英文:
Rescale model linear predictor in rms::cph
问题
我正在使用 rms 包来推导一个用于预测对药物的响应的得分。
实际上，我得出了两个得分：一个仅包括治疗前变量 (x 和 y)，另一个还包括只能在治疗6个月后测量的变量 (z)。
因此，这两个模型分别是：
score_baseline <– cph(Surv(t, response)~ x + y, data = df)
score_6months <– cph(Surv(t, response)~ x + y + z, data = df)
通过这样做，这两个得分具有不同的线性预测器比例尺和不同的基线生存值 (S0(t))。是否有一种方法可以重新缩放其中一个模型的系数（例如 score_6months），使两个线性预测器具有相同的比例尺和相同的基线生存估计值？
感谢帮助。
英文:
I'm working with the rms package to derive a score for predicting response to a drug.
I actually derived two scores: one including only pre-treatment variables (x and y), and a second including also variable that can be measured only after 6 months of therapy (z).
So the models are:
score_baseline <– cph(Surv(t, response)~ x + y, data = df)
score_6months <– cph(Surv(t, response)~ x + y + z, data = df)
By doing that, the two scores have different scales of the linear predictor and different baseline survival values (S0(t)). Is there a way to rescale the coefficients of one of the two models (e.g. score_6months) and have the two linear predictors on the same scale and also same baseline survival estimates?
Thanks for help.
答案1
得分: 1
不仅我认为没有办法这样做，我也认为尝试使用相同的零时间来建模这两种结果并不是一个好主意。您将z视为在零时间测量的，但实际上并不是，患者必须在存活至少6个月后才能应用z。因此，第二个模型似乎需要是一个条件模型，即在患者至少存活6个月的情况下进行条件分析。
英文:
Not only do I not think there is a way to do that, I don't think it's a good idea to try to model the two outcomes using the same time zero. You are treating z as if it's measured at time zero but it's not, and patients must survive 6 months to have z apply. So it seems that the second model needs to be a conditional one, that is, conditional on (subsetting on) patients surviving at least 6m.
通过集体智慧和协作来改善编程学习和解决问题的方式。致力于成为全球开发者共同参与的知识库，让每个人都能够通过互相帮助和分享经验来进步。
评论