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Survival Modeling for the Estimation of Transition Probabilities in Model-Based Economic Evaluations in the Absence of Individual Patient Data: A Tutorial

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Abstract

Background

Survival modeling techniques are increasingly being used as part of decision modeling for health economic evaluations. As many models are available, it is imperative for interested readers to know about the steps in selecting and using the most suitable ones. The objective of this paper is to propose a tutorial for the application of appropriate survival modeling techniques to estimate transition probabilities, for use in model-based economic evaluations, in the absence of individual patient data (IPD). An illustration of the use of the tutorial is provided based on the final progression-free survival (PFS) analysis of the BOLERO-2 trial in metastatic breast cancer (mBC).

Methods

An algorithm was adopted from Guyot and colleagues, and was then run in the statistical package R to reconstruct IPD, based on the final PFS analysis of the BOLERO-2 trial. It should be emphasized that the reconstructed IPD represent an approximation of the original data. Afterwards, we fitted parametric models to the reconstructed IPD in the statistical package Stata. Both statistical and graphical tests were conducted to verify the relative and absolute validity of the findings. Finally, the equations for transition probabilities were derived using the general equation for transition probabilities used in model-based economic evaluations, and the parameters were estimated from fitted distributions.

Results

The results of the application of the tutorial suggest that the log-logistic model best fits the reconstructed data from the latest published Kaplan–Meier (KM) curves of the BOLERO-2 trial. Results from the regression analyses were confirmed graphically. An equation for transition probabilities was obtained for each arm of the BOLERO-2 trial.

Conclusions

In this paper, a tutorial was proposed and used to estimate the transition probabilities for model-based economic evaluation, based on the results of the final PFS analysis of the BOLERO-2 trial in mBC. The results of our study can serve as a basis for any model (Markov) that needs the parameterization of transition probabilities, and only has summary KM plots available.

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Acknowledgments

The author contributions are presented below.

Study concept and design: Vakaramoko Diaby, Georges Adunlin, and Alberto J. Montero.

Data acquisition: Vakaramoko Diaby, Georges Adunlin.

Data analyses and interpretation: Vakaramoko Diaby.

Drafting of the article: Vakaramoko Diaby, Georges Adunlin, and Alberto J. Montero drafted the manuscript.

Revision for intellectual content: All Authors.

Guarantor: Vakaramoko Diaby.

The authors are grateful to Dr. Patricia Guyot for her help in the implementation of the algorithm in the statistical package R. The authors would also like to thank Moussa K. Richard, Gordon Blackhouse, Dr. Robert Hopkins, and Askal Ali for their insightful comments on earlier versions of the paper.

Conflict of interests

Dr. Vakaramoko Diaby, Georges Adunlin, and Dr. Alberto J. Montero certifies that they have no conflicts of interest with any financial organization regarding the material discussed in the manuscript.

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Authors and Affiliations

  1. Programs for Assessment of Technology in Health (PATH) Research Institute, St Joseph’s Healthcare Hamilton, 25 Main St. W., Suite 2000, Hamilton, ON, L8P 1H1, Canada

    Vakaramoko Diaby

  2. Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada

    Vakaramoko Diaby

  3. Division of Economic, Social and Administrative Pharmacy, College of Pharmacy and Pharmaceutical Sciences, Florida A&M University, Tallahassee, FL, USA

    Vakaramoko Diaby & Georges Adunlin

  4. Cleveland Clinic, Taussig Cancer Institute, Cleveland, OH, USA

    Alberto J. Montero

Authors
  1. Vakaramoko Diaby
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  2. Georges Adunlin
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  3. Alberto J. Montero
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Correspondence to Vakaramoko Diaby.

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Diaby, V., Adunlin, G. & Montero, A.J. Survival Modeling for the Estimation of Transition Probabilities in Model-Based Economic Evaluations in the Absence of Individual Patient Data: A Tutorial. PharmacoEconomics 32, 101–108 (2014). https://doi.org/10.1007/s40273-013-0123-9

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  • DOI: https://doi.org/10.1007/s40273-013-0123-9

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