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Types of analysis

Explore simulation techniques for comparing stroke treatments, including sensitivity analysis and stochastic methods to assess cost-effectiveness. Learn about SPM, base case analysis, and combining bootstrap with sensitivity analysis.

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Types of analysis

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  1. Types of analysis

  2. Simulation rationale • Each patient’s natural history is random, but guided by underlying parameters. • With sufficiently large number of patients, Monte Carlo variability can be made as small as possible. • In this case, the SPM essentially serves as a “counting machine” to estimate expected outcomes.

  3. Analysis plan • To compare two stroke treatments, set the natural history parameters for the first treatment and run the simulation to obtain expected outcomes. • Then, reset the natural history parameters to correspond to the second treatment and rerun the simulation to obtain a second set of expected outcomes. • Finally, compare the two sets of outcomes.

  4. Example • To assess the cost-effectiveness of an acute stroke drug for 70-year old males with ischemic stroke… • Group Cost Effectiveness • Usual care 170,000 3.67 QALY • Intervention 180,000 4.17 QALY • ICER= 10,000 / .50 = 20,000 $/QALY

  5. Types of analysis • Base case • Sensitivity • Bootstrapping • Stochastic sensitivity • ……..

  6. Base case analysis • 1 SPM run • 1 patient type (e.g., 50,000 simulated patients, all with the same characteristics) • 1 set of fixed input parameters (e.g., fix the natural history parameters, utilities, cost parameters, efficacy of intervention, etc.)

  7. Sensitivity analysis • 1 patient type • Multiple SPM runs • Each SPM run applies a separate set of pre-specified parameters. • One or more parameters could be changed at a time.

  8. One-way sensitivity analysis • Discount Rate ICER • 0% 24,576 • 3% 21,864 • 5% 17,987 • 7% 13,747 • As discount rate increases, intervention becomes increasingly cost effective.

  9. Two-way sensitivity analysis • Discount Efficacy ICER • 0% 1.30 305,987 • 5% 1.30 865,483 • 0% 1.40 5,076 • 5% 1.40 12,946 • Discount rate doesn’t matter, but intervention’s efficacy does: small changes in efficacy imply very different conclusions about cost -effectiveness.

  10. Bootstrapped analysis • 1 patient type • 1 SPM run • SPM parameters remain the same • Resampling of patients (i.e., conceptually, the RCT is repeated a large number of times, and the ICER is estimated for each replication; the variability of the ICER describes the precision of the results)

  11. Stochastic sensitivity analysis • 1 patient type • Multiple SPM runs • Multiple parameters changed simultaneously • Parameters obtained by random sampling from prior distributions • (in comparison with sensitivity analysis, more emphasis on estimating overall precision of results)

  12. Example • Run Discount Efficacy ICER* • 1 3.21% 1.52 21,056 • 2 4.56% 1.67 29,059 • 3 3.12% 1.34 22,356 • 4 2.18% 1.68 12,967 • … … … … • *Mean ICER = 20,000; s.d. = 5,000

  13. Combined bootstapped and stochastic sensitivity analysis • For each bootstrapped sample, rerun the SPM using input parameters randomly selected from prior distributions. • Bootstrapping accounts for first-order uncertainty (i.e., patient-level). • Sampling from parameters accounts for second-order uncertainty (i.e., in SPM parameters).

  14. Standard of practice • The current standard of practice is to use modeling to attach expected values for long-term outcomes to each patient in the trial. One and multi-way sensitivity analyses are performed. Bootstrapping (perhaps combined with stochastic sensitivity analysis) is the state-of-the science, in order to assess the precision associated with the CEA.

  15. Comment • Precision is very important to consider, as it is critical in determining the strength of the CEA’s conclusions. • ICER = 20,000 with s.d. = 5,000 is strong evidence in favor of the treatment. • ICER = 20,000 with s.d. = 500,000 is very weak evidence in favor of the treatment.

  16. SPM structure

  17. SPM Structure TIA IS HS DTH ASY Bleed MI

  18. States, events, and transitions • States are asymptomatic (ASY), transient ischemic attack (TIA), ischemic stroke (IS), hemorrhagic stroke (HS), myocardial infarction (MI) and death (DTH). • An event is a transition between states (e.g., a TIA in a previously asymptomatic patient moves the patient from ASY to TIA). • Recurrent events are allowed (e.g., a second IS for a patient in the IS state). • The intervention language can also count other complications of treatment.

  19. Sample patient history from a patient with ischemic stroke* • Month State Event Cost Utility • 1 IS None C(I,1) U(I) • 2 IS None C(I,2) U(I) • 3 IS IS C(I,1) U(I) • 4 MI MI C(M,1) U(M) • 5 DT DT 0 0 • *Note: if U(M)<U(I) then use U(I)

  20. Modules • Natural history module -- generates patient histories • Cost module -- attaches costs to patient histories • Utility module -- attaches QOL to patient histories • Intervention module -- modifies natural history parameters

  21. Basic philosophy • Use each data source to its best purpose. • For example, administrative files are used to estimate utilization (and thus costs), but not treatment efficacy. • Expert judgement is minimized, but used when other information is insufficient or implausible.

  22. Data sources • Natural history -- Framingham; Rochester, Minnesota / Mayo Clinic; US life tables • Costs -- most categories from Medicare • Utilities -- from national patient survey and literature • Intervention effects -- meta-analysis / synthesis of RCTs • Expert judgement -- as needed

  23. Natural history module • Natural history module reflects the epidemiology of stroke. • All information presented as transition functions. • The traditional survival curve is an example of a transition function,(outcome=death). • Transition functions use proportional hazards model (i.e., baseline curve + effect of covariates). • Default cycle time is 1 month.

  24. Cost module • The basic idea is that each new event places the patient at “month 0” of a cost curve (reflecting medical costs, over time, after an event such as IS). • Costs can be attached to patient histories either deterministically or stochastically. • Costs are currently in 1996 US dollars.

  25. Cost categories • Direct medical -- acute care hospital, physician, outpatient, home health, skilled nursing facility, durable medical equipment, outpatient drugs, rehabilitation units, rehabilitation hospitals, nursing home (non-SNF) • Direct non-medical -- caregiver, modifications to environment • Indirect -- lost earnings, lost non-market productivity

  26. Cost sources • Medicare -- institutional costs (acute care hospitals, rehabilitation, some skilled nursing), home health, hospital-based outpatient, physician, durable medical equipment • Medicare plus imputation -- skilled and other nursing home • UHC -- under 65s, drugs • Literature -- caregiver, environmental modifications, indirect costs

  27. Utility module • The basic idea is that each event leads to a change (typically, a decrease) in QOL. • Utilities are one measure of QOL • Utilities can be attached to patient histories either deterministically or stochastically.

  28. Utility sources • PORT patient survey + literature • 613 AMCC inpatients • 321 CHS population-based aged 65+ • 319 UHC managed care, inpatients and outpatients, mostly aged <65 • Oversampling ensured sufficiently large numbers of patients in asymptomatic, TIA, and minor stroke categories • TTO and CS for current health state and hypothetical major stroke

  29. SPM structure -- intervention module • Interventions can change natural history, costs, and/or utilities. • Parameters are obtained by meta-analysis / literature synthesis

  30. Intervention specification (example) • Carotid endarterectomy has the following effects: • 1-cycle decrement in QOL of .xx • 1-time cost of $xxx • Probability of stroke, MI, and death in next cycle increased by xx%, xx%, and xx% • Risk of stroke and MI multiplied by .xx in subsequent cycles • Duration of benefit of xx years

  31. Covariates • Patient characteristics (covariates) affect natural history, cost, and QOL. • For example, in the natural history module, the effect of covariates is described by terms in the proportional hazards model. • Users select degree of detail / complexity.

  32. Extrapolation • Epidemiologic cohorts had reliable follow-up for approximately 6 years; Medicare files include 24-36 months per patient. • However, the pattern of hazards and costs was nearly linear by the conclusion of follow-up. We used constrained linear extrapolation technique. • Constraints -- hazard for symptomatic patients can never fall below US population life table, monthly costs can never fall below those of a comparison sample...

  33. Discounting • Discount rate can be varied. • Default is to discount both life years and costs by 3% risk-less rate.

  34. SPM outputs • Survival • Quality-adjusted survival • Event-free survival • Costs • Costs by category • Patient histories

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