Non‐linearity in the cost‐effectiveness frontier

Lord, Joanne; Laking, George; Fischer, Asja

doi:10.1002/hec.1083

Cited by 12 publications

(11 citation statements)

References 24 publications

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“…Thus, the outcome and costs can be controlled by setting this threshold. As suggested by a previous study [32], the cost-effectiveness decision rule might be modeled in a nonlinear form. For example, the value of improvements may vary among the outcome levels.…”

Section: Discussionmentioning

confidence: 99%

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis

Bremer¹,

Becker²,

Kolovos³

et al. 2018

J Med Internet Res

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BackgroundDifferent treatment alternatives exist for psychological disorders. Both clinical and cost effectiveness of treatment are crucial aspects for policy makers, therapists, and patients and thus play major roles for healthcare decision-making. At the start of an intervention, it is often not clear which specific individuals benefit most from a particular intervention alternative or how costs will be distributed on an individual patient level.ObjectiveThis study aimed at predicting the individual outcome and costs for patients before the start of an internet-based intervention. Based on these predictions, individualized treatment recommendations can be provided. Thus, we expand the discussion of personalized treatment recommendation.MethodsOutcomes and costs were predicted based on baseline data of 350 patients from a two-arm randomized controlled trial that compared treatment as usual and blended therapy for depressive disorders. For this purpose, we evaluated various machine learning techniques, compared the predictive accuracy of these techniques, and revealed features that contributed most to the prediction performance. We then combined these predictions and utilized an incremental cost-effectiveness ratio in order to derive individual treatment recommendations before the start of treatment.ResultsPredicting clinical outcomes and costs is a challenging task that comes with high uncertainty when only utilizing baseline information. However, we were able to generate predictions that were more accurate than a predefined reference measure in the shape of mean outcome and cost values. Questionnaires that include anxiety or depression items and questions regarding the mobility of individuals and their energy levels contributed to the prediction performance. We then described how patients can be individually allocated to the most appropriate treatment type. For an incremental cost-effectiveness threshold of 25,000 €/quality-adjusted life year, we demonstrated that our recommendations would have led to slightly worse outcomes (1.98%), but with decreased cost (5.42%).ConclusionsOur results indicate that it was feasible to provide personalized treatment recommendations at baseline and thus allocate patients to the most beneficial treatment type. This could potentially lead to improved decision-making, better outcomes for individuals, and reduced health care costs.

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Section: Discussionmentioning

confidence: 99%

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis

Bremer¹,

Becker²,

Kolovos³

et al. 2018

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Thus, the outcome and costs can be controlled by setting this threshold. As suggested by [32], it might also be required to model the cost-effectiveness decision rule in a non-linear form. The value of improvements may vary amongst the outcome levels, for example.…”

Section: Principal Resultsmentioning

confidence: 99%

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis (Preprint)

Bremer¹,

Becker²,

Kolovos³

et al. 2018

Preprint

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Background: Different treatment alternatives exist for psychological disorders. Clinical and cost-effectiveness of treatment is a crucial aspect for policy makers, therapists, as well as patients and thus plays a major role for healthcare decision-making. At the start of an intervention, however, it is often not clear which specific individuals benefit from a particular intervention alternative most and how costs will be distributed on an individual patient level. Objective: This paper aimed at predicting the individual outcome and costs for patients before the start of an Internet-based intervention. Based on these predictions, treatment recommendations can be provided on an individual patient level. Thus, we expand the discussion of personalized treatment recommendation. Methods: Outcomes and costs were predicted based on baseline data of 350 patients from a two-arm randomized controlled trial focusing on the comparison of treatment as usual and blended therapy for depressive disorders. For this purpose, we evaluated various machine learning techniques, compared predictive accuracy, and revealed features that contributed most to the prediction performance. We then combined these predictions and utilized the incremental cost-effectiveness ratio in order to derive individual treatment recommendations before the start of treatment. Results: Predicting clinical outcomes and costs was a challenging task that came with high uncertainty when only utilizing baseline information. However, we were able to predict more accurate than a predefined reference measure in the shape of mean values of the outcome and costs. Questionnaires that include anxiety/depression items and questions regarding the mobility of individuals and their energy level contributed to the prediction performance. It was then illustrated how patients can be allocated to the individually most appropriate treatment type. For an incremental cost-effectiveness threshold of 25,000 €/quality adjusted life years, we demonstrated that our recommendations would have led to slightly worse outcomes (1.8%) but simultaneously a decrease in costs (5.9%). Conclusions: Our results indicate that it was feasible to provide personalized treatment recommendations at baseline and thus allocate patients to the most beneficial treatment type. This approach can potentially lead to an improved decision-making, better outcomes for individuals, and simultaneously reduced health care costs.

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“…Given the non-linearity in the ranking of INB, a cost-effectiveness expansion curve (CEEC) (Lord et al, 2005) was used to illustrate the impact of shifting villages gradually from policy 1 (no intervention) to policy 2 (intervention) (Fig. 4).…”

Section: Resultsmentioning

confidence: 99%

Modelling geographic variation in the cost-effectiveness of control policies for infectious vector diseases: The example of Chagas disease

Castillo-Riquelme

Chalabi

Lord

et al. 2008

Journal of Health Economics

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Non‐linearity in the cost‐effectiveness frontier

Cited by 12 publications

References 24 publications

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis

Predicting Therapy Success and Costs for Personalized Treatment Recommendations Using Baseline Characteristics: Data-Driven Analysis (Preprint)

Modelling geographic variation in the cost-effectiveness of control policies for infectious vector diseases: The example of Chagas disease

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