drizopoulos
commented
5 years ago Thanks for your e-mail and interest in my package. Regarding your question, please check the following:
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If there are any event times that are exactly equal to zero, set those to a small positive number, e.g., 1e-05.
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Order the longitudinal data according the subject id variable, and order the time points within each subject with respect to time. You can use the order() function for this, e.g., my_data <- my_data[order(my_data$id, my_data$time), ].
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Remove any missing data you have in the variable you are going to use in the analysis beforehand, e.g., my_data_nomiss <- my_data[complete.cases(my_data[c("long_outcome", "time", "age", "bmi", "sex")]), ].
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Assuming the you have the data as described above, and that the event time variable is also in the longitudinal dataset, construct the dataset for the survival submodel using my_data_surv <- my_data[!duplicated(my_data$id), ].
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If you have longitudinal measurements after the event times, these are not allowed in my package.
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Set both model = TRUE and x = TRUE in the call to coxph().
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If you have any missing data, then first exclude them and then run the linear mixed and Cox models.
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It is better to use the adaptive Gauss-Hermite rule, i.e., in the definition of the 'method' argument use, e.g., "piecewise-PH-aGH" instead of "piecewise-PH-GH".
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It is better to use the piecewise-constant or spline-approximated baseline hazard instead of the unspecified hazard (i.e., method = "piecewise-PH-aGH" or "spline-PH-aGH" instead of "Cox-PH-aGH").
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If the knots for the B-spline or the piecewise-constant baseline hazard are placed in parts where there are no or very few events. You can override the default placing of the knots by manually setting them at specific time points based on, e.g., the Kaplan-Meier estimator. In this case you can use the 'knots' argument.
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The time scale must be the same in both the longitudinal and survival submodels.
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If in the linear mixed model it appears that some coefficients have very different magnitudes than others, then try centering or scaling some of the offending covariates.
On 5/20/2019 9:11 AM, Chaochen Wang wrote:
Hi, thanks for the amazing package. I am wondering in the two parts of the joint modeling framework, can they accept patients entering the study/observation from different time points?
I am dealing with data from a patient database, therefore, in a real-world setting, patients come to visit the doctor and with their blood sample drawn at totally different time points/intervals. Some patients started early some with their initial recording later.
So my question is whether the survival part of the Joint Modeling within this package can be fitted using the following form?
|coxFit.DF <- coxph(Surv(start, stop, event) ~ sex + age + group, data, x = TRUE) |
Because when I fitted the above Cox model with the difference between the times they were followed |time = stop-start|:
|coxFit.DF <- coxph(Surv(time, event) ~ sex + age + group, data, x = TRUE) |
There were errors telling me that some patients had observations after the events. And when I use the |Surv(start, stop, event)| one, error message also came up and ask me to refit the Cox model using |x = TRUE| argument which I had already added.
One of the flexibility of the time-dependent Cox regression model is that patients are allowed to enter the study from different start points, If there is also a way to allow for this in the Joint Modeling framework, I would like to see how and it will be greatly appreciated.
Many thanks.
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-- Dimitris Rizopoulos Professor of Biostatistics Department of Biostatistics Erasmus University Medical Center
Address: PO Box 2040, 3000 CA Rotterdam, the Netherlands Tel: +31/(0)10/7043478 Fax: +31/(0)10/7043014 Web (personal): http://www.drizopoulos.com/ Web (work): http://www.erasmusmc.nl/biostatistiek/ Blog: http://iprogn.blogspot.nl/
winterwang
Hi, thanks for the amazing package. I am wondering in the two parts of the joint modeling framework, can they accept patients entering the study/observation from different time points?
I am dealing with data from a patient database, therefore, in a real-world setting, patients come to visit the doctor and with their blood sample drawn at totally different time points/intervals. Some patients started early some with their initial recording later.
So my question is whether the survival part of the Joint Modeling within this package can be fitted using the following form?
Because when I fitted the above Cox model with the difference between the times they were followed
time = stop-start:There were errors telling me that some patients had observations after the events. And when I use the
Surv(start, stop, event)one, error message also came up and ask me to refit the Cox model usingx = TRUEargument which I had already added.One of the flexibility of the time-dependent Cox regression model is that patients are allowed to enter the study from different start points, If there is also a way to allow for this in the Joint Modeling framework, I would like to see how and it will be greatly appreciated.
Many thanks.