Table 1.
Summary of model structural framework and the method of disease progression across identified chronic kidney disease and diabetes models
| N | Model structure | Model structure sub-category | CKD progression (as described by model publication) | References |
|---|---|---|---|---|
| CKD models (N = 13) | ||||
| 9 | Markov model | Multistate Markov model | Transition probabilities | [23] |
| Markov model | GFR decline | [24] | ||
| Semi-Markov model | Transition probabilities | [25] | ||
| Markov model | Estimated GFR decline, transition probabilities | [26] | ||
| Markov model | Transition probabilities | [27] | ||
| Markov model | Estimated GFR decline, transition probabilities | [28] | ||
| Markov model | Transition probabilities | [30] | ||
| Markov model | Transition probabilities | [29] | ||
| Markov model | Risk equations | [32] | ||
| 1 | Simulation model | Microsimulation model | Estimated GFR decline | [163] |
| 3 | Combination | Markov model and Monte Carlo simulation | Relative risk of progression | [36] |
| Decision tree and Markov model | Transition probabilities | [35] | ||
| Markov model and Monte Carlo simulation | GFR decline | [31] | ||
| Diabetes models (N = 48) | ||||
| 25 | Markov model | Markov model | Transition probabilities | [115] |
| Markov model | Transition probabilities | [118] | ||
| Markov model | Transition probabilities | [90] | ||
| Markov model | Transition probabilities | [79] | ||
| Markov model | Transition probabilities | [98] | ||
| Markov model | Transition probabilities | [116] | ||
| Markov model | Transition probabilities | [53] | ||
| Markov model | Creatinine clearance decline | [124] | ||
| Markov model | Transition probabilities | [92] | ||
| Markov model | Transition probabilities | [100] | ||
| Markov model | Transition probabilities | [89] | ||
| Markov model | Transition probabilities | [164] | ||
| Markov model | Transition probabilities | [85] | ||
| Markov model | Transition probabilities | [120] | ||
| Markov model | Transition probabilities | 105 | ||
| Markov model | Transition probabilities | [88] | ||
| Markov model | Transition probabilities | [110] | ||
| Markov model | HbA1c levels | [86] | ||
| Markov model | Transition probabilities | [102] | ||
| Markov model | Transition probabilities | [103] | ||
| Markov model | Risk equations | [104] | ||
| Markov model | Transition probabilities | [113] | ||
| Markov model | Transition probabilities | [74] | ||
| Semi-Markov model | Transition probabilities | [117] | ||
| Semi-Markov model | Transition probabilities | [107] | ||
| 13 | Simulation model | Discrete-event simulation model | Transition probabilities | [121] |
| Microsimulation model | Risk equations | [109] | ||
| Microsimulation model | Incidence rates | [91] | ||
| Discrete-event simulation model | Risk equations | [95] | ||
| Microsimulation model | Risk equations | [93] | ||
| Microsimulation model | Transition probabilities | [83] | ||
| Object-oriented simulation model | Risk equations | [112] | ||
| Microsimulation model | Risk equations | [99] | ||
| Discrete-event simulation model | Transition probabilities | [101] | ||
| Microsimulation model | Transition probabilities | [96] | ||
| Discrete-event simulation model | Transition probabilities | [84] | ||
| Monte Carlo simulation model | Transition probabilities | [87] | ||
| Microsimulation model | Not reported | [106] | ||
| 2 | Decision tree | Decision tree | Creatinine clearance decline | [96] |
| Decision tree | Probabilities | [82] | ||
| 8 | Combination | Markov model and Monte Carlo simulation | Transition probabilities | [94] |
| Markov model and Monte Carlo simulation | Transition probabilities | [119] | ||
| Markov model and microsimulation | Transition probabilities | [122] | ||
| Semi-Markov model and Monte Carlo simulation | Transition probabilities | [81] | ||
| Markov model and Monte Carlo simulation | Hazard rate (per year) | [97] | ||
| Markov model and Monte Carlo simulation | Risk equations | [114] | ||
| Markov model and Monte Carlo simulation | Transition probabilities | [80] | ||
| Decision tree and Markov model | Transition probabilities | [111] | ||
Decision tree: Defined as a cohort-level model that uses a tree-like model of decisions and their possible consequences, where transitions are limited to those specified by the particular nodes included in the decision tree. Markov model: A more fluid extension of the decision tree principle, defined as a type of cohort-based mathematical model containing a finite number of mutually exclusive health states, with time periods of uniform length, in which the probability of movement from one state to another depends on the current state. Semi-Markov model: As a Markov model but incorporating a time-dependency factor whereby transition rates are dependent on the time spent in a health state and are, thus, not constant. Multi-state Markov model: As a Markov model but has explicitly described the calculation of transition rates as accounting for dependencies between events. Simulation model: Defined as a patient-level model in which patient disease progression is simulated individually and where health states are not modelled as mutually exclusive. Microsimulation model: As a simulation model but providing more granularity/detail (e.g. where a simulation model may model ESRD as a single state, a microsimulation model may model health states within the ESRD state such as dialysis or transplant). Object-oriented simulation model: Defined as a person-by-person, object-by-object simulation, spanning from biological details to the care processes, logistics, resources and costs of healthcare systems. Monte-Carlo simulation model: Defined as a form of modelling where model inputs are drawn from distributions and are not treated as fixed values, with the model run multiple times to provide a probabilistic distribution of results. Discrete-event simulation: Discrete-event simulation is a computer-modelling technique used in economic evaluation of health interventions in which individual patient experience is simulated over time, and events occurring to the patient and the consequences of such events are tracked and summarised. Unlike other models, in discrete-event simulation, movements between patients’ health states are usually driven by events that may occur at varying times (rather than during cycles of fixed length), and time-to-event distributions are required for each event. Event likelihoods are driven by individual patient characteristics, which are recorded at baseline and may be updated as the patient experience (events, new health states) accumulates
CKD chronic kidney disease, ESRD end-stage renal disease, GFR glomerular filtration rate, HbA1c glycated haemoglobin