The sample size calculation method for health economic evaluation in clinical trials_Chinese Journal of Evidence-Based Medicine

Authors：

XIN Yu ¹ , WU Changjin ² , HAO Jun ³ , ZUO Ling ¹ , CAI Yuanyi ⁴ ,  HUI Wen ¹

1. West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. School of Public Health, Chongqing Medical University, Chongqing 400016, P. R. China;
3. Medical Research and Biometrics Center, National Center for Cardiovascular Diseases and Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 102308, P. R. China;
4. Department of Health Service Management, China Medical University, Shenyang 110122, P. R. China;

Corresponding author：

HUI Wen, Email: huiwen@scu.edu.cn

Keywords：

Sample size calculation; Health economic evaluation; Clinical trials; Frequentist approach

DOI：

10.7507/1672-2531.202502014

Video：

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The calculation of sample size is a critical component in the design phase of clinical trials incorporating health economic evaluations. A reasonable sample size is essential to ensure the scientific validity and accuracy of trial results. This paper summarizes the sample size calculation methods in the frequentist framework based on two health economic evaluation indicators: incremental cost-effectiveness ratio (ICER) and net benefit and examines these methods in terms of their applicable conditions, advantages, and limitations. The ICER method derives the sample size calculation formula by computing the ratio of incremental cost to incremental effect, while the net benefit method determines the economic viability of interventions by calculating incremental net benefit, subsequently leading to the formulation of the sample size calculation. Furthermore, this paper briefly discusses other sample size calculation methods, such as the classical Bayesian approach and the value of information analysis, providing a reference for calculating sample size in clinical trials with integrated health economic evaluations.

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2.	Abbott JH, Wilson R, Pryymachenko Y, et al. Economic evaluation: a reader's guide to studies of cost-effectiveness. Arch Physiother, 2022, 12(1): 28.
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4.	Ramsey SD, Willke RJ, Glick H, et al. Cost-effectiveness analysis alongside clinical trials II-An ISPOR Good Research Practices Task Force report. Value Health, 2015, 18(2): 161-172.
5.	Wu C, Hao J, Xin Y, et al. Poor sample size reporting quality and insufficient sample size in economic evaluations conducted alongside pragmatic trials: a cross-sectional survey. J Clin Epidemiol, 2024, 176: 111535.
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7.	Agency for Health Technology Assessment and Tariff System. Health technology assessment guidelines version 3.0. 2016.
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10.	Norwegian Medicines Agency. Guidelines on how to conduct pharmacoeconomic analyses. 2012.
11.	Pharmaceutical Management Agency. Prescription for pharmacoeconomic analysis: methods for cost-utility analysis (version 2.2). 2014.
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13.	Hollingworth W, McKell-Redwood D, Hampson L, et al. Cost-utility analysis conducted alongside randomized controlled trials: are economic end points considered in sample size calculations and does it matter. Clin Trials, 2013, 10(1): 43-53.
14.	陈平雁. 临床试验中样本量确定的统计学考虑. 中国卫生统计, 2015, 32(4): 727-731.
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16.	Bertram MY, Lauer JA, De Joncheere K, et al. Cost-effectiveness thresholds: pros and cons. Bull World Health Organ, 2016, 94(12): 925-930.
17.	刘国恩. 中国药物经济学评价指南导读(2022). 北京: 中国市场出版社, 2020.
18.	Briggs AH, Gray AM. Power and sample size calculations for stochastic cost-effectiveness analysis. Med Decis Making, 1998, 18(2 Suppl): S81-S92.
19.	Gardiner JC, Huebner M, Jetton J, et al. Power and sample assessments for tests of hypotheses on cost-effectiveness ratios. Health Econ, 2000, 9(3): 227-234.
20.	Willan AR, O'Brien BJ. Sample size and power issues in estimating incremental cost-effectiveness ratios from clinical trials data. Health Econ, 1999, 8(3): 203-211.
21.	O'Brien BJ, Drummond MF, Labelle RJ, et al. In search of power and significance: issues in the design and analysis of stochastic cost-effectiveness studies in health care. Med Care, 1994, 32(2): 150-163.
22.	Briggs AH, O'Brien BJ, Blackhouse G. Thinking outside the box: recent advances in the analysis and presentation of uncertainty in cost-effectiveness studies. Annu Rev Public Health, 2002, 23: 377-401.
23.	孙振球. 医学统计学(第五版). 北京: 人民卫生出版社, 2020.
24.	Willan AR. Analysis, sample size, and power for estimating incremental net health benefit from clinical trial data. Control Clin Trials, 2001, 22(3): 228-237.
25.	Lipscomb J, Ancukiewicz M, Parmigiani G, et al. Predicting the cost of illness: a comparison of alternative models applied to stroke. Med Decis Making, 1998, 18(2 Suppl): S39-56.
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27.	Greenland S, Senn SJ, Rothman KJ, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol, 2016, 31(4): 337-350.
28.	Willan AR, Pinto EM. The value of information and optimal clinical trial design. Stat Med, 2005, 24(12): 1791-1806.
29.	David M, Michael JC, Say BT, et al. Sample sizes for clinical, laboratory and epidemiology studies. 4^th edition. Wiley-Blackwell: New Jersey, 2018.
30.	O'Hagan A, Stevens JW. Bayesian assessment of sample size for clinical trials of cost-effectiveness. Med Decis Making, 2001, 21(3): 219-230.
31.	Rothery C, Strong M, Koffijberg HE, et al. Value of information analytical methods: report 2 of the ISPOR value of information analysis emerging good practices task force. Value Health, 2020, 23(3): 277-286.
32.	Pham CT, Visvanathan R, Strong M, et al. Cost-effectiveness and value of information analysis of an ambient intelligent geriatric management (AmbIGeM) system compared to usual care to prevent falls in older people in hospitals. Appl Health Econ Health Policy, 2023, 21(2): 315-325.
33.	Bader C, Cossin S, Maillard A, et al. A new approach for sample size calculation in cost-effectiveness studies based on value of information. BMC Med Res Methodol, 2018, 18(1): 113.

1. O'Sullivan AK, Thompson D, Drummond MF. Collection of health-economic data alongside clinical trials: is there a future for piggyback evaluations. Value Health, 2005, 8(1): 67-79.
2. Abbott JH, Wilson R, Pryymachenko Y, et al. Economic evaluation: a reader's guide to studies of cost-effectiveness. Arch Physiother, 2022, 12(1): 28.
3. Ramsey S, Willke R, Briggs A, et al. Good research practices for cost-effectiveness analysis alongside clinical trials: the ISPOR RCT-CEA Task Force report. Value Health, 2005, 8(5): 521-533.
4. Ramsey SD, Willke RJ, Glick H, et al. Cost-effectiveness analysis alongside clinical trials II-An ISPOR Good Research Practices Task Force report. Value Health, 2015, 18(2): 161-172.
5. Wu C, Hao J, Xin Y, et al. Poor sample size reporting quality and insufficient sample size in economic evaluations conducted alongside pragmatic trials: a cross-sectional survey. J Clin Epidemiol, 2024, 176: 111535.
6. 刘国恩. 中国药物经济学评价指南2020. 北京: 中国市场出版社, 2020.
7. Agency for Health Technology Assessment and Tariff System. Health technology assessment guidelines version 3.0. 2016.
8. Canadian Agency for Drugs and Technologies in Health. Guidelines for the economic evaluation of health technologies: Canada (4th Edition). 2017.
9. German National Institute for Quality and Efficiency in Health Care. General methods for the assessment of the relation of benefits to costs (version 6.1). 2021.
10. Norwegian Medicines Agency. Guidelines on how to conduct pharmacoeconomic analyses. 2012.
11. Pharmaceutical Management Agency. Prescription for pharmacoeconomic analysis: methods for cost-utility analysis (version 2.2). 2014.
12. Thiry N, Neyt M, Van De Sande S, et al. Belgian guidelines for economic evaluations: second edition. Int J Technol Assess Health Care, 2014, 30(6): 601-607.
13. Hollingworth W, McKell-Redwood D, Hampson L, et al. Cost-utility analysis conducted alongside randomized controlled trials: are economic end points considered in sample size calculations and does it matter. Clin Trials, 2013, 10(1): 43-53.
14. 陈平雁. 临床试验中样本量确定的统计学考虑. 中国卫生统计, 2015, 32(4): 727-731.
15. 董芬, 李超, 彭晓霞, 等. 临床研究中样本含量计算的意义、计算方法及注意事项. 中国卒中杂志, 2009, 4(10): 854-859.
16. Bertram MY, Lauer JA, De Joncheere K, et al. Cost-effectiveness thresholds: pros and cons. Bull World Health Organ, 2016, 94(12): 925-930.
17. 刘国恩. 中国药物经济学评价指南导读(2022). 北京: 中国市场出版社, 2020.
18. Briggs AH, Gray AM. Power and sample size calculations for stochastic cost-effectiveness analysis. Med Decis Making, 1998, 18(2 Suppl): S81-S92.
19. Gardiner JC, Huebner M, Jetton J, et al. Power and sample assessments for tests of hypotheses on cost-effectiveness ratios. Health Econ, 2000, 9(3): 227-234.
20. Willan AR, O'Brien BJ. Sample size and power issues in estimating incremental cost-effectiveness ratios from clinical trials data. Health Econ, 1999, 8(3): 203-211.
21. O'Brien BJ, Drummond MF, Labelle RJ, et al. In search of power and significance: issues in the design and analysis of stochastic cost-effectiveness studies in health care. Med Care, 1994, 32(2): 150-163.
22. Briggs AH, O'Brien BJ, Blackhouse G. Thinking outside the box: recent advances in the analysis and presentation of uncertainty in cost-effectiveness studies. Annu Rev Public Health, 2002, 23: 377-401.
23. 孙振球. 医学统计学(第五版). 北京: 人民卫生出版社, 2020.
24. Willan AR. Analysis, sample size, and power for estimating incremental net health benefit from clinical trial data. Control Clin Trials, 2001, 22(3): 228-237.
25. Lipscomb J, Ancukiewicz M, Parmigiani G, et al. Predicting the cost of illness: a comparison of alternative models applied to stroke. Med Decis Making, 1998, 18(2 Suppl): S39-56.
26. Nickerson RS. Null hypothesis significance testing: a review of an old and continuing controversy. Psychol Methods, 2000, 5(2): 241-301.
27. Greenland S, Senn SJ, Rothman KJ, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol, 2016, 31(4): 337-350.
28. Willan AR, Pinto EM. The value of information and optimal clinical trial design. Stat Med, 2005, 24(12): 1791-1806.
29. David M, Michael JC, Say BT, et al. Sample sizes for clinical, laboratory and epidemiology studies. 4^th edition. Wiley-Blackwell: New Jersey, 2018.
30. O'Hagan A, Stevens JW. Bayesian assessment of sample size for clinical trials of cost-effectiveness. Med Decis Making, 2001, 21(3): 219-230.
31. Rothery C, Strong M, Koffijberg HE, et al. Value of information analytical methods: report 2 of the ISPOR value of information analysis emerging good practices task force. Value Health, 2020, 23(3): 277-286.
32. Pham CT, Visvanathan R, Strong M, et al. Cost-effectiveness and value of information analysis of an ambient intelligent geriatric management (AmbIGeM) system compared to usual care to prevent falls in older people in hospitals. Appl Health Econ Health Policy, 2023, 21(2): 315-325.
33. Bader C, Cossin S, Maillard A, et al. A new approach for sample size calculation in cost-effectiveness studies based on value of information. BMC Med Res Methodol, 2018, 18(1): 113.

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