From: Independence estimators for re-randomisation trials in multi-episode settings: a simulation study
Estimand | Definition | Description | Estimator |
---|---|---|---|
Per-episode added-benefit | \({\upbeta}_E^{AB}=E\left({Y}_{(IJ)^E}^{\left(Z=1,\overset{\sim }{Z}\right)}-{Y}_{(IJ)^E}^{\left(Z=0,\overset{\sim }{Z}\right)}\right)\) | Provides the additional effect of being assigned the intervention in the current episode, over and above the benefit of being assigned the intervention in previous episodes Provides an average effect across episodes | \({\hat{\upbeta}}_E^{AB}=\frac{\sum_{ij}{Y}_{ij}{Z}_{ij}}{\sum_{ij}{Z}_{ij}}-\frac{\sum_{ij}{Y}_{ij}\left(1-{Z}_{ij}\right)}{\sum_{ij}\left(1-{Z}_{ij}\right)}\) |
Per-episode policy-benefit | \({\upbeta}_E^{PB}=E\left({Y}_{(IJ)^E}^{\left(Z=1,\overset{\sim }{Z}=\overset{\sim }{1}\right)}-{Y}_{(IJ)^E}^{\left(Z=0,\overset{\sim }{Z}=\overset{\sim }{0}\right)}\right)\) | Provides the effect of a treatment policy where patients are assigned intervention vs. control for all episodes Provides an average effect across episodes | Step 1: \({Y}_{ij}=\upalpha +\upbeta {Z}_{ij}+\upgamma {Z}_{i,j-1}+\updelta {Z}_{ij}{Z}_{i,j-1}+{\beta}_{ep}{X}_{e{p}_{ij}}+{\upvarepsilon}_{ij}\) Step 2: \({\hat{\upbeta}}_E^{PB}=\frac{N_1}{M_T}\left(\hat{\upbeta}\right)+\frac{N_2}{M_T}\left(\hat{\upgamma}+\hat{\upbeta}+\hat{\updelta}\right)\) |
Per-patient added-benefit | \({\upbeta}_P^{AB}=E\left({Y}_{(IJ)^P}^{\left(Z=1,\overset{\sim }{Z}\right)}-{Y}_{(IJ)^P}^{\left(Z=0,\overset{\sim }{Z}\right)}\right)\) | Provides the additional effect of being assigned the intervention in the current episode, over and above the benefit of being assigned the intervention in previous episodes Provides an average effect across patients | \({\hat{\upbeta}}_P^{AB}=\frac{\sum_{ij}{W}_i{Y}_{ij}{Z}_{ij}}{\sum_{ij}{W}_i{Z}_{ij}}-\frac{\sum_{ij}{W}_i{Y}_{ij}\left(1-{Z}_{ij}\right)}{\sum_{ij}{W}_i\left(1-{Z}_{ij}\right)}\) |
Per-patient policy-benefit | \({\upbeta}_P^{PB}=E\left({Y}_{(IJ)^P}^{\left(Z=1,\overset{\sim }{Z}=\overset{\sim }{1}\right)}-{Y}_{(IJ)^P}^{\left(Z=0,\overset{\sim }{Z}=\overset{\sim }{0}\right)}\right)\) | Provides the effect of a treatment policy where patients are assigned intervention vs. control for all episodes Provides an average effect across patients | Step 1: \({Y}_{ij}=\upalpha +\upbeta {Z}_{ij}+\upgamma {Z}_{i,j-1}+\updelta {Z}_{ij}{Z}_{i,j-1}+{\beta}_{ep}{X}_{e{p}_{ij}}+{\upvarepsilon}_{ij}\) using weighted least squares, with weights \({W}_i=\frac{1}{M_i}\). Step 2: \({\hat{\upbeta}}_P^{PB}=\frac{M_{T(1)}}{N_T}\left(\hat{\upbeta}\right)+\frac{M_{T(2)}}{N_T}\left(\frac{1}{2}\hat{\upbeta}+\left(\frac{1}{2}\right)\left(\hat{\upbeta}+\hat{\upgamma}+\hat{\updelta}\right)\right)\) |