Patient Assisted Intervention for Neuropathy: Comparison of Treatment in Real Life Situations (PAIN-CONTRoLS)
Phase: PHASE4
Conditions: Cryptogenic Sensory Polyneuropathy
Sponsor: University of Kansas Medical Center

 
 
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Patient Assisted Intervention for 
Neuropathy: Comparison of Treatment in 
Real Life Situations (PAIN-CONTRoLS) 
 
 
Richard J. Barohn, MD (Principal Investigator) 
 
 
 
 
 
 
 
Version 1.4 
 
 
 
 
 
 
 

 
 
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Table of Contents 
I. 
Key Personnel………………………………………………………………………………………3        
II. 
Abstract………………………………………………………………………………………………..4 
III. 
Specific Aims…………………………………………………………………………………………4 
IV. 
Prevalence and Background…………………….……………………………………………4-10 
V. 
Study Design and rational………………………………………………….………………….11-21 
VI. 
Risks to Human Subjets………………………………………………………………………..22-24 
II. 
Adequacy of Protection Against Risks .................................................................................... 23 
III. 
Potential Benefits of the Proposed Research to Human Subjects and Others .................. 24-24 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
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Key personnel (Primary Site): 
Principal Investigator:  
Richard J. Barohn, MD 
Co-Investigators: 
 
Mamatha Pasnoor, MD 
 
 
 
 
Mazen M. Dimachkie, MD 
                                               Byron Gajewski, PhD 
 
 
 
 
Omar Jawdat, MD 
 
 
 
 
April L. McVey, MD 
 
 
 
 
Melanie Glenn, MD 
 
 
 
 
Andrea Nicol, MD 
Project Manager: 
 
Angela Watson, MPH, CCRP 
Co-Project Manager:  
Laura Herbelin, BA 
 
Steering Committee: 
Patrick Peterson (Patient Advocate) Richard J. Barohn MD, PI 
Mazen Dimachkie, MD 
Aziz Shaibani, MD 
Vera Bril, MD 
Jaya Trivedi, MD 
Gil Wolfe, MD 
Omar Jawdat, MD 
Andrea Nicol, MD 
Byron Gajewski, PhD 
Linda Ward 
Kim Kimminau, PhD  
 
Data and Safety Monitoring Board (DSMB): 
Anthony Amato, MD 
Zarife Sahenk, MD 
Elliott Frohman, MD 
Tina Tockarshewsky 
Leslie McClure, PhD 
 
 Physician Safety Monitor/Chair Safety Committee: 
 Jeffrey Statland, MD 
 
 
 
 
 
 
 

 
 
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A. 
Abstract: 
 
Peripheral Neuropathy affects over 20 million Americans. Some neuropathies are 
secondary to readily identifiable causes, with diabetic sensory polyneuropathy being the most 
common cause in the United States. Once known causes are excluded, approximately 25 to 
50% remain idiopathic and are referred to as cryptogenic sensory polyneuropathy (CSPN). This 
usually affects people over the age of 50.1Neuropathic pain is a presenting symptom in 70-80% 
of CSPN.  According to a poll obtained by the Neuropathy Association, 87% of patients rated 
pain management as the most challenging in managing their neuropathy.  Currently there have 
been no prospective trials performed in the large CSPN group.  
 
The overall objective of this trial is to determine which pharmaceutical therapy is most 
effective and causes the fewest side effects in CSPN. To do this comparative effectiveness 
study, we will use an adaptive design model. 
 
Specific Aim 1: Determine which drug is most effective in producing pain relief and improving 
quality of life in patients with CSPN. We will perform a prospective randomized comparative 
effectiveness adaptive design study in neuropathy patients with pain who do not have diabetes 
and for whom no other cause has been found. The four drugs we will use are nortriptyline, 
duloxetine, pregabalin and mexiletine.  
 
Specific Aim 2: Determine which drug has the fewest and which has the most side effects. We 
will use the MedDRA adverse event coding system and the number of dropouts due to side 
effects. 
 
B. 
Prevalence, Background: 
 
Prevalence: 
Cryptogenic Sensory Polyneuropathy (CSPN) is a common but under recognized and 
understudied condition that is debilitating due to pain (1).  It is estimated that ~20 million people 
in the United States have peripheral neuropathy (PN), with an annual cost to Medicare 
exceeding $3.5 billion (2). 
 
Background: 
Many peripheral neuropathies are secondary to identifiable causes, such as diabetes, alcohol 
abuse, medications. However, once known etiologies are excluded, at least 25% of 
neuropathies remain idiopathic (3-4).  We refer to these as CSPN (1, 3-7). The onset is usually in 
the sixth and seventh decades of life. The mean age of patients in prior published series ranges 
from 51 to 63 years old.1,6,12  Prior reports describing CSPN have used other terms such as 
idiopathic neuropathy or small fiber sensory peripheral neuropathy, but we prefer CSPN.  The 
diagnostic criteria for CSPN have been established by Wolfe et al (6).  One recent study of 
peripheral neuropathy, which tested for impaired glucose tolerance and celiac disease in 
patients with abnormal skin biopsy findings, found 50% to be idiopathic(8).  Our retrospective 
review of databases from two North American (NA) and one South America (SA) tertiary 
neuropathy clinic  showed that CSPN represented at least one-fourth of all referred PN patients 
and is the most common form of neuropathy evaluated at these sites (Table 1)(7). 
 
 

 
 
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Table 1. Total number of PN cases in six major categories (7) 
Major category 
NA # of pts (%)
SA # of pts (%) 
Total # of cases 
1090
1034
Immune mediated 
215 (19.7%)
191 (18%)
Diabetic 
148 (13.5%)
236 (23%)
Hereditary/degenerative 
292 (26.7%)
103 (10%)
Infection/inflammation 
53 (4.8%)
141 (14%)
Syst./metab./toxic (Non-diabetic) 
71 (6.5%)
124 (12%)
Cryptogenic 
311(28.5%)
239 (23%)
 
In 2009, the National Chapter of the Neuropathy Association polled 1,000 members 
asking what form of neuropathy they had.  Fifty-two percent described their neuropathy as 
idiopathic (9)—twice what the academic institutions found based on referrals—which might 
understate the actual frequency of idiopathic (cryptogenic) neuropathy. Regardless, 5 to 10 
million people (25 to 50% of all neuropathies) in the US have CSPN. Clearly CSPN is a 
common, but under recognized and understudied condition. 
 
CSPN is usually diagnosed on the basis of pain, numbness and/or tingling in the distal 
extremities. There are equal numbers of men and women with this condition, and it occurs in 
all races and geographic regions.  The most common symptoms are pain (54% to 100% of 
patients), sensory loss (86%), and paresthesia (86% to 100%) (1, 5, 6, 10-13).  Lower extremity 
symptoms usually precede upper extremity symptoms (6, 10).  Approximately one-third to one-
half of patients will have symptoms confined to their lower extremities (10, 13).  The average time 
for symptoms to spread to the upper extremities appears to be about 5 years (10). Worsening of 
sensory symptoms with heat exposure, activity, or fatigue is commonly reported (13).   
 
Genetic testing is not generally indicated in CSPN.  However, recently a mutation in a 
gene for peripheral nerve sodium channel was discovered in 8 of 28 (29%) patients with CSPN 
(14). While this finding has not been confirmed in a larger population, it is intriguing and may 
explain why some neuropathy patients with pain respond well to drugs that block sodium 
channel conductance in peripheral nerves. 
 
As reported in the May 2012 National Neuropathy Association newsletter, out of 1,922 
people polled, ~66% of those with neuropathy currently receive some form of treatment for their 
pain (Figure 1) (15) and ~87% view pain management as the most challenging aspect of their 
neuropathy (Figure 2) (15). 

 
 
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Figure 1: Forms of therapies currently receiving (15) 
 
 
 
Figure 2: Most Challenging Aspects of Neuropathy and Pain Management (15) 
 
We recently asked 38 of the 161 patients with CSPN who are in the neuromuscular 
division database at the University of Kansas Medical Center to rate their symptoms of pain, 
numbness, tingling, weakness, and poor balance from 1 to 5, with 1 being the most important 
and 5 being the least important. Approximately 63% rated pain as either the most or the 2nd 
most important symptom.   In contrast, we surveyed patient with neuropathy due to Guillain–
Barré syndrome and Chronic Inflammatory Demyelinating Polyneuropathy at a recent Guillain–
Barré syndrome/Chronic Inflammatory Demyelinating Polyneuropathy support group meeting in 
Kansas City on June 29, 2013 and only 33% rated pain as most important. For those patients, 
weakness and loss of balance were rated as most important. 
 
The goal of this research project is to find the best drug for the treatment of pain in 
patients with CSPN. While the pharmaceutical industry has focused attention on drugs for 
 
 
 
 
Neuropathic pain medications (e.g., pregabalin, 
gabapentin, duloxetine, amitriptyline, opioids) 
 
I do not receive any treatments for pain relief 
 
Complementary therapies (e.g., vitamin supplements, 
acupuncture, TENS unit) 
 
Topical medications (e.g., lidocaine patch, capsaicin) 
 
Rehabilitative therapy (e.g., physical therapy,  
occupational therapy) 
 
Intravenous immunoglobulin/IVIG, plasmapheresis 
 
Interventional therapies (e.g., local anesthetic blocks, spinal 
neurostimulators, intrathecal pumps) 
 
 
 
Controlling my sensory neuropathy symptoms (e.g., 
numbness, tingling, neuropathic pain, shooting pains, 
impaired balance, loss of sensations, hypersensitivity to 
sensations) 
 
Controlling my motor neuropathy symptoms (e.g., weakness 
in the arms and legs, muscle wasting, lack of coordination) 
 
Finding the right healthcare specialists to manage my pain 
 
Understanding how my neuropathic pain is treated and 
managed 
 
Controlling my autonomic neuropathy symptoms (e.g., dry 
skin to due to lack of sweating or excessive sweating; 
lightheadedness; urinary discomforts; gastrointestinal 
discomforts; and sexual dysfunction) 

 
 
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treating diabetic sensory neuropathy (DSPN), and two drugs are now FDA approved, there have 
not been any prospective trials in CSPN.  And, because there are no studies with CSPN 
patients, insurance carriers often reject authorizing prescriptions for some drugs for patients 
with CSPN.  Additionally, while recommendations from diabetes associations and neurologists 
for treating DSPN (16-17) exist, no information is available to guide physicians and patients in 
treating CSPN. Therefore, we propose conducting a comparative effectiveness 
prospective study using drugs commonly prescribed by physicians when caring for 
CSPN patients in order to identify the best therapy for CSPN.  Scientifically establishing a 
treatment for CSPN will substantially improve care and outcomes for these patients. 
 
Most randomized, controlled pharmacologic studies in neuropathic pain have evaluated 
antidepressants and anticonvulsants.  Prospective randomized controlled clinical trials in 
neuropathic pain using placebos share a number of challenges and shortcomings.  The 
response to placebo is considerable, typically observed in one-third (18-19) and at times in more 
than one-half of patients (21).  Although statistically significant differences may be demonstrated 
between active treatment and placebo groups, the actual clinical benefit of a 1- or 2-point drop 
on an 11-point Likert-type scale for pain intensity is debatable.  And, although they provide 
objective data, neurophysiologic outcome measurements have not been helpful (22).     
 
Another consideration for U.S. clinicians is that labeled indications in the U.S. are limited 
to duloxetine and pregabalin for painful diabetic neuropathy; carbamazepine for trigeminal 
neuralgia; and gabapentin, pregabalin, lidocaine, and high-dose capsaicin patches for 
postherpetic neuralgia.  Some countries permit more liberal indications that cover neuropathic 
pain in general, whereas others require labeling for only specific forms of painful neuropathy (20, 
23).  In the United States this seems to vary based on the insurance carriers.  Some carriers 
deny the use of a neuropathic pain drug if it is not one of the two FDA approved drugs for 
diabetic neuropathy patients. 
 
For this study, we will use two drugs that are FDA approved for diabetic peripheral 
neuropathy (pregabalin and duloxetine) and two drugs (mexiletine and nortriptyline) that are 
often used in U.S. clinics to treat painful peripheral neuropathy.  We will not use a placebo 
group in this comparative effectiveness study. Below we discuss these drugs; further rationale 
for choosing these is provided under Criterion 3. 
 
Nortriptyline 
Nortriptyline is a tricyclic antidepressant.  Tricyclic antidepressants presumably exert 
their analgesic effect by modulating voltage-gated sodium channels and by inhibiting the 
reuptake of biogenic amines, norepinephrine and serotonin.  They are the most extensively 
studied agents for neuropathic pain, especially in diabetic neuropathy (31-32).  Efficacy for the 
different tricyclics is roughly similar (33-34).  We selected nortriptyline because, in general, 
nortriptyline is preferred by our clinical experts as it has fewer side effects than the older drug, 
amitriptyline.  The recommended daily dose for treatment for pain ranges from 75 mg to 200 
mg. Tricyclics have been effective in relieving pain in both depressed and non-depressed 
patients, with an analgesic action independent of mood alteration and clinically evident as early 
as 1 to 2 weeks (35-36).  
 
 
 

 
 
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Duloxetine 
Duloxetine is a serotonin-norepinephrine reuptake inhibitor (SNRI).  Selective serotonin 
reuptake inhibitors (SSRIs and SNRIs) are most commonly used as antidepressants.  In 
general, however, they have been less effective than tricyclic antidepressants in controlled 
studies of neuropathic pain (22, 33, 37).  The exception has been duloxetine which was released in 
the fall of 2004 as the first FDA-approved agent for treatment of diabetic peripheral neuropathic 
pain. Its analgesic effects are believed to be related to potentiation of descending pain inhibition 
by blocking serotonin and norepinephrine reuptake.  Duloxetine has a higher, more balanced 
affinity at these transporter sites than do prior generations of antidepressants. In several 
diabetic neuropathy trials, delayed-release duloxetine at doses of 60 and 120 mg/day was more 
effective than placebo in reducing pain severity scores, with significant improvement seen as 
early as one week (38-39). The target for improvement in these studies was a 50% reduction in 
pain.  A lower 20 mg/day dose was not effective.  Patients with major depression were excluded 
from studies.  The 120 mg/day dose was no more effective than 60 mg/day and caused more 
side effects.  Significant improvement in several secondary outcomes also was observed, 
including night pain severity, the SF-MPQ, and a brief inventory on the effects of pain on daily 
activities and mood.    
The main adverse events were nausea, which tended to occur in the first week and last 
~6 days, somnolence and dizziness.  Approximately 15% of patients in the duloxetine arms 
(groups) dropped out due to adverse events. Contraindications include hypersensitivity, use of 
monoamine oxidase inhibitors, and uncontrolled narrow-angle glaucoma.  No specific laboratory 
monitoring is needed, but duloxetine is not recommended in cases of severe renal or chronic 
hepatic insufficiency.  While labeled at a dose of 60 mg/day, 20 and 30 mg capsules are 
available, titrating up from lower doses may be better tolerated from the standpoint of nausea.            
 
Pregabalin 
Pregabalin, an analog of GABA, is an anticonvulsant that has been shown to have 
analgesic, anticonvulsant, and anxiolytic activity, although via GABA-independent mechanisms.  
Similar to gabapentin, it binds potently to the 2 subunit of voltage-activated calcium channels, 
reducing the release of several excitatory neurotransmitters.  Unlike gabapentin, pregabalin 
exhibits linear pharmacokinetics across the standard dose range, and can be initiated at a 
therapeutic dose without a long titration.  Pregabalin significantly reduced mean pain scores at a 
dose of 100 mg tid (3x/day) versus placebo in painful diabetic neuropathy (47).  The target for 
improvement was a 50% reduction in pain. No titration was used, and significant pain relief was 
observed by the end of the first week.  Secondary efficacy variables also improved significantly, 
including the SF-MPQ, sleep outcomes, pain domains in the SF-36, tension-anxiety, total mood 
scores, and both patient and clinician-rated global impressions.  Other studies in diabetic 
neuropathy have demonstrated efficacy versus placebo using fixed or flexible dosing regimens 
ranging from 150 to 600 mg/day (48-50), whereas 75 mg daily in one study (48) and 150 mg in 
another study (49) did not differ from placebo.  As a result, increasing the dose to 300 mg/day or 
higher in partial or non-responders who tolerate the medication seems appropriate.   
The main adverse events of pregabalin were dizziness (up to 39%), somnolence (up to 
22%), infection (15%), and peripheral edema (up to 17%), a profile similar to gabapentin.  The 
frequency of side effects increases with higher doses.  Adverse events were mostly mild to 
moderate in severity, and overall this drug is well tolerated (47-48).  Infections were mainly of the 
upper respiratory variety.  Eleven to 15 percent of pregabalin-treated patients discontinued 
taking it due to adverse events.   
Pregabalin was released in the fall of 2005 with indications for both painful diabetic 
neuropathy and postherpetic neuralgia.  It is a Schedule V medication in the US due to a low 

 
 
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frequency of euphoria, insomnia and headache with sudden discontinuation, and from 
responses on surveys of non-dependent sedative or hypnotic users. 
 
Mexiletine 
Trials of mexiletine, a class IB antiarrhythmic agent and oral analogue of lidocaine, 
have generated conflicting data in studies involving patients who are diabetic (55-57), alcoholic (58), 
or have HIV-related neuropathy (59-60).  In a crossover study of diabetic painful neuropathy in 
which mexiletine was titrated up to a daily dose of 10 mg/kg, the pain score and clinical 
symptom scale showed significant improvement (p< 0.02 and p<0.01, respectively) compared to 
placebo (56).  A larger study involving 126 diabetics also suggested improvement in sleep 
disturbance and nocturnal pain at 675 mg/day (55).  In contrast, others (57) failed to differentiate 
between mexiletine and placebo using two different pain scales, although a subanalysis 
suggested stabbing or burning pain, heat sensations, and formication improved.  Clinical trials 
with HIV-related neuropathy patients have failed to demonstrate any benefit for mexiletine at 
doses up to 600 mg/day (59-60). 
We have considerable experience with mexiletine in multicenter trials of patients with 
muscle stiffness (61).  Since the drug works at the sodium channel, we are intrigued with the 
possibility of using mexiletine in CSPN based on the recent publication that up to a third of 
CSPN patients may have a sodium channel mutation (14). The starting dose is 150 mg/day, with 
titration up to 600 mg/day divided into two or three doses per day. The half-life of mexiletine is 
10 to 12 hours.  Main side effects include nausea, vomiting, dizziness, tremor, nervousness, 
headache, and liver function abnormalities.  Mexiletine is contraindicated in patients with 
second- or third-degree atrioventricular blockade or cardiogenic shock (62). 
 
Past Experience at University of Kansas Medical Center 
We performed a retrospective chart review of 143 patients who were started on 
pregabalin or duloxetine, primarily for painful neuropathy, during a 10-month period at the 
University of Kansas Medical Center (63). Ninety-two patients tried only one of these two 
medications, while both medications were used at different time periods in 51 of the 143 
patients. Follow-up was available for 87 of the 91 patients who took pregabalin and 89 of the 
103 patients who took duloxetine. We found more patients with neuropathic pain reported an 
improvement with pregabalin (49%) than with duloxetine (41%), but duloxetine had a higher 
frequency (38%) of side effects compared to pregabalin (30%). However, these differences 
between pregabalin and duloxetine were not statistically significant, suggesting that both 
pregabalin and duloxetine may be effective for neuropathic pain secondary to diabetes or 
cryptogenic sensory peripheral neuropathy (Table 2) and are worthy of further investigation. 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
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TABLE 2.   Comparison  of  efficacy  and  side  effects  in  143 neuropathic  pain  patients  
using  pregabalin  and  duloxetine 
 
 
 
 
 
 
Pregabalin
Duloxetine
 
 
 
 
 
 
 
 
 
Numbera
Percentage
Numbera 
Percentage
p
Number  of  patients 
91
63.6
103 
72
 
 
Dose  (in  mg) 
217  ± 128
(range:  
50Ð600)
59  ± 14 
(range:  30Ð120)
 
Follow-up  available 
87
97
89 
86
 
.03
Efficacy 
 
 
 
 
 
 
No  improvement 
36/69
52.2
46/78 
59.0
 
.47
Minimally  improved 
10/69
14.5
16/78 
20.5
 
.35
Much improved 
23/69
33.3
16/78 
20.5
 
.09
Side  effects 
22/74
30.0
32/85 
37.6
 
.29
Medication  continued 
25/76
32.9
24/89 
27.0
 
.61
Medication  stopped  due  to  lack  of  
efficacy 
25/51
49.0
32/65 
49.2
 
.58
Medication  stopped  due  to  side  
effects 
12/51
23.5
16/65 
24.6
 
.81
Medication  stopped  due  to  a  
combination  of  lack 
8/51
15.7
12/65 
18.5
 
.51
of  efficacy  and  side  effects 
 
 
 
 
 
 
Medication  stopped  due  to financial  
reasons 
6/51
11 .8
3/65 
4.6
 
.22
 
a Due to the unavailability follow-up data for all patients, the denominator is the number in that 
group for whom follow-up data were available.  Also, recall that 51 patients took both Pregabalin 
and Duloxetine, although at different times, so the N’s do not add up to 143. 
 
Measures to Assess Outcomes 
A variety of outcome measures have been used in neuropathic pain studies.  Visual 
analogue scales and 10-point and 11-point numeric rating scales such as the Likert-type scale 
(0=no pain, 10=worst possible pain) are conventional primary outcome measures. Patients 
record their pain levels in daily diaries, and mean values (averages) over a specified interval are 
compared to baseline values.  Although these instruments provide reliable and valid 
measurements of pain intensity and unpleasantness, they do not address other aspects of the 
neuropathic pain experience (25).  As a result, a variety of quality-of-life and daily activity 
measures have been introduced, especially in more recent trials (26).       
 
Recently the NIH published a Toolbox to measure pain in research. They recommend 
using an eight item pain interference measure from their Patient Reported Outcome 
Measurement Information System (PROMIS) that has undergone validation testing in pain 
studies (27-30). The patient is asked to score each from 1 to 5, where 1=not at all, 2=a little bit, 
3=somewhat, 4=quite a bit, and 5= very much.  In this study, at all study visits, we will use this 6 
item pain interference scale, a Likert-type pain experience scale, the SF12, and the 8 item 
scales for fatigue and sleep disturbance from the NIH Toolbox .  The latter two measures were 
added to our design after meeting with our Patient Advisory Group and hearing from them that 

 
 
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sleep disturbance and fatigue were very important to them with respect to their pain.  All 
measures are in the Appendix.  
 
C. 
Study Design 
If a patient agrees to participate in the study, they will be randomized to one of the 4 
drug options. They cannot be currently taking one of the study medications or similar class of 
medication and they must have a Likert type pain score of 4 or higher. They will then be written 
a prescription for the drug at the study dose. The drug is not provided by the study; the cost of 
the drug will be the responsibility of the patient or their insurance provider. Target drug daily 
doses are nortriptyline 75 mg; duloxetine 60 mg; pregabalin 300 mg; mexiletine 600 mg. Doses 
will be escalated weekly during the first month until the target doses are achieved (all by 4 
weeks).  
 
Doses will be escalated by the following schedule: 
 
Nortriptyline: 25 mg at bedtime for 1 week, then 50 mg at bedtime for 1 week, then 75 
mg at bedtime 
 
Duloxetine:  20 mg for 1 week, then 40 mg for 1 week, then 60 mg daily     
 
Pregabalin: 100 mg at bedtime for 1 week, then 100 mg 2 times a day for 1 week, then 
100 mg 3 times a day for the remainder of the study  
 
Mexiletine: 200 mg at bedtime for 1 week, then 200 mg 2 times a day for 1 week, then 
200 mg 3 times a day for the remainder of the study 
 
If suggested medications are not available in the dosage outlined in this protocol, then an 
alternate dosing schedule may be utilized providing that patients achieve the target daily drug 
dosage at week 3 of the study. 
 
The length of the study for each patient is 3 months. After this point, they can stay on the 
medication of their choice. 
 
If the patient cannot tolerate the maximum dose, then (at the discretion of the physician) they 
may be titrated down to the previously tolerable dose.  Patients who are currently taking 
narcotics for pain control are eligible to participate in this study if they meet all other inclusion 
criteria. 
 
  
a. Inclusion/Exclusion Criteria: 
Inclusion Criteria: 
1. Age 30 or older. 
2. Diagnosis of cryptogenic sensory polyneuropathy. 
3. Likert Pain Score of greater than or equal to 4. 
4. Must not currently be on nortriptyline, duloxetine, pregabalin or mexiletine or similar 
class of medication for at least 7 days from baseline study visit. 
 
Exclusion Criteria: 
1. Any medical condition or current medication (Appendix G) that would prevent them 
from taking either nortriptyline, duloxetine, pregabalin or mexiletine. 

 
 
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2. Unable to give consent. 
3. Unable or not willing to comply with the study. 
4. Other causes for polyneuropathy. 
 
b. Study Procedures: 
 
Prescreening: Subjects must be identified with having CSPN prior to the baseline visit into the 
study. 
 
Screening/Baseline Visit: (Screening/baseline period may take up to 14 days if patients need 
to titrate down off of medication) 
1. Subjects will be asked to read and sign the consent form.  
2. Medical history 
3. List of concomitant medication 
4. Likert pain scale 
5. NIH Pain Interference Scale 
6. NIH Fatigue Interference Scale 
7. NIH Sleep Disturbance Scale 
8. A three month prescription will be given to the subject at this visit. 
9. SF-12 
 
Month 1 and 2:  
1. Visits will be completed via phone unless a clinical visit is requested by the subject or 
the physician. 
2. Likert pain scale 
3. NIH Pain Interference Scale 
4. NIH Fatigue Interference Scale 
5. NIH Sleep Disturbance Scale 
6. Concomitant medication 
7. Adverse events 
8. SF-12 
 
Month 3: (titration down of the medication is not required) 
1. In-person clinical visit is required for the final month 3 visit (no phone option). 
2. Adverse events 
3. Likert pain scale 
4. NIH Pain Interference Scale 
5. NIH Fatigue Interference Scale 
6. NIH Sleep Disturbance Scale 
7. Concomitant medication 
8. Subjects can either can go off medication or be prescribed medication they were on 
(titration down is not necessary) 
9. SF-12 
10. 
28 day post call. 
1. There will be a call by the study coordinator to close out any adverse events 
left open at the end of the study. 
 

 
 
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Choice of Study Design 
             We chose the Bayesian Adaptive Design with efficiency in mind. Using adaptive 
randomization (being able to change how we assign patients to the drugs during the study 
based on information gained during the study) not only allows for substantially smaller sample 
sizes, but also provides better conclusions about what treatments are the most effective 
because it lets us make changes to our approach or stop the study early if we find strong results 
before the scheduled end of the study (64).  We conducted extensive trial simulations comparing 
different designs measuring the resources (time and number of patients required) and the ability 
to draw important conclusions about relative efficacy of the four drugs (i.e., which is better) and 
selected the proposed design as the most effective and efficient.  
 
Choice of Outcomes 
            Patients will complete a Likert-type scale for pain, the PROMIS 6 item pain interference 
scale, the SF-12, and the PROMIS fatigue and sleep disturbance scales. The first endpoint, or 
outcome, is a patient responder-defined measure of efficacy (i.e., if the patient reports a pain 
score at the end of 12 weeks that is 50% or greater from what they reported when they started 
the study, then it is efficacious for that patient).  The second endpoint is the percentage of 
patients who discontinue treatment.  Our secondary endpoints, SF-12, pain interference, 
fatigue, and sleep disturbance, also will be longitudinally modeled (see content after Table 5.) 
 
D. 
Analytic Methods 
 Overview 
The primary aim of this study is to determine which drug is most effective and most 
tolerable in producing pain relief. We will perform a prospective randomized comparative 
effectiveness adaptive design study with those who do not have diabetes and in whom no other 
cause for neuropathy has been found. We have an agreement from 40 sites to participate in the 
study and four drugs will be tested. In this design we will perform several interim analyses 
(interims) while controlling the overall Type I error rate.  
 
The following sections focus on specific details of this design and detail how we 
determined power, sample size, and duration of this trial.  Throughout we add information to 
help those less familiar with our design and analyses better understand what we did and plan to 
do with this study. 
  
Bayesian Adaptive Design 
Patients will be randomized to one of four treatment arms (groups) with a maximum 
number of patients nmax= 400. Using a Bayesian Adaptive Design, at each interim analysis a 
decision will be made to either continue enrolling patients or to stop the trial for success. If 
patient enrollment continues, the randomization structure (i.e., how we randomize patients to 
each drug) will also be updated.  Two endpoints are used to drive the adaptive randomization 
and stopping criteria.  The first endpoint is the percentage of patients who report a 50% or 
greater decrease on the Likert-type pain scale after 12 weeks.  The second endpoint is the 
percentage of patients that discontinue treatment. After 80 patients are enrolled (see definition 
of “enrolled” in the consent form section towards the end of this protocol) and equally 
randomized to the four arms, we begin adapting the randomization. Then, after we have 
endpoint data on 100 patients, the data will be analyzed (a first interim analysis) and an updated 
randomization schedule will be used.   Specifically, the arm, or drug, that looks to be the best 

 
 
Version date: 15 Feb 2015 
14 
 
will get more participants allocated to it in this subsequent randomization.  A new adaptive 
randomization schedule will be updated and an interim analysis will occur every 13 weeks after 
the first interim analysis, using up to date outcome data, until the trial is stopped (i.e. for early 
success). Early success stopping criteria will be if the probability of the maximum arm (i.e., the 
best drug), measured by a combined utility of the pain and treatment discontinuation endpoints, 
is larger than 0.925. While this will halt new patient enrollment, we will confirm this finding with a 
subsequent analysis and evaluation after all data from all enrolled patients are obtained as 
some will still be actively in the study when the early stopping criterion is identified.  Also 
determined is which of the arms are losers, and is defined as an arm that has a probability of 
being maximum as less than 0.01. This will be a useful result in cases where a best arm could 
not be identified. If a loser or two is identified then the drug(s) would not be recommended for 
use in clinical practice.  
A summary of the overall general design is as follows. 
• 
Begin a new adaptive randomization schedule once 20 patients have been enrolled on 
each treatment. 
• 
Stop for success if the probability a treatment is best, defined as pr(ti is best) > .925, for 
some treatment. 
• 
Perform an interim analysis after 100 patients have 12 week data and every 13 weeks 
thereafter.  
• 
For interim analyses, all data are used on all enrolled patients with at least 4 weeks of 
data. 
• 
For the final analysis: (1) a treatment is best if pr(ti is best) > .925 or (2) a treatment is 
loser if pr(ti is best) < .01. 
 
Combining Endpoints 
We are combining the two endpoints using a utility function framework, which has been 
discussed from a Bayesian adaptive design point of view (65).  The following describes how we 
built our utility function. Suppose Drug B has better efficacy than Drug A, but Drug A has a lower 
quit rate due to tolerability than Drug B.  What would the “quit rates” and “efficacies” have to be 
in order for Drug A to be clinically the same as Drug B? An elicitation tool that looks at various 
comparisons (seven of them) was created (66) that asked three of the clinical experts on this 
proposal (i.e. Barohn, Dimachkie, and Pasnoor) to provide the point of equivalency (Table 3). 
 
Table 3.  This table was presented to clinical experts and they were asked to fill in the 
underlined values, which are filled-in here but were blank when given to the experts. 
  
Drug A 
 
 
Drug B 
  
Scenario %Efficacy
%Quit
 
%Efficacy %Quit 
#1 
30% 
15% 
Equals
40% 
30% 
#2 
30% 
5% 
Equals 
40% 
10% 
#3 
50% 
20% 
Equals 
40% 
15% 
#4 
50% 
30% 
Equals 
40% 
20% 
#5 
30% 
15% 
Equals 
50% 
30% 
#6 
20% 
2% 
Equals 
50% 
10% 
#7 
25% 
15% 
Equals 
50% 
35% 
 

 
 
Version date: 15 Feb 2015 
15 
 
We then plotted the points from Table 3 that were considered "equal” and then plotted lines of 
constant utility. This resulted in identifying that 4 points of efficacy = 3 points of quit rate.   This 
utility function was acceptable to the clinical team.  Specifically, the utility function is 
U(E,Q)= .75E+1-Q.    
Combining patient ratings of efficacy with data on the percentage of patients who stop 
taking the drug to which they were assigned provides us with an important single measure of 
which drug is ‘best.’ 
 
 
Virtual Participant Response 
For the purposes of this investigation we looked at several virtual (or “pretend”) 
responses to determine the power, sample size and time (duration) needed for our study.  We 
created several scenarios for both efficacy and quit rates using five patterns for efficacy and 
three patterns for quit rates (Table 4).  These were then combined into several possible 
combinations (e.g., one combination would be a ‘best’ and ‘2nd best’ drug for efficacy and a ‘no 
differences’ for quit rate, another might be ‘no differences’ for efficacy and ‘no differences’ for 
quit rate, etc.). 
Table 4.  Virtual response patterns for each endpoint. 
1 
2
3
4
 
Efficacy
 
No Difference 
0.30 
0.30
0.30
0.30
all drugs are equally efficacious
Best and 2nd Best 
0.30 
0.40
0.50
0.30
one drug is best, one is 2nd best
All Different 
0.40 
0.45
0.50
0.30
all have different efficacy
One Strong Best 
0.30 
0.30
0.50
0.30
one drug is much better 
One Modest Best 
0.30 
0.30
0.40
0.30
one drug is modestly better
Quit
 
No Difference 
0.20 
0.20
0.20
0.20
all drugs have same quit rates
Positive Relation 
0.30 
0.25
0.15
0.30
Most efficacious drug has lowest 
quit rate 
Negative Relation 
0.20 
0.40
0.60
0.20
Most efficacious drug has highest 
quit rate 
NOTE:  “Positive” relation refers to both efficacy and quit percentages being good; “Negative” 
relation refers to either good efficacy and high quit percentages or low efficacy and low quit 
percentages. 
 
Accrual (patient enrollment) Patterns 
Accrual patterns are important to Bayesian adaptive designs and refer to how rapidly 
each site enrolls patients in the trial.  We assume that the distribution of the accrual patterns 
follows a Poisson distribution with a mean, or average, number of patients accrued per week. 
The accrual patterns depend on two factors: (1) the number of sites actively enrolling patients in 
the trial, and (2) how fast the sites can enroll, which we assume is a constant for each. We 
expect after a ramp-up of around 8 weeks of enrolling patients, using information from the 
participating 40 site investigators, we should accrue an average of 4.2 patients/week.  
Determining this is important for identifying how long the trial will last. 
   
Statistical Model  
Of necessity the following is fairly technical, but it is needed to allow for appropriate 
statistical review and because it is the statistical model that will evaluate final determination of 
which drug is “best”.  This is referred to as the arm of maximum utility (i.e., the drug with the 

 
 
Version date: 15 Feb 2015 
16 
 
best combination of patient reported efficacy and percentage of patients who quit taking the 
drug.)  For this study the two endpoints are respectively SEjT for efficacy and SQjT for quitting. 
The number who did not quit and were not efficacious is SnEjT. These are modeled as a 
multinomial distribution (SQjT,SEjT, SnEjT)~MultNomial(θq
j, θe
j, θne
j).  In addition, we provide 
“weakly informative” priors, (θq
j, θe
j, θne
j)~ MultNomial(1/3,1/3,1/3)).  Using the endpoint data and 
the prior probabilities, we then use Markov Chain Monte Carlo computations to obtain the 
Bayesian posterior distributions of 

|
e
j
EjT
S

and 

|
q
j
QjT
S

respectively for each endpoint (i.e., 
efficacy and quitting.)   
 
The utility function previously discussed is used for determining whether we have met 
our stopping criterion.  Specifically, the rule is we will stop the trial if the probability of an arm 
(i.e., a drug) having maximum utility is greater than 0.925. This may first be determined after 
enrolling 100 of the 400 potential patients.  The utility function is  




.75
|
1
|
e
q
jT
j
EjT
j
QjT
U
S
S




 and the arm (or drug) having the maximum utility is


max,
1
2
3
4
5
max
,
,
,
,
T
T
T
T
T
T
U
U
U
U
U
U

.  The stopping rule is mathematically P(Umax,T)>.925). If a 
maximum utility arm (drug) is not identified after completed data on 100 patients, this procedure 
and accrual will continue until a maximum arm is identified or we reach 400 enrolled patients.  
At the end of the trial we will also decide that an arm is a “loser” if there is a very unlikely chance 
that it is the best, P(Umax,T)<.01). 
       
Adaptive Randomization: Allocation  
After the stopping rule is evaluated the next round of patients are randomized using a 
formula that takes advantage of the information gained from our analyses up to that point.  
Using this formula, each arm (or drug) is allocated for the next patients to be enrolled in the jth 
arm proportional to




max,
*
Pr
1
jT
T
jT
j
jT
U
U
Var U
V
n



. This type of allocation tends to have more 
desirable properties then simply using


max,
Pr
jT
T
U
U

.  Using this approach will allow us to 
assign more patients to the most promising arm or drug, and fewer patients to the least 
promising drug. 
 
Longitudinal Model 
This model predicts patients 12 week data from data at early time points (4 and 8 
weeks). Estimate transition probabilities from outcome at early time point to final outcome. The 
number of transitions to final outcome given early outcome is distributed as multinomial with the 
following parameters.  If a patient quits treatment early, we transition the patient to quit at 12 
weeks. Let p21, p22, and p32 be conditional on a patient showing early efficacy, the respective 
final probabilities of quit, efficacy, and not efficacy.   For these we use a Dirichlet prior on 
transition probabilities, (p21,p22,p32)~Dirichlet(1,7,2).  Similarly for a patient that shows no 
efficacy early, the final prior probabilities are (p31,p32,p33)~Dirichlet(1,2,7).  These are fairly 
diffuse, each having a prior sample size equivalent to ten patients.  
We estimate transition probabilities separately for each treatment group and for 4 and 8 
week early time points. Here is the algorithm for the longitudinal model. Calculate the posterior 
probability each treatment is best by: 
1. Sample posterior transition probabilities from longitudinal model  
2. Impute 12 week data values for patients with at least 4 week values given 
sampled transition probabilities  

 
 
Version date: 15 Feb 2015 
17 
 
3. Sample values from the posterior distribution under each treatment given 
imputed 12 week data   
4. Given each vector calculate the utility under each treatment 
5. Report the treatment with the max utility 
6. Repeat 1-5 and summarize posterior probability that each treatment is best as 
the proportion of times it had the max utility. 
Here is the longitudinal scenario based on elicitations from the clinical team, which 
resulted in transition probabilities for evaluating the trial’s operating characteristics. To simulate 
the early outcomes, one first simulates the final outcomes from a multinomial distribution. Then 
the early outcomes are also multinomial simulations conditional on these final outcomes.  
Specifically, if the final outcome is a quit, then the probabilities of early quit, efficacy, non-
efficacy is 0.80, 0.10, and 0.10 respectively.  If the final is effective then we have 0, .67, and .33 
respectively.  If not effective then is 0, .33, and .67.   
 
Simulation Algorithm  
The following steps summarize the algorithm (or rule) used to determine power, sample 
size, and duration of the trial.  These are all necessary elements for establishing that the trial is 
of sufficient size and length to yield valid results.  For this algorithm we used the “virtual patient 
responses” parameters (or simulated data) previously discussed.  Step 0:  Set the index for 
simulation iteration to be b=0.  Step 1: Set b=b+1. Step 2: Simulate the initial observed data. 
Step 3: estimate posterior parameters via simulation and calculate the stopping rule and the 
possible next allocation. Step 4: repeat steps 2 and 3 after collecting four more weeks of data. 
Step 5: evaluate all of the data after collecting all of the endpoints. Step 6: go to step 1 unless 
b=1000, then stop.  Initially we used this algorithm in a software package called FACTSTM 
(Fixed and Adaptive Clinical Trials Simulator) (65).  FACTSTM is very powerful and can handle a 
wide variety of models for exploring the operating characteristics of Bayesian clinical trials 
designs. It was developed by S. Berry, who co-authored the PCORI methods guidelines and 
wrote the Bayesian adaptive design portion of those guidelines.  Dr. Berry is a consultant on this 
application and the University of Kansas Medical Center is the first academic institution with 
license to use FACTSTM. We then programmed the algorithm in the software R(66) to handle the 
multinomial case for the simulation and adapted it for use during the actual trial.  
 
Power, Sample Size, and Trial Duration 
We performed several trial simulations based on the various combinations of efficacy (5 
types) and quit (3 types) endpoints that were shown in Table 4.  We varied the maximal accrual 
into the study after several weeks and, as previously identified, found 4.2 patients/week to be 
very close to the optimal combination of sample size and trial duration (66).  These simulations 
resulted in identifying power (the probability of success) in two components—one for early 
success (i.e., being able to stop the trial early) and one for late success of the trial (i.e., after 
enrolling all 400 patients) (see Table 5).  While some of these combinations are very unlikely to 
occur, we include all possible combinations in Table 5, ordered from most to least power.  We 
highlight four scenarios, here.   
 
First we want to highlight the unlikely scenario that serves as our null hypothesis 
(scenario #1 in Table 6).  In this scenario there are no differences in efficacy or in quit rates 
among the drugs.  Therefore, the extent to which this scenario is “successful” actually reflects 
our Type I error rate.  For this scenario, we estimated (identified) that 5.5% of the simulated 
trials had early success, .6% late success. Thus this trial scenario produced an appropriate 
expected Type I error (α~.05).  The sample size of this scenario on average was 393 patients.  
The average length of the trials under this scenario was 108 weeks. Second (scenario #2 in 

 
 
Version date: 15 Feb 2015 
18 
 
Table 6), if there is a best drug and second best drug in terms of efficacy and quit rates, we 
estimated (identified) that 78% of the simulated trials had early success, 2% had late success, 
and 20% had incomplete results. This trial scenario had 80% power and the sample size of this 
trial scenario was on average 298.  The average length of this trial scenario was 78 weeks.  
Third (scenario #3 in Table 6), if there is one strong best drug in terms of efficacy and quit 
rates, we estimated (identified) that 92% of the simulated trials had early success, 2% late 
success, and only 6% had incomplete results. Thus this simulation had 94% power.  The 
average sample size of this trial scenario was 266.  The average length of these simulated trials 
was 68 weeks. Close to half of the sample size, 108, was on the best treatment.  Fourth 
(scenario #4 in Table 6), a very unlikely case, is if there is one strong best drug in terms of 
efficacy and but quit rates that are higher for the drug with high efficacy, we estimated 
(identified) that 10% of the simulated trials had early success, 2% late success, and 88% had 
incomplete results. Thus this simulation had 12% power.  The average sample size of this trial 
scenario was 387.  The average length of these simulated trials was 107 weeks. However, this 
trial does a decent job correctly identifying treatment 2 as the loser (65%).   
 
Table 5. Simulation scenarios. 
 
  
  
  
Treatment 
  
  
  
1 
2 
3 
4 
Scenario 1 
Pr(Quit) 
0.2 
0.2 
0.2 
0.2 
Eff: No Difference 
Pr(Efficacy) 
0.3 
0.3 
0.3 
0.3 
Quit: No Difference 
Utility 
1.025 
1.025 
1.025 
1.025 
Scenario 2 
Pr(Quit) 
0.3 
0.25 
0.15 
0.3 
Eff: Best and 2nd Best 
Pr(Efficacy) 
0.3 
0.4 
0.5 
0.3 
Quit: Positive 
Utility 
0.925 
1.050 
1.225 
0.925 
Scenario 3 
Pr(Quit) 
0.3 
0.25 
0.15 
0.3 
Eff: One Strong Best 
Pr(Efficacy) 
0.3 
0.3 
0.5 
0.3 
Quit: Positive 
Utility 
0.925 
0.975 
1.225 
0.925 
Scenario 4 
Pr(Quit) 
0.2 
0.4 
0.4 
0.2 
Eff: One Strong Best 
Pr(Efficacy) 
0.3 
0.3 
0.5 
0.3 
Quit: Negative  
Utility 
1.025 
0.825 
0.975 
1.025 
 
 
Table 6. Simulated Trial Operating Characteristics. 
 
  
  
  
  
  
  
  
  
Mean 
Patients
Scenario 
Prob. 
Success 
Prob. 
Early 
Success
Mean 
Trial 
Weeks
Total 
T1 
T2 
T3 
T4 
1 (No Difference) 
0.061 
0.055 
108 
393 
99 
99 
97 
99 
2 (Best & 2nd Best+) 
0.798 
0.777 
78 
298 
51 
80 
116 
52 
3 (One Strong Best+) 
0.939 
0.923 
68 
266 
51 
58 
105 
51 
4 (One Strong Best-) 
0.115 
0.103 
107 
387 
108 
62 
107 
109 
 
  
Prob. Drug 
Wins 
 
  
  
Prob. 
Drug 
 
  
  

 
 
Version date: 15 Feb 2015 
19 
 
Loses 
Pr(Treatj 
Best)>.925
 
Pr(Treatj 
Best<.01) 
 
Scenario 
T1 
T2 
T3 
T4 
T1 
T2 
T3 
T4 
1 (No Difference) 
0.015 
0.015 
0.014 
0.017 
0.075 
0.064 
0.088 
0.068 
2 (Best & 2nd Best+) 
0.000 
0.001 
0.797 
0.000 
0.744 
0.238 
0.000 
0.736 
3 (One Strong Best+) 
0.000 
0.000 
0.939 
0.000 
0.661 
0.461 
0.000 
0.682 
4 (One Strong Best-) 
0.053 
0.000 
0.011 
0.051 
0.030 
0.647 
0.104 
0.034 
NOTE:  For Quit, "positive" means quit percentages are low and "negative" means quit 
percentages are high  
 
Secondary Endpoints 
The previous discussion focused on our primary endpoints, efficacy and quite rates.  The 
temporal patterns of our secondary endpoints, SF-12, pain interference, fatigue, and sleep 
disturbance, will be longitudinally modeled using a two-level (time, patient) hierarchical linear 
modeling approach to evaluate these variables within each arm across time (67). 
Missing data: 
Our primary statistical inference of the intervention measures (efficacy and medication 
quit rates) will label all patients with missing VAS data to be imputed as a failure.  Therefore our 
study has valid type I error rates and has nominal confidence interval coverage properties as 
detailed in the proposal. A patient that is enrolled and randomized (note: after signing the 
consent form) into the study will either continue to take the randomly assigned pain medication 
(quit=0) or discontinue (quit=1).  Among the patients that continue the pain medication, and 
have follow-up VAS outcomes, that patient will either drop their VAS pain score by ½ or better 
(efficacy=1) or they will not (efficacy=0).  Among the patients that discontinue treatment of 
assigned pain medication, or having missing VAS values, they will automatically be recorded as 
non-efficacious (efficacy=0).  Patients that are lost from the study will be considered to have 
discontinued treatment (quit=1, efficacy =0). The rule above implies that missing data will result 
in an imputed failure (and hence there are no missing primary outcomes). The primary inference 
in our study automatically addresses MD-3 of the PCORI Methodology Standards.   Note: our 
secondary statistical inference of the intervention measure of reported VAS changes will be 
conducted using Bayesian mixed-effect model repeated measures (MMRM) where missing data 
will be addressed using multiple imputation. 
 
E. 
Stakeholders   
 
The key stakeholders for the PAIN-CONTRoLS project are patients with Cryptogenic 
Sensory Polyneuropathy (CSPN), their families who cope with loved ones struggling with pain 
on a daily basis, providers who treat these patients, scientists involved in this study, patients’ 
local and national associations (the local and national chapters of the Neuropathy Association), 
and ethicists working in healthcare in general and pain management in particular. We already 
have engaged a number of critical stakeholders—including in discussions of study design, for 
feedback on measures to be used, and for support for recruitment and completion of the 
proposed study.  Indeed, the decision to propose this study and how it will proceed are a direct 
result of working with patients and tracking their symptoms.  Our patients made it very clear that 
a rigorous comparative effectiveness study is desperately needed. The organizational 

 
 
Version date: 15 Feb 2015 
20 
 
Committees and 
Activities
Stakeholder Engagement
Researcher 
Team
Barohn
Dimachkie
Pasnoor
Jawdat
Herbelin
Gejewski
Kimminau
Site PIs
Patient Advisory Group
Communications 
Committee
Data Safety and 
Monitoring Board
Community Engagement:
‐PAINS Action Alliance
‐National Neuropathy Association
‐Kansas City Neuropathy 
Association
PAIN CONTRoLS Steering 
Committee
Patient Advisory Board
Technical Experts:
‐Spertus
‐Berry
‐Frontiers Ethics Committee
‐Center for Practical Bioethics
framework we present in Figure 3 visually demonstrates our commitment to inform our research 
with patient, caregiver, and consumer input at every level.  Bold/underlined text in Figure 3 
indicates that activity or membership includes patients and other stakeholders (see Engagement 
Activities text below for details.)  
 
 
 
 
Figure 3.  Organizational Structure for Stakeholder Engagement 
 
 
 
Engagement Activities: Researcher and Technical Expert Collaboration 
 
Our engagement activities began over a year ago.  We had an initial brainstorming 
meeting with approximately 30 researchers and staff at the Personalized Medicine and 
Outcomes Center within the NIH CTSA supported Frontiers: Heartland Institute for Clinical and 
Translational Research program in May 2012. We presented our initial concept of a comparative 
effectiveness study for painful neuropathy and received outstanding feedback.  They advised 
that we should develop the study so that it most closely resembles real world activity and 
decision-making between physicians and patients in the management of neuropathy pain.  This 
influenced us a great deal in deciding on the design to randomize patients to drugs that are 
currently given routinely, but for which there have been no systematic studies for use in CSPN.  
We also were mindful to design the study to dispense these drugs as they would be advised by 
their doctor—for example, one or two or three times a day, and not try to create a common time 
or strict number of pills dispensed for each drug. Dr. Barohn heard Scott Berry, PhD, talk about 
adaptive design models for clinical trials at a CTSA meeting in the spring of 2012, and this led to 
Dr. Berry joining the study development team as a consultant. 
  
The Data and Safety Monitoring Board (DSMB) will be chaired by Dr. Anthony Amato, 
Professor of Neurology at Harvard Medical School. Other members of the DSMB will be Zarife 
Safety Committee

 
 
Version date: 15 Feb 2015 
21 
 
Sahenk, MD at Ohio State University, a peripheral nerve specialist; Elliott Frohman MD, a 
multiple sclerosis and clinical trials expert at UT Southwestern Medical Center in Dallas; Kim 
Kimminau, PhD, co-director of our CTSA’s Community Partnership for Health program, Ms. 
Tockarshewsky; and Leslie McClure PhD, a biostatistician from a non-participating academic 
institution.  The DSMB will meet via phone 3-4 times a year to review the status of the study and 
to make decisions about study continuation. After each meeting, they will prepare a letter that 
will be disseminated to all members of the PAIN-CONTRoLS Steering Committee and to each 
participating site.  By including all members of the Steering Committee, we ensure that 
information will be available to our patient Committee members and if appropriate will be 
included in Newsletter and other communications. 
While we will have this official Data and Safety Monitoring Board (DSMB), we also will 
have a physician safety monitor, Jeffrey Statland, MD, who will chair a Safety Committee to 
discuss issues regarding side effects and adverse events in real time as they happen.  This is 
standard for clinical trials.  Our Safety Committee will have at least one patient with CSPN as a 
member, and at least one physician who is not associated with the study.  Together they 
will assess side effects as they occur and make determinations regarding particular patient 
safety issues. They will provide this information to the study PI and Co PIs and the official 
DSMB.   
 
The last committee we have previously referred to but did not detail is the PAIN-
CONTRoLS Steering Committee.  The Steering Committee is made up of the following: at 
least one representative from the Patient Advisory Board, Richard J. Barohn MD, PI, Mazen 
Dimachkie MD, Aziz Shaibani MD, Vera Bril MD, Jaya Trivedi MD, Gil Wolfe MD, Omar Jawdat 
MD, Andrea Nicol MD, Byron Gajewski PhD, statistician, Linda Ward from the Center for 
Practical Bioethics, and Kim Kimminau PhD, representing the community engagement function 
for Frontiers (CTSA grant at KUMC).  Because we actively involved patients in developing the 
PAIN-CONTRoLS project, several have already agreed to serve on the Steering Committee. 
The Steering Committee will oversee additional development of the protocol and execution of 
the study. They will approve the final protocol before it is distributed to each site, and will be 
kept up to date on reports from the Data and Safety Monitoring Board. They also may ask the 
DSMB to convene for review of safety issues at any time. 
 
The Steering Committee will review all manuscripts and presentations prepared about 
the PAIN-CONTRoLS project and using data from this project.  The patient member of the 
Steering Committee will be charged with ensuring these are shared with the full Patient Advisory 
Board.  The Steering Committee also will ensure that each site investigator maintains contact 
with their local chapter of the Neuropathy Association (if one exists near their site) and visits or 
sends a representative to the local chapter meetings several times a year. 
We are committed to conduct this project in the most transparent way possible, seeking 
meaningful communication with and participation by all of our stakeholders.  In particular, 
patients and other stakeholders have been fully integrated into the planning and the design of 
this project and all will be actively involved in the conduct of this study and the dissemination of 
findings from this study.  Patients and other stakeholders sit on every committee, and a patient 
will co-chair the important Communications Committee. 
 
KUMC intends to utilize a “remote or virtual” monitoring plan for this trial. The goal with 
remote monitoring is to help site investigators and research staff with the prevention or 
mitigation of important and likely risks to data quality, protection of human subjects and trial 
integrity.  
 
The remote monitor will ask for copies of signed consent forms and other source 
documents to verify legal informed consent, subject eligibility and outcome measures entered 
into the database. We will try to request remote monitoring 2-3 subjects per site at random. 
Completion and accuracy of subject eligibility, the Likert Pain Scale, the NIH Pain Interference 

 
 
Version date: 15 Feb 2015 
22 
 
Scale, the NIH Fatigue Interference Scale, NIH Sleep Disturbance Scale, SF-12 and adverse 
event reporting will also be verified during remote monitoring. 
Each site will be asked to send protected documents containing individually identifiable 
personal and/or health information through KUMC's secure portal called Secure File Transfer 
Network. 
The project managers will monitor the database and to confirm that all data has been 
entered on a weekly basis. They will report to Richard J. Barohn, MD if they identify a site that is 
unable to enter data in a timely manner. 
 
Protection of Human Subjects 
 
The University of Kansas Medical Center will serve as Internal Review Board (IRB) of 
record for this study for all sites. Each site must obtain approval from their IRB as well as from 
the IRB of record (KUMC) before enrollment at their site can begin. This process will be followed 
carefully by the Research Institute Clinical Research Administration office at KUMC (JoAnn 
Miller, BA, CCRP) to ensure that all sites comply. 
 
Patients will need to be consented prior to participation in the study. All sites will need to 
document their consenting processes and the original signed and dated consent forms will be 
kept at each site. Each patient will be given a copy of their consent forms. 
 
Each subject will sign two consent forms. The main consent form will explain the study, 
outline study visits and procedures and identify the risks that will be the same in each group. 
After all study procedures are performed and the subject is randomized, the subjects will then 
sign a drug specific supplemental consent form. This consent form will list the risks unique to 
the drug the subject is randomized. 
 
Each main consent form will contain the following information found from the National Institutes 
of Health (NIH) website (www.grants.nih.gov/grants/funding/phs398/phs398.doc). The 
components of the consent form must contain the following information (copied from the above 
website): 
 
I. Risks to Human Subjects 
a. 
Human Subjects Involvement, Characteristics, and Design  
 
Describe and justify the proposed involvement of human subjects in the work outlined in 
the Research Strategy section.  
 
Describe the characteristics of the subject population, including their anticipated number, 
age range, and health status if relevant.  
 
Describe and justify the sampling plan, as well as the recruitment and retention 
strategies and the criteria for inclusion or exclusion of any subpopulation.  
 
Explain the rationale for the involvement of special vulnerable populations, such as 
fetuses, neonates, pregnant women, children, prisoners, institutionalized individuals, or 
others who may be considered vulnerable populations. Note that 'prisoners' includes all 
subjects involuntarily incarcerated (for example, in detention centers) as well as subjects 
who become incarcerated after the study begins.  
 
If relevant to the proposed research, describe procedures for assignment to a study 
group. As related to human subjects’ protection, describe and justify the selection of an 
intervention’s dose, frequency and administration. 

 
 
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 
List any collaborating sites where human subjects research will be performed, and 
describe the role of those sites and collaborating investigators in performing the 
proposed research. Explain how data from the site(s) will be obtained, managed, and 
protected. 
b. 
Sources of Materials  
 
Describe the research material obtained from living individuals in the form of specimens, 
records, or data.  
 
Describe any data that will be collected from human subjects for the project(s) described 
in the application.  
 
Indicate who will have access to individually identifiable private information about human 
subjects.  
 
Provide information about how the specimens, records, and/or data are collected, 
managed, and protected as well as whether material or data that include individually 
identifiable private information will be collected specifically for the proposed research 
project. 
c. 
Potential Risks  
 
Describe the potential risks to subjects (physical, psychological, financial, legal, or 
other), and assess their likelihood and seriousness to the human subjects.  
 
Where appropriate, describe alternative treatments and procedures, including the risks 
and potential benefits of the alternative treatments and procedures, to participants in the 
proposed research. 
 
II. 
Adequacy of Protection against Risks 
a. 
Recruitment and Informed Consent 
 
Describe plans for the recruitment of subjects (where appropriate) and the process for 
obtaining informed consent. If the proposed studies will include children, describe the 
process for meeting requirements for parental permission and child assent. 
 
Include a description of the circumstances under which consent will be sought and 
obtained, who will seek it, the nature of the information to be provided to prospective 
subjects, and the method of documenting consent. If a waiver of some or all of the 
elements of informed consent will be sought, provide justification for the waiver. 
Informed consent document(s) need not be submitted to the PHS agencies unless 
requested. 
b. 
Protections against Risk 9 
 
Describe planned procedures for protecting against or minimizing potential risks, 
including risks to privacy of individuals or confidentiality of data, and assess their likely 
effectiveness. 
 
Research involving vulnerable populations, as described in the DHHS regulations, 
Subparts B-D must include additional protections. Refer to DHHS regulations, and 
OHRP guidance:  
o Additional Protections for Pregnant Women, Human Fetuses and Neonates: 
http://www.hhs.gov/ohrp/humansubjects/guidance/45cfr46.html#subpartb  
o Additional Protections for Prisoners: 
http://www.hhs.gov/ohrp/humansubjects/guidance/45cfr46.html#subpartc  
o OHRP Subpart C Guidance: 
http://www.hhs.gov/ohrp/policy/index.html#prisoners 
o Additional Protections for Children: 
http://www.hhs.gov/ohrp/humansubjects/guidance/45cfr46.html#subpartd 
o OHRP Subpart D Guidance: 
http://www.hhs.gov/ohrp/policy/index.html#children 

 
 
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 
Where appropriate, discuss plans for ensuring necessary medical or professional 
intervention in the event of adverse effects to the subjects. Studies that involve clinical 
trials (biomedical and behavioral intervention studies) must include a general description 
of the plan for data and safety monitoring of the clinical trials and adverse event 
reporting to the IRB, the NIH and others, as appropriate, to ensure the safety of subjects. 
 
III. 
Potential Benefits of the Proposed Research to Human Subjects and Others 
 
Discuss the potential benefits of the research to research participants and others.  
 
Discuss why the risks to subjects are reasonable in relation to the anticipated benefits to 
research participants and others. 
IV. 
Importance of the Knowledge to be Gained  
 
Discuss the importance of the knowledge gained or to be gained as a result of the 
proposed research.  
 
Discuss why the risks to subjects are reasonable in relation to the importance of the 
knowledge that reasonably may be expected to result. 
 
NOTE: Test articles (investigational new drugs, devices, or biologics) including test articles that 
will be used for purposes or administered by routes that have not been approved for general 
use by the Food and Drug Administration (FDA) must be named. State whether the 30-day 
interval between submission of applicant certification to the FDA and its response has elapsed 
or has been waived and/or whether use of the test article has been withheld or restricted by the 
FDA, and/or the status of requests for an Investigational New Drug (IND) or Investigational 
Device Exemption (IDE) covering the proposed use of the test article in the Research Plan. 
 
All sites will be trained to address potential susceptibility to therapeutic misconception by 
making sure that the participants understand the research, its purpose and their role, have been 
counseled about it, and know that additional support may be available for them should they 
withdraw from the study through alternative support from the Pain Action Alliance to Implement 
a National Strategy (PAINS) project and advocacy groups that may offer bio-psychosocial 
alternatives to pharmaceutical interventions that have failed them. (Note, the Pain Action 
Alliance to Implement a National Strategy (PAINS) project is different from this research study 
we call PAIN-CONTRoLS).  
Children will not be entered into the study. Most of the study medications offered in this 
study is safe for pregnant females. Due to the short duration of the study (12 weeks) we are 
going to ask all females of childbearing potential use birth control. There will not be a caregiver 
form, so only patients that can comprehend the consent form will be enrolled.  
 
 
 
 
 
 
 

 
 
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APPENDIX 
 
Appendix A: Likert Numeric Pain Rating Scale 
Appendix B: Adverse Event 
Appendix C: SF-12 Patient Questionnaire 
Appendix D: NIH Pain Interference Scale- Short Form 8a 
Appendix E: NIH Fatigue Scale- Short Form 8a 
Appendix F: NIH Sleep Disturbance Scale- Short Form 8a 
Appendix G: Exclusionary Medication List