Optimal healthcare decision making under multiple mathematical models: application in prostate cancer screening

Abstract

Important decisions related to human health, such as screening strategies for cancer, need to be made without a satisfactory understanding of the underlying biological and other processes. Rather, they are often informed by mathematical models that approximate reality. Often multiple models have been made to study the same phenomenon, which may lead to conflicting decisions. It is natural to seek a decision making process that identifies decisions that all models find to be effective, and we propose such a framework in this work. We apply the framework in prostate cancer screening to identify prostate-specific antigen (PSA)-based strategies that perform well under all considered models. We use heuristic search to identify strategies that trade off between optimizing the average across all models’ assessments and being “conservative” by optimizing the most pessimistic model assessment. We identified three recently published mathematical models that can estimate quality-adjusted life expectancy (QALE) of PSA-based screening strategies and identified 64 strategies that trade off between maximizing the average and the most pessimistic model assessments. All prescribe PSA thresholds that increase with age, and 57 involve biennial screening. Strategies with higher assessments with the pessimistic model start screening later, stop screening earlier, and use higher PSA thresholds at earlier ages. The 64 strategies outperform 22 previously published expert-generated strategies. The 41 most “conservative” ones remained better than no screening with all models in extensive sensitivity analyses. We augment current comparative modeling approaches by identifying strategies that perform well under all models, for various degrees of decision makers’ conservativeness.

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Correspondence to John Silberholz.

Appendices

Appendix A: Literature Review

We searched PubMed (January 1, 2010, through October 3, 2015) using the following query:

figurea

We also searched the Tufts Cost-Effectiveness Analysis Registry [28] (from inception to October 3, 2015) for the term “prostate”. Two citations were retrieved in full text, but were also identified in the PubMed searches. Fig. 5 shows the results of searches and reasons for exclusion.

Fig. 5
figure5

Literature identification

One modification was required to use Model Z [53] to assess an arbitrary PSA-based screening strategy. Given a patient’s cancer status, Model Z assigns a probability that the patient will have a PSA value in the ranges [0,1), [1,2.5), [2.5,4), [4,7), [7,10), and [10,). We assume all PSA values in a range are equally likely to occur, and we limit to PSA values between 10 n g/m L and 20 n g/m L for the highest range.

Appendix B: Sensitivity Analysis

We varied parameters based on sensitivity ranges used in the papers describing each model. The variables names are from the respective papers.

B.1: Model G [24], parameters governing the course of the disease

We varied each of the following five parameters to the maximum and minimum value in the 100 sets of sensitivity parameters used in Gulati et al. [24].

  • grade.onset.rate: A rate controlling how quickly patients experience prostate cancer onset.

  • grade.metastasis.rate: A rate controlling how quickly patients with undetected prostate cancer experience metastasis.

  • grade.clinical.rate.baseline: A rate controlling how quickly a patient’s cancer is clinically detected.

  • grade.clinical.rate.distant: A rate controlling how quickly a patient’s cancer is clinically detected after metastasis.

  • grade.clinical.rate.high:: A rate controlling how quickly a patient’s high-grade cancer is clinically detected.

  • low.grade.slope: A parameter controlling the likelihood that a patient who developed cancer has a low-grade cancer.

B.2: Model U [49], parameters governing the course of the disease

We varied parameters using sensitivity ranges from [49].

  • d t : The rate of other-cause (non-prostate cancer) mortality at age t was varied ±20 % from the base-case parameter values from [3, 42].

  • w t : The prostate cancer incidence rate for a man at age t was varied using sensitivity ranges from [8]. For patients aged 40–49, the sensitivity range was defined as [0.00020, 0.00501]; for patients aged 50–59, the sensitivity range was defined as [0.00151, 0.00491]; for patients aged 60–69, the sensitivity range was defined as [0.00243, 0.00852]; for patients aged 70–79, the sensitivity range was defined as [0.00522, 0.01510]; and for patients aged 80 or more, the sensitivity range was defined as [0.00712, 0.01100].

  • b t : The annual probability of metastasis among patients with detected cancer treated with radical prostatectomy was varied ±20 % from the base-case parameter value of 0.006 derived from the Mayo Clinic Radical Prostatectomy Repository.

  • e t : The annual probability of metastasis among patients with undetected cancer was varied ±20 % from the base-case parameter value of 0.069 from [22, 46].

  • z t : The annual probability of dying from prostate cancer among men aged t with metastatic disease was varied using the sensitivity range [0.07, 0.37] from [4, 40] around the base-case values of 0.074 for patients aged 40–64 and 0.070 for patients aged 75 and older [42].

  • f: The probability of a biopsy detecting cancer in a patient with prostate cancer was varied ±20 % from its base-case value of 0.8 from [26].

B.3: Model Z [53], parameters governing the course of the disease

The sensitivity analyses in [53] did not vary any parameters governing the course of the disease, and pertained only to costs and literature-derived quality-of-life decrements. Because of the similarities with model U, we used the sensitivity ranges from model U for model Z’s d t ,w t , and f parameters, additionally varying the following parameters:

  • b t : The annual probability of a man of age t with detected prostate cancer treated with radical prostatectomy dying of the disease was varied ±20 % from its base-case value of 0.0067 for men aged 40–64 and 0.0092 for men aged 65 and older [42].

  • e t : The annual probability of a man of age t with undetected prostate cancer dying of the disease was varied ±20 % from its base-case value of 0.033 from [1].

B.4: All models, quality-of-life decrements

Each model in this work uses the literature review-based quality-of-life decrements (utility weights) from a re-analysis of the ERSPC study from [29]. The sensitivity analysis ranges used in that work are as follows:

  • Screening attendance: The utility estimate for the week following screening was varied in range [0.99, 1.00] from base estimate 0.99.

  • Biopsy: The utility estimate for the three weeks following biopsy was varied in range [0.87, 0.94] from base estimate 0.90.

  • Cancer diagnosis: The utility estimate for the month following cancer diagnosis was varied in range [0.75, 0.85] from base estimate 0.80.

  • Radiation therapy: The utility estimate for the first two months after radiation therapy was varied in range [0.71, 0.91] from base estimate 0.73, and the utility estimate for the next 10 months after radiation therapy was varied in range [0.61, 0.88] from base estimate 0.78.

  • Radical prostatectomy: The utility estimate for the first two months after radical prostatectomy was varied in range [0.56, 0.90] from base estimate 0.67, and the utility estimate for the next 10 months after radical prostatectomy was varied in range [0.70, 0.91] from base estimate 0.77.

  • Active surveillance: The utility estimate for the first seven years of active surveillance was varied in range [0.85, 1.00] from base estimate 0.97.

  • Postrecovery period: The utility estimate for years 1–10 following radical prostatectomy or radiation therapy was varied in range [0.93, 1.00] from base estimate 0.95.

  • Palliative therapy: The utility estimate during 30 months of palliative therapy was varied in range [0.24, 0.86] from base estimate 0.60.

  • Terminal illness: The utility estimate during six months of terminal illness was varied in range [0.24, 0.40] from base estimate 0.40.

Appendix C: Building an Efficient Frontier of Screening Strategies

Given a screening strategy s, let A(s) be the average assessment of the strategy across all mathematical models and let P(s) be the pessimistic assessment of the strategy across all mathematical models. To construct an efficient frontier of strategies trading off the average and most pessimistic assessment, we use mathematical optimization via an iterated local search heuristic to maximize the objective function λ A(s)+(1−λ)P(s) for λ∈{0,0.1,0.2,…,1.0} over annual screening strategies and biennial screening strategies, optimizing a total of 22 times. From the set of all screening strategies encountered during the optimization process (not just the final values identified through optimization), we construct an efficient frontier trading off the average and pessimistic assessments. Solutions encountered while optimizing the objective with parameter value λ using iterated local search may not be optimal for the objective with that λ but may still lie on the efficient frontier trading off the average and pessimistic assessments, so the final efficient frontier may contain more than 22 efficient strategies.

The key step in constructing the efficient frontier is solving maxsS λ A(s)+(1−λ)P(s), where S is the set of all feasible screening strategies. We consider strategies with age-specific PSA cutoffs limited to 0.5, 1.0, 1.5, …, 6.0 n g/m L, fixed cutoffs for 5-year age ranges, and cutoffs that are non-decreasing in a patient’s age. We consider screening from ages 40 through 99, so there are 10.4 million possible screening strategies; as a result, it would be time consuming to use enumeration to identify the strategy with the highest average incremental QALE compared to not screening. Instead, we use constrained iterated local search to identify a locally optimal strategy that cannot be improved by changing a single age-specific PSA threshold.

The central step in the iterated local search is the local search, which takes as input a screening strategy s and a single age range r and searches a small number of similar strategies to s. For each possible PSA threshold (0.5, 1.0, …, 6.0 n g/m L), the local search procedure constructs a new strategy by modifying s to use that threshold in age range r, additionally making the smallest possible changes to the remaining PSA thresholds in s to retain non-decreasing PSA thresholds in age. Each of these 12 screening strategies is evaluated, and if any improves over s then the one with the best objective value is selected to replace s. In the case where r is either the first or last age range in s in which patients screen, the procedure also considers a no-screening option for age range r.

As an example, consider a screening strategy for which annual screening is performed for ages 45–69, with cutoff 2.0 n g/m L from ages 45–49, 3.0 n g/m L from ages 50–54, and 5.0 n g/m L from ages 55–59, 60–64, and 64–69. We can write this screening strategy compactly as (2, 3, 5, 5, 5), with each value in the vector representing the cutoff for a 5-year period. If we apply local search to the cutoff for ages 55–59, then we will consider changing the cutoff for that age range to each value in {0.5, 1.0, 1.5, …, 6.0} n g/m L, adjusting other cutoffs the smallest amount possible to ensure all cutoffs are non-decreasing in age. For instance, if the cutoff for ages 55–59 were set to 2.5 n g/m L, then the cutoff for ages 50–54 would also need to be decreased to 2.5 n g/m L in order to maintain non-decreasing cutoffs, yielding final screening strategy (2, 2.5, 2.5, 5, 5). The set of all possible screening strategies considered by a local search on age range 55–59 is:

  • (0.5, 0.5, 0.5, 5, 5)

  • (1, 1, 1, 5, 5)

  • (1.5, 1.5, 1.5, 5, 5)

  • (2, 2, 2, 5, 5)

  • (2, 2.5, 2.5, 5, 5)

  • (2, 3, 3, 5, 5)

  • (2, 3, 3.5, 5, 5)

  • (2, 3, 4, 5, 5)

  • (2, 3, 4.5, 5, 5)

  • (2, 3, 5, 5, 5)

  • (2, 3, 5.5, 5.5, 5.5)

  • (2, 3, 6, 6, 6)

Among these strategies, the one resulting in the largest objective value λ A(s) + (1 − λ)P(s) is the one selected by the local search.

The iterated local search begins with a strategy of never screening for prostate cancer. The procedure repeatedly loops through a random permutation of the age ranges, performing local search on an age range if it’s within 5 years of an age range for which the current strategy screens with PSA. The procedure terminates when the current screening strategy cannot be improved by applying local search to any valid age range.

Appendix D: Details of Optimizing Screening Strategies

The iterated local search procedure was implemented in python. The C source code for model G and the C++ source code of model U were provided by the authors of those works; model U was re-implemented in python to improve the efficiency of the procedure. Model Z was implemented in python based on the published description of that model. All procedures were tested on a Dell Precision T7600 with 128 GB RAM and two Intel Xeon E5-2687W Processors, each with 8 cores and a clock speed of 3.1 GHz.

The runtime of the iterated local search procedure for each objective function is provided in Table 4.

Table 4 Computation time required for iterated local search procedure

To validate the performance of the local search optimization approach, we computed the exact optimal solution for models Z and U by evaluating all 5.2 million feasible biennial strategies and all 5.2 million feasible annual strategies with each model, a process that required 60.1 CPU hours for model Z and 369.1 CPU hours for model U. The local search heuristic had identified the global optimal solution for models Z and U. Given the heavy computational burden of evaluating strategies with model G, we did not compute exact optimal solutions for model G or for any of the objectives used to compute the efficient frontier.

Appendix E: Sensitivity Analysis: Model Averaging of Normalized Assessments

As a sensitivity analysis, we reproduced the efficient frontier using a normalized version of the objective function. For each model, we normalized the QALE change compared to not screening to have a maximum value of 1, ensuring that models with systematically more optimistic assessments of screening strategies are not weighted more heavily than others in the model averaging objective.

We computed an efficient frontier as before, trading off the average and most pessimistic assessment of the normalized objective function. The efficient frontier, single-model solutions, and expert strategies are plotted in Fig. 6.

Fig. 6
figure6

Average and most pessimistic assessments of identified and expert-generated screening strategies. The 6 strategies on the efficient frontier are shown as black circles. The optimal strategies according to models G (G-best), U (U-best), or Z (Z-best) are shown as red circles. The 22 expert-generated strategies are shown as gray circles. Normalized assessments of QALE over no screening with each model are shown in parentheses for some strategies. For example strategy EF-1 was assessed as 0.8 proportion of the maximum attainable QALE improvement by models G, U, and Z

The efficient frontier with the normalized objective function is qualitatively different from the efficient frontier with the non-normalized objective. No screening strategy optimized with a single model falls on the efficient frontier, and all strategies in the efficient frontier dominate the single-model and expert-generated strategies in both the most pessimistic and the average model assessments. The efficient frontier is smaller, comprising only six screening strategies, and the strategies in the frontier are more homogeneous. The strategy optimizing the most pessimistic assessment prescribes biennial screening with threshold 0.5 n g/m L from ages 40–54, 1.5 n g/m L from ages 55–64, 4.0 n g/m L from ages 65–69, and 5.0 n g/m L from ages 70–74. The strategy optimizing the average assessment is similar, prescribing biennial screening with threshold 0.5 n g/m L from ages 40–49, 1.0 n g/m L from ages 50–59, 1.5 n g/m L from ages 60–64, 2.0 n g/m L from ages 65–69, and 6.0 n g/m L from ages 70–79. For all six strategies on the efficient frontier model U was the most pessimistic in the normalized assessment.

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Bertsimas, D., Silberholz, J. & Trikalinos, T. Optimal healthcare decision making under multiple mathematical models: application in prostate cancer screening. Health Care Manag Sci 21, 105–118 (2018). https://doi.org/10.1007/s10729-016-9381-3

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Keywords

  • Comparative modeling
  • Decision analysis
  • Sensitivity analysis
  • Model averaging
  • Optimization
  • Prostate cancer screening
  • Simulation modeling