Mapping Studies to Estimate Health-State Utilities From Non–Preference-Based Outcome Measures: A Systematic Review on How Repeated Measurements are Taken Into Account

Published:November 10, 2022DOI:


      • Authors often use data sets with repeated measurements per individual over time to develop their mapping algorithms. Nevertheless, our review suggests that many authors do not account for the interdependence of data or do not clearly describe the methods used.
      • This updated systematic review of mapping articles found that, when data sets with repeated measurements are used, authors often only use 1 time point in the estimation data set and another time point for its validation, hence ignoring that health states with different levels of severity may be present only at a specific time point.
      • When multiple time points are used, often the methodological implications are not explicitly considered; this means that variance-covariance matrices from these models—often used in probabilistic sensitivity analyses—are not accurate. Mapping guidelines should expand on how to deal with repeated measurements, and journals should make sure that these updated guidelines are followed.



      Mapping algorithms are developed using data sets containing patient responses to a preference-based questionnaire and another health-related quality-of-life questionnaire. When data sets include repeated measurements from the same individuals over time, the assumption of observations’ independence, required by standard models, is violated, and standard errors are underestimated. This review aimed to identify how studies deal with methodological challenges of repeated measurements, provide an overview of practice to date, and potential implications for future work.


      We conducted a systematic literature search of MEDLINE, Cumulative Index to Nursing and Allied Health Literature, specialized databases, and previous systematic reviews. A data template was used to extract, among others, start and target instruments if the data set(s) used for estimation and validation had repeated measurements per patient, used regression techniques, and which (if any) adjustments were made for repeated measurements.


      We identified 278 publications developing at least 1 mapping algorithm. Of the 278 publications, 121 used a data set with repeated measurements, among which 92 used multiple time points for estimation, and 39 selected specific time points to have 1 observation per participant. A total of 36 studies did not account for repeated measurements. An adjustment was conducted using cluster-robust standard errors (21), random-effects models (30), generalized estimating equations (7), and other methods (7).


      The inconsistent use of methods to account for interdependent observations in the literature indicates that mapping guidelines should include recommendations on how to deal with repeated measurements, and journals should update their guidelines accordingly.


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