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Quality of Life Research

Gepubliceerd in:

19-01-2024

Quantifying bias due to missing data in quality of life surveys of advanced-stage cancer patients

Auteurs: Nina Haug, Martina Jänicke, Benjamin Kasenda, Norbert Marschner, Melanie Frank

Gepubliceerd in: Quality of Life Research | Uitgave 4/2024

Abstract

Purpose

Many studies on cancer patients investigate the impact of treatment on health-related quality of life (QoL). Typically, QoL is measured longitudinally, at baseline and at predefined timepoints thereafter. The question is whether, at a given timepoint, patients who return their questionnaire (available cases, AC) have a different QoL than those who do not return their questionnaire (non-AC).

Methods

We employed augmented inverse probability weighting (AIPW) to estimate the average QoL of non-AC in two studies on advanced-stage cancer patients. The AIPW estimator assumed data to be missing at random (MAR) and used machine learning (ML)-based methods to estimate answering probabilities of individuals at given timepoints as well as their reported QoL, as a function of auxiliary variables. These auxiliary variables were selected by medical oncologists based on domain expertise. We aggregated results both by timepoint and by time until death and compared AIPW estimates to the AC averages. Additionally, we used a pattern mixture model (PMM) to check sensitivity of our AIPW estimates against violation of the MAR assumption.

Results

Our study included 1927 patients with advanced pancreatic and 797 patients with advanced breast cancer. The AIPW estimate for average QoL of non-AC was below the average QoL of AC when aggregated by timepoint. The difference vanished when aggregated by time until death. PMM estimates were below AIPW estimates.

Conclusions

Our results indicate that non-AC have a lower average QoL than AC. However, estimates for QoL of non-AC are subject to unverifiable assumptions about the missingness mechanism.

vorige artikel Administering selected subscales of patient-reported outcome questionnaires to reduce patient burden and increase relevance: a position statement on a modular approach

volgende artikel Assessing health-related quality of life and clinical outcomes of patients with diabetes accessing healthcare in two public hospitals in south-eastern Ghana: a cross-sectional descriptive study

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Titel: Quantifying bias due to missing data in quality of life surveys of advanced-stage cancer patients
Auteurs: Nina Haug
Martina Jänicke
Benjamin Kasenda
Norbert Marschner
Melanie Frank
Publicatiedatum: 19-01-2024
Uitgeverij: Springer International Publishing
Gepubliceerd in: Quality of Life Research / Uitgave 4/2024
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-023-03588-7

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Abstract

Purpose

Methods

Results

Conclusions

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