Imputation of Recurrent event data for partial observed off-treatment data

Latest update 23 March 2018

Quick summary

In the past, many trials have stopped collection of data following discontinuation of randomised treatment. However, more recently data collection continues after randomised treatment discontinuation, since the occurrence of this event is irrelevant to the calculation of a treatment policy estimand. When all such data are fully collected, analysis simply ignores the treatment adherence and categorizes patients by their randomisation allocation, grouping together those who complete their randomised treatment and those who do not.

Often patients who discontinue randomised treatment will leave the trial before completion. This leads to missing data all of which is in the off-treatment period. This suggests that it should be imputed using experience off treatment.

For continuous outcome methods are described in the section https://blogs.lshtm.ac.uk/missingdata/2017/04/06/stepwise-imputation-for-marginal-model-based-on-previous-residuals/ where multiple imputation (MI) is used to complete the missed data under models which borrow information from experience in the off-treatment period rather than either the on-treatment period or a combination of both on and off.

The macros described here extend the MI approaches for recurrent event data as described in https://blogs.lshtm.ac.uk/missingdata/2017/04/07/reference-based-mi-for-negative-binomial-discrete-data/ to impute using information borrowed from the off-treatment period only. Patients potentially go through three periods; on randomized treatment, off randomized treatment and finally missing. The data are assumed to follow  a log-linear model with a Negative trinomial distribution (equivalent to Gamma-Poisson model). Details are available in the program headers and examples supplied. A paper has been submitted for publication.

Downloads

The following list of files that can be downloaded here  NegMult20180322

NM_Reg20.sas     Main macro using Negative Multinomial computational approach

NM_Rand11.sas    Main macro using Gamma-Poisson computational approach (slower)

NB_Analze4.sas  Macro to fit Negative Binomial log-linear model to multiple imputed data sets and summarize using Rubin’s formula

NM_Simdata2.sas Program to simulate data based on characteristics or a real trial.

NM_Simdemo4.sas Program which uses these macros to fit a series of possible imputation models including ones that allow piecewise constant, seasonal variation and Delta.

NM_Simdemo4-results.pdf Results from the SimDemo3 program.

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