A comparative study of different methods for treatment switching analysis in clinical trials

doi:10.12122/j.issn.1673-4254.2025.05.23

Abstract

Abstract:

Objective To compare the commonly used methods for analyzing treatment switching in clinical trials to facilitate selection of optimal methods in different scenarios. Methods Based on the data characteristics of patient conversion in oncology clinical trials, we simulated the survival time of patients across different scenarios and compared the bias, mean square error and coverages of the treatment effects derived from different methods. Results The sample size had an almost negligible impact on the outcomes of the various methods. Compared to conventional methods, more complex methods (RPSFTM, IPCW, TSE, and IPE) resulted in lower errors across different scenarios. The IPCW method could cause a significant increase in errors in cases where the probability of conversion was high. The TSE method had the lowest error and mean squared error when the risk was low and the probability of conversion was high. The IPE method had an obvious advantage in the scenario with a low probability of conversion, but it may slightly underestimate the treatment effect when the inflation factor was small. Conclusion The choice of a specific method for analyzing cohort transition should be made based on considerations of both the probability of conversion and inflation factor in different scenarios.

Key words: treatment switching, inverse probability censoring weighting, rank preserving structural failure time models, two-stage estimation method, iterative parameter estimation method

Zhiyue LIANG, Lishan XU, Keke LI, Milai YU, Shengli AN. A comparative study of different methods for treatment switching analysis in clinical trials[J]. Journal of Southern Medical University, 2025, 45(5): 1093-1102.

Figures/Tables 9

Fig.1 IPCW process diagram.

Tab.1 Setting of the simulation parameters

Variables	Assignment/Distribution
Simulation times	1000
Sample size	200, 500, 1000
Shape parameter $γ$	0.5
Scale parameter $λ$	1.33
observation time	3 years
Survival time	Weibull distribution
Entry time	Uniform distribution (0-1distribution)
Prognostic probability (good/poor)	Good: 30%, 75%
	Poor: 70%, 25%
Switching probability (good, poor)	(0.1, 0.25)
	(0.5, 0.75)
Switching time	Uniform distribution
Hazard ratios	0.7, 0.9
Inflation factor	1.2, 2

Tab.1 Setting of the simulation parameters

Variables	Assignment/Distribution
Simulation times	1000
Sample size	200, 500, 1000
Shape parameter $γ$	0.5
Scale parameter $λ$	1.33
observation time	3 years
Survival time	Weibull distribution
Entry time	Uniform distribution (0-1distribution)
Prognostic probability (good/poor)	Good: 30%, 75%
	Poor: 70%, 25%
Switching probability (good, poor)	(0.1, 0.25)
	(0.5, 0.75)
Switching time	Uniform distribution
Hazard ratios	0.7, 0.9
Inflation factor	1.2, 2

Tab.2 Parameter values in 16 scenarios

Scenario	Hazard Ratios		Probability of good prognosis		Inflation factor		Switching probability (good : poor)
Scenario	0.7	0.9	30%	75%	1.2	2	10%:25%	50%:75%
1	√		√		√		√
2	√		√		√			√
3	√		√			√	√
4	√		√			√		√
5	√			√	√		√
6	√			√	√			√
7	√			√		√	√
8	√			√		√		√
9		√	√		√		√
10		√	√		√			√
11		√	√			√	√
12		√	√			√		√
13		√		√	√		√
14		√		√	√			√
15		√		√		√	√
16		√		√		√		√

Fig.2 Impact of sample size on errors of different methods in Scenario 4 and Scenario 14. A: Scenario 4; B: Scenario 14. ITT: Intention-to-Treat; PPExc: Per-Protocol method; PPCens: Censoring Method; Treated: As-Treated Method; IPCW: Inverse Probability Censoring Weighting; TSE: Two-Stage Estimation; RPSFTM: Rank Preserving Structural Failure Time Models; IPE: Iterative Parameter Estimation Algorithm.

Fig.3 Forest plot for 16 scenarios with a sample size of 500.

Tab.3 Impact of different hazard ratios on outcomes of different methods

Scenario	Method	Mean Estimate	Bias	MSE	SE	Coverage (%)
Scenario6 beta=0.7 prob=0.75 a=1.2 prob1=(0.50, 0.75)	ITT	0.98	0.28	0.09	0.11	95.2
	PPEXC	0.76	0.06	0.02	0.11	94.9
	PPCENS	2.08	1.38	2.01	0.30	94.6
	Treated	0.92	0.22	0.06	0.11	95.9
	IPCW	0.85	0.15	0.03	0.10	95.2
	Two-Stage	0.84	0.14	0.03	0.10	95.2
	RPSFTM	0.98	0.28	0.08	0.05	99.4
	IPE	0.90	0.20	0.05	0.10	95.1
Scenario7 beta=0.7 prob=0.75 a=2 prob1=(0.10, 0.25)	ITT	0.98	0.28	0.09	0.11	95.4
	PPEXC	0.93	0.23	0.06	0.11	94.3
	PPCENS	1.11	0.41	0.19	0.13	95.3
	Treated	1.09	0.39	0.17	0.13	94.3
	IPCW	0.94	0.24	0.07	0.11	95.4
	Two-Stage	0.94	0.24	0.07	0.11	95.3
	RPSFTM	0.96	0.26	0.08	0.07	98.8
	IPE	0.80	0.10	0.02	0.09	95.6
Scenario14 beta=0.9 prob=0.75 a=1.2 prob1=(0.50, 0.75)	ITT	1.20	0.30	0.10	0.13	95.5
	PPEXC	0.98	0.08	0.02	0.13	94.9
	PPCENS	2.62	1.72	3.07	0.36	95.7
	Treated	1.01	0.11	0.02	0.11	95.8
	IPCW	1.06	0.16	0.04	0.11	94.8
	Two-Stage	1.04	0.14	0.03	0.11	94.6
	RPSFTM	1.00	0.10	0.01	0.01	100.0
	IPE	1.10	0.20	0.06	0.12	95.3
Scenario15 beta=0.9 prob=0.75 a=2 prob1=(0.10, 0.25)	ITT	1.24	0.34	0.13	0.14	95.3
	PPEXC	1.19	0.29	0.10	0.14	95.1
	PPCENS	1.42	0.52	0.30	0.17	95.2
	Treated	1.23	0.33	0.13	0.13	95.0
	IPCW	1.20	0.30	0.11	0.13	94.9
	Two-Stage	1.20	0.30	0.11	0.13	95.3
	RPSFTM	1.14	0.24	0.07	0.12	97.8
	IPE	1.01	0.11	0.02	0.11	95.1

Tab.4 Impact of the probability of "good" prognosis when the hazard ratio is low on outcomes of different methods

Scenario	Method	Mean Estimate	Bias	MSE	SE	Coverage (%)
Scenario1 beta=0.7 prob=0.3 a=1.2 prob1= (0.10, 0.25)	ITT	0.82	0.12	0.02	0.09	94.0
	PPEXC	0.73	0.03	0.01	0.09	93.9
	PPCENS	0.99	0.29	0.10	0.12	93.8
	Treated	1.19	0.49	0.25	0.13	95.3
	IPCW	0.76	0.06	0.01	0.09	93.7
	Two-Stage	0.77	0.07	0.01	0.09	93.4
	RPSFTM	0.84	0.14	0.03	0.09	96.4
	IPE	0.68	-0.02	0.01	0.08	93.6
Scenario5 beta=0.7 prob=0.75 a=1.2 prob1=(0.10, 0.25)	ITT	0.81	0.11	0.02	0.09	94.4
	PPEXC	0.75	0.05	0.01	0.09	94.3
	PPCENS	0.92	0.22	0.06	0.11	94.3
	Treated	0.89	0.19	0.05	0.10	95.1
	IPCW	0.77	0.07	0.01	0.09	94.6
	Two-Stage	0.78	0.08	0.01	0.09	94.3
	RPSFTM	0.82	0.12	0.02	0.09	97.6
	IPE	0.66	-0.04	0.01	0.07	94.9

Tab.5 Impact of the probability of "good" prognosis with a high hazard ratio on outcomes of different methods

Scenario	Method	Mean Estimate	Bias	MSE	SE	Coverage (%)
Scenario9 beta=0.9 prob=0.3 a=1.2 prob1=(0.10, 0.25)	ITT	1.02	0.12	0.03	0.11	95.0
	PPEXC	0.93	0.03	0.01	0.10	95.7
	PPCENS	1.25	0.35	0.14	0.14	96.0
	Treated	1.10	0.2	0.05	0.12	95.9
	IPCW	0.97	0.07	0.01	0.10	95.0
	Two-Stage	0.97	0.07	0.01	0.10	95.5
	RPSFTM	0.99	0.09	0.01	0.05	99.7
	IPE	0.85	-0.05	0.01	0.09	94.9
Scenario13 beta=0.9 prob=0.75 a=1.2 prob1=(0.10, 0.25)	ITT	1.02	0.12	0.03	0.11	94.8
	PPEXC	0.97	0.07	0.02	0.11	95.1
	PPCENS	1.17	0.27	0.09	0.13	94.2
	Treated	1.02	0.12	0.02	0.11	94.6
	IPCW	0.99	0.09	0.02	0.11	95.0
	Two-Stage	0.99	0.09	0.02	0.11	94.9
	RPSFTM	1.00	0.10	0.01	0.07	98.6
	IPE	0.83	-0.07	0.01	0.09	94.9

Tab.6 Best Method Selection Under Different Scenarios

Scenario	Hazard ratios		Prognostic probability (good)		Inflation factor		Switching probability (good, poor)		Method
Scenario	0.7	0.9	30%	75%	1.2	2	10%∶25%	50%∶75%	Method
1	√		√		√		√		IPE/IPCW
2	√		√		√			√	TSE
3	√		√			√	√		IPE
4	√		√			√		√	RPSFTM
5	√			√	√		√		IPE/IPCW
6	√			√	√			√	TSE
7	√			√		√	√		IPE
8	√			√		√		√	TSE
9		√	√		√		√		IPE/IPCW
10		√	√		√			√	RPSFTM
11		√	√			√	√		IPE
12		√	√			√		√	RPSFTM
13		√		√	√		√		IPE/IPCW
14		√		√	√			√	RPSFTM
15		√		√		√	√		IPE
16		√		√		√		√	RPSFTM

References 30

1	Harmonisation ICF. E9(R1) Statistical Principles for Clinical Trials: Addendum: Estimands and Sensitivity Analysis in Clinical Trials; International Council for Harmonisation; Draft Guidance for Industry; Availability[J]. Fed Regist, 2017, 82(209): 7-14.
2	Shih A, Hsu CY, Shyr Y. Power and sample size calculation for non-inferiority trials with treatment switching in intention-to-treat analysis comparing RMSTs.[J]. Research square, 2024.
3	Lejun D, ChihYuan H, Yu S. Power and sample sizes estimation in clinical trials with treatment switching in intention-to-treat analysis: a simulation study.[J]. BMC Med Res Methodol, 2023, 23(1): 49.
4	吴文凯, 何俏, 姚明宏, 等. 罕见事件场景下处理双向治疗转换方法的模拟研究[J]. 中华流行病学杂志, 2025, 46(2): 334-44. DOI: 10.3760/cma.j.cn112338-20240522-00295
5	Latimer NR, Dewdney A, Campioni M. A cautionary tale: an evaluation of the performance of treatment switching adjustment methods in a real world case study[J]. BMC Med Res Methodol, 2024, 24(1): 17.
6	Morden JP, Lambert PC, Latimer N, et al. Assessing methods for dealing with treatment switching in randomised controlled trials: a simulation study[J]. BMC Med Res Methodol, 2011, 11: 4.
7	Latimer NR, Abrams KR, Lambert PC, et al. Assessing methods for dealing with treatment switching in clinical trials: a follow-up simulation study[J]. Stat Methods Med Res, 2018, 27(3): 765-84.
8	Xuan J, Isa SM, Latimer N, et al. Is inverse probability of censoring weighting a safer choice than per-protocol analysis in clinical trials?[J]. Stat Methods Med Res, 2024, 34(2): 1029045591.
9	宋佳丽, 孙凤宇, 卢宇红, 等. MIPE在临床试验转组研究中的应用[J]. 中国卫生统计, 2021, 38(2): 193-7, 203. DOI: 10.3969/j.issn.1002-3674.2021.02.008
10	EunJin A, Hyun K. Intention-to-treat versus as-treated versus per-protocol approaches to analysis.[J]. Korean J Anesthesiol, 2023, 76(6): 531-9.
11	Law MG, Kaldor JM. Author's Reply: Survival analysis of randomized clinical trials adjusted for patients who switch treatments by M. G. Law and J. M. Kaldor, Statistics in Medicine, 15, 2069-2076 (1996)[J]. Statist Med, 1997, 16(22): 2620-1.
12	Xuan J, Isa SM, Latimer N, et al. Is inverse probability of censoring weighting a safer choice than per-protocol analysis in clinical trials[J]? Stat Methods Med Res, 2024, 34(2): 1029045591.
13	Reck M, De TL, Paz-Ares L, et al. Treatment-switching adjustment of overall survival in CheckMate 227 part 1 evaluating first-line nivolumab plus ipilimumab versus chemotherapy for metastatic nonsmall cell lung cancer[J]. Clin Lung Cancer, 2024, 25(7): e362-8.
14	Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology[J]. Epidemiology, 2000, 11(5): 550-60.
15	李玉森. 单向交叉设计生存资料数据IPCW分析方法比较研究[D]. 西安: 第四军医大学, 2013.
16	Gorrod HB, Isa SM, Xuan J, et al. Adjusting for switches to multiple treatments: Should switches be handled separately or combined?[J]. Stat Methods Med Res, 2025, 34(2): 1029056081.
17	于全骥, 倪森淼, 杨旻, 等. 两阶段估计法在肿瘤临床试验转组分析中的应用[J]. 中国临床药理学与治疗学, 2021, 26(4): 395-400. DOI: 10.12092/j.issn.1009-2501.2021.04.006
18	Latimer NR, Abrams KR, Lambert PC, et al. Adjusting for treatment switching in randomised controlled trials - A simulation study and a simplified two-stage method[J]. Stat Methods Med Res, 2017, 26(2): 724-51.
19	Latimer NR, White IR, Abrams KR, et al. Causal inference for long-term survival in randomised trials with treatment switching: Should re-censoring be applied when estimating counterfactual survival times?[J]. Stat Methods Med Res, 2019, 28(8): 2475-93.
20	School Of Health And Related Research UOSS, MRC Clinical Trials Unit UCLL, Population Health Sciences BMSU, et al. Improved two-stage estimation to adjust for treatment switching in randomised trials: g-estimation to address time-dependent confounding[J]. Stat Methods Med Res, 2020, 29(10): 2900-18.
21	Jackson D, Ran D, Zhang F, et al. New methods for two-stage treatment switching estimation[J]. Pharm Stat, 2025, 24(1): e2462.
22	Robins JM, Tsiatis AA. Correcting for non-compliance in randomized trials using rank preserving structural failure time models[J]. Commun Stat Theory Meth, 1991, 20(8): 2609-31.
23	Sullivan TR, Latimer NR, Gray J, et al. Adjusting for Treatment Switching in Oncology Trials: A Systematic Review and Recommendations for Reporting[J]. Value Health, 2020, 23(3): 388-96.
24	Bell Gorrod H, Latimer NR, Damian D, et al. Assessing the long-term effectiveness of cladribine vs. placebo in the relapsing-remitting multiple sclerosis CLARITY randomized controlled trial and CLARITY extension using treatment switching adjustment methods[J]. Adv Ther, 2020, 37(1): 225-39.
25	Latimer NR, Abrams KR, Amonkar MM, et al. Adjusting for the confounding effects of treatment switching-the BREAK-3 trial: dabrafenib versus dacarbazine[J]. Oncologist, 2015, 20(7): 798-805.
26	Bender R, Augustin T, Blettner M. Generating survival times to simulate Cox proportional hazards models[J]. Stat Med, 2005, 24(11): 1713-23.
27	Walker AS, White IR, Babiker AG. Parametric randomization-based methods for correcting for treatment changes in the assessment of the causal effect of treatment[J]. Stat Med, 2004, 23(4): 571-90.
28	Collett D. Modelling survival data in medical research[M]. 3rd ed. Taylor and Francis, 2015: 204-219
29	Burton A, Altman DG, Royston P, et al. The design of simulation studies in medical statistics[J]. Stat Med, 2006, 25(24): 4279-92.
30	Latimer NR. Treatment switching in oncology trials and the acceptability of adjustment methods[J]. Expert Rev Pharmacoecon Outcomes Res, 2015, 15(4): 561-4.