How do HEOR research deal with lacking information? – Healthcare Economist


That’s the questioned answered in a paper by Mukherjee et al. (2023). The authors outline an “HEOR research” for this paper as

…real-world proof research that carried out a secondary/post-hoc evaluation utilizing randomized
managed trial (RCT) information, and a within-trial cost-utility evaluation wherein the result of curiosity was prices or PROs together with preference-based utilities (e.g., EQ-5D).

Essentially the most applicable method for imputing lacking information relies on the assumptions about how the info are lacking:

  • Lacking fully at random (MCAR): the noticed or unobserved values of all variables in a research should not have any affect on the likelihood of an commentary being lacking
  • Lacking at random (MAR). The likelihood of lacking information for a selected variable is related to the noticed values of variables (both noticed values of different variables within the dataset or noticed values for a similar variable at earlier timepoints) within the dataset, however not upon the lacking information. One can not check for whether or not MAR holds in a dataset.
  • Lacking Not at Random (MNAR). On this case, the likelihood of lacking information for a selected variable is said to the underlying worth of that particular variable. MNAR could be ignorable (when lacking values happen independently of the info assortment course of) or non-ignorable (when there’s a structural trigger to the missingness mechanism that relies on unobserved variables or the lacking worth itself).

To deal with the lacking information, varied strategies can be found together with: complete-case evaluation (CCA), available-case (AC) evaluation, a number of imputation (MI), a number of imputation by chained equation (MICE), and predictive imply matching.

To raised perceive which approaches are generally utilized in well being economics and outcomes analysis (HEOR), the authors carried out a scientific literature evaluate in PubMed and examined what sort of statistical strategies had been used to handle lacking value, utility or patient-reported end result measures.

The authors discovered that a number of imputation, a number of imputation by chained equation and complete-case analyses had been mostly used:

From 1433 recognized data, 40 papers had been included. 13 research had been financial evaluations. Thirty research used a number of imputation with 17 research utilizing a number of imputation by chained equation, whereas 15 research used a complete-case evaluation. Seventeen research addressed lacking value information and 23 research handled lacking end result information. Eleven research reported a single technique whereas 20 research used a number of strategies to handle lacking information.

The authors be aware that whereas they discovered a considerable amount of HEOR methodological literature on how one can deal with lacking information in a RCT context; nonetheless, there have been only a few research which have tried to really implement these suggestions and impute the lacking information. You’ll be able to learn the complete article right here.


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