 1218 participants, 321 (26%) died within 12 months.
  • Methods focusing only on survivors lead to biased and difficult-to-interpret results.
  • Alternative methods (e.g., 'while-alive' strategy, composite endpoints) provide more complete QoL views.

Conclusions:

  • The choice of method significantly influences the interpretation of longitudinal PROs and associated conclusions.
  • Addressing mortality is crucial for accurate estimands and meaningful clinical interpretations.
  • Selecting appropriate methods based on the research question is essential for clinicians and patients.