A systematic review and checklist presenting the main challenges for health economic modeling in personalized medicine: towards implementing patient-level models

Figures & data

Table 1. Checklist presenting the main challenges described in literature and used for analysis of the final sample. A positive score on challenges 1–7 indicates that the corresponding challenge is addressed in the model presented in the publication. A positive score in challenges 8–10 indicates that the authors identified the corresponding challenge and reported on the challenge in the publication. Negative scores indicate that the corresponding challenge is not addressed or identified and reported by the authors.

	Challenge	Specification
1.	Modeling patient-level processes	Is the model defined on a patient level?
2.	Modeling patients’ preferences	Are patients’ preferences modeled to take their effect on the outcomes into account?
3.	Modeling physicians’ preferences	Are physicians’ preferences modeled to take their effect on the outcomes into account?
4.	Taking into account the diagnostic performance of tests	Is the effect of the sensitivity, specificity, positive predictive value, and/or negative predictive value on the outcomes taken into account?
5.	Modeling combinations of tests	Does the modeled process include combinations of tests and/or prediction models?
6.	Modeling companion diagnostics	Does the modeled process include combinations of test(s) and treatment(s)?
7.	Study-specific outcome measures	Does the modeled process include study-specific outcomes, such as disease-specific adverse events?
8.	Data gaps	Do the authors mention any evidence gaps? If so, do they mention that these evidence gaps exist due to stratification of patients based on risk models and/or test results?
9.	Greater uncertainty due to more complex analysis	Do the authors mention greater uncertainty with respect to the outcomes, due to more complex analysis, as a result of personalization of the model?
10.	Absence of guidelines	Do the authors mention any difficulties related to the absence of guidelines for HEM in the context of PM?

Figure 1. Graphical representation of the search- and selection process.

Table 2. Summary of the study characteristics of the final sample of publications.

Reference	Disease	PM process^2	Modeling method^3	Model structure^4	Analyses^5
[47]^1	Breast cancer	Scre. (Prog.)	POMDP	HS, TR	QALY, DRO, DSA
[48]^1	Breast cancer	Scre. (Prog.)	Statistical Model (Math.)	HS	CEA (LE, LY, QALY), DRO, DSA
[49]	Human Immunodeficiency Virus	Targ. (Pred.)	DT	HS, PS, BS	NNT, CEA (QALY), DSA, PSA
[50]	Alzheimer’s disease	Targ. (Pred.)	MM (12, 30)	HS	CEA (QALY), CEAC, EVPI, EVPPI, DSA, PSA
[51]	Atrial fibrillation	Targ. (Pred.)	DT + MM (1, LT)	HS	CEA (QALY), CEAC, DSA, PSA
[52]	Cardiovascular Disease	Targ. (Pred.)	MSM (12, LT)	HS	TLE, DRO, PSA
[53]	Neonatal disease	Targ. (Pred.)	MM (6, 30)	HS	CEA (LY, QALY), CEAC, NMB, DSA, PSA
[54]	Coronary syndrome	Targ. (Pred.)	DT + MM (1, LT)	HS	CEA (QALY), DSO, DSA, PSA
[55]	Lung cancer	Targ. (Pred.)	DT	TR, PS	CEA (QALY), CEAC, DSA, PSA
[56]	Coronary artery disease	Targ. (Pred.)	DT	TR, PS	Costs, budget Impact, PSA
[57]^1	Human Immunodeficiency Virus	Targ. (Pred.)	DDCM	HS	CEA (QALY), DRO, DSA
[58]^1	Colorectal cancer	Scre. (Prog.)	DT + MM (–, LT)	HS, TR	CEA (LY), DSA
[59]	Prostate cancer	Scre. (Prog.)	MM (12, 30)^6	HS, TS	CEA (QALY), DSA
[60]	Rheumatoid arthritis	Targ. (Pred.), Mon. (Prog.)	MSM (3, 3)	HS	CEA (QALY), CEAC, DSA, PSA
[61]	Colorectal cancer	Scre. (Prog.), Targ. (Pred.)	DT + MM (12, age 100)	HS, TR	CEA (LY), CEAC, DSA, PSA
[62]^1	Coronary artery disease	Scre. (Prog.)	MSM (12, LT)	HS, TR	CEA (QALY), DRO, DSA
[63]	Acute myeloid leukemia	Targ. (Pred.), Mon. (Prog.)	DES	PS	DRO, OS, DFS, PSA
[64]^1	Lung cancer	Targ. (Pred.)	DT	HS, TR	CEA (LY), DSA
[65]^1	Lung cancer	Targ. (Pred.)	DT	HS, TR	CEA (QALY), DSA
[66]^1	Hepatitis C virus	Targ. (Pred.)	DT + MM (6, LT)	HS, TR	CEA (QALY) DSA, PSA
[67]^1	Colorectal cancer	Scre. (Prog.)	DT	TR	CEA (LY, QALY), DSA
[68]	Breast cancer	Scre. (Prog.)	Cost equation (Math.)	-	Costs, PSA
[69]	Depressive disorder	Targ. (Pred.), Mon. (Prog.)	MM (3, 3)	HS, TR, PS	CEA (QALY), DSA
[70]	Hepatitis C virus	Targ. (Pred.), Mon. (Prog.)	MM (12, LT)	HS	CEA (LY, QALY), CEAC, DSA, PSA
[71]^1	Colorectal cancer	Scre. (Prog.)	DT	TR, BS	CEA (LY), DSA, PSA
[72]^1	Breast cancer	Scre. (Prog.)	Multiple MSM (-, LT)	HS, TR	DRO, LY, QALY, DSA
[73]^1	Colorectal cancer	Scre. (Prog.)	DT	TR	CEA(Case Detected), DSA
[74]	Breast cancer	Scre. (Prog.)	MM (1, LT)^7	HS	CEA (QALY), NNT, DSA, PSA
[75]	Type 2 diabetes	Targ. (Pred.)	DT + MM (12, 5)	HS, TR	CEA (QALY), CEAC, DSA, PSA
[76]^1	Acute myeloid leukemia	Targ. (Pred.)	DT	HS, TR	QALY, DSA
[77]	Breast cancer	Scre. (Prog.)	Prob. Function (Math.)	HS	CEA (QALY), LE, DSA

¹Obtained by cross-referencing.

²Scr.: screening; Targ.: targeting; Mon.: monitoring; Pred.: predictive; Prog.: prognostic.

³DT: decision tree; MM (…, …): Markov model (cycle length in months, time horizon in years); MSM (…, …): Microsimulation model (cycle length in months, time horizon in years); POMDP: partially observable Markov decision process; DDCM: deterministic dynamic compartment model; DES: discrete event simulation; Math.: mathematical; LT: life-time.

⁴HS: health states; PS: process states; BS: behavior states; TR: Test Results.

⁵NNT: Number Needed to Treat; CEA(…): Cost-effectiveness Analysis (effect. outcome); QALY: quality adjusted life years; LE: lives extended; LY: life years; DSA: deterministic sens. analysis; PSA: probabilistic sens. analysis; CEAC: cost-effectiveness acceptability curve; EVPI: expected value of perfect information; EVPPI: expected value of partial perfect information; TLE: total life expectancy; DRO: disease related outcomes; NMB: net monetary benefit; OS: overall survival; DSF: disease-free survival.

⁶A cycle length of 24 months was also used. Several other time horizons were included: 25, 20, 15 and 10 years.

⁷Cycle lengths of 24 months and 36–48 months were also used.

Table 3. Summary of the distribution of the publications and the use of cohort modeling³ or alternative modeling methods over time.

Time period	Publications		Cohort modeling		Alternative methods
	#	%^1	#	%^2	#	%^2
≤2005	4	13	4	100	0	0
2006–2010	5	16	4	80	1	20
≥2011	22	71	13	59	9	41

¹As a percentage of all publications included in the final sample.

²As a percentage of the publications in the corresponding time period.

³Cohort modeling was defined as decision tree analysis and Markov modeling.

Table 4. Results of the analysis on whether the challenges for HEM in PM are addressed or identified and reported in the final sample of publications.

Reference	Addressed in model							Identified and reported
	Patient-level	Patients’ preferences	Physicians’ preferences	Diagnostic performance	Multiple tests	Companion diagnostics	Study spec. outcomes	Data gaps	Uncertainty	Guidelines
[47]^1	+	-	-	+	+^5	-	+	+	-	-
[48]^1	-	-	-	-	+^5	+	-	+^4	-	-
[49]	-	+^2	-	-	-	+	-	-	-	-
[50]	-	-	-	+^3	-	+	-	+^4	+^4	-
[51]	-	-	-	+^3	-	+	-	-	-	-
[52]	+	+^2	-	-	-	+	+	+^4	-	-
[53]	-	-	-	+^3	-	+	-	+^4	-	-
[54]	-	-	-	-	-	+	-	+	-	-
[55]	-	-	-	+^6	-	+	-	+	-	-
[56]	-	-	-	+	+^5	+	-	-	-	-
[57]^1	-	+^2	-	-	-	+	-	-	-	-
[58]^1	-	+^7	+^2	+	-	-	-	-	-	-
[59]	-	-	-	-	-	-	-	+	-	-
[60]	+	-	-	-	+^5	+	-	+	-	-
[61]	-	+^2	-	+	+^5	+	-	+^4	-	-
[62]^1	+	-	-	+	+^5	+	+	+	-	-
[63]	+	-	-	-	-	+	-	-	-	-
[64]^1	-	-	-	+	-	+	-	-	-	-
[65]^1	-	-	-	+^6	-	+	-	+	-	-
[66]^1	-	-	-	+^6	+^5	+	-	-	-	-
[67]^1	-	+	-	+	+^5	-	-	+	-	-
[68]	-	+^7	-	-	-	-	-	-	-	-
[69]	-	-	-	+^6	-	+	-	+	-	-
[70]	-	-	-	-	-	+	-	+	-	-
[71]^1	-	+^7	-	+	+^5	+	-	+^4	-	-
[72]^1	+	+^2	-	+	-	-	-	-	-	-
[73]^1	-	+^7	-	+	+^5	-	-	-	-	-
[74]	-	-	-	+	-	-	-	+	-	-
[75]	-	-	-	-	+^5	+	-	+	-	-
[76]^1	-	-	-	+	-	+	-	+^4	-	-
[77]	-	-	-	+	-	-	-	-	-	-
Total positive scores	19%	32%	3%	65%	35%	71%	10%	61%	3%	0%

¹Publication obtained by cross-referencing.

²Only adherence taken into account as patients’ preferences.

³Diagnostic performance taken into account, but 100% sensitivity and specificity assumed.

⁴Evidence gap exists in general, not due to PM.

⁵Multiple tests are taken into account, but fixed sequence of tests is assumed.

⁶Chance on positive test result is taken into account as diagnostic performance.

⁷Acceptance of test or treatment is taken into account as the only patients’ preferences.

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