Clinical trial NCT03070470
Conditions: Drug-induced QT Prolongation Pharmacokinetics Pharmacodynamics
Sponsor: Food and Drug Administration (FDA)
Start date: 2017-03-14; Completion date: 2017-06-26
Enrollment: 60 participants; Allocation: RANDOMIZED; Intervention model: PARALLEL

Brief summary:
This study will assess whether exposure response analysis of the electrocardiographic QTc and J-Tpeakc intervals in Phase 1 clinical pharmacology studies can be used to confirm that drugs that predominantly block the potassium channel encoded by the human ether-à-go-go-related gene (hERG) with approximately equipotent late sodium and/or calcium block ("balanced ion channel" drugs) do not cause J-Tpeakc prolongation and that drugs that predominantly block hERG without late sodium or L-type calcium current block ("predominant hERG" drugs) cause QTc prolongation.

Detailed description:
This study will assess whether exposure response analysis of the electrocardiographic QTc and J-Tpeakc intervals in Phase 1 clinical pharmacology studies can be used to confirm that "balanced ion channel" drugs do not cause J-Tpeakc prolongation and that "predominant hERG" drugs cause QTc prolongation. This clinical study consists of 2 parts: a 50-subject parallel part (Part 1) and a 10-subject crossover part (Part 2). Up to 74 healthy subjects will be enrolled (including 14 potential replacement subjects).

Part 1 will be a double-blind, randomized, placebo-controlled, 1 period parallel design to assess the effect of 4 marketed drugs and 1 placebo on the QTc and J-Tpeakc intervals in 50 healthy subjects. A parallel design similar to a single or multiple ascending dose (SAD/MAD) Phase 1 study will be used that will result in each study drug being administered to 10 subjects, and placebo to 10 subjects, in 1 period of 3 consecutive days to achieve low and high drug exposure.

Part 2 will be a double-blind, randomized, 2-period crossover design to assess the effect of hERG block (dofetilide) versus calcium block (diltiazem) on the QTc and J-Tpeakc intervals in 10 healthy subjects on Days 1, 2, and 3 (Period 1) and Days 8, 9, and 10 (Period 2).

Arms: arm 1: Ranolazine 1500 mg two times per day for 2.5 days arm 2: Verapamil 120 mg immediate release (IR) morning and afternoon doses on Days 1 and 2, 240 mg extended release (ER) evening dose on Days 1 and 2, and 120 mg IR morning dose on Day 3 arm 3: Lopinavir / Ritonavir 800 mg / 200 mg two times per day for 2.5 days arm 4: Chloroquine 1000 mg on Day 1, 500 mg on Day 2, 1000 mg on Day 3 arm 5: Placebo capsules arm 6: In one period subjects receive Dofetilide 0.125 mg on Day 1, 0.375 mg on Day 3.

In a second period (randomized cross-over) subject receive Diltiazem 120 mg IR morning dose on Day 8, 240 mg ER evening dose on Days 8 and 9, and 120 mg IR on Day 10 with coadministration of 0.25 mg dofetilide on Day 10.
Interventions: intervention 1: Ranolazine intervention 2: Verapamil intervention 3: Lopinavir / Ritonavir intervention 4: Chloroquine intervention 5: Placebo intervention 6: Dofetilide and Diltiazem
Intervention descriptions: intervention 1: Ranolazine 1500 mg orally two times per day for 2.5 days intervention 2: Verapamil 120 mg immediate release (IR) morning and afternoon doses on Days 1 and 2, 240 mg extended release (ER) evening dose on Days 1 and 2, and 120 mg IR morning dose on Day 3 (all oral doses) intervention 3: Lopinavir / Ritonavir 800 mg / 200 mg orally two times per day for 2.5 days intervention 4: Chloroquine 1000 mg on Day 1, 500 mg on Day 2, 1000 mg on Day 3 (all oral doses) intervention 5: Placebo (administered orally) intervention 6: In one period subjects receive Dofetilide 0.125 mg on Day 1, 0.375 mg on Day 3.

In a second period (randomized cross-over) subject receive Diltiazem 120 mg IR morning dose on Day 8, 240 mg ER evening dose on Days 8 and 9, and 120 mg IR on Day 10 with coadministration of 0.25 mg dofetilide on Day 10.

Protocol documents:
Prot_000.pdf: 







































































































































































SAP_001.pdf: 
Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 1 of 45
Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Sponsor:
U.S. Food and Drug Administration
White Oak Building #64, Room 2072
10903 New Hampshire Avenue
Silver Spring, MD 20993
Sponsor Study Lead and 
Medical Monitor:
David Strauss, MD, PhD
Director, Division of Applied Regulatory Science 
U.S. Food and Drug Administration
Telephone: 301-796-6323
Email: david.strauss@fda.hhs.gov
FDA RIHSC Sponsor:
David Strauss, MD, PhD
Project Manager:
Jose Vicente, PhD
U.S. Food and Drug Administration
Study Monitor:
Jill Brown
RIHSC Project Manager
U.S. Food and Drug Administration
Version of SAP:
5.1
Date of SAP:
25 March 2017
CONFIDENTIAL
The concepts and information contained in this document or generated during the study are 
considered proprietary and may not be disclosed in whole or in part without the expressed 
written consent of the U.S. Food and Drug Administration.


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 2 of 45
Sponsor Signatures Page 
Prepared by
March 25, 2017
Jose Vicente, PhD
Visiting Scientist, Division of Cardiovascular 
and Renal Products
U.S. Food and Drug Administration
Date
Reviewed by
March 25, 2017
Robbert Zusterzeel, MD, PhD
Staff Fellow, Division of Applied Regulatory 
Science 
U.S. Food and Drug Administration
Date
March 25, 2017
Approved by
David Strauss, MD, PhD
Director, Division of Applied Regulatory 
Science 
U.S. Food and Drug Administration
Date
Jose Vicente 
Ruiz -S
Digitally signed by Jose Vicente Ruiz -S 
DN: c=US, o=U.S. Government, ou=HHS, 
ou=FDA, ou=People, 
0.9.2342.19200300.100.1.1=2002160424, 
cn=Jose Vicente Ruiz -S 
Date: 2017.03.25 11:39:29 -04
'00
'
Robbert 
Zusterzeel -S
Digitally signed by Robbert Zusterzeel -S 
DN: c=US, o=U.S. Government, ou=HHS, 
ou=FDA, ou=People, 
0.9.2342.19200300.100.1.1=2001114905, 
cn=Robbert Zusterzeel -S 
Date: 2017.03.25 11:45:53 -04
'00
'
Digitally signed by David Strauss -S 
DN: c=US, o=U.S. Government, ou=HHS, 
ou=FDA, ou=People, cn=David Strauss -S, 
0.9.2342.19200300.100.1.1=2000507494 
Date: 2017.03.25 14:01:10 -04
'00
'


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 3 of 45
Contents 
Sponsor Signatures Page................................................................................................................. 2 
Contents .......................................................................................................................................... 3 
1 Study Objectives...................................................................................................................... 6
 
1.1 Primary Objective 1 (Part 1) ............................................................................................ 6 
1.2 Primary Objective 2 (Part 2) ............................................................................................ 6 
2 Sample Size ............................................................................................................................. 6
 
2.1 Resampling analysis methodology................................................................................... 7 
2.2 Part 1 ................................................................................................................................ 8 
2.3 Part 2 ................................................................................................................................ 9 
2.4 Replacement Algorithm for Parts 1 and 2...................................................................... 10 
2.4.1 Crossover part (Part 2):........................................................................................... 11 
2.4.2 Parallel part (Part 1):............................................................................................... 12 
3 Analysis Populations ............................................................................................................. 15 
4 General Statistical Considerations, Subject Disposition and Demographics and Baseline 
Characteristics............................................................................................................................... 15 
5 Electrocardiogram Analyses.................................................................................................. 16 
5.1 Analysis and removal of heart rate dependency for QT and J-T
peak.............................. 16
 
5.2 Primary Analyses ........................................................................................................... 17 
5.2.1 Parts 1 and 2............................................................................................................ 17 
5.2.2 Part 1 
....................................................................................................................... 18 
5.2.3 Part 2 
....................................................................................................................... 19 
5.3 Exploratory Analyses 
..................................................................................................... 20 
5.3.1 Exposure-response analysis of secondary ECG biomarkers................................... 20 
5.3.2 ΔΔECG measurements by time point.....................................................................20 
5.3.3 Analyses using population-based heart rate corrected QT and J-Tpeak................. 21 
5.3.4 Use of placebo from parallel part in crossover part 
................................................ 21 
6 Pharmacokinetic Analyses..................................................................................................... 22 
7 Safety Analyses ..................................................................................................................... 22
 
8 Missing Data.......................................................................................................................... 23 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 4 of 45
9 Data Quality Assurance 
.........................................................................................................23 
10 
References.......................................................................................................................... 24
 
A Randomization schedule 
........................................................................................................... 25 
A.1 Abstract .......................................................................................................................... 25
 
A.2 Study design (pre-specified balanced design within period/cohort) 
.............................. 26 
A.3 Treatment codes and subjects IDs from 3 blocks by Part and Cohort ........................... 26 
A.4 Randomization ............................................................................................................... 31 
A.4.1 Seed random number generator .............................................................................. 31 
A.4.2 Randomization schedule code................................................................................. 31 
A.5 For clinical site unblinded pharmacist ........................................................................... 32 
A.5.1 The random generator seed was: 123456................................................................ 32 
A.5.2 Randomized treatment codes 
.................................................................................. 32 
A.5.3 Randomization schedule table ................................................................................ 33 
A.5.4 Randomized summary table.................................................................................... 35 
A.6 Appendix: technical information.................................................................................... 36 
B Step by step example of replacement algorithm ....................................................................... 37
 
B.1 Replacement in crossover part ....................................................................................... 37 
B.1.1 Move RANDIDs from completers to the next period............................................. 37 
B.1.2 Replace dropouts..................................................................................................... 38 
B.2 Replacement in parallel part........................................................................................... 40 
B.2.1 Step 1: (replace subject 1014 from placebo)........................................................... 41 
B.2.2 Step 2: (replace subject 1002 from ranolazine) ...................................................... 41
 
B.2.3 Step 3: (replace subject 1004 from verapamil) 
....................................................... 42 
B.2.4 Step 4: (replace subject 1011 from chloroquine).................................................... 42 
B.2.5 Step 5: (replace subject 1007 from lopinavir+ritonavir) ........................................ 43 
B.2.6 Step 6: (replace subject 1028 from placebo)........................................................... 43 
B.2.7 Step 7: (replace subject 1018 from ranolazine) ...................................................... 43
 
B.2.8 Step 8: (replace subject 1019 from verapamil) 
....................................................... 44 
B.2.9 Step 9: (replace subject 1025 from chloroquine).................................................... 44 
B.2.10 Step 10: (replace subject 1024 from lopinavir+ritonavir) ...................................... 45 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 5 of 45
B.2.11 Step 11: End (maximum number of 14 replacements reached).............................. 45 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 6 of 45
1 Study Objectives 
The primary objectives of this study are:
1. To confirm that exposure-response analysis of the electrocardiographic QTc and J-T
peakC
intervals in Phase 1 clinical pharmacology studies can be used to confirm that “balanced ion 
channel” drugs do not cause J-T
peakCprolongation and “predominant hERG” drugs cause 
QTc prolongation.
2. To test the hypothesis that calcium channel block (diltiazem) can reduce the QTc 
prolongation from hERG block (dofetilide) by shortening J-TpeakC.
1.1 Primary Objective 1 (Part 1) 
Part 1 of the study will assess primary objective 1. Part 1 will include 4 oral drugs with well-
characterized ion channel effects, QTc effects, and torsade de pointes (TdP) risk. Three (3) drugs 
will be “balanced ion channel” blockers (approximately equipotent hERG and late sodium and/or 
calcium block) with low TdP risk (ranolazine, verapamil and lopinavir + ritonavir) and 1 drug 
will be a “predominant hERG” blocker with TdP risk (chloroquine). 
1.2 Primary Objective 2 (Part 2) 
Part 2 of the study will assess primary objective 2. Part 2 will include 2 oral drugs (dofetilide and 
diltiazem) with well-characterized individual ion channel effects and TdP risk. Dofetilide is a 
“predominant hERG” blocker while diltiazem is a calcium channel blocker. 
2 Sample Size 
Approximately 60 healthy subjects are planned for enrollment, of which 50 will be assigned to 
Part 1 and 10 will be assigned to Part 2 at randomization. Up to 14 subjects may be qualified as 
replacements. Thus, a maximum of 74 subjects will be exposed to study drugs and procedures 
during the study. 
The sample size (10 subjects per arm) was selected based on analysis by the sponsor of ECG 
biomarkers in Studies SCR-0021and SCR-003
2and by resampling of data from previously 
conducted thorough QT (TQT) clinical studies, similar to the methodology of Ferber et al.
3,4The 
data sources5,6used in this analysis are appropriate to determine the sample size because (i) the 
inclusion and exclusion criteria of this study are similar to inclusion and exclusion criteria in 
SCR-002, SCR-003 and typical TQT studies; (ii) this study will be conducted at the same clinical 
site than SCR-002 and SCR-003; (iii) the ECG analysis will be performed at FDA using the 
same methodology used in SCR-002 and SCR-003; and (iv) we observed consistent drug-
induced ECG signatures between SCR-002 and SCR-003 (Figure 1 and Figure 2).
7


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 7 of 45
Figure 1. ECG signatures of predominant hERG block with dofetilide in both SCR-002 (Study 
1, dark grey) and SCR-003 (Study 2, light grey) and with moxifloxacin in SCR-003 (yellow). 
Vicente et al. 2016.7
Figure 2. ECG signatures of multichannel block with ranolazine in SCR-002 (Study 1, blue) and 
dofetilide+lidocaine (green) and dofetilide+mexiletine (orange) vs. predominant hERG block 
with dofetilide alone (gray) in SCR-003 (Study 2). Vicente et al. 2016.7
2.1 Resampling analysis methodology 
The overall methodology in this analysis is based on work by Ferber et al.,3,4where data from 
TQT studies is bootstrapped using sampling without replacement, likely due to the resampling 
size << number of subjects in the study, under different conditions: number of subjects on active 
drug and number on placebo. In the case of cross-over studies, subjects in either placebo or on 
active are treated as two different subjects, and the data is modeled using the following model 
(for QTc): 
,=
,+ 
,+
,+
+
+
,


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 8 of 45
where subscripts i and j refer to subject ith measurement jth, α is the slope, β is the intercept and 
there is a random effect on both the intercept and slope, and the random effects and the error 
term are assumed to be independent and identically distributed (i.i.d) normal. Of note, all time 
points are included in the analysis.
In our analysis, we bootstrapped the data from SCR-002 and SCR-003 using sampling with 
replacement because the number of subjects in these studies (22 subjects in each study) was 
similar to the sample size of the largest sample size of the simulated studies (from 6 to 24 
subjects).
2.2 Part 1 
To estimate the sample size required to meet the endpoint of Part 1, we used data from a 
“predominant hERG” blocking drug (moxifloxacin from SCR-003), a “balanced ion channel” 
drug (ranolazine from SCR-002) and placebo (from both SCR-002 and SCR-003). We simulated 
1000 parallel studies for each drug + placebo and scenario ranging from 3 subjects on placebo 
and 3 subjects on drug to 10 subjects on placebo and 14 subjects on drug (i.e. 160,000 studies). 
We bootstrapped the data using sampling with replacement because the number of subjects in 
these studies (22 subjects in each study) was similar to the sample size of the largest sample size 
of the simulated studies (up to 24 subjects). The results of resampling analysis suggest that 
9 subjects on active study drug and 6 subjects on placebo will be sufficient to detect QTc 
prolongation for the “predominant hERG” drug and exclude J-TpeakCprolongation for the 
“balanced ion channel” drugs (Figure 3). Figure 3 below shows simulation results using data 
from our previous studies and the following model using the lme4 package in R:
CHG~TPT+TRTA+CONC+(1+CONC|RANDID)
where CHG is change from baseline in the ECG biomarker (e.g. ΔQTc), CONC is the drug 
concentration (set to 0 for placebo), TPT is time, TRT is the treatment (coded as 0 for placebo, 1 
for active drug) and USUBJID is the subject identifier.
Due to the long half-life of some drugs that are included in this study and the desire to more 
closely mimic SAD/MAD studies, Part 1 of the current clinical study will use an entirely parallel 
study design with 10 subjects in each arm. Specifically, fifty (50) healthy subjects will be 
enrolled. Multiple doses of each drug will be given to 40 subjects on 3 consecutive days to 
achieve low and high exposures on Days 1 and 3, respectively, and 10 subjects will receive 
placebo.
It is anticipated that this study will be completed in 3 cohorts with approximately equal numbers 
of subjects per cohort. Cohorts 1 and 2 will have 15 subjects each (3 subjects per arm). Cohort 3 
will have 20 subjects (4 subjects per arm). Up to 14 replacement subjects may be added to 
Cohort 3 if it is estimated that fewer than 8 subjects will complete the study in a study arm


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 9 of 45
following the replacement algorithm specified under “2.4 Replacement Algorithm for Parts 1 and 
2”.
(a)
(b)
Figure 3. Percentage of studies correctly classified as a function of the number of subjects on 
active drug with 6 subjects on placebo for (a) a hERG blocker (moxifloxacin) with QTc>=10ms; 
and (b) a “balanced ion channel” blocker (ranolazine) with J-TpeakC<10 ms.
2.3 Part 2 
To estimate the sample size required to meet the endpoint of Part 2, we used data from a 
“predominant hERG” blocking drug alone (dofetilide from SCR-003) and in combination with 
an inward current blocker (mexiletine from SCR-003). We simulated 1000 crossover studies for 
each scenario ranging from 3 to 10 subjects completing the study (i.e. 8,000 studies = 8 different 
sample sizes x 1000 simulations). We bootstrapped the data using sampling with replacement 
because the number of subjects in study SCR-003 (10 subjects in the each treatment sequence)
was equal to the sample size of the largest sample size of the simulated studies (up to 10
subjects). The results of resampling analysis demonstrated that inward current block (mexiletine) 
effects on the QTc and J-T
peakCslopes could be detected approximately 99% and 99% of the 
time, respectively, using a crossover design with 8 subjects (i.e., the inward current blocker 
(mexiletine) significantly reduced prolongation from the hERG blocker (dofetilide)). Figure 4 
below shows simulation results using dofetilide and mexiletine data from our previous study 
SCR-003 and the following model using the lme4 package in R:
CHG~DOF+MEXI+DOF*MEXI+(1+DOF|USUBJID)+(1+MEXI
|USUBJID)


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 10 of 45
whereCHG is change from baseline in the ECG biomarker (e.g. ΔQTc), DOF is the dofetilide 
concentration, MEXI is mexiletine concentration and USUBJID is the subject identifier.
(a)
(b)
Figure 4:Percentage of studies where late sodium blocker (mexiletine) significantly (p<0.05) 
reduced prolongation from the hERG blocker (dofetilide) for QTc (left panel) and for QTc first 
and then J-T
peakC(right panel). Each sample size included 1,000 simulated studies using data 
from SCR-003 Vicente et al. 2016.
7
Ten (10) healthy subjects will be enrolled for this part. Multiple doses of each drug (dofetilide 
and diltiazem) will be given either separately or together on 3 consecutive days in the first period
and again on 3 consecutive days in the second period, depending on the randomization, to 
achieve low and high exposures on Days 1 and 3, respectively. There will be no placebo arm. 
It is anticipated that Part 2 of this study will be completed in 1 cohort with 10 subjects with a 
crossover design. Similar to Part 1, subjects may be replaced in a later cohort if it is estimated 
that fewer than 8 subjects will complete Part 2. A maximum of 14 replacement subjects may be 
enrolled in the study.
2.4 Replacement Algorithm for Parts 1 and 2 
Randomization identifiers for the potential replacements (REPLCMNTID) will be provided in 
the randomization schedule (see example in appendix A). The replacement algorithm to guide 
the unblinded Spaulding pharmacist is described below. A step by step example is provided in 
appendix B.


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 11 of 45
2.4.1 Crossover part (Part 2): 
After Period 1 and before check-in of Period 2:
1. After period 1 assess number of dropouts in the crossover part  (Part 2)
2. Allocate 1 replacement for each dropout up to 4 replacements to start on Period 2 
together with those already participating.
a. If there are more than 4 dropouts, replacements will be done one subject per 
sequence at a time ordering dropouts by RANDID within sequence and starting 
with the lowest RANDID (example in appendix A).
Flow-chart of management of replacements for the crossover part. Assessment will be done after 
Period 1 and before check-in of Period 2
Nxo: Number of dropouts in the crossover part in Period 1 (i.e. before Period 2 check-in).
 
Number of dropouts 
in crossover part 
(Nxo)
Add 4 replacements 
to crossover part
Nxo > 4
Add Nxo
replacements to 
crossover part
Nxo ≤ 4
End
Nxo=0


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 12 of 45
2.4.2 Parallel part (Part 1): 
Parallel part (Part 1) will be assessed after its second cohort (Cohort 3) finishes and before the 
check-in of its last cohort (Cohort 4):
1. After the second cohort of the parallel part (Cohort 3) finishes assess number of dropouts 
in the parallel part (Part 1)
2. Replace each dropout up to the maximum number of 14 for the whole study (Parts 1 and 
2 together). If the maximum will be reached, prioritize which treatment arms will receive 
replacements to bring the total number of completers in a treatment arm closer to 10 in 
the following order: 1) placebo, 2) ranolazine, 3) verapamil, 4) chloroquine, 5) 
lopinavir/ritonavir. For example, first bring all arms up to having at least 9 subjects per 
arm, and then if there are 2 remaining potential replacements, allocate them to placebo 
and ranolazine (see flow chart for parallel part in following pages and example in 
appendix B ).


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 13 of 45
Flow-chart of management of replacements for the parallel part. Assessment will be done after 
the second cohort (Cohort 3) finishes and before the check-in of the last cohort (Cohort 4) of the 
parallel part
PLACEBO
P1Cpbo + P2Cpbo + Rpbo + 4
<10
Number of 
replacements 
available (Nrpl)
Nrpl=0
End
RANOLAZINE
P1Cran + P2Cran + Rran + 4
< 10
Add 1 replacement to 
ranolazine arm
(Rran = Rran +1)
Nrpl
Nrpl=0
End
Nrpl ≥ 1
A
(to next page)
B
(from next page)
Nrpl
Add 1 replacement to 
placebo arm
(Rpbo = Rpbo +1)
Nrpl=0
End
Nrpl ≥ 1
Nrpl ≥ 1


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 14 of 45
VERAPAMIL
P1Cver + P2Cver + Rver + 4
< 10
Nrpl
Nrpl=0
End
Nrpl ≥ 1
CHLOROQUINE
P1Cchl + P2Cchl + Rchl + 4
< 10
Nrpl
Nrpl=0
End
Nrpl ≥ 1
LOPINAVIR+RITONAVIR
P1Clvrv + P2Clvrv + Rlvrv 
< 10
Nrpl
Nrpl=0
End
Nrpl ≥ 1
A
(from previous page)
B
(to previous page)
Add 1 replacement to 
verapamil arm
(Rver = Rver +1)
Add 1 replacement to 
chloroquine arm
(Rchl = Rchl +1)
Add 1 replacement to 
lopinavir+ritonavir arm
(Rlvrv = Rlvrv +1)
(P1Cpbo+P2Cpbo+Rpbo+4)<10 OR
(P1Cran+P2Cran+Rran+4)<10 OR
(P1Cver+P2Cver+Rver+4)<10 OR
(P1Cchl+P2Cchl+Rchl+4)<10 OR
(P1Clvrv+P2Clvrv+Rlvrv+4)<10
YES
NO


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 15 of 45
Table of acronyms in the flow chart:
Acronym Description
Nrpl 
Number of replacement that are available (i.e. how many of the 14 potential 
replacements have not been allocated yet)
C1Cpbo 
Number of subjects that completed Cohort 1 of placebo arm
C3Cpbo 
Number of subjects that completed Cohort 3 of placebo arm
Rpbo 
Number of replacements allocated to the ranolazine arm
C1Cran 
Number of subjects that completed Cohort 1 of ranolazine arm
C3Cran 
Number of subjects that completed Cohort 3 of ranolazine arm
Rran 
Number of replacements allocated to the ranolazine arm
C1Cver 
Number of subjects that completed Cohort 1 of verapamil arm
C3Cver 
Number of subjects that completed Cohort 3 of verapamil arm
Rver 
Number of replacements allocated to the verapamil arm
C1Cchl 
Number of subjects that completed Cohort 1 of chloroquine arm
C3Cchl 
Number of subjects that completed  Cohort 3 of chloroquine arm
Rchl 
Number of replacements allocated to the chloroquine arm
C1Clvrv Number of subjects that completed Cohort 1 of lopinavir+ritonavir arm
PC3Clvrv Number of subjects that completed Cohort 3 of lopinavir+ritonavir arm
Rlvrv 
Number of replacements allocated to the lopinavir+ritonavir arm
3 Analysis Populations 
The exposure-response population will include all subjects who receive at least 1 dose of any of 
the study drugs and have digital ECG (QTc and J-TpeakC) data for the treatment period collected 
before dosing and at 1 or more time points after dosing as well as plasma concentration data 
(except for the placebo arm) from the same time points after dosing. Subjects in this population 
will be used for the exposure-response analysis.
The PK population will include all subjects who receive study drug and have at least 1 estimable 
PK parameter after dosing.
The safety population will include all subjects who receive at least 1 dose of any of the study 
drugs.
4 General Statistical Considerations, Subject Disposition and 
Demographics and Baseline Characteristics 
All data will be presented in data listings. Data from subjects excluded from an analysis 
population will be presented in the data listings but not included in the calculation of summary 
statistics. The number of subjects who enroll in the study and the number and percentage of 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 16 of 45
subjects who complete each assessment will be presented. The frequency and percentage of 
subjects who withdraw or discontinue from the study and the reason for withdrawal or 
discontinuation will be summarized. Continuous demographic and baseline characteristic 
variables (age, height, weight, body mass index) will be summarized overall and by treatment 
using descriptive statistics (number of subjects, mean, standard deviation [SD], median, and 
minimum and maximum). The number and percentage of subjects in each class of categorical 
demographic and baseline characteristic variables will also be summarized.
5 Electrocardiogram Analyses 
5.1 Analysis and removal of heart rate dependency for QT and J-T
peak 
For the primary analysis, the QT interval will be corrected for heart rate using Fridericia’s 
formula (QTc=QT/RR
1/3). The J-T
peak interval will be corrected for heart rate using the 
Johannesen et al (2014)
8formula (J-T
peakC= J-TpeakC/RR
0.58). 
If any drug causes an absolute placebo corrected change from baseline in heart rate (ΔΔHR) 
greater than 10 beats per minute (i.e. mean effect >10bpm change in either direction) at any time 
point, then we will assess goodness of HR correction (visual assessment on plots like the 
example ones in Figure 5):
1. QT vs RR and QTcF vs RR
2. J-Tpeakvs RR and JTpeakC vs RR
Figure 5: Example of plots for assessing goodness of HR correction. QT and RR data from 
placebo arm and baseline time points from SCR-003.


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 17 of 45
If there are HR effects >10 bpm and the relationship does not look appropriate (per visual 
assessment) then we will perform an analysis using population-based HR correction (described 
below).
5.2 Primary Analyses 
Analysis flow chart (for each endpoint):
5.2.1 Parts 1 and 2 
Investigation of hysteresis:Prior to model selection for the exposure-response analysis, the 
presence of hysteresis will be assessed for QTc and J-TpeakC. To detect hysteresis, individual 
change-from-baseline in J-T
peakC(ΔJ-T
peakC) will be computed. For each day, the time of the 
largest mean ΔJ-T
peakC(U
max) will be determined. If the largest mean ΔJ-T
peakCexceeds 5 msec at 
≥3time points, the time difference between U
maxand the T
maxof the drug level exceeds 1 hour, 
and the 1-sided, 1-sample Wilcoxon test for the difference between ΔJ-T
peakCat T
maxand at U
max
is formally significant at the 1% level, it will be concluded that hysteresis existed. In such a case, 
a PK model with an additional effect compartment will replace the model described below. The 
same steps will be followed for QTc as part of the primary endpoint. 
Model selection:To assess the appropriateness of a linear model, normal QQ-plots for the 
residuals and plots of weighted residuals versus concentration and versus fitted values will be 
produced. A model with a quadratic term in concentration will be fitted and the quadratic term 
will be tested on the 2-sided 5% alpha level. If there is a significant quadratic term, nonlinear 
models, such as a log-linear model and an Emaxmodel, will be investigated and the primary 
Hysteresis
GoF plots
(QQ, residuals…)
Compartment 
model
(PK + PK/PD)
Yes 
(Tmax – Umax > 1hr)
No
(Tmax –Umax ≤ 1hr)
ER with lme
ER linear
ER non-linear
Model selection (AIC):
- quadratic
- log-linear
-Emax


Statistical Analysis Plan 
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(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 18 of 45
model will be selected based on the Akaike Information Criterion (AIC) and plausibility 
arguments.
Exposure-response analysis:In the absence of hysteresis and unless the pre-specified test 
procedure for linearity indicates otherwise, the primary analysis will be based on a linear
mixed-effects model implemented in SAS®or R software, with ΔJ-T
peakCas the dependent 
variable, drug plasma concentration as continuous covariate, treatment and time point as 
categorical factors, and subject-specific random effects for the intercept and slope, depending on 
whether Part 1 or Part 2 of the study is being considered (details for each part below). All 
postdose data will be used. The degrees of freedom for the model estimates will be determined 
by the Kenward-Rogers method. From the model, the slope (i.e., the regression parameter for the 
concentration) and the treatment effect will be estimated together with 2-sided 90% CIs.
The predicted mean placebo-adjusted change-from-baseline J-TpeakC(ΔΔJ-T
peakC) at the observed 
geometric mean Cmax(i.e., the product with the slope estimate
 +
 treatment effect [ΔJ-T
peakCactive–
ΔJ-T
peakCplacebo]) and the 2-sided 90% CI of the estimate will be calculated. The same steps will 
be followed for QTc as part of the primary endpoint.
5.2.2 Part 1 
The primary variable for the exposure-response analysis will be the change-from-baseline in QTc 
(ΔQTc) for the “predominant hERG” drug and change-from-baseline in J-T
peakC(ΔJ-T
peakC) for 
the “balanced ion channel” drugs, where the mean of the 3 predose ECG readings on Day 1 will 
be used as the Baseline. The concentration of the drug will be used as a covariate. Exposure-
response analysis will be done following most recent best practices in concentration-QTc 
modeling.
Criteria for primary QTc and J-TpeakCassessment:
Criteria for the 3 “balanced ion channel” drugs (ranolazine, verapamil, lopinavir + ritonavir) will 
be based on the predicted J-TpeakCeffect on the third day of dosing. To demonstrate a lack of 
placebo-adjusted change-from-baseline J-TpeakC(ΔΔJ-T
peakC) prolongation for each of the 
3drugs: 
The upper bound of the 2-sided 90% CI of the predicted mean ΔΔJ-T
peakCmust be 
<10 msec at the observed geometric mean Cmaxon Day 3.
o H0:ΔΔJ-T
peakC≥ 10 ms
o HA:ΔΔJ-T
peakC< 10 ms
Criteria for the “predominant hERG” drug (chloroquine) will be based on the predicted QTc 
effect on the first day of dosing. To demonstrate the presence of placebo-adjusted 
change-from-baseline QTc(ΔΔQTc) prolongation for chloroquine: 


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(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 19 of 45
The upper bound of the 2-sided 90% CI of the predicted mean ΔΔQTc must be ≥10msec 
at the observed geometric mean C
maxon Day 1.
o H0:ΔΔQTc < 10 ms
o HA:ΔΔQTc ≥ 10 ms
We will use the following linear mixed effects model (QTc as example):
ΔQTc ~ time + treatment + concentration + (1+concentration|subjid)
where concentration is set to 0 for placebo, treatment is 1 for drug and 0 for placebo, and time is 
a categorical variable coded as time after first dose (e.g. ‘1 hr’, 1.5 hr’, ‘2 hr’, ‘4 hr’, … ’72 hr’). 
Note that the prediction from the model above is ΔΔQTc because placebo treatment is coded as 
0.
In addition to the tests specified above (e.g. ΔΔQTc ≥10 ms at Cmax of Day 1), it will be 
assessed:
1. whether exploratory plots suggest model misspecification
a. Exploratory plot will show the model fit (mean and 95%CIs) plot on top of the 
observed ΔΔQTc (ΔQTcdrug-mean ΔQTcplaceboby time) and the corresponding 
deciles of the observed data
b. QQ-plots of the model residuals
c. Plot of the standardized residuals by time, treatment and concentration 
(continuous for continuous variables, otherwise boxplot)
If there is an effect (e.g. abs(ΔΔQTc) ≥10 ms) and the fit is acceptable then we accept the linear 
model. If not (i.e. either or both criteria are not met) then further models will be assessed. The 
same assessment will be performed for J-Tpeakc in a similar fashion.
5.2.3 Part 2 
The primary variable for the exposure-response analysis will be the change-from-baseline in QTc 
(ΔQTc) for the pooled dofetilide alone, diltiazem alone, and dofetilide+ diltiazem, where the 
mean of the 3 predose ECG readings on Day 1 will be used as the Baseline. The concentration of 
dofetilide and diltiazem will be used as covariates.
Criteria for primary QTc and J-TpeakCassessment:
To demonstrate that calcium channel block (diltiazem) reduces QTc prolongation from hERG 
block (dofetilide) by shortening J-TpeakC:
It will be assessed whether the projected QTc effect of dofetilide alone is significantly 
greater (i.e., p<0.05) than the projected QTc effect of the combination of dofetilide +


Statistical Analysis Plan 
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(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 20 of 45
diltiazem. This will be assessed at the dofetilide peak plasma level on Day 3 (computed 
from the combination of dofetilide + diltiazem) on the pooled dofetilide alone, diltiazem 
alone, and dofetilide + diltiazem data using a linear mixed effects model:
o ΔQTc ~ DOF+DILT+DOF*DILT+(1+DOF| USUBJID)+(1+DILT| USUBJID)
where DOF is concentration of dofetilide, and DILT is concentration of diltiazem. 
H0:ΔQTc (Dof Cmax, Dilt=0) ≤ ΔQTc(Dof Cmax, Dilt Cmax)
HA:ΔQTc(Dof Cmax, Dilt=0) > ΔQTc(Dof Cmax, Dilt Cmax)
If the previous test is statistically significant for QTc, the same test will be performed for 
J-Tpeakc.
o ΔJTpeakc ~ DOF+DILT+DOF*DILT+(1+DOF| USUBJID)+(1+DILT|
USUBJID)
H0: ΔJTpeakc(Dof Cmax, Dilt=0) ≤ ΔJTpeakc (Dof Cmax, Dilt Cmax)
HA: ΔJTpeakc (Dof Cmax, Dilt=0) > ΔJTpeakc (Dof Cmax, Dilt Cmax)
In addition to the tests specified above, it will be assessed:
1. whether exploratory plots suggest model misspecification
a. Exploratory plot will show the model fit (mean and 95%CIs) plot on top of the 
observed ΔQTcand the corresponding deciles of the observed data.
b. QQ-plots of the model residuals
c. Plot of the standardized residuals by time, treatment and concentration 
(continuous for continuous variables, otherwise boxplot)
If there is a ΔQTceffect and the fit is acceptable then we accept the linear model. If not (i.e. 
either or both criteria are not met) then further models will be assessed. The same assessment 
will be performed for J-Tpeakc in a similar fashion.
5.3 Exploratory Analyses 
5.3.1 Exposure-response analysis of secondary ECG biomarkers 
Exposure-response analysis of secondary ECG biomarkers:For Part 1, exposure-response 
analysis similar to that described for QTc and J-T
peakCwill be applied to PR, QRS, QTc, J-TpeakC,
and T
peak-T
endfor Day 1 and Day 3. This will also include analysis of change from baseline for 
dofetilide alone, diltiazem alone, and dofetilide + diltiazem combination from Part 2 of the study.
5.3.2
ECG measurements by time point  
For each time point, an analysis of variance model will be fitted with ΔJ-T
peakCas the dependent 
variable, treatment (active or placebo) as factor, and baseline J-T
peakCas a covariate. From this 


Statistical Analysis Plan 
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(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 21 of 45
model, the difference (ΔJ-T
peakCactive–ΔJ-T
peakCplacebo) will be estimated with a 2-sided 90% CI. 
Separate models will be fitted for each treatment, all of them using the same placebo data. The 
same steps will be followed for QTc. Change from Baseline in heart rate, PR, QRS, and 
Tpeak-T
endwill be calculated using descriptive summary statistics.
5.3.3 Analyses using population-based heart rate corrected QT and J-Tpeak 
If any drug causes an absolute placebo-corrected change from baseline in heart rate greater than 
10 beats per minute, we will perform exposure-response andΔΔECG measurements by time 
point using population-based heart rate correction factors for QT and J-Tpeak. We will use 
continuous/Holter 12-lead ECGs recorded at check-in from early afternoon until the subjects go 
to sleep. During this period, subjects will perform a sequence of postural maneuvers (e.g., 
10 minutes supine, 10 minutes sitting, 10 minutes standing, 10 minutes sitting, 10 minutes 
supine) to characterize ECG biomarkers and heart rate relationship (i.e. QT/RR and J-T
peak/RR 
relationships). From each Holter and postural maneuver window, triplicate 10-second 12-lead 
ECGs will be extracted at different heart rates. Additional details will be available in the ECG 
analysis plan. The dependency of the different electrocardiographic intervals on heart rate will be 
evaluated using the extracted ECGs from the Holters. We will compute a population-based 
correction factor using an exponential model. The model will include random effects for the 
intercept (log(α)) and β (i.e., allowing each individual to have his or her own slope). We will 
consider that there is heart rate dependency for an ECG biomarker if the ECG biomarker and 
heart rate relationship is significant and there is at least 10% of change in the ECG biomarker 
within the heart rate range as described in Johannesen et al 2014.8For heart rate dependent ECG 
biomarkers, the individual correction factors will be applied using the exponential model. Using 
QT as an example: QTcI = QT/RRβi, where βi is the slope of the relationship of the i-th subject.
5.3.4 Use of placebo from parallel part in crossover part 
The 10-subject placebo group from the parallel part of the study can potentially be used 
interchangeably with the crossover part. This analysis will test that calcium channel block 
(diltiazem) reduces QTc prolongation from hERG block (dofetilide) by shortening J-T
peakC. The 
placebo corrected changes from baseline (ΔΔ) will be computed subtracting the time-matched 
average placebo changes from baseline (Δ) of the subjects participating in Part 1 of the study 
from the individual changes from baseline of subjects participating in Part 2.
It will be assessed whether the projected QTc effect of dofetilide alone is significantly 
greater (i.e., p<0.05) than the projected QTc effect of the combination of dofetilide + 
diltiazem. This will be assessed at the dofetilide peak plasma level on Day 3 (computed 
from the combination of dofetilide + diltiazem) on the pooled dofetilide alone, diltiazem 
alone, and dofetilide + diltiazem data using a linear mixed effects model using the 
placebo-corrected change from baseline as dependent variable:
o ΔΔQTc ~ DOF+DILT+DOF*DILT+(1+DOF| USUBJID)+(1+DILT| USUBJID)


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Page 22 of 45
where DOF is concentration of dofetilide, and DILT is concentration of diltiazem. 
H0:ΔΔQTc (Dof Cmax, Dilt=0) ≤ ΔΔQTc(Dof Cmax, Dilt Cmax)
HA:ΔΔQTc(Dof Cmax, Dilt=0) > ΔΔQTc(Dof Cmax, Dilt Cmax)
If the previous test is statistically significant for QTc, the same test will be performed for 
J-Tpeakc.
o ΔΔJTpeakc ~ DOF+DILT+DOF*DILT+(1+DOF| USUBJID)+(1+DILT|
USUBJID)
H0:ΔΔJTpeakc(Dof Cmax, Dilt=0) ≤ ΔΔJTpeakc (Dof Cmax, Dilt Cmax)
HA:ΔΔJTpeakc (Dof Cmax, Dilt=0) > ΔΔJTpeakc (Dof Cmax, Dilt 
Cmax)
6 Pharmacokinetic Analyses 
PK parameters Cmax, T
max, and AUC will be summarized using descriptive statistics (number of 
subjects, mean, standard deviation [SD], coefficient of variation [CV], median, minimum, and 
maximum) for Day 1 and Day 3 for each active drug and period (i.e., Days 1 and 3 of Part 1 and 
Days 1, 3, 8 and 10 of Part 2). The PK parameters will be analyzed using non-compartmental 
methods based on actual sampling times. Mean and individual concentration-time profiles will 
be presented in graphs.
7 Safety Analyses 
All AEs will be coded using the latest version of the Medical Dictionary for Regulatory 
Activities (MedDRA). The incidence of treatment-emergent adverse events (TEAEs), organized 
by system organ class and frequency, will be summarized by seriousness, severity, relationship 
to treatment, and by treatment at onset of the TEAE. A detailed listing of serious AEs and 
TEAEs leading to withdrawal will also be provided. 
Clinical laboratory results (hematology, serum chemistry, and urinalysis) will be summarized 
using descriptive statistics (number of subjects, mean, SD, minimum, median, and maximum). 
Clinical laboratory results will be classified as normal or abnormal, according to the reference 
ranges of the individual parameter. The number and percentage of subjects with abnormal 
laboratory results will be provided. No statistical testing will be performed on clinical laboratory 
data.
Vital sign measurements (blood pressure, heart rate, respiratory rate, and oral body temperature), 
safety 12-lead ECG results, and changes from Baseline for these parameters will be summarized 
by treatment and time point using descriptive statistics (number of subjects, mean, SD, 
minimum, median, and maximum). Physical examination findings will be presented in a data 
listing, and abnormal physical examination findings will be recorded as AEs. 


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(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 23 of 45
All concomitant medication usage and medications that changed in daily dose, frequency, or 
both since the subject provided informed consent will be summarized for each subject.
8 Missing Data 
Missing data will not be imputed. Data that are excluded from the descriptive or inferential 
analyses will be included in the subject data listings. This will include data from subjects not in 
the particular analysis population, measurements from unscheduled visits, or extra measurements 
that may arise from 2 or more analyses of a plasma sample at the same time point.
9 Data Quality Assurance 
Completed eCRFs are required for each subject randomly assigned to study drug. Electronic data 
entry will be accomplished through the ClinSpark remote electronic data capture (EDC) system, 
which allows for on-site data entry and data management. This system provides immediate, 
direct data transfer to the database, as well as immediate detection of discrepancies, enabling site 
coordinators to resolve and manage discrepancies in a timely manner. Each person involved with 
the study will have an individual identification code and password that allows for record 
traceability. Thus, the system, and subsequently any investigative reviews, can identify 
coordinators, investigators, and individuals who have entered or modified records.
Furthermore, the investigator retains full responsibility for the accuracy and authenticity of all 
data entered into the electronic data capture system. The completed dataset and their associated 
files are the sole property of the sponsor and should not be made available in any form to third 
parties, except for appropriate governmental health or regulatory authorities, without written 
permission of the sponsor.
 


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(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
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10 References 
1. Johannesen L, Vicente J, et al. Differentiating drug-induced multichannel block on the 
electrocardiogram: randomized study of dofetilide, quinidine, ranolazine, and verapamil. Clin Pharmacol 
Ther. 2014 Nov;96(5):549-58. 
2. Johannesen L, Vicente J, et al. Late sodium current block for drug-induced long QT syndrome: Results 
from a prospective clinical trial. Clin Pharmacol Ther. 2016 Feb;99(2):214-23. 
3. Ferber G, Zhou M, Darpo B. Detection of QTc effects in small studies--implications for replacing the 
thorough QT study. Ann Noninvasive Electrocardiol. 2015;20(4):368-77. 
4. Ferber G, Lorch U, Taubel J. The Power of Phase I Studies to Detect Clinical Relevant QTc Prolongation: 
A Resampling Simulation Study. Biomed Res Int. 2015;2015:293564. 
5. Vicente J, Johannesen L, Strauss DG.ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine 
in Healthy Subjects. doi:10.13026/C2HP45. url: https://physionet.org/physiobank/database/ecgrdvq/. 
6. Vicente J, Johannesen L, Strauss DG. ECG effects of Dofetilide, Moxifloxacin, Dofetilide+Mexiletine, 
Dofetilide+Lidocaine and Moxifloxacin+Diltiazem in Healthy Subjects. doi:10.13026/C2D016. url: 
https://physionet.org/physiobank/database/ecgdmmld/. 
7. Vicente, J., L. Johannesen, M. Hosseini, J. W. Mason, P. T. Sager, E. Pueyo and D. G. Strauss (2016). 
"Electrocardiographic Biomarkers for Detection of Drug-Induced Late Sodium Current Block." PLOS ONE 
11(12): e0163619.
 
8. Johannesen, L., J. Vicente, R. A. Gray, L. Galeotti, Z. Loring, C. E. Garnett, J. Florian, M. Ugander, N. 
Stockbridge and D. G. Strauss (2014). "Improving the assessment of heart toxicity for all new drugs 
through translational regulatory science." Clin Pharmacol Ther 95(5): 501-508. 
 


Statistical Analysis Plan 
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(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 25 of 45
A Randomization schedule 
This code does not necessarily reflect any position of the Government or the Food and Drug 
Administration.
A.1 Abstract 
This script generates the randomization schedule for the CiPA ECG clinical study (SCR-004). 
After screening, 60 subjects will be randomized to participate in one of the 7 treatment sequences 
of the study. Example of treatment codes from the study protocol:
Treatment sequences of Part 1 (parallel part) 
– A: Ranolazine (10 subjects) 
– B: Verapamil (10 subjects) 
– C: Lopinavir+Ritonavir (10 subjects) 
– D: Chloroquine (10 subjects) 
– E: Placebo (10 subjects) 
Treatment sequences of Part 2 (crossover part) 
– F,G: Dofetilide, Diltiazem+Dofetilide (5 subjects) 
– G,F: Diltaizem+Dofetilide, Dofetilide (5 subjects) 
This script performs randomization in permuted blocks to achieve balance across treatment 
groups. The blocks are defined as follows: 
Block 1: First 15 subjects of parallel part (Cohort 1, Period 1) and 10 subjects of crossover 
part (Cohort 2, Periods 1 and 2) 
– Treatment sequences: 
Parallel part: A, B, C, D, E 
Crossover part: F-G, G-F 
Block 2: Second 15 subjects of parallel part (Cohort 3, Period 2) 
– Treatment sequences: 
Parallel part: A, B, C, D, E 
Block 3: Remaining 20 subjects of parallel part (Cohort 4, Period 3) 
– Treatment sequences: 
Parallel part: A, B, C, D, E 
IDs for the potential replacements (REPLCMNTID) are computed from the randomization IDs 
(RANDID) as follows. 
In the parallel part (Part 1) REPLCMNTID = RANDID+2000 
In the crossover part (Part 2) there are 2 replacements (one per treatment sequence) for 
each drop out (up to 4 replacement total). Thus, each subject has two potential 
replacements IDs: 


Statistical Analysis Plan 
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(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 26 of 45
– For the replacement with same sequence than the dropout REPLCMNTID = 
10*(RANDID+2000) + 1 (e.g. 40011 for subject 2001) 
– For the replacement with the alternative sequence than the dropout REPLCMNTID 
= 10*(RANDID+2000) + 2 (e.g. 40012 for subject 2001) 
A.2 Study design (pre-specified balanced design within period/cohort) 
A.3 Treatment codes and subjects IDs from 3 blocks by Part and Cohort 
## 
# List of randomization IDs 
## 
randids <- c(seq(1001,1050,1),seq(2001,2010,1)) 
 
## 
# Randomization of treatment codes 
## 
treatmentcodespart1 <- c('A','B','C','D','E') 
treatmentnamespart1 <- c('Ranolazine','Verapamil','Lopinavir+Ritonavir','Chlo
roquine','Placebo') 
treatmentcodespart2 <- c('F','G') 
treatmentnamespart2 <- c('Dofetilide','Diltiazem+Dofetilide') 
 
## 
# Treatment codes data.frame 
## 
treatments <- data.frame(CODE=c(treatmentcodespart1,treatmentcodespart2),TREA
TMENT=c(treatmentnamespart1,treatmentnamespart2),PART=c(1,1,1,1,1,2,2)) 
 
## 
# Treatment sequences 
## 
part1seqs <- c('A','B','C','D','E') 
part2seqs <- c('F-G','G-F') 
 
## 
# Block1 
## 
block1 <- data.frame(SEQ=c(part1seqs,part2seqs),NSUBJ=c(3,3,3,3,3,5,5),PART=
c(1,1,1,1,1,2,2),COHORT=c(1,1,1,1,1,2,2)) 
block1 <- block1 %>% group_by(PART) %>% mutate(CUMSUBJ=cumsum(NSUBJ)) %>% ung
roup()  
block1 <- block1 %>% mutate(Group_number=group_indices(block1,.dots=c(SEQ))) 
%>% group_by(Group_number) %>% mutate(RANDIDs=paste0(PART*1000+seq(CUMSUBJ-NS
UBJ+1,CUMSUBJ,1),collapse=', ')) %>% ungroup() %>% data.frame() %>% mutate(SE
Q=as.character(SEQ)) 
 


Statistical Analysis Plan 
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## 
# Block2 
## 
block2 <- data.frame(SEQ=part1seqs,NSUBJ=3,PART=1,COHORT=3) 
block2 <- block2 %>% group_by(PART) %>% mutate(CUMSUBJ=cumsum(NSUBJ)) %>% ung
roup()  
block2 <- block2 %>% mutate(Group_number=group_indices(block2,.dots=c(SEQ))) 
%>% group_by(Group_number) %>% mutate(RANDIDs=paste0(15+PART*1000+seq(CUMSUB
J-NSUBJ+1,CUMSUBJ,1),collapse=', ')) %>% ungroup() %>% data.frame() %>% mutat
e(SEQ=as.character(SEQ)) 
 
## 
# Block3 
## 
block3 <- data.frame(SEQ=part1seqs,NSUBJ=4,PART=1,COHORT=4) 
block3 <- block3 %>% group_by(PART) %>% mutate(CUMSUBJ=cumsum(NSUBJ)) %>% ung
roup()  
block3 <- block3 %>% mutate(Group_number=group_indices(block3,.dots=c(SEQ))) 
%>% group_by(Group_number) %>% mutate(RANDIDs=paste0(30+PART*1000+seq(CUMSUB
J-NSUBJ+1,CUMSUBJ,1),collapse=', ')) %>% ungroup() %>% data.frame() %>% mutat
e(SEQ=as.character(SEQ)) 
 
## 
# Schedule 
## 
schedule <- bind_rows(block1, block2, block3) %>% select(-c(Group_number,CUMS
UBJ,NSUBJ)) 
# Next statements were used during development to validate wide/long c
onversion operation 
#schedule.summary <- schedule %>% spread(COHORT, RANDIDs) 
#schedule.summary[is.na(schedule.summary)] <- '' 
#pander(schedule.summary,split.table=Inf) 
 
# Save schedule in long format to facitilate shuffle operations in the 
randomization process below 
schedulelong <- schedule %>% group_by(COHORT,SEQ) %>% mutate(ID1=strsplit(RAN
DIDs,',')[[1]][1],ID2=strsplit(RANDIDs,',')[[1]][2],ID3=strsplit(RANDIDs,',')
[[1]][3],ID4=strsplit(RANDIDs,',')[[1]][4],ID5=strsplit(RANDIDs,',')[[1]][5]
) %>% ungroup() %>% select(-RANDIDs) %>% gather(ID,RANDID,c(ID1,ID2,ID3,ID4,I
D5))  %>% filter(!is.na(RANDID)) %>% select(-ID) %>% distinct() %>% mutate(RA
NDID=as.numeric(RANDID)) %>% select(RANDID,PART,COHORT,SEQ) %>% arrange(RANDI
D) 
 
# Schedule printed in section below 
# pander(schedulelong,split.table=Inf) 
 


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(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
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#Format in summary table 
schedulelong.summary <- schedulelong %>% group_by(COHORT,SEQ) %>% mutate(RAND
IDs=paste0(RANDID,collapse=', ')) %>% select(-RANDID) %>% ungroup() %>% disti
nct() %>% spread(COHORT,RANDIDs) 
schedulelong.summary[is.na(schedulelong.summary)] <- '' 
 
# Add potential replacements IDS to the long format 
addpotentialreplacementids<-function(schdf){ 
  return (schdf %>% mutate(REPLCMNTID=RANDID+2000)) 
} 
 
schedulelongwithrplids <- addpotentialreplacementids(schedulelong) 
# Print table 
# pander(schedulelong.summary,split.table=Inf) 
A.3.1.1 Treatment codes 
pander(treatments,split.table=Inf) 
CODE
 TREATMENT
 PART
 
A
 Ranolazine
 1
 
B
 Verapamil
 1
 
C
 Lopinavir+Ritonavir
 1
 
D
 Chloroquine
 1
 
E
 Placebo
 1
 
F
 Dofetilide
 2
 
G
 Diltiazem+Dofetilide
 2
 
A.3.1.2 Treatment schedule 
pander(schedulelongwithrplids ,split.table=Inf) 
RANDID  PART  COHORT  SEQ   REPLCMNTID  
1001
 1
  1
  A
  3001
 
1002
 1
  1
  A
  3002
 
1003
 1
  1
  A
  3003
 
1004
 
1
 1
 B
 3004
 
1005
 
1
 1
 B
 3005
 
1006
 
1
 1
 B
 3006
 
1007
 
1
 1
 C
 3007
 
1008
 
1
 1
 C
 3008
 
1009
 
1
 1
 C
 3009
 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 29 of 45
1010
 1
  1
  D
  3010
 
1011
 1
  1
  D
  3011
 
1012
 1
  1
  D
  3012
 
1013
 1
  1
  E
  3013
 
1014
 1
  1
  E
  3014
 
1015
 1
  1
  E
  3015
 
1016
 1
  3
  A
  3016
 
1017
 1
  3
  A
  3017
 
1018
 1
  3
  A
  3018
 
1019
 
1
 3
 B
 3019
 
1020
 
1
 3
 B
 3020
 
1021
 
1
 3
 B
 3021
 
1022
 
1
 3
 C
 3022
 
1023
 
1
 3
 C
 3023
 
1024
 
1
 3
 C
 3024
 
1025
 1
  3
  D
  3025
 
1026
 1
  3
  D
  3026
 
1027
 1
  3
  D
  3027
 
1028
 1
  3
  E
  3028
 
1029
 1
  3
  E
  3029
 
1030
 1
  3
  E
  3030
 
1031
 1
  4
  A
  3031
 
1032
 1
  4
  A
  3032
 
1033
 1
  4
  A
  3033
 
1034
 1
  4
  A
  3034
 
1035
 
1
 4
 B
 3035
 
1036
 
1
 4
 B
 3036
 
1037
 
1
 4
 B
 3037
 
1038
 
1
 4
 B
 3038
 
1039
 
1
 4
 C
 3039
 
1040
 
1
 4
 C
 3040
 
1041
 
1
 4
 C
 3041
 
1042
 
1
 4
 C
 3042
 
1043
 1
  4
  D
  3043
 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 30 of 45
1044
 1
  4
  D
  3044
 
1045
 1
  4
  D
  3045
 
1046
 1
  4
  D
  3046
 
1047
 1
  4
  E
  3047
 
1048
 1
  4
  E
  3048
 
1049
 1
  4
  E
  3049
 
1050
 1
  4
  E
  3050
 
2001
 2
  2
  F-G
  4001
 
2002
 2
  2
  F-G
  4002
 
2003
 2
  2
  F-G
  4003
 
2004
 2
  2
  F-G
  4004
 
2005
 2
  2
  F-G
  4005
 
2006
 2
  2
  G-F
  4006
 
2007
 2
  2
  G-F
  4007
 
2008
 2
  2
  G-F
  4008
 
2009
 2
  2
  G-F
  4009
 
2010
 2
  2
  G-F
  4010
 
A.3.1.3 Balanced design summary table 
# Print summary table 
pander(schedulelong.summary,split.table=Inf) 
PART SEQ  
1 
2 
3 
4 
1
 A
 1001,
 
1002,
 
1003 
1016, 1017, 
1018 
1031, 1032, 1033, 
1034 
1
 B
 1004,
 
1005,
 
1006 
1019, 1020, 
1021 
1035, 1036, 1037, 
1038 
1
 C
 1007,
 
1008,
 
1009 
1022, 1023, 
1024 
1039, 1040, 1041, 
1042 
1
 D
 1010,
 
1011,
 
1012 
1025, 1026, 
1027 
1043, 1044, 1045, 
1046 
1
 E
 1013,
 
1014,
 
1015 
1028, 1029, 
1030 
1047, 1048, 1049, 
1050 
2  F-G 
2001, 2002, 2003, 2004, 
2005 
2  
G-F 
2006, 2007, 2008, 2009, 
2010 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 31 of 45
A.4 Randomization 
A.4.1 Seed random number generator 
TODO: The unblinded statistician of the sponsor team will be responsible of selecting the seed 
for the random number generator and update the set.seed statement below accordingly. 
# Seed the random number generator 
myseed <- 123456 
set.seed(myseed) 
A.4.2 Randomization schedule code 
## 
# Randomization within blocks by cohort 
## 
randomizedschedulelong <- data.frame() 
# Randomize treatment codes within each cohort 
for(cohort in unique(schedulelong$COHORT)){ 
  rsdf <- schedulelong %>% filter(COHORT==cohort) 
  # Randomly permute RANDIDs 
  rsdf$RANDID <- levels(fct_shuffle(as.character(rsdf$RANDID))) 
  rsdf <- rsdf %>% arrange(RANDID) 
  randomizedschedulelong <- bind_rows(randomizedschedulelong,rsdf) 
} 
randomizedschedulelong$RANDID <- as.numeric(randomizedschedulelong$RANDID) 
randomizedschedulelongwithrplcids <- addpotentialreplacementids(randomizedsch
edulelong) 
# Randomization schedule printed in unblinded pharmacist section below 
# pander(randomizedschedulelongwithrplcids,split.table=Inf) 
A.4.2.1 Randomized summary table 
#Format in summary table 
randomizedschedulelong.summary <- randomizedschedulelong %>% group_by(COHORT,
SEQ) %>% mutate(RANDIDs=paste0(RANDID,collapse=', ')) %>% select(-RANDID) %>% 
ungroup() %>% distinct() %>% spread(COHORT,RANDIDs) 
randomizedschedulelong.summary[is.na(randomizedschedulelong.summary)] <- '' 
 
# Randomization summary table printed in unblinded pharmacist section 
below 
#pander(randomizedschedulelong.summary,split.table=Inf) 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 32 of 45
A.4.2.2 Randomized treatment codes 
A.4.2.3 ## 
# Randomized treatment codes data.frame 
## 
randomizetreatmentcodes<-F 
randomizedtreatments <- data.frame() 
if (randomizetreatmentcodes){ 
  # Randomize treatment codes within each part 
  for(part in unique(treatments$PART)){ 
    rtrt <- treatments %>% filter(PART==part) 
    # Randomly permute the levels of the treatment CODEs 
    rtrt$CODE <- levels(fct_shuffle(as.character(rtrt$CODE))) 
    rtrt <- rtrt %>% arrange(CODE) 
    randomizedtreatments <- bind_rows(randomizedtreatments,rtrt) 
  } 
} else { 
  randomizedtreatments <- treatments 
} 
# Randomized treatment codes printed in unblinded pharmacist 
section below 
#pander(randomizedtreatments,split.table=Inf) 
A.5 For clinical site unblinded pharmacist 
pandoc.header(paste0('The random generator seed was: ', myseed),level=2) 
A.5.1 The random generator seed was: 123456 
A.5.2 Randomized treatment codes 
if(randomizetreatmentcodes){ 
  mystr <- 'Treatments code were randomized\n' 
}else{ 
  mystr <- ('Treatments code as per protocol. Treatment codes NOT randomized\
n') 
} 
sprintf(mystr) 
## [1] "Treatments code as per protocol. Treatment codes NOT randomize
d\n" 
pander(randomizedtreatments,split.table=Inf) 
CODE
 TREATMENT
 PART
 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 33 of 45
A
 Ranolazine
 1
 
B
 Verapamil
 1
 
C
 Lopinavir+Ritonavir
 1
 
D
 Chloroquine
 1
 
E
 Placebo
 1
 
F
 Dofetilide
 2
 
G
 Diltiazem+Dofetilide
 2
 
pdf(file = "SCR-004.treatment.codes.pdf") 
grid.table(paste0('The random generator seed was: ', myseed, '\n', mystr, '\n
Table in next page')) 
grid::grid.newpage() 
grid.table(randomizedtreatments,rows=NULL) 
dev.off() 
## png  
##   2 
A.5.3 Randomization schedule table 
pander(randomizedschedulelongwithrplcids,split.table=Inf) 
RANDID  PART  COHORT  SEQ   REPLCMNTID  
1001
 
1
 1
 C
 3001
 
1002
 
1
 1
 B
 3002
 
1003
 1
  1
  D
  3003
 
1004
 
1
 1
 B
 3004
 
1005
 
1
 1
 B
 3005
 
1006
 1
  1
  A
  3006
 
1007
 
1
 1
 C
 3007
 
1008
 1
  1
  E
  3008
 
1009
 1
  1
  E
  3009
 
1010
 1
  1
  D
  3010
 
1011
 1
  1
  A
  3011
 
1012
 1
  1
  A
  3012
 
1013
 1
  1
  E
  3013
 
1014
 1
  1
  D
  3014
 
1015
 
1
 1
 C
 3015
 
1016
 
1
 3
 C
 3016
 
1017
 
1
 3
 B
 3017
 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 34 of 45
1018
 1
  3
  A
  3018
 
1019
 1
  3
  D
  3019
 
1020
 
1
 3
 B
 3020
 
1021
 1
  3
  E
  3021
 
1022
 1
  3
  E
  3022
 
1023
 
1
 3
 C
 3023
 
1024
 
1
 3
 B
 3024
 
1025
 
1
 3
 C
 3025
 
1026
 1
  3
  E
  3026
 
1027
 1
  3
  D
  3027
 
1028
 1
  3
  A
  3028
 
1029
 1
  3
  A
  3029
 
1030
 1
  3
  D
  3030
 
1031
 1
  4
  E
  3031
 
1032
 
1
 4
 B
 3032
 
1033
 1
  4
  A
  3033
 
1034
 1
  4
  D
  3034
 
1035
 1
  4
  A
  3035
 
1036
 1
  4
  E
  3036
 
1037
 
1
 4
 C
 3037
 
1038
 1
  4
  D
  3038
 
1039
 
1
 4
 C
 3039
 
1040
 1
  4
  D
  3040
 
1041
 1
  4
  A
  3041
 
1042
 
1
 4
 B
 3042
 
1043
 1
  4
  D
  3043
 
1044
 
1
 4
 B
 3044
 
1045
 1
  4
  E
  3045
 
1046
 
1
 4
 B
 3046
 
1047
 1
  4
  A
  3047
 
1048
 1
  4
  E
  3048
 
1049
 
1
 4
 C
 3049
 
1050
 
1
 4
 C
 3050
 
2001
 2
  2
  G-F
  4001
 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 35 of 45
2002
 2
  2
  G-F
  4002
 
2003
 2
  2
  G-F
  4003
 
2004
 2
  2
  F-G
  4004
 
2005
 2
  2
  F-G
  4005
 
2006
 2
  2
  G-F
  4006
 
2007
 2
  2
  F-G
  4007
 
2008
 2
  2
  F-G
  4008
 
2009
 2
  2
  F-G
  4009
 
2010
 2
  2
  G-F
  4010
 
pdf(file = "SCR-004.randomization.schedule.pdf") 
grid.table(paste0('The random generator seed was: ', myseed, '\n Tables by co
hort in next page')) 
grid::grid.newpage() 
for(cohort in unique(randomizedschedulelongwithrplcids$COHORT)){ 
  gridExtra::grid.table(randomizedschedulelongwithrplcids %>% filter(COHORT==
cohort),rows=NULL) 
  grid::grid.newpage() 
} 
dev.off() 
## png  
##   2 
A.5.4 Randomized summary table 
pander(randomizedschedulelong.summary,split.table=Inf) 
PART SEQ  
1 
2 
3 
4 
1
 A
 1006,
 
1011,
 
1012 
1018, 1028, 
1029 
1033, 1035, 1041, 
1047 
1
 B
 1002,
 
1004,
 
1005 
1017, 1020, 
1024 
1032, 1042, 1044, 
1046 
1
 C
 1001,
 
1007,
 
1015 
1016, 1023, 
1025 
1037, 1039, 1049, 
1050 
1
 D
 1003,
 
1010,
 
1014 
1019, 1027, 
1030 
1034, 1038, 1040, 
1043 
1
 E
 1008,
 
1009,
 
1013 
1021, 1022, 
1026 
1031, 1036, 1045, 
1048 
2  F-G 
2004, 2005, 2007, 2008, 
2009 
2  
G-F 
2001, 2002, 2003, 2006, 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 36 of 45
2010 
A.6 Appendix: technical information 
sessionInfo() 
## R version 3.3.2 (2016-10-31) 
## Platform: x86_64-w64-mingw32/x64 (64-bit) 
## Running under: Windows 7 x64 (build 7601) Service Pack 1 
##  
## locale: 
## [1] LC_COLLATE=English_United States.1252  
## [2] LC_CTYPE=English_United States.1252    
## [3] LC_MONETARY=English_United States.1252 
## [4] LC_NUMERIC=C                           
## [5] LC_TIME=English_United States.1252     
##  
## attached base packages: 
## [1] stats     graphics  grDevices utils     datasets  methods   bas
e      
##  
## other attached packages: 
##  [1] gridExtra_2.2.1 forcats_0.1.1   pander_0.6.0    dplyr_0.5.0     
##  [5] purrr_0.2.2     readr_1.0.0     tidyr_0.6.0     tibble_1.2      
##  [9] ggplot2_2.2.0   tidyverse_1.0.0 
##  
## loaded via a namespace (and not attached): 
##  [1] Rcpp_0.12.7      knitr_1.14       magrittr_1.5     munsell_0.4
.3    
##  [5] colorspace_1.2-7 R6_2.2.0         stringr_1.2.0    plyr_1.8.4       
##  [9] tools_3.3.2      grid_3.3.2       gtable_0.2.0     DBI_0.5-1        
## [13] htmltools_0.3.5  yaml_2.1.13      lazyeval_0.2.0   assertthat_
0.1   
## [17] digest_0.6.12    formatR_1.4      evaluate_0.10    rmarkdown_1
.1    
## [21] stringi_1.1.2    scales_0.4.1 
 
 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 37 of 45
B Step by step example of replacement algorithm  
This is just an example of a potential but very unlikely scenario with more than 14 dropouts and 
with number of dropouts above 20% in at least 2 treatment sequences. In particular, this example 
shows how replacement will be done with 5 dropouts in the crossover part and 11 dropouts in the 
parallel part using the randomization schedule example above.
B.1 Replacement in crossover part 
Potential snapshot of completers and dropouts after Period 1
RANDID  PART  COHORT  SEQ  REPLCMNTID  Period 1  Period 2  Period 3 
2001
 2
  2
 F-G
  4001
 Complete
    
 
2002
 2
  2
 F-G
  4002
 Complete
    
 
2003
 2
  2
 F-G
  4003
  Dropout
    
 
2004
 2
  2
 F-G
  4004
  Dropout
    
 
2005
 2
  2
 F-G
  4005
 Complete
    
 
2006
 2
  2
 G-F
  4006
 Complete
    
 
2007
 2
  2
 G-F
  4007
  Dropout
    
 
2008
 2
  2
 G-F
  4008
  Dropout
    
 
2009
 2
  2
 G-F
  4009
  Dropout
    
 
2010
 2
  2
 G-F
  4010
 Complete
    
 
B.1.1 Move RANDIDs from completers to the next period 
RANDID  PART  COHORT  SEQ  REPLCMNTID  Period 1  Period 2  Period 3 
2001
 2
  2
 F-G
  4001
 Complete
 
2001
  
 
2002
 2
  2
 F-G
  4002
 Complete
 
2002
  
 
2003
 2
  2
 F-G
  4003
  Dropout
    
 
2004
 2
  2
 F-G
  4004
  Dropout
    
 
2005
 2
  2
 F-G
  4005
 Complete
 
2005
  
 
2006
 2
  2
 G-F
  4006
 Complete
 
2006
  
 
2007
 2
  2
 G-F
  4007
  Dropout
    
 
2008
 2
  2
 G-F
  4008
  Dropout
    
 
2009
 2
  2
 G-F
  4009
  Dropout
    
 
2010
 2
  2
 G-F
  4010
 Complete
 
2010
  
 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 38 of 45
B.1.2 Replace dropouts 
Add one (1) replacement (with lowest RANDID) at a time from each sequence until there are no 
more replacements to be done or we reach 4 replacements (whichever happens first)
B.1.2.1 Step 1: (replace 2003 from F-G with 4003) 
RANDID  PART  COHORT  SEQ  REPLCMNTID  Period 1  Period 2  Period 3 
2001
 2
  2
 F-G
  4001
 Complete
 
2001
  
 
2002
 2
  2
 F-G
  4002
 Complete
 
2002
  
 
2003
 2
  2
 F-G
  4003
  Dropout
 
4003
  
 
2004
 2
  2
 F-G
  4004
  Dropout
    
 
2005
 2
  2
 F-G
  4005
 Complete
 
2005
  
 
2006
 2
  2
 G-F
  4006
 Complete
 
2006
  
 
2007
 2
  2
 G-F
  4007
  Dropout
    
 
2008
 2
  2
 G-F
  4008
  Dropout
    
 
2009
 2
  2
 G-F
  4009
  Dropout
    
 
2010
 2
  2
 G-F
  4010
 Complete
 
2010
  
 
B.1.2.2 Step 2: (replace 2007 from G-F with 4007) 
RANDID  PART  COHORT  SEQ  REPLCMNTID  Period 1  Period 2  Period 3 
2001
 2
  2
 F-G
  4001
 Complete
 
2001
  
 
2002
 2
  2
 F-G
  4002
 Complete
 
2002
  
 
2003
 2
  2
 F-G
  4003
  Dropout
 
4003
  
 
2004
 2
  2
 F-G
  4004
  Dropout
    
 
2005
 2
  2
 F-G
  4005
 Complete
 
2005
  
 
2006
 2
  2
 G-F
  4006
 Complete
 
2006
  
 
2007
 2
  2
 G-F
  4007
  Dropout
 
4007
  
 
2008
 2
  2
 G-F
  4008
  Dropout
    
 
2009
 2
  2
 G-F
  4009
  Dropout
    
 
2010
 2
  2
 G-F
  4010
 Complete
 
2010
  
 
B.1.2.3 Step 3: (replace 2004 from F-G with 4004) 
RANDID  PART  COHORT  SEQ  REPLCMNTID  Period 1  Period 2  Period 3 
2001
 2
  2
 F-G
  4001
 Complete
 
2001
  
 
2002
 2
  2
 F-G
  4002
 Complete
 
2002
  
 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 39 of 45
2003
 2
  2
 F-G
  4003
  Dropout
 
4003
  
 
2004
 2
  2
 F-G
  4004
  Dropout
 
4004
  
 
2005
 2
  2
 F-G
  4005
 Complete
 
2005
  
 
2006
 2
  2
 G-F
  4006
 Complete
 
2006
  
 
2007
 2
  2
 G-F
  4007
  Dropout
 
4007
  
 
2008
 2
  2
 G-F
  4008
  Dropout
    
 
2009
 2
  2
 G-F
  4009
  Dropout
    
 
2010
 2
  2
 G-F
  4010
 Complete
 
2010
  
 
B.1.2.4 Step 4: (replace 2008 from G-F with 4008) 
RANDID  PART  COHORT  SEQ  REPLCMNTID  Period 1  Period 2  Period 3 
2001
 2
  2
 F-G
  4001
 Complete
 
2001
  
 
2002
 2
  2
 F-G
  4002
 Complete
 
2002
  
 
2003
 2
  2
 F-G
  4003
  Dropout
 
4003
  
 
2004
 2
  2
 F-G
  4004
  Dropout
 
4004
  
 
2005
 2
  2
 F-G
  4005
 Complete
 
2005
  
 
2006
 2
  2
 G-F
  4006
 Complete
 
2006
  
 
2007
 2
  2
 G-F
  4007
  Dropout
 
4007
  
 
2008
 2
  2
 G-F
  4008
  Dropout
 
4008
  
 
2009
 2
  2
 G-F
  4009
  Dropout
    
 
2010
 2
  2
 G-F
  4010
 Complete
 
2010
  
 
B.1.2.5 End (maximum number of replacements reached) 
We have reached the maximum number of replacements (4) for the crossover part, thus we stop 
adding replacements (subject 2009 is not replaced)
 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 40 of 45
B.2 Replacement in parallel part 
Potential snapshot of completers and dropouts after Cohorts 1 and 3 finished.
RANDID  PART  COHORT  SEQ  REPLCMNTID   Status  
1001
 1
  1
  A
  3001
  Complete
 
1002
 1
  1
  A
  3002
  Dropout
 
1003
 1
  1
  A
  3003
  Complete
 
1004
 1
  1
  B
  3004
  Dropout
 
1005
 1
  1
  B
  3005
  Complete
 
1006
 1
  1
  B
  3006
  Complete
 
1007
 1
  1
  C
  3007
  Dropout
 
1008
 1
  1
  C
  3008
  Complete
 
1009
 1
  1
  C
  3009
  Complete
 
1010
 1
  1
  D
  3010
  Complete
 
1011
 1
  1
  D
  3011
  Dropout
 
1012
 1
  1
  D
  3012
  Complete
 
1013
 1
  1
  E
  3013
  Complete
 
1014
 1
  1
  E
  3014
  Dropout
 
1015
 1
  1
  E
  3015
  Dropout
 
1016
 1
  3
  A
  3016
  Complete
 
1017
 1
  3
  A
  3017
  Complete
 
1018
 1
  3
  A
  3018
  Dropout
 
1019
 1
  3
  B
  3019
  Dropout
 
1020
 1
  3
  B
  3020
  Complete
 
1021
 1
  3
  B
  3021
  Complete
 
1022
 1
  3
  C
  3022
  Complete
 
1023
 1
  3
  C
  3023
  Complete
 
1024
 1
  3
  C
  3024
  Dropout
 
1025
 1
  3
  D
  3025
  Dropout
 
1026
 1
  3
  D
  3026
  Complete
 
1027
 1
  3
  D
  3027
  Complete
 
1028
 1
  3
  E
  3028
  Dropout
 
1029
 1
  3
  E
  3029
  Complete
 
1030
 1
  3
  E
  3030
  Complete
 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 41 of 45
Table of the 10 dropouts to be replaced:
RANDID  PART  COHORT  SEQ  REPLCMNTID   Status  
1002
 1
  1
  A
  3002
  Dropout
 
1004
 1
  1
  B
  3004
  Dropout
 
1007
 1
  1
  C
  3007
  Dropout
 
1011
 1
  1
  D
  3011
  Dropout
 
1014
 1
  1
  E
  3014
  Dropout
 
1015
 1
  1
  E
  3015
  Dropout
 
1018
 1
  3
  A
  3018
  Dropout
 
1019
 1
  3
  B
  3019
  Dropout
 
1024
 1
  3
  C
  3024
  Dropout
 
1025
 1
  3
  D
  3025
  Dropout
 
1028
 1
  3
  E
  3028
  Dropout
 
B.2.1 Step 1: (replace subject 1014 from placebo) 
RANDID  PART  COHORT  SEQ  REPLCMNTID   Status   Replacement  
1002
 1
  1
  A
  3002
  Dropout
  
 
1004
 1
  1
  B
  3004
  Dropout
  
 
1007
 1
  1
  C
  3007
  Dropout
  
 
1011
 1
  1
  D
  3011
  Dropout
  
 
1014
 1
  1
  E
  3014
  Dropout
  3014
 
1015
 1
  1
  E
  3015
  Dropout
  
 
1018
 1
  3
  A
  3018
  Dropout
  
 
1019
 1
  3
  B
  3019
  Dropout
  
 
1024
 1
  3
  C
  3024
  Dropout
  
 
1025
 1
  3
  D
  3025
  Dropout
  
 
1028
 1
  3
  E
  3028
  Dropout
  
 
B.2.2 Step 2: (replace subject 1002 from ranolazine) 
RANDID  PART  COHORT  SEQ  REPLCMNTID   Status   Replacement  
1002
 1
  1
  A
  3002
  Dropout
  3002
 
1004
 1
  1
  B
  3004
  Dropout
  
 
1007
 1
  1
  C
  3007
  Dropout
  
 
1011
 1
  1
  D
  3011
  Dropout
  
 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 42 of 45
1014
 1
  1
  E
  3014
  Dropout
  3014
 
1015
 1
  1
  E
  3015
  Dropout
  
 
1018
 1
  3
  A
  3018
  Dropout
  
 
1019
 1
  3
  B
  3019
  Dropout
  
 
1024
 1
  3
  C
  3024
  Dropout
  
 
1025
 1
  3
  D
  3025
  Dropout
  
 
1028
 1
  3
  E
  3028
  Dropout
  
 
B.2.3 Step 3: (replace subject 1004 from verapamil) 
RANDID  PART  COHORT  SEQ  REPLCMNTID   Status   Replacement  
1002
 1
  1
  A
  3002
  Dropout
  3002
 
1004
 1
  1
  B
  3004
  Dropout
  3004
 
1007
 1
  1
  C
  3007
  Dropout
  
 
1011
 1
  1
  D
  3011
  Dropout
  
 
1014
 1
  1
  E
  3014
  Dropout
  3014
 
1015
 1
  1
  E
  3015
  Dropout
  
 
1018
 1
  3
  A
  3018
  Dropout
  
 
1019
 1
  3
  B
  3019
  Dropout
  
 
1024
 1
  3
  C
  3024
  Dropout
  
 
1025
 1
  3
  D
  3025
  Dropout
  
 
1028
 1
  3
  E
  3028
  Dropout
  
 
B.2.4 Step 4: (replace subject 1011 from chloroquine) 
RANDID  PART  COHORT  SEQ  REPLCMNTID   Status   Replacement  
1002
 1
  1
  A
  3002
  Dropout
  3002
 
1004
 1
  1
  B
  3004
  Dropout
  3004
 
1007
 1
  1
  C
  3007
  Dropout
  
 
1011
 1
  1
  D
  3011
  Dropout
  3011
 
1014
 1
  1
  E
  3014
  Dropout
  3014
 
1015
 1
  1
  E
  3015
  Dropout
  
 
1018
 1
  3
  A
  3018
  Dropout
  
 
1019
 1
  3
  B
  3019
  Dropout
  
 
1024
 1
  3
  C
  3024
  Dropout
  
 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 43 of 45
1025
 1
  3
  D
  3025
  Dropout
  
 
1028
 1
  3
  E
  3028
  Dropout
  
 
B.2.5 Step 5: (replace subject 1007 from lopinavir+ritonavir) 
RANDID  PART  COHORT  SEQ  REPLCMNTID   Status   Replacement  
1002
 1
  1
  A
  3002
  Dropout
  3002
 
1004
 1
  1
  B
  3004
  Dropout
  3004
 
1007
 1
  1
  C
  3007
  Dropout
  3007
 
1011
 1
  1
  D
  3011
  Dropout
  3011
 
1014
 1
  1
  E
  3014
  Dropout
  3014
 
1015
 1
  1
  E
  3015
  Dropout
  
 
1018
 1
  3
  A
  3018
  Dropout
  
 
1019
 1
  3
  B
  3019
  Dropout
  
 
1024
 1
  3
  C
  3024
  Dropout
  
 
1025
 1
  3
  D
  3025
  Dropout
  
 
1028
 1
  3
  E
  3028
  Dropout
  
 
B.2.6 Step 6: (replace subject 1028 from placebo) 
RANDID  PART  COHORT  SEQ  REPLCMNTID   Status   Replacement  
1002
 1
  1
  A
  3002
  Dropout
  3002
 
1004
 1
  1
  B
  3004
  Dropout
  3004
 
1007
 1
  1
  C
  3007
  Dropout
  3007
 
1011
 1
  1
  D
  3011
  Dropout
  3011
 
1014
 1
  1
  E
  3014
  Dropout
  3014
 
1015
 1
  1
  E
  3015
  Dropout
  
 
1018
 1
  3
  A
  3018
  Dropout
  
 
1019
 1
  3
  B
  3019
  Dropout
  
 
1024
 1
  3
  C
  3024
  Dropout
  
 
1025
 1
  3
  D
  3025
  Dropout
  
 
1028
 1
  3
  E
  3028
  Dropout
  3028
 
B.2.7 Step 7: (replace subject 1018 from ranolazine) 
RANDID  PART  COHORT  SEQ  REPLCMNTID   Status   Replacement  


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 44 of 45
1002
 1
  1
  A
  3002
  Dropout
  3002
 
1004
 1
  1
  B
  3004
  Dropout
  3004
 
1007
 1
  1
  C
  3007
  Dropout
  3007
 
1011
 1
  1
  D
  3011
  Dropout
  3011
 
1014
 1
  1
  E
  3014
  Dropout
  3014
 
1015
 1
  1
  E
  3015
  Dropout
  
 
1018
 1
  3
  A
  3018
  Dropout
  3018
 
1019
 1
  3
  B
  3019
  Dropout
  
 
1024
 1
  3
  C
  3024
  Dropout
  
 
1025
 1
  3
  D
  3025
  Dropout
  
 
1028
 1
  3
  E
  3028
  Dropout
  3028
 
B.2.8 Step 8: (replace subject 1019 from verapamil) 
RANDID  PART  COHORT  SEQ  REPLCMNTID   Status   Replacement  
1002
 1
  1
  A
  3002
  Dropout
  3002
 
1004
 1
  1
  B
  3004
  Dropout
  3004
 
1007
 1
  1
  C
  3007
  Dropout
  3007
 
1011
 1
  1
  D
  3011
  Dropout
  3011
 
1014
 1
  1
  E
  3014
  Dropout
  3014
 
1015
 1
  1
  E
  3015
  Dropout
  
 
1018
 1
  3
  A
  3018
  Dropout
  3018
 
1019
 1
  3
  B
  3019
  Dropout
  3019
 
1024
 1
  3
  C
  3024
  Dropout
  
 
1025
 1
  3
  D
  3025
  Dropout
  
 
1028
 1
  3
  E
  3028
  Dropout
  3028
 
B.2.9 Step 9: (replace subject 1025 from chloroquine) 
RANDID  PART  COHORT  SEQ  REPLCMNTID   Status   Replacement  
1002
 1
  1
  A
  3002
  Dropout
  3002
 
1004
 1
  1
  B
  3004
  Dropout
  3004
 
1007
 1
  1
  C
  3007
  Dropout
  3007
 
1011
 1
  1
  D
  3011
  Dropout
  3011
 
1014
 1
  1
  E
  3014
  Dropout
  3014
 


Statistical Analysis Plan 
SCR-004: Comprehensive in vitroProarrhythmia Assay 
(CiPA) Clinical Phase 1 ECG Biomarker Validation Study
Page 45 of 45
1015
 1
  1
  E
  3015
  Dropout
  
 
1018
 1
  3
  A
  3018
  Dropout
  3018
 
1019
 1
  3
  B
  3019
  Dropout
  3019
 
1024
 1
  3
  C
  3024
  Dropout
  
 
1025
 1
  3
  D
  3025
  Dropout
  3025
 
1028
 1
  3
  E
  3028
  Dropout
  3028
 
B.2.10 Step 10: (replace subject 1024 from lopinavir+ritonavir) 
RANDID  PART  COHORT  SEQ  REPLCMNTID   Status   Replacement  
1002
 1
  1
  A
  3002
  Dropout
  3002
 
1004
 1
  1
  B
  3004
  Dropout
  3004
 
1007
 1
  1
  C
  3007
  Dropout
  3007
 
1011
 1
  1
  D
  3011
  Dropout
  3011
 
1014
 1
  1
  E
  3014
  Dropout
  3014
 
1015
 1
  1
  E
  3015
  Dropout
  
 
1018
 1
  3
  A
  3018
  Dropout
  3018
 
1019
 1
  3
  B
  3019
  Dropout
  3019
 
1024
 1
  3
  C
  3024
  Dropout
  3024
 
1025
 1
  3
  D
  3025
  Dropout
  3025
 
1028
 1
  3
  E
  3028
  Dropout
  3028
 
B.2.11 Step 11: End (maximum number of 14 replacements reached) 
We have reached the maximum number of replacements (14) for the study, thus we stop adding 
replacements (subject 1015 is not replaced in this example)