Online Appendix A: Data generation process for the simulation study
The following steps describe the general data generating process.

Choose the sample size n, the total number of study subjects per data set.

For subject i (𝑖 = 1, ⋯ , 𝑛), the observed data {𝜏𝑖 , 𝒁𝑖 , 𝑿𝑖 , 𝑑𝑖 , 𝑚𝑖 ; (𝑡𝑖𝑗 , 𝛿𝑖𝑗 ): 𝑗 = 1, … , 𝑚𝑖 } are
simulated as follows:

The maximum follow-up time 𝜏 is pre-specified by the study design. Simulate the rightcensoring time 𝐶𝑖 according to the right-censoring hazard function ℎ𝑐 (𝑠). Then the length
of study interval 𝜏𝑖 = min(𝜏, 𝐶𝑖 ).

𝑇
Simulate 𝒁𝑖 and 𝑿𝑖 . Assume 𝑿𝑖 = (𝑥𝑖1 , … , 𝑥𝑖𝑝 ) and 𝒁𝑖 = (1, 𝑿𝑇𝑖 )𝑇 , with 𝑥𝑖1 indicating
the group allocation. Of the 𝑛 (an even integer) study subjects, 𝑛/2 are allocated to the
control group (𝑥𝑖1 = 0) and 𝑛/2 to the intervention group (𝑥𝑖1 = 1).

Simulate 𝑘𝑖 : 𝑘𝑖 ~Bernoulli(𝜋𝑖 ) with 𝜋𝑖 = {1 + exp(−𝒁𝑇𝑖 𝜽 )}−1.
o If 𝑘𝑖 = 1, simulate the subject-specific frailty 𝜔𝑖 from Gamma ( ψ−1 , ψ ). Suppose
∗
∗
∗
𝑢𝑖0
= 0. Given 𝑢𝑖,𝑗−1
( 𝑗 = 1, … ), generate 𝑢𝑖𝑗
, time to the 𝑗 -th event, using the
following event-order specific hazard function
∗
∗
∗
∗
ℎ𝑖𝑗 (𝑢𝑖𝑗
− 𝑢𝑖,𝑗−1
| 𝜔𝑖 , 𝑿𝑖 , 𝑘𝑖 = 1) = 𝜔𝑖 ℎ0𝑗 (𝑢𝑖𝑗
− 𝑢𝑖,𝑗−1
) exp(𝑿𝑇𝑖 𝜷 ).
∗
The observed number of gap times is 𝑚𝑖 = min{𝑗: 𝑢𝑖𝑗
≥ 𝜏𝑖 }. The observed number
∗
of events is 𝑑𝑖 = max{𝑗: 𝑢𝑖𝑗
≤ 𝜏𝑖 }. Thus, the observed gap-time process for (𝑡𝑖𝑗 , 𝛿𝑖𝑗 )
∗
∗
∗
∗
∗
is {(𝑢𝑖1
, 1), ⋯ , (𝑢𝑖,𝑑
− 𝑢𝑖,𝑑
, 1), (𝜏𝑖 − 𝑢𝑖,𝑑
, 0)}. If 𝜏𝑖 = 𝑢𝑖,𝑑
then the last duplet
𝑖
𝑖 −1
𝑖
𝑖
∗
(𝜏𝑖 − 𝑢𝑖,𝑑
, 0, 𝑚𝑖 ) is omitted.
𝑖
o If 𝑘𝑖 = 0, then 𝑑𝑖 = 0 and the observed gap time process is {(𝜏𝑖 , 0)}.
1
Note that under each simulation scenario, the right-censoring hazard functions ℎ𝑐 (𝑠) and the
first-event baseline hazard function ℎ01 (𝑠) are chosen such that a negligible number of
susceptible subjects have their first events being right-censored after the largest observed
uncensored event time within the first event stratum.
We consider two sets of (ℎ𝑐 (𝑠), ℎ0𝑗 (𝑠), 𝜏) and details are given as follows:
(1) For time to the first event, the baseline hazard is a monotonically increasing function in
the form of a Weibull distribution: ℎ01 (𝑠) = 𝑠 5 . And after the first event, the baseline hazard
is a constant within each stratum: ℎ0𝑗 (𝑠) = 3/𝑗 2 for 2 ≤ 𝑗 ≤ 4 and ℎ0𝑗 (𝑠) = 3/16 for 𝑗 > 4.
Three right-censoring settings are chosen: ℎ𝑐 (𝑠) = 4𝑠/9 with 𝜏 = 4, ℎ𝑐 (𝑠) = 4𝑠/13 with
𝜏 = 3 , and ℎ𝑐 (𝑠) = 4𝑠/7 with 𝜏 = 3 , which lead to the right-censoring proportions of,
respectively, about 10%, 25% and 40% among the susceptible subjects in the first event
strata in the control groups.
(2) For time to the first event, the baseline hazard first increases to a peak value and then
gradually decreases, which can be formulated as a log-logistic distribution: ℎ01 (𝑠) =
(1/8)21/8 𝑡 −7/8 /(1 + (2𝑡)1/8 ). After the first event, the baseline hazard is a constant within
each stratum: ℎ0𝑗 (𝑠) = 1/𝑗 2 for 2 ≤ 𝑗 ≤ 4 and ℎ0𝑗 (𝑠) = 1/16 for 𝑗 > 4 . Three rightcensoring settings are chosen: ℎ𝑐 (𝑠) = 1/(16𝑠) with 𝜏 = 16, ℎ𝑐 (𝑠) = 1/(6𝑠) with 𝜏 = 16,
and ℎ𝑐 (𝑠) = 1/(9𝑠 4/5 ) with 𝜏 = 16 , which lead to the right-censoring proportions of,
respectively, about 10%, 25% and 40% among the susceptible subjects in the first event
strata in the control groups.
2
Online Appendix B: Simulation scenario settings
To evaluate the performance of the estimator with the three tail completion methods (0TAIL,
ETAIL and WTAIL), we consider the following two sets of simulation scenarios:
(i) A single binary independent variable: 𝑿𝑖 = 𝑥𝑖1 , 𝒁𝑖 = (1, 𝑥𝑖1 )𝑇 . Set 𝛽1 = 𝜃1 = −0.5, with
three values considered for 𝜃0 : (i.1) 𝜃0 = log(9), (i.2) 𝜃0 = log(1.5) and (i.3) 𝜃0 = −log(9).
In total, there are three parameter specifications for (𝜃0 , 𝜃1 , 𝛽1 ), resembling the situations
where the non-susceptible (or cured) fractions in the control groups are small (10%),
moderate (40%) and large (90%), respectively.
(ii) Two correlated covariates: 𝑿𝑖 = (𝑥𝑖1 , 𝑥𝑖2 )𝑇 , 𝒁𝑖 = (1, 𝑥𝑖1 , 𝑥𝑖2 )𝑇 . Here 𝑥𝑖2 is a quantitative
variable defined as 𝑥𝑖2 = 5 × Beta(2, 5) − 2.5 if 𝑥𝑖1 = 0 and 𝑥𝑖2 = 5 × Beta(5, 2) − 2.5 if
𝑥𝑖1 = 1 , such that 𝑥𝑖1 and 𝑥𝑖2 are strongly correlated (correlation coefficient=0.8).
Furthermore, the respective expectations of 𝑥𝑖2 within the control (𝑥𝑖1 = 0) and intervention
(𝑥𝑖1 = 1) groups are approximately −1.07 and 1.07. The notable difference between the
control and intervention groups in terms of their distributions of 𝑥𝑖2 is set up deliberately. In
this way, the true values of the three versions of summary PE are distinguishable, so are their
corresponding estimates, and this helps to avoid any incidental doubt about mismatch
between the parameter estimates and their true identities in the simulation results. Three
parameter specifications are considered: (ii.1) 𝜃0 = log(9), (ii.2) 𝜃0 = log(1.5) and (ii.3)
𝜃0 = − log(9) , each with (𝜃1 , 𝜃2 , 𝛽1 , 𝛽2 ) = (−0.5, 0.25, −0.5, −0.25). The three
parameter settings imply that 𝑥𝑖2 is a factor representing predisposition to susceptibility, but
if a subject is susceptible, 𝑥𝑖2 becomes a protective factor, protecting from event
occurrence/recurrence.
3
For each of the parameter specifications in sets (i) and (ii), two values are considered for the
frailty parameter ψ (0.5 or 1), representing two different scenarios in which the variance of the
frailty are 0.5 and 1, respectively. Moreover, sample sizes of 500 and 1000 are considered for the
simulation scenarios with small or moderate non-susceptible fractions, i.e., 𝜃0 = log(9) or
log(1.5), whereas 1000 and 3000 for those with high non-susceptible fractions, i.e., 𝜃0 =
−log(9). These sample size settings correspond to realistic situations where a large sample size
is used for a rare event outcome. Therefore, by combination, there are twelve (three parameter
specifications × two values of ψ × two sample sizes) simulation scenarios for each set as
specified in (i) and (ii) above, and altogether there are twenty-four simulation scenarios.
To further evaluate the estimator’s performance with 0TAIL method, the following two
additional simulation settings are considered:
(iii) Sensitivity analysis: we consider two situations where the frailty follows a Log-Normal or
Binary distribution. For clarity,
o Log-Normal
frailty: 𝜔𝑖 = 𝑒 𝑣𝑖 with 𝑣𝑖 ~Normal (−
log(1+ψ)
2
, √log(1 + ψ)) .
Here
Normal(μ, σ) refers to a Normal distribution with mean μ and standard deviation σ;
ψ(1−𝑝1 )
o Binary frailty: Pr ( 𝜔𝑖 = 1 − √
𝑝1
ψ𝑝
) = 𝑝1 and Pr ( 𝜔𝑖 = 1 + √1−𝑝1 ) = 1 − 𝑝1.
1
2
Here 𝑝1 = is arbitrarily chosen to ensure 𝜔𝑖 > 0 for ψ = 0.5 and 1.
3
Note that the above two frailty distributions both satisfy the assumptions E(𝜔𝑖 ) = 1
and Var( 𝜔𝑖 ) = ψ, so as to provide some qualitative insight into the sensitivity of the estimator’s
performance to the Gamma distribution assumption for the frailty but with the same mean and
variances. Simulations are conducted with the parameter specifications in (i.2) and (ii.2) for each
4
frailty distribution. The considered sample sizes are 500 or 1000. There are a total of sixteen
simulation scenarios in the sensitivity analysis set.
(iv) “All susceptible”: We examine the estimator’s performance under the simulation scenarios
with all subjects being susceptible and the parameter specifications are: (iv.1) 𝑿𝑖 = 𝑥𝑖1 with
𝛽1 = −0.5 and (iv.2) 𝑿𝑖 = (𝑥𝑖1 , 𝑥𝑖2 )𝑇 with ( 𝛽1 , 𝛽2 ) = (−0.5, −0.25) . Under either (iv.1)
or (iv.2), the three distributions defined earlier (i.e. Gamma, Log-Normal and Binary) are
assumed for the frailty. Two values, 0.5 and 1, are used for the frailty parameter ψ. In the
interest of space, we use only the sample size 500 for each scenario. This leads to twelve
simulation scenarios in total.
Online Appendix C: Interpretation of results for the simulation scenarios for the settings (iii)
sensitivity analysis and (iv) “all susceptible” using the 0TAIL method.
In what follows, the simulation results concern the scenarios where the baseline hazard of time to
the first event follows a Weibull distribution, the right-censoring proportions among the
susceptible are about 10% in the first event stratum in the control group, the frailty parameter
ψ = 0.5. Results for and conclusions from other simulation settings are similar and thus omitted.
(All simulation result tables are available from the first author on request).
The results of simulation scenarios with one binary independent variable, 𝜃0 = log(1.5), a
mis-specified frailty distribution (Log-Normal or Binary) and sample size 𝑛 = 500 or 1000 are
summarized in Online Table 1. In the case of mis-specified frailty distributions, the estimates for
the frailty parameter ψ are no longer valid as indicated by the large negative bias. Nonetheless,
the estimates with respect to the parameters 𝜽, 𝛽1 and 𝛾 still perform very satisfactorily. The
5
results of the “two correlated covariates” scenarios with Log-Normal and Binary distributed
frailty are similar to their analogues in Online Table 1 and can be found in Online Table 2.
Online Table 3 concerns with the simulation scenarios where all subjects are susceptible, the
frailty distribution follows Gamma, Log-Normal or Binary form and sample size 𝑛 = 500. In
line with all subjects being susceptible, the average parameter estimates for the intercepts of the
logistic regression in predicting the probability of being susceptible are all very large. Across the
simulation scenarios, there is no fixed pattern for the average parameter estimates for 𝜃1 . This is
because the detection of a null intervention effect requires an infinite sample size. Moreover, in
the context of studying common (or rare) events, fitting the data using the logistic regression
with only one binary independent variable would yield a large positive (or negative) intercept
estimate reflecting the high (or low) event prevalence observed in the control group. On this
point, as the logistic function is subject to “saturation” at both tails, a small difference between
groups in the observed prevalences based on finite sample size would translate into very
dramatic and irrational estimate for the intervention effect. The estimated hessian matrix for 𝜽
involved multipliers 𝜋̂𝑖 (1 − 𝜋̂𝑖 ) where 𝜋̂𝑖 was the predicted probability of being susceptible and
very close to 1, thus 𝜋̂𝑖 (1 − 𝜋̂𝑖 ) was close to 0 and the matrix inversion produced very large
values for the variance estimates. Nonetheless, under all scenarios regardless of whether the
frailty distribution is correctly specified, the estimation results for 𝛽1 and 𝛾 are satisfactory.
Online Table 4 shows the results of the “two correlated covariates” scenarios where all
subjects are susceptible, the frailty follows the Gamma, Log-Normal or Binary distribution, and
sample size 𝑛 = 500. We observed very large negative values in the estimates for 𝛾1 and 𝛾2
under these scenarios. This happens for the following reason. The quantitative variable 𝑥𝑖2
consisted mostly of negative values in the control group and positive values in the intervention
6
group, and the distributions of these sample value 𝑥𝑖2 ’s for the two groups may overlap very
little due to the finite sample size. If this was the case and in the context of common event
outcomes, fitting the logistic regression model to the data tends to produce a large negative value
of parameter estimate for 𝜃2 and large positive values of parameter estimates for 𝜃0 and 𝜃1 . As a
result, the predicted probabilities and the predicted counterfactuals based on the values of 𝑥𝑖2 in
the control group were all very close to 1, thus the summary PE parameter 𝛾0 can be reasonably
estimated. In contrast, though the predicted probabilities for the subjects in the intervention
group were very close to 1, their predicted counterfactuals (after removing the large intervention
effect) might be grossly biased downwards. The resulting estimates for 𝛾1, hence 𝛾2 which was
a weighted average of 𝛾0 and 𝛾1, would become irrationally small. In return, the averages of
these estimates will be affected by these outliers and driven downwards. In light of this, all the
summary statistics with respect to the three summary PE parameters 𝛾𝑙 (𝑙 = 0, 1, 2) were
calculated after omitting those corresponding to the smallest 5% of the parameter estimates
for 𝛾1. The choice of the trimming proportion was rather arbitrary, depend on the severity of the
outliers and the number of replications, and we chose 5%. The parameter estimates for 𝛾𝑖 after
trimming are very close to the truth. Across all the simulation scenarios, the estimator
performance is satisfactory.
7
Online Table 1: Simulation results using 0TAIL method for the scenarios with a single binary
independent variable, Log-Normal or Binary distributed frailty and parameter specifications in set (i.2)
where 𝜃0 = log(1.5).
Frailty
LogNormal
Binary
Parameter
Sample size
True value
Estimate
Bias
Empirical
SD
𝜃0
𝜃1
𝛽1
ψ
𝛾
500
0.4055
-0.5
-0.5
0.5
0.5184
0.4071
-0.5070
-0.4949
0.3230
0.5114
0.0016
-0.0070
0.0051
-0.1770
-0.0070
0.1361
0.1883
0.1399
0.1098
0.0783
0.1376
0.1935
0.1310
0.0651
0.0762
95.0
96.6
92.0
-94.4
----94.4
𝜃0
𝜃1
𝛽1
ψ
𝛾
1000
0.4055
-0.5
-0.5
0.5
0.5184
0.4087
-0.5105
-0.5010
0.3534
0.5160
0.0032
-0.0105
-0.0010
-0.1466
-0.0024
0.1300
0.1899
0.1298
0.1164
0.0738
0.1377
0.1936
0.1334
0.0675
0.0765
95.2
94.8
95.0
-95.2
----96.2
𝜃0
𝜃1
𝛽1
𝜓
𝛾
500
0.4055
-0.5
-0.5
0.5
0.5184
0.4023
-0.5013
-0.4974
0.3477
0.5144
-0.0032
-0.0013
0.0026
-0.1523
-0.0040
0.0988
0.1390
0.0985
0.0859
0.0567
0.0971
0.1365
0.0937
0.0469
0.0540
94.6
94.4
95.4
-94.8
----94.4
𝜃0
𝜃1
𝛽1
𝜓
𝛾
1000
0.4055
-0.5
-0.5
0.5
0.5184
0.4052
-0.4989
-0.4983
0.3672
0.5141
-0.0003
0.0011
0.0017
-0.1328
-0.0043
0.0993
0.1464
0.1000
0.0869
0.0574
0.0972
0.1366
0.0947
0.0480
0.0544
95.6
93.4
93.8
-93.2
----92.6
8
Average
SE
CPN
(%)
CPLog
(%)
Online Table 2: Simulation results using 0TAIL method for the scenarios with two correlated covariates,
Log-Normal or Binary distributed frailty and parameter specifications in set (ii.2) where 𝜃0 = log(1.5).
Frailty
LogNormal
Binary
True value
Estimate
Bias
Empirical
SD
Average
SE
CPN
(%)
CPLog
(%)
500
0.4055
-0.5
0.25
-0.5
-0.25
0.5
0.5340
0.5026
0.5183
0.4069
-0.4995
0.2557
-0.4852
-0.2466
0.3128
0.5119
0.4755
0.4937
0.0014
0.0005
0.0057
0.0148
0.0034
-0.1872
-0.0221
-0.0271
-0.0246
0.1898
0.3288
0.1193
0.2182
0.0850
0.1075
0.1397
0.1323
0.1352
0.1903
0.3261
0.1224
0.2137
0.0802
0.0653
0.1343
0.1266
0.1305
95.6
94.6
96.0
94.0
93.8
-93.6
94.8
94.6
------94.6
94.6
94.8
𝜃0
𝜃1
𝜃2
𝛽1
𝛽2
ψ
𝛾0
𝛾1
𝛾2
1000
0.4055
-0.5
0.25
-0.5
-0.25
0.5
0.5340
0.5026
0.5183
0.4066
-0.4940
0.2506
-0.5000
-0.2490
0.3393
0.5247
0.4918
0.5083
0.0012
0.0060
0.0006
0.0000
0.0010
-0.1607
-0.0093
-0.0107
-0.0100
0.1291
0.2252
0.0812
0.1569
0.0560
0.0791
0.0953
0.0900
0.0921
0.1343
0.2302
0.0862
0.1534
0.0573
0.0473
0.0935
0.0874
0.0905
96.0
94.6
96.0
95.0
95.4
-94.6
95.0
94.6
------94.6
94.2
94.6
𝜃0
𝜃1
𝜃2
𝛽1
𝛽2
ψ
𝛾0
𝛾1
𝛾2
500
0.4055
-0.5
0.25
-0.5
-0.25
0.5
0.5340
0.5026
0.5183
0.4020
-0.4922
0.2486
-0.4941
-0.2411
0.3347
0.5112
0.4755
0.4934
-0.0035
0.0078
-0.0014
0.0059
0.0089
-0.1653
-0.0228
-0.0271
-0.0249
0.2013
0.3493
0.1301
0.2291
0.0902
0.1121
0.1503
0.1422
0.1455
0.1907
0.3271
0.1227
0.2180
0.0815
0.0680
0.1357
0.1288
0.1323
93.6
92.0
93.0
93.4
92.6
-94.2
94.2
93.8
------92.0
93.4
92.6
𝜃0
𝜃1
𝜃2
𝛽1
𝛽2
ψ
𝛾0
𝛾1
𝛾2
1000
0.4055
-0.5
0.25
-0.5
-0.25
0.5
0.5340
0.5026
0.5183
0.4121
-0.5099
0.2506
-0.5037
-0.2413
0.3523
0.5309
0.4971
0.5140
0.0067
-0.0099
0.0006
-0.0037
0.0087
-0.1477
-0.0031
-0.0055
-0.0043
0.1350
0.2325
0.0846
0.1570
0.0570
0.0799
0.0914
0.0864
0.0883
0.1344
0.2307
0.0864
0.1551
0.0580
0.0487
0.0931
0.0873
0.0902
95.0
95.0
96.0
95.2
96.6
-94.0
95.0
94.6
------96.0
96.0
96.4
Parameter
Sample size
𝜃0
𝜃1
𝜃2
𝛽1
𝛽2
ψ
𝛾0
𝛾1
𝛾2
9
Online Table 3: Simulation results using 0TAIL method for the scenarios with “all susceptible”, a single
binary independent variable (𝛽1 = −0.5), Gamma (or Log-Normal, Binary) distributed frailty and
sample size of 500.
Estimate
Bias
Empirical SD
Average
SE
32.5254
-0.1262
-0.4959
0.4659
0.3877
--0.0041
-0.0341
-0.0058
6.7270
5.3996
0.1065
0.0981
0.0649
2107209
2974994
0.1009
0.0531
0.0617
--93.4
-93.4
----93.6
3.4084
2.8697
0.1011
0.0849
0.0622
2337499
3307376
0.0941
0.0466
0.0579
--91.6
-93.0
----91.4
1.2472
2416672
-1.4218
3411796
-0.1017
0.0961
93.0
0.0893
0.0490
-0.0622
0.0588
93.6
Note: as all study subjects are susceptible, the true value for 𝛾 is 1 − exp(𝛽1 ) = 0.3935.
----93.0
Frailty
Gamma
𝜃0
𝜃1
𝛽1
ψ
𝛾
True value
---0.5
0.5
0.3935
LogNormal
𝜃0
𝜃1
𝛽1
ψ
𝛾
---0.5
0.5
0.3935
34.2354
-0.1412
-0.4887
0.3358
0.3835
--0.0113
-0.1642
-0.0100
---0.5
0.5
0.3935
34.7197
-0.0759
-0.4951
0.3710
0.3873
--0.0049
-0.1290
-0.0061
Binary
Parameter
𝜃0
𝜃1
𝛽1
ψ
𝛾
10
CPN
(%)
CPLog
(%)
Online Table 4: Simulation results using 0TAIL method for the scenarios with “all susceptible”, two
correlated covariates (𝛽1 = −0.5, 𝛽2 = −0.25), Gamma (or Log-Normal, Binary) distributed frailty
where ψ = 0.5, and sample size of 500.
𝜃0
𝜃1
𝜃2
𝛽1
𝛽2
ψ
𝛾0
𝛾1
𝛾2
True value
----0.5
-0.25
0.5
0.3935
0.3935
0.3935
Estimate
30.3359
0.4524
-0.0855
-0.4910
-0.2499
0.4714
0.3949
0.3810
0.3879
Bias
---0.0090
0.0001
-0.0286
0.0014
-0.0125
-0.0055
Empirical
SD
9.0845
7.5923
2.2753
0.1690
0.0627
0.0939
0.1084
0.1012
0.1016
Average
SE
2528714
4339710
1620692
0.1686
0.0632
0.0540
0.1087
0.1258
0.1217
CPN
(%)
---94.8
95.4
-94.7
96.4
96.0
CPLog
(%)
------95.4
95.8
96.0
Log-Normal
𝜃0
𝜃1
𝜃2
𝛽1
𝛽2
ψ
𝛾0
𝛾1
𝛾2
----0.5
-0.25
0.5
0.3935
0.3935
0.3935
31.6197
0.5004
-0.4677
-0.5017
-0.2447
0.3319
0.3931
0.3859
0.3895
31.6197
0.5004
-0.4677
-0.0017
0.0053
-0.1681
-0.0003
-0.0076
-0.0040
7.5290
5.8322
2.3212
0.1558
0.0596
0.0799
0.0997
0.0957
0.0958
2741682
4696193
1758908
0.1560
0.0585
0.0470
0.0983
0.1271
0.1191
---94.8
94.2
-94.3
95.0
94.7
------95.0
95.2
95.0
Binary
𝜃0
𝜃1
𝜃2
𝛽1
𝛽2
ψ
𝛾0
𝛾1
𝛾2
----0.5
-0.25
0.5
0.3935
0.3935
0.3935
32.7035
0.3226
-0.4661
-0.4896
-0.2479
0.3446
0.3838
0.3791
0.3815
32.7035
0.3226
-0.4661
0.0104
0.0021
-0.1554
-0.0097
-0.0143
-0.0120
Frailty
Parameter
Gamma
6.1156
2918981
--4.8995
5010071
--2.0984
1873854
--0.1661
0.1571
93.8
-0.0629
0.0589
93.0
-0.0775
0.0487
--0.1021
0.1003
92.9
94.5
0.1008
0.1386
93.5
94.7
0.1005
0.1278
93.5
94.7
Note: as all study subjects are susceptible, the true values for 𝛾0 , 𝛾1 and 𝛾2 are 1 − exp(𝛽1 ) = 0.3935.
11