. Author manuscript; available in PMC: 2023 Mar 18.

Published in final edited form as: Med Decis Making. 2022 Dec 13;43(3):337–349. doi: 10.1177/0272989X221143058

Table 2.

Detailed description of net benefit (NB) estimation for ‘treat all with treatment j’, and ‘treat patients according to the prediction model’ strategies on the RRMS example. The threshold values for glatiramer acetate, dimethyl fumarate, and natalizumab are noted as T_GA, T_DF, and T_N respectively.

	Treat all with				Treat according to the model
	Placebo (‘treat none’)	Glatiramer acetate	Dimethyl fumarate	Natalizumab	Placebo (‘treat none’)	Glatiramer acetate	Dimethyl fumarate	Natalizumab	Total
Treatment rate	0%	100%	100%	100%	$p_{0}^{D a t a_{s}}$	$p_{1}^{D a t a_{s}}$	$p_{2}^{D a t a_{s}}$	$p_{3}^{D a t a_{s}}$	$\sum_{j = 0}^{J} p_{j}^{D a t a_{s}}$
Event rate	${\hat{ε}}_{0}$ as a meta-analysis of all placebo arms in Data	${\hat{ε}}_{1} = {\hat{ε}}_{0} \times R R_{1}^{D a t a}$	${\hat{ε}}_{2} = \hat{ε_{0}} \times R R_{2}^{D a t a}$	${\hat{ε}}_{3} = \hat{ε_{0}} \times R R_{3}^{D a t a}$	${\hat{ε}}_{s, 0}$ as a meta-analysis of all placebo arms in Data_s	${\hat{ε}}_{s, 1} = {\hat{ε}}_{s, 0} \times R R_{1}^{D a t a}$	${\hat{ε}}_{s, 2} = {\hat{ε}}_{s, 0} \times R R_{2}^{D a t a}$	${\hat{ε}}_{s, 3} = {\hat{ε}}_{s, 0} \times R R_{3}^{D a t a_{s}}$	${\hat{ε}}_{s} = \sum_{j = 0}^{J} p_{j}^{D a t a_{s}} \times {\hat{ε}}_{s, j}$
Decrease in event rate	0	${\hat{ε}}_{0} - {\hat{ε}}_{1}$	${\hat{ε}}_{0} - {\hat{ε}}_{2}$	${\hat{ε}}_{0} - {\hat{ε}}_{3}$	${\hat{ε}}_{0} - \sum_{j = 0}^{J} p_{j}^{D a t a_{s}} \times {\hat{ε}}_{s, j}$
Net strategy Benefit	0	$N B_{1} = {\hat{ε}}_{0} - {\hat{ε}}_{1} - T_{GA}$	$N B_{2} = {\hat{ε}}_{0} - {\hat{ε}}_{2} -$	$N B_{3} = {\hat{ε}}_{0} - {\hat{ε}}_{3} - T_{N}$	$N B_{m o d e l} = {\hat{ε}}_{0} - \sum_{j = 0}^{J} p_{j}^{D a t a_{s}} \times {\hat{ε}}_{s, j} - \sum_{j = 1}^{J} p_{j}^{D a t a_{s}} \times T_{j}$