Table 1.
Summary of Challenges to Evidence Generation for People with Multimorbidity
| 1. Definition and Measurement of Multimorbidity Status | |
| There are several distinct concepts of multimorbidity: specific disease patterns, latent health status, or comorbidity. | |
| Multimorbidity can be measured several ways: on presence of diseases alone or also using disease severity; conditions; measures of function; treatment or utilization. | |
| Multimorbidity status or severity may be ascertained in various ways: self report, physician report, clinical examination, administrative, pharmacy, lab data, other. | |
| Measurement of multimorbidity status may not be done comparably across studies and across research and clinical settings. | |
| 2. Multimorbidity-related Effects on Study Design and Implementation | |
| Internal validity | |
| Multimorbidity may lead to misclassification of treatment or inaccurate measure of treatment intensity (e.g. initiation—identification of ‘time zero’, duration, dosage). | |
| Multimorbidity may affect selection of treatments or treatment intensity. | |
| Multimorbidity may lead to confounding and interactions due to concomitant treatments. | |
| Multimorbidity may lead to misdiagnosis of outcomes. | |
| Multimorbidity may increase the likelihood of losses to follow-up. | |
| External validity | |
| Multimorbidity poses challenges for selection of participants from the at-risk population, especially recruitment. | |
| Multimorbidity may affect adherence to treatment. | |
| Multimorbidity may alter real-world effectiveness of treatments due to harms and competing risks. | |
| 3. Heterogeneity of Treatment Effect | |
| People with multimorbidity are frequently excluded from trials—sometimes appropriately, sometimes inappropriately. | |
| Even when people with multimorbidity are included in trials, summary treatment effects may not apply. | |
| Multidimensionality is a fundamental problem when considering multimorbidity, as the number of potential multimorbidity-related questions are too large to be answered in any given clinical trial/trials. | |
| A multiplicity of outcomes may be important when patients with multimorbidity are enrolled. | |
| Subgroup analyses frequently yield false positive or false negative results due to multiple comparisons or low statistical power. | |
| Multimorbidity-related multi dimensionality makes it challenging to pre-specify a small number of clinically important hypotheses related to subgroups and outcomes. |