Application of the subpopulation treatment effect pattern plot (STEPP) method in clinical trials_Chinese Journal of Evidence-Based Medicine

Authors：

LI Yufei ¹ , LIU Jianping ¹ , LIANG Shibing ^1,2 , CHENG Hongjie ³ , ZHANG Qiaoyan ³ ,  ZHANG Ying ¹

1. Centre for Evidence-Based Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, P. R. China;
2. Clinical Research Center, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan 250013, P. R. China;
3. Fangshan Hospital Beijing University of Chinese Medicine, Beijing 102401, P. R. China;

Corresponding author：

ZHANG Ying, Email: yingzhang@bucm.edu.cn

Keywords：

Subpopulation treatment effect pattern plot method; Subgroup analyses; Interaction; R language; Randomized controlled trial

DOI：

10.7507/1672-2531.202308169

Video：

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Abstract Full text Figures/Tables Video References Cited by

Subpopulation treatment effect pattern plot (STEPP) method is a method for examining the relationship between treatment effects and continuous covariates and is characterized by dividing the study population into multiple overlapping subpopulations to be analyzed based on continuous covariate values. STEPP method has a different purpose than traditional subgroup analyses, and STEPP has a clear advantage in exploring the relationship between treatment effects and continuous covariates. In this study, the concepts, advantages, and subpopulation delineation methods of the STEPP method are introduced, and the specific operation process and result interpretation methods of STEPP method analysis using the STEPP package in R language are presented with examples.

1.	Peterson SJ, Foley S. Clinician's guide to understanding effect size, alpha level, power, and sample size. Nutr Clin Pract, 2021, 36(3): 598-605.
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12.	Lazar AA, Bonetti M, Cole BF, et al. Identifying treatment effect heterogeneity in clinical trials using subpopulations of events: STEPP. Clin Trials, 2016, 13(2): 169-179.
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14.	Venturini S, Bonetti M, Lazar AA, et al. Subpopulation treatment effect pattern plot (STEPP) methods with R and Stata. J Data Sci, 2022, 21(1): 106-126.
15.	Lambertini M, Fielding S, Loibl S, et al. Impact of age on clinical outcomes and efficacy of adjuvant dual anti-HER2 targeted therapy. J Natl Cancer Inst, 2022, 114(8): 1117-1126.
16.	Wang C, Fan C, Lu Q, et al. Exploration of dose tapering strategies for methadone maintenance treatment based on relapse risks: a subpopulation treatment effect pattern plot (STEPP) analysis. Drug Alcohol Depend, 2022, 238: 109581.
17.	Pagani O, Francis PA, Fleming GF, et al. Absolute improvements in freedom from distant recurrence to tailor adjuvant endocrine therapies for premenopausal women: results from text and soft. J Clin Oncol, 2020, 38(12): 1293-1303.
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19.	Yip W, Lazar A, Zahrieh D, et al. Stepp: subpopulation treatment effect pattern plot (STEPP). R package version 3.2. 5, 2020.
20.	Lagakos SW. The challenge of subgroup analyses--reporting without distorting. N Engl J Med, 2006, 354(16): 1667-1669.
21.	Liang SB, Han M, Cheng HJ, et al. Chinese herbal formula Tongxie Yaofang for diarrhea-predominant irritable bowel syndrome: study protocol for a randomized, multiple-blind, placebo-controlled trial. Trials, 2022, 23(1): 226.
22.	Van Oudenhove L, Crowell MD, Drossman DA, et al. Biopsychosocial aspects of functional gastrointestinal disorders. Gastroenterology, 2016, 2: S0016-5085.
23.	Zung WW. A rating instrument for anxiety disorders. Psychosomatics, 1971, 12(6): 371-379.
24.	Francis CY, Morris J, Whorwell PJ. The irritable bowel severity scoring system: a simple method of monitoring irritable bowel syndrome and its progress. Aliment Pharmacol Ther, 1997, 11(2): 395-402.
25.	Krinsley JS. The long and winding road toward personalized glycemic control in the critically ill. J Diabetes Sci Technol, 2018, 12(1): 26-32.

1. Peterson SJ, Foley S. Clinician's guide to understanding effect size, alpha level, power, and sample size. Nutr Clin Pract, 2021, 36(3): 598-605.
2. 朱泉, 郭新峰, 赖世隆. 临床试验效应值的分析和解释. 中国中西医结合杂志, 2005, 25(8): 677-681.
3. Cornell CN. Patient diversity is our reality: the covariates matter. HSS J, 2019, 15(2): 101-102.
4. Byar DP, Green SB. The choice of treatment for cancer patients based on covariate information. Bull Cancer, 1980, 67(4): 477-490.
5. Lewis JA. Statistical principles for clinical trials (ICH E9): an introductory note on an international guideline. Stat Med, 1999, 18(15): 1903-1942.
6. EMA. Guideline on adjustment for baseline covariates in clinical trials. London: EMA, 2015.
7. FDA. Adjusting for covariates in randomized clinical trials for drugs and biologics with continuous outcomes. Washington DC: FDA, 2019.
8. Bonetti M, Gelber RD. A graphical method to assess treatment-covariate interactions using the Cox model on subsets of the data. Stat Med, 2000, 19(19): 2595-2609.
9. Bonetti M, Zahrieh D, Cole BF, et al. A small sample study of the STEPP approach to assessing treatment-covariate interactions in survival data. Stat Med, 2009, 28(8): 1255-1268.
10. Lazar AA, Cole BF, Bonetti M, et al. Evaluation of treatment-effect heterogeneity using biomarkers measured on a continuous scale: subpopulation treatment effect pattern plot. J Clin Oncol, 2010, 28(29): 4539-4544.
11. Bonetti M, Gelber RD. Patterns of treatment effects in subsets of patients in clinical trials. Biostatistics, 2004, 5(3): 465-481.
12. Lazar AA, Bonetti M, Cole BF, et al. Identifying treatment effect heterogeneity in clinical trials using subpopulations of events: STEPP. Clin Trials, 2016, 13(2): 169-179.
13. Yip WK, Bonetti M, Cole BF, et al. Subpopulation treatment effect pattern plot (STEPP) analysis for continuous, binary, and count outcomes. Clin Trials, 2016, 13(4): 382-390.
14. Venturini S, Bonetti M, Lazar AA, et al. Subpopulation treatment effect pattern plot (STEPP) methods with R and Stata. J Data Sci, 2022, 21(1): 106-126.
15. Lambertini M, Fielding S, Loibl S, et al. Impact of age on clinical outcomes and efficacy of adjuvant dual anti-HER2 targeted therapy. J Natl Cancer Inst, 2022, 114(8): 1117-1126.
16. Wang C, Fan C, Lu Q, et al. Exploration of dose tapering strategies for methadone maintenance treatment based on relapse risks: a subpopulation treatment effect pattern plot (STEPP) analysis. Drug Alcohol Depend, 2022, 238: 109581.
17. Pagani O, Francis PA, Fleming GF, et al. Absolute improvements in freedom from distant recurrence to tailor adjuvant endocrine therapies for premenopausal women: results from text and soft. J Clin Oncol, 2020, 38(12): 1293-1303.
18. Shen B, Liu J, Wu D, et al. Evaluation of the safety and efficacy of high-dose rate brachytherapy for radiorecurrent prostate cancer: a systematic review and meta-analysis. Strahlenther Onkol, 2024.
19. Yip W, Lazar A, Zahrieh D, et al. Stepp: subpopulation treatment effect pattern plot (STEPP). R package version 3.2. 5, 2020.
20. Lagakos SW. The challenge of subgroup analyses--reporting without distorting. N Engl J Med, 2006, 354(16): 1667-1669.
21. Liang SB, Han M, Cheng HJ, et al. Chinese herbal formula Tongxie Yaofang for diarrhea-predominant irritable bowel syndrome: study protocol for a randomized, multiple-blind, placebo-controlled trial. Trials, 2022, 23(1): 226.
22. Van Oudenhove L, Crowell MD, Drossman DA, et al. Biopsychosocial aspects of functional gastrointestinal disorders. Gastroenterology, 2016, 2: S0016-5085.
23. Zung WW. A rating instrument for anxiety disorders. Psychosomatics, 1971, 12(6): 371-379.
24. Francis CY, Morris J, Whorwell PJ. The irritable bowel severity scoring system: a simple method of monitoring irritable bowel syndrome and its progress. Aliment Pharmacol Ther, 1997, 11(2): 395-402.
25. Krinsley JS. The long and winding road toward personalized glycemic control in the critically ill. J Diabetes Sci Technol, 2018, 12(1): 26-32.

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