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Therapeutic lag: Is treatment effect delayed in progressive MS?
Multiple Sclerosis : Clinical and Laboratory Research 2024 April 14
BACKGROUND: Randomized clinical trials (RCTs) in progressive multiple sclerosis (MS) often revealed non-significant treatment effects on disability progression.
OBJECTIVES: To investigate whether the failure to detect a significant benefit from treatment may be motivated by a delay in treatment effect, possibly related to baseline characteristics.
METHODS: We re-analyzed data from two RCTs testing interferon-beta and glatiramer-acetate versus placebo in progressive MS with no significant effect on EDSS progression. We first designed a time-dependent Cox model with no treatment effect up to time = t0 , and constant hazard ratio (HR) after time = t0 . We selected the best-fitting t0 from 0 (standard Cox model) to 2.5 years. Furthermore, we modeled the delay as a function of baseline EDSS and fitted the resulting Cox model to the merged dataset.
RESULTS: The time-dependent Cox model revealed a significant benefit of treatment delayed by t0 = 2.5 years for the SPECTRIMS study (HR = 0.65 (0.43-0.98), p = 0.041), and delayed by t0 = 2 years for the PROMISE study (HR = 0.65, (0.42-0.99), p = 0.044). In the merged dataset, the HR for the EDSS-dependent delayed effect was 0.68 (0.56, 0.82), p < 0.001.
CONCLUSION: The assumption of a delayed treatment effect improved the fit to the data of the two examined RCTs, uncovering a significant, although shifted, benefit of treatment.
OBJECTIVES: To investigate whether the failure to detect a significant benefit from treatment may be motivated by a delay in treatment effect, possibly related to baseline characteristics.
METHODS: We re-analyzed data from two RCTs testing interferon-beta and glatiramer-acetate versus placebo in progressive MS with no significant effect on EDSS progression. We first designed a time-dependent Cox model with no treatment effect up to time = t0 , and constant hazard ratio (HR) after time = t0 . We selected the best-fitting t0 from 0 (standard Cox model) to 2.5 years. Furthermore, we modeled the delay as a function of baseline EDSS and fitted the resulting Cox model to the merged dataset.
RESULTS: The time-dependent Cox model revealed a significant benefit of treatment delayed by t0 = 2.5 years for the SPECTRIMS study (HR = 0.65 (0.43-0.98), p = 0.041), and delayed by t0 = 2 years for the PROMISE study (HR = 0.65, (0.42-0.99), p = 0.044). In the merged dataset, the HR for the EDSS-dependent delayed effect was 0.68 (0.56, 0.82), p < 0.001.
CONCLUSION: The assumption of a delayed treatment effect improved the fit to the data of the two examined RCTs, uncovering a significant, although shifted, benefit of treatment.
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