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1
Detailed statistical analysis plan for the outcomes quality of life and
mortality and cardiovascular outcomes of the randomised 2x3 factorial
Copenhagen Insulin and Metformin Therapy (CIMT) trial
By
Thomas P Almdal chief physician1,7, Trine W Boesgaard physician3, Leif Breum chief physician4,
Birthe Gade-Rasmussen chief physician1 , Elsebeth Duun chief physician2, Christian Gluud head of
department5, Christoffer Hedetoft chief physician4, Bianca Hemmingsen physician5,6, Tonny Jensen
chief physician7,Thure Krarup chief physician8 ,Louise Lundby-Christensen physician,3,5 9, Søren S
Lund physician3, Sten Madsbad professor1, 13,Elisabeth R Mathiesen professor7,13, Oluf Pedersen
professor3,12,13, Hans Perrild associate professor8, Michael Røder chief physician1,6, Simone B
Sneppen chief physician2 , Ole Snorgaard chief physician1, Lise Tarnow professor 3,6, Birger
Thorsteinsson professor6,13, Henrik Vestergaard associate professor10,12,13, Allan Vaag
professor3,7,13, Jørn Wetterslev chief physician4, Niels Wiinberg physician11, The CIMT Trial Group,
Janus C Jakobsen physician, statistician5
Affiliations
1
Department of Endocrinology, Hvidovre, Copenhagen University Hospital, Kettegaard Allé 30,
2650 Hvidovre, Denmark.
2
Department of medicine, Gentofte, Copenhagen University Hospital, Kildegaardsvej 28, 2900
Hellerup, Denmark.
3
Steno Diabetes Center, Niels Steensens Vej 2, 2820 Gentofte, Denmark.
4
Department of Medicine, University Hospital Køge, Lykkebækvej 1,4600 Køge, Denmark:
5
Copenhagen Trial Unit, Centre for Clinical Intervention Research, Rigshospitalet, Copenhagen
University Hospital, Blegdamsvej 9, 2100 Copenhagen, Denmark.
6
Department of Cardiology, Nephrology and Endocrinology, Nordsjællands University Hospital Hillerød, Dyrehavevej 29, 3400 Hillerød, Denmark
7
Department of Endocrinology, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100
Copenhagen, Denmark
8
Department of Endocrinology, Bispebjerg, Copenhagen University Hospital, Bispebjerg Bakke 23,
2400 Copenhagen, Denmark
9
Department of Paediatrics, Hvidovre, Copenhagen University Hospital, Kettegaard Allé 30, 2650
Hvidovre, Denmark
10
Department of Endocrinology, Herlev, Copenhagen University Hospital, Herlev Ringvej 75, 2730
Herlev, Denmark
2
11
Department of Physiology and nuclear medicine, Bispebjerg Hospital, Copenhagen University
Hospital, Bispebjerg Bakke 23, 2400 Copenhagen, Denmark
12
The Novo Nordisk Foundation Center for Basic Metabolic Research, Section of Metabolic
Genetics, University of Copenhagen
13
University of Copenhagen, Denmark
List of E-mail adresses
Thomas P Almdal - [email protected]
Trine W Boesgaard - [email protected]
Leif Breum - [email protected]
Birthe Gade-Rasmussen [email protected]
Elsebeth Duun - [email protected]
Christian Gluud – [email protected]
Christoffer Hedetoft – [email protected]
Bianca Hemmingsen – [email protected]
Tonny Jensen – [email protected]
Thure Krarup – [email protected]
Louise Lundby-Christensen – Louise.Lundby.Christensen
Søren S Lund – [email protected]
Sten Madsbad – [email protected]
Elisabeth R Mathiesen – [email protected]
Oluf Pedersen – [email protected]
Hans Perrild – [email protected]
Michael Røder – [email protected]
Simone B Sneppen – [email protected]
Ole Snorgaard – [email protected]
Lise Tarnow – [email protected]
Birger Thorsteinsson – birger.thorsteinsson
Henrik Vestergaard – [email protected]
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Allan Vaag – [email protected]
Jørn Wetterslev – [email protected]
Niels Wiinberg – [email protected]
Janus C Jakobsen – [email protected]
Correspondence to:
Thomas Almdal Dr. Med. Sci.
Department of Endocrinology,
Rigshospitalet
Copenhagen University Hospital,
Blegdamsvej 9,
2100 Copenhagen,
Denmark
E-mail: [email protected]
4
Abstract
Background: The evidence on the effects of metformin and insulin in type 2 diabetes (T2D) on
quality of life, mortality, and cardiovascular outcomes is unclear.
Methods: The Copenhagen Insulin and Metformin Therapy (CIMT) trial is an investigator-initiated,
randomised, placebo-controlled trial with a 2x3 factorial design conducted at eight hospitals in
Denmark. Participants with type 2 diabetes were randomised to: metformin (n=206) versus placebo
(n=206); in combination with open-label biphasic insulin aspart one to three times daily (n=137)
versus insulin aspart three times daily in combination with insulin detemir once daily (n=138)
versus insulin detemir once daily (n=137).
The quality of life outcomes in the CIMT trial are assessed by the Short Form Health Survey (SF36), physical component score and mental component score, the Diabetes Medication Satisfaction
Questionnaire to be completed score and Insulin Treatment Satisfaction Questionnaire score.
These outcomes are assessed at randomisation and 18 months after . The time to a clinical event
outcomes are: (1) The composite of time to first of the following: death, myocardial infarction,
stroke, peripheral amputation, coronary revascularization, or peripheral revascularisation; (2) The
composite of time to the first of the following: death, myocardial infarction, stroke, or peripheral
amputation; (3) time to cardiovascular death. The individual components of each of these
outcomes will be evaluated separately as well. These outcomes are assessed from start of trial in
May 2008 and until June 11th 2013. This article describes our analytic strategies for these
outcomes before any assessment of the data.
We will report the results of these exploratory outcomes in two papers, one in relation to the
metformin versus placebo comparison and one in relation to the three insulin comparisons.
Trial registration: The protocol was approved by the Regional Committee on Biomedical
Research Ethics (H-D-2007-112), the Danish Medicines Agency, and registered within
ClinicalTrials.gov (NCT00657943, 8th of april 2008)
Key words: Type 2 diabetes, metformin, insulin, randomised clinical trial, clinical outcomes, quality
of life, and statistical analysis plan.
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Update
Clinical trials ought to be conducted according to a pre-specified analysis plan in order to prevent
reporting bias and data driven analysis results (1-4). The Copenhagen Insulin and Metformin
Therapy (CIMT) trial is an investigator initiated multicentre, randomized, placebo-controlled
superiority trial with a 2x3 factorial design. The CIMT trial evaluated the effect of an 18-month
treatment with metformin versus placebo and the effect of three insulin analogue regimens on the
progression of mean carotid carotid intima media thickness (IMT) in patients with type 2 diabetes
(5). The CIMT trial was conducted from May 2008 to December 2012 and the trial results in
relation to changes in carotid IMT have been reported previously (6, 7). In the trial protocol (5), we
pre-specified to assess the time to a number of clinical outcomes as well as measures of quality of
life as secondary and additional outcomes.
The CIMT trial participant inclusion criteria were: type 2 diabetes; age >30 years; body-mass index
>25 kg/m2; HbA1c ≥7.5% (58 mmol/mol); and treatment with oral antidiabetic drugs for at least one
year and/or insulin treatment for at least three months (5). After a screening visit, participants were
centrally randomised at the Copenhagen Trial Unit according to the 2x3 factorial design.
Participants were randomised 1:1:1 to treatment with one of three insulin analogue regimens and
in a factorial way randomised 1:1 to treatment with metformin versus placebo. Randomisation was
stratified by age >65 years (yes/ no), insulin treatment at trial entry (yes /no), and treatment at
Steno Diabetes Center (yes/ no). The insulin treatment was open-labelled, whereas participants,
investigators, and medical staff were blinded to the metformin and placebo intervention (numbered
identical containers).
The protocol was approved by the Regional Committee on Biomedical Research Ethics (H-D-2007112) and the Danish Medicines Agency, registered within ClinicalTrials.gov (NCT00657943), and
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conducted in accordance with The Declaration of Helsinki and guidelines for Good Clinical
Practice. Informed consent was obtained from each participant enrolled in the trial.
In this paper we describe the detailed statistical analyses of the patient-relevant clinical outcomes
in the CIMT trial.
Sample size and power considerations
Based on the sample size calculation in relation to the primary outcomes of the trial (5), we
planned to include a total of 950 patients in the trial. However, the trial was stopped at the end of
the financing grant after the inclusion of 412 participants. For this factorial trial, this means that
there is a comparison on 206 on metformin versus 206 participants on placebo and another
comparison of 3 groups on different insulin regimens each including 137 and 138 participants. For
simplicity, we have only included power calculations based on the metformin versus placebo (206
versus 206) comparison, but it must be noted that the power for the insulin comparisons will be
less because of the lower number of randomised participants in each intervention group. We have
used an alfa on 1% in the power estimations because this threshold for significance was chosen as
the multiplicity adjusted (due to multiple outcomes) threshold in the protocol (5). The following
power estimations are based on the 412 randomised patients:.
Short Form Health Survey (SF-36)
Using an acceptable risk of type I error of 1%, an anticipated mean SF-36 score in the placebo
group of 45 points, and a SD of 9.5 points (8), we estimate that we will have 95.4% power to
confirm or reject a 4 point increase or decrease on the SF-36 score. We will consider 4 points as
the minimal clinically relevant difference (9). The minimal detectable mean difference with 80%
power will be 3.21 points.
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Diabetes Medication Satisfaction Questionnaire (Diab-Medsat)
Using an acceptable risk of type I error of 1%, an anticipated mean Diab Medsat score in the
control group of 60 , and a SD of 18 points in relation to the three domains burden, efficacy and
symptoms (9), we estimate that we will have 37 % power to confirm or reject a 4 point increase or
decrease in the DTSQ score. We will consider 4 points as the minimal clinically relevant difference
(10). The minimal detectable mean difference with 80% power will be 9.
Insulin Treatment Satisfaction Questionaire (ITSQ)
Using an acceptable risk of type I error of 1%, an anticipated mean ITSQ score in the control group
of 80 points, and a SD of 9 points (10), we estimate that we will have 97.2% power to confirm or
reject a 4 point increase or decrease on the ITSQ score. We will consider 4 points as the minimal
clinically relevant difference (10). The minimal detectable mean difference with 80% power will be
3.04 points.
Time to death, myocardial infarction, stroke, peripheral amputation, coronary
revascularisation, or peripheral revascularisation
We will report the number of outcomes for the composite as well as the individual components per
intervention group at both 18-month landmark follow up as well as at maximal follow-up. However,
due to our power problems and in order to reduce multiplicity we will only conduct analyses of the
latter data. Using an acceptable risk of type I error of 1% and an overall estimated 29% risk of
experiencing one or more of the following outcomes: death, non-fatal myocardial infarction, stroke,
peripheral amputation, coronary revascularization, or peripheral revascularisation in the placebo
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group (12), we estimate that we will have 26.6% power to confirm or reject a hazard ratio reduction
of 30% on this composite outcome in the metformin group.
Time to the first of the following clinical outcomes death, myocardial infarction, stroke, or
peripheral amputation
We will report the number of outcomes for the composite per intervention group at both 18-month
landmark follow up as well as at maximal follow-up. However, due to our power problems and in
order to reduce multiplicity we will only conduct analyses of the latter data. Using an acceptable
risk of type I error of 1% and an overall estimated 12.5% risk of experiencing one or more of the
following outcomes: death, non-fatal myocardial infarction and stroke, and peripheral amputation in
the placebo group (12), we estimate that we will have 9.75% power to confirm or reject a hazard
ratio reduction of 30% on this composite outcome in the metformin group.
Time to cardiovascular death
We will report the number of outcomes per intervention group at both 18-month landmark follow up
as well as at maximal follow-up. However, due to our power problems and in order to reduce
multiplicity we will only conduct analyses of the latter data. Using an acceptable risk of type I error
of 1% and an overall estimated 3.8% risk of experiencing death from cardiovascular disease in the
placebo group (12), we estimate that we will have only 3.1% power to confirm or reject a hazard
ratio reduction of 30% on this outcome in the metformin group.
Stratification and design variables
The randomisations were performed using three stratification variables: age >65 years (yes/ no),
insulin treatment at trial entry (yes/ no), and treatment at Steno Diabetes Center (yes/ no).
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Other design variables were sex (male/ female), prior cardiovascular disease (yes/ no), statin
treatment at baseline (yes/ no), and glutamic acid decarboxylase antibodies (positive/ negative)
(5).
Outcomes
All outcomes will be considered as exploratory.
1. Short Form Health Survey (SF-36) physical component score.
2. Short Form Health Survey (SF-36) mental component score.
3. Diabetes Medication Satisfaction Questionnaire burden, efficacy and
symptoms scores.
4. Insulin Treatment Satisfaction Questionnaire (ITSQ) score.
5. The composite of time to death, myocardial infarction, stroke, peripheral
amputation, coronary revascularisation, or peripheral revascularisation.
6. The composite of time to death, myocardial infarction, stroke, or peripheral
amputation.
7. Time to cardiovascular death.
Outcomes 1 to 4 were evaluated only at the end of intervention after 18 months treatment. The
outcomes were all assessed by the participants who were blinded to metformin versus placebo but
not to the three insulin regimens.
The observation period in relation to outcomes 5 to 7 is from inclusion of the individual patients,
May 2008 to April 2011, and followed up to June 11th 2013. The outcomes were assessed by an
adjudication committee blinded to all interventions ( 13, 14, 15, 16, 17).
Populations and handling of missing data
The following populations will be analysed:
A modified intention-to-treat population. This population includes all patient, which passed the
initial investigations and were randomized – but excluding the following:
- randomised participants who did not fulfill the inclusion criteria;
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- randomised participants fulfilling one or more exclusion criteria;
- randomised participants who did not receive any of the planned interventions.
A per protocol population. This population includes all those includes in the modified intention-totreat population, but excludes those characterised by the following major protocol violations:
- patients who did not attend more than 4 out of the planned 6 visits following randomisation.
We do not except any missing values for the time to a clinical outcomes as all deaths and
admissions to Danish hospitals are registered in the Civil Registry and the National Patient
Registry and were adjudicated for clinical events by the blinded adjudication committee (13, 14, 15,
16, 17). If missing data for the remaining outcomes or covariates represents less than 5%, a
complete participant analysis without multiple imputation of missing values will be conducted. If
more than 5% of data are missing, a blinded statistician will assess whether missing completely at
random may be ruled out based on a rational assessment of the pattern of missing data (18).
Little’s test will be used if there is doubt (19). If more than 5% of the data are missing and missing
completely at random is ruled out, multiple imputation using chained equations will be performed
by creating ten imputed data sets that will be analysed under the assumption that data are missing
at random (19, 20). We will use the following three stratification and four design variables during
the multiple imputation procedure to estimate the missing values, i.e., age >65 years (yes/ no),
insulin treatment at trial entry (yes/ no), and treatment at Steno Diabetes Center (yes/ no) as well
as sex (male/ female), prior cardiovascular disease (yes/ no), statin treatment at baseline (yes/ no),
and glutamic acid decarboxylase antibodies (positive/ negative).
In relation to quality of life measures, participants who died during the 18 month trial period will be
assigned the value zero. If multiple imputation is used, then the primary result of the trial will be
based on these data. To take account of the possibility that data may be ‘missing not at random’,
we will use a best-worst and worst-best case scenario as sensitivity analyses, which will assess
the potential impact of the missing data on the trial results (10). In the ’best-worst-case’ scenario it
is assumed that all patients lost to follow-up in the experimental group have had a beneficial
outcome; and all those with missing outcomes in the control group have had a harmful outcome.
Conversely, in the ’worst-best-case’ scenario, it is assumed that all patients who were lost to follow
up in the experimental group have had a harmful outcome; and that all those lost to follow-up in the
control group have had a beneficial outcome. When continuous outcomes are analysed, a
‘beneficial outcome’ will be defined as the group mean plus two SDs of the group mean, and a
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‘harmful outcome’ will be defined as the group mean minus two SDs of the group mean for ‘bestworst case’ imputation.
Statistical analysis
We will assess if the thresholds for statistical significance and clinical significance are crossed
using the five-point procedure as suggested by Jakobsen et al. (9). This procedure will include
adjustments of thresholds for significance according to the number of outcome comparisons (Pvalue threshold 0.01) and number of randomised participants. We will use a Bayes factor threshold
for significance of 0.1 based on the a priory anticipated intervention effect (10).
Analysis of quality of life outcomes
Primary analysis: linear regression adjusted for the three stratification variables (age >65 years
(yes/ no), insulin treatment at trial entry (yes/ no), and treatment at Steno Diabetes Center (yes/
no) (5)
Secondary analysis: linear regression adjusted for stratification variables (see above) plus the
design variables (sex (male/ female), prior cardiovascular disease (yes/ no), statin treatment at
baseline (yes/ no), and glutamic acid decarboxylase antibodies (positive/ negative) (5).
All analyses will primarily include the modified intention-to-treat population. We will also perform
the analyses adjusted for the stratification variables including the per protocol population.
Assumptions behind the linear regression will be tested by ‘qq’ plots of the residuals and plots of
residuals against co-variates and fitted values.
Analysis of the time to a clinical outcome
Primary analysis: hazard ratios estimated by Cox regression analysis adjusted for the three
stratification variables (age >65 years (yes/ no), insulin treatment at trial entry (yes/ no), and
treatment at Steno Diabetes Center (yes/ no).
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Secondary analysis: hazard ratios estimated by Cox regression analysis adjusted for stratification
variables (see paragraph above) and other design variables (sex (male/ female), prior
cardiovascular disease (yes/ no), statin treatment at baseline (yes/ no), and glutamic acid
decarboxylase antibodies (positive/ negative) (5).
The assumption of proportional hazards will be tested by (a) plots of Shoenfeld residuals against
each variable (continuous and categorical) and (b) against fitted values (22).
Possible interactions between treatment and covariates will be investigated by tests of interaction.
Characteristics of patients at baseline
We will present a description of baseline characteristics by intervention group. Discrete variables
will be summarised by frequencies and percentages. Percentages will be calculated according to
the number of patients where data are available. Where values are missing, the actual
denominator will be stated.
Continuous variables will be summarised using standard measures of central tendency and
dispersion using either mean + SD for data with normal distribution or median and interquartile
range for non-normally distributed data.
Outline of figures and tables
The first figure will be a consort flow chart of respectively metformin versus placebo or the three
insulin regimens.
The first table will describe the baselines characteristics as shown in Table 1.
The quality of life outcomes: The second to fifth figures will be whiskers and box plots (median
with interquartile ranges) of the four measures of quality of life comparing respectively metformin
versus placebo and the three insulin regimens compared.
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The clinical outcomes: The sixth to eight figures will be Kaplan Meier curves for the clinical
outcomes 5, 6, and 7 in relation to metformin versus placebo respectively the three insulin
regimens compared.
The individual clinical outcomes will in relation to metformin versus placebo be reported as shown
in Table 2.
In the insulin comparison, one of the groups (A) will randomly be identified as the reference group
and compared with the two other groups (B, C) as shown in Table 3.
Deviations from the initial design and methods of the CIMT trial
Based on our sample size estimation for the primary outcome of the CIMT trial allowing for five
comparisons of the involved intervention groups we planned to include 950 patients (5). However,
eventually only 412 present were included, as the trial had to be stopped at the scheduled duration
of the trial and lack of additional financial support. In the present analysis plan we schedule to
analyse the quality of life measures and the clinical outcomes based on a strict factorial 2 x 3
design comparing the effects of metformin versus placebo and comparing the three different insulin
analogue regimens. We originally planned to analyse the clinical outcomes as secondary
outcomes and the quality of life data as exploratory outcomes. Given the low power in relation to
the clinical outcomes we consider all outcomes to be exploratory and that most emphasis should
be given to the quality of life data according to our result of the power calculations.
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In the original protocol it was not specified how the patient-important quality of life data and clinical
outcomes would be analysed and presented. Given the fact that the trial is a 2 x 3 factorial design
and two sets of data are presented we anticipate to publish the data in two separate papers.
Discussion
In order to avoid risks of outcome reporting bias and data-driven results, this paper presents the
detailed statistical analysis plan for the CIMT trial analysing patient-relevant outcomes at a time
when the data have been gathered but not yet inspected or analysed. Contrary to our pre-specified
plans for the trial (5), we now plan to report clinical outcomes as exploratory outcomes. We will use
the five-step procedure proposed by Jakobsen et al. to assess whether the thresholds for statistical
and clinical significance are crossed or not (10). We will analyse data in accordance to the
modified intention-to-treat principle and, if necessary, use data sets generated by multiple
imputations, and a best-worst/worst-best case scenario to assess the potential impact of the
missing data on the results.
Strengths
This statistical analysis plan has a number of strengths. First of all, we want to make the reporting
of the patient-important outcomes from the CIMT trial transparent facilitating the use of these data
in systematic reviews and meta-analyses of the effect of metformin and the insulin analogues used
in the trial (23, 24, 25). The overall trial methodology has been pre-defined (5), we take the
multiplicity problem into account, use validated statistical methods, we systematically test for
underlying statistical assumptions, and we use multiple imputations and best-worst and worst-best
case scenarios to take account of incomplete outcome data bias. In order to minimize the risk of
multiplicity we restrict our analyses to the outcomes that were predefined (5). As an example, it
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might also have been of interest to analyse a composite outcome of cardiovascular death and
other cardiovascular outcomes but we abstained from this due to the risks of multiplicity
Limitations
The statistical analysis plan has limitations. First, the effects of the interventions on the primary
unvalidated surrogate outcome in the main publications were neutral and has been accepted for
publication. Due to multiplicity caused by multiple outcome comparisons our results should be
interpreted as exploratory and thus hypothesis generating (10). Second, we plan to use Cox
regression in the analysis of the time to a clinical outcomes. We plan to use the data based on the
entire trial observation period, i.e., some participants (those who are randomised early in the trial
period) will be observed much longer than others. This may make the interpretation of the trial
results difficult with events occurring long time after the intervention was stopped maybe less likely
to have been caused by the intervention. Third, we are assessing a number of clinically relevant
outcome measures using survival analysis but we do not have sufficient power to reliable access
and conclude on these time to event outcomes. Therefore, all neutral as well as significant findings
should be interpreted cautiously. To try to control the risks of type I errors we will assess
significance using the Lan-DeMets monitoring boundaries for outcomes with power less than 80%
(10). Forth, the power estimations on the QOL outcomes show power estimations over 90%, which
result in a high risk of showing statistically significant results without any clinical relevance (10). We
have pre-specified a minimal clinically relevant difference to limit this risk. Finally, we use multiple
imputation to handle missing data on the remaining outcome measures and it cannot be ruled out
that missingness are not at random and multiple imputation may produce biased results and the
intervention effect estimate based on the multiple imputation might therefore also be biased.
Conclusion
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This article describes the principles of the statistical analyses of a number of patient important
exploratory outcomes used in the CIMT trial in order to minimize risk of data-driven results and
outcome reporting bias.
List of abbreviations:
CIMT = The Copenhagen Insulin and Metformin Therapy
IMT = intima media thickness
SF-36 = Short Form Health Survey 36
SD = Standard Deviation
Diab Medsat = Diabetes Medication Satisfaction Questionnaire
ITSQ = Insulin Treatment Satisfaction Questionaire
Competing Interest. LLC, LT, TA, AV, OP, TB, BC, SL, TJ have reported shares in Novo Nordisk
A/S; SL owns shares in dynamically traded investment funds, which may own stocks from
pharmaceutical companies LLC, LT, TA, AV, TB, SL have reported former employment at Steno
Diabetes Center, which is a diabetes hospital and academic institution owned by Novo Nordisk; SL
is an employee at Boehringer Ingelheim, Germany. SL contribution was his alone and does not
necessarily reflect the official position of Boehringer Ingelheim. AV has received fees from Novo
Nordisk; TB is employed at Novo Nordisk A/S; BT is member of advisory board for Eli Lilly; OS has
received fees from AstraZeneca, Sanofi, MSD, Boehringer-Ingelheim, Eli Lilly, NovoNordisk; CH
has received fees from Bristol-Myers Squibb; LB has received fees from and attended advisory for
Novo Nordisk A/S; SM has served as a consultant or adviser to: Novartis Pharma, Novo Nordisk,
Merck Sharp & Dome, Sanofi-Aventis, AstraZeneca, Johnson & Johnson, Rosche, Mankind, AstraZeneca, Boehringer-Ingelheim, Zeeland, E Lilly, Intarcia Therapeutics, Bristol-Meyer Squibb, has
received fee for speaking from Novo Nordisk, Merck, Sharp & Dome, Astra-Zeneca, Johnson and
17
Johnson, Rosche, Shering-Ploug, Sanofi-Aventis, Novartis Pharma, E Lilly, Bristol-Meyer Squibb,
Boehringer Ingelheim, and has received 2 research grants from Novo Nordisk. BH, BGR, JW, CG,
NW, MR, HV, ED, HP, TK, SS, EM have no conflicts of interests to declare.
Author Contributions.
TA: Drafted the present manuscript and revised and approved the final version.
TB: Revised and approved the final version of the manuscript
LB: Revised and approved the final version of the manuscript
BG-R: Revised and approved the final version of the manuscript
ED: Revised and approved the final version of the manuscript
CG: Drafted the present manuscript and revised and approved the final version.
CH: Revised and approved the final version of the manuscript
BH: Revised and approved the final version of the manuscript
TJ: Revised and approved the final version of the manuscript
TK: Revised and approved the final version of the manuscript
LL-C: Revised and approved the final version of the manuscript
SSL: Revised and approved the final version of the manuscript
SM: Revised and approved the final version of the manuscript
ERM: Revised and approved the final version of the manuscript
OP: Revised and approved the final version of the manuscript
HP: Revised and approved the final version of the manuscript
MR: Revised and approved the final version of the manuscript
SBS: Revised and approved the final version of the manuscript
OS: Revised and approved the final version of the manuscript
LT: Revised and approved the final version of the manuscript
BT: Revised and approved the final version of the manuscript
HV: Revised and approved the final version of the manuscript
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AV: Revised and approved the final version of the manuscript
JW: Drafted the present manuscript and revised and approved the final version.
NW: Revised and approved the final version of the manuscript
JCJ: Drafted the present manuscript and revised and approved the final version.
Funding. The investigators received an unrestricted grant from Novo Nordisk to enable conduct of
the trial. The trial and its main 2x3 factorial design was 100% initiated and conducted by the
investigators. Novo Nordisk was allowed to comment on the protocol, on protocol changes during
the trial, and on the manuscript prior to submission.
19
References
1. Dwan K, Gamble C, Williamson PR, Altman DG. Reporting of clinical trials: a review of research
funders guidelines. Trials 2008; 9: 66 – 77.
2. Dwan K, Altman DG, Clarke M, Gamble C, Higgins JPT, et al. Evidence for the selective reporting of
analyses and discrepancies in clinical trials: a systematic review of cohort studies of clinical trials.
PLoS Med 2014;11(6): e1001666. doi:10.1371/ journal.pmed.1001666.
3. Ebrahim S, Sohani ZN, Montoya L, Agarwal A, Thorlund K, Mills EJ, Ioannidis JPA. Reanalyses of
randomized clinical trial data. JAMA. 2014;312(10):1024-1032. doi:10.1001/ jama.2014.9646.
4. Ioannidis JPA. How to make more published research true. PLoS Med 2014;11(10):
e1001747.doi:10.1371/journal.pmed.1001747
5. Lundby Christensen, L., Almdal, T., Boesgaard, et al. Study rationale and design of the CIMT trial:
the Copenhagen Insulin and Metformin Therapy trial. Diabetes, Obesity & Metabolism, 2009;11(4),
315–22. doi:10.1111/j.1463-1326.2008.00959.
6. Lundby-Christensen L, The CIMT Trial Group. Effects of metformin versus placebo in combination
with insulin analogues in patients with type 2 diabetes mellitus: The CIMT randomized clinical trial.
BMJ-Open, accepted for publication 2015
7. Lundby-Christensen L, The CIMT Trial Group. Effect of three insulin regimens on carotid intimamedia thickness in patients with type 2 diabetes: the randomised Copenhagen Insulin and Metformin
Therapy (CIMT) trial. BMJ-Open, accepted for publication 2015
8. Donk M v d, Griffins S, Stellato R et al. Effects of early intensive multifactorial therapy compared with
routine care on self-reported health status, general well-being, diabetes specific quality of life, and
treatment satisfaction in screen detected type 2 diabetes mellitus patients (ADDITION-Europe): a
cluster randomized trial. Diabetologia; 2013: 56: 2367-77.
9. Al-Aujan A, AL-Aqeel S, Al-Harbi A, Al-Abdulltif E. Patients satisfaction with diabetes medications in
one hospital, Saudi Arabia. Patients Preferences and Adherence 2012: 6: 735 - 740
10. Jacobsen JC, Gluud C, Winkel P, Lange T, Wetterslev J. The thresholds for statistical and clinical
significance – a five step procedure for evaluation of intervention effects in randomized clinical trials.
BMC Med Res Methodol 2014; 14: 34 – 40.
11. Farmer AJ, Oke J, Stevens R, Holmann RR. Differences in insulin treatment satisfaction following
randomized addition of biphasic, prandial or basal insulin to oral therapy in type 2 diabetes. DOM
2011; 13: 1136 – 41.
12. Hemmingsen, B., Lund, S. S., Gluud, C., Vaag, A., Almdal, T., Hemmingsen, C., & Wetterslev, J.
Intensive glycaemic control for patients with type 2 diabetes: systematic review with meta-analysis
and trial sequential analysis of randomised clinical trials. BMJ (Clinical Research Ed.),
2011;343(November), d6898. doi:10.1136/bmj.d6898.
13. Jespersen CM, Als-Nielsen B, Damgaard M, Hansen JF, Hansen S, Helø OH, Hildebrandt P, Hilden
J, Jensen GB, Kastrup J, Kolmos HJ, Kjøller E, Lind I, Nielsen H, Petersen L, Gluud C; CLARICOR
Trial Group Randomised placebo controlled multicentre trial to assess short term clarithromycin for
patients with stable coronary heart disease: CLARICOR trial. BMJ. 2006;332(7532):22-7.
14. Gluud C, Als-Nielsen B, Damgaard M, Fischer Hansen J, Hansen S, Helø OH, Hildebrandt P, Hilden
J, Jensen GB, Kastrup J, Kolmos HJ, Kjøller E, Lind I, Nielsen H, Petersen L, Jespersen CM;
CLARICOR Trial Group. Clarithromycin for 2 weeks for stable coronary heart disease: 6-year follow-
20
up of the CLARICOR randomized trial and updated meta-analysis of antibiotics for coronary heart
disease. Cardiology. 2008;111(4):280-7. doi: 10.1159/000128994. Epub 2008 May 2.
15. Kjoller E, Hilden J, Winkel P, Frandsen NJ, Galatius S, Jensen G, Kastrup J, Hansen JF, Kolmos HJ,
Jespersen CM, Hildebrandt P, Gluud C; CLARICOR Trial Group. Good interobserver agreement was
attainable on outcome adjudication in patients with stable coronary heart disease. J Clin Epidemiol.
2012;65(4):444-53. doi: 0.1016/ j.jclinepi.2011.09.011. Epub 2012 Jan 17.
16. Kjøller E, Hilden J, Winkel P, Galatius S, Frandsen NJ, Jensen GB, Fischer Hansen J, Kastrup J,
Jespersen CM, Hildebrandt P, Kolmos HJ, Gluud C; CLARICOR Trial Group. Agreement between
public register and adjudication committee outcome in a cardiovascular randomized clinical trial.Am
Heart J. 2014;168(2):197-204.e1-4. doi: 10.1016/j.ahj.2013.12.032. Epub 2014 May 4.
17. Winkel P, Hilden J, Hansen JF, Kastrup J, Kolmos HJ, Kjøller E, Jensen GB, Skoog M, Lindschou J,
Gluud C; the CLARICOR trial group. Clarithromycin for stable coronary heart disease increases allcause and cardiovascular mortality and cerebrovascular morbidity over 10years in the CLARICOR
randomised, blinded clinical trial. Int J Cardiol. 20156;182C:459-465. doi: 10.1016/j.ijcard.
2015.01.020.
18. Mallinckrodt CH, Lin Q, Molensbergh M. A structured framework for assessing sensitivity to missing
data assumptions in longitudinal clinical trials. Pharm Stat. 2013; 12: 1 – 6.
19. Little RJA. A test of missing completely at random multivariate data ith missing values. J Am Stat
Assoc. 1988; 83: 1198.
20. Schafer JL Multiple imputations: a primer. Stat Methods Med Res. 1999; 8: 3 – 15.
21. Vickers AJ, Altman DG. Statistical notes: missing outcomes in randomised trials. BMJ 2013: 346:
f3438.
22. Mario Cleves WG, Gutierrez RG, Marchenko YV. An Introduction to Survival Analysis Using Stata,
Third Edition. Stata Press. 2010
23. Guyatt GH, Mills EJ, Elbourne D. In the era of systematic reviews, does the size of an individual trial
still matter. PLoS Medicine 2008;5(1):e4.doi:10.1371/ journal.pmed.0050004
24. Borm GF, Lemmers O, Fransen J, Donders R. The evidence provided by a single trial is less reliable
than its statistical analysis suggest. J Clin Epidemiol 2009; 62: 711 – 715
25. Inthout J, Ioannidis JPA, Borm GF. Obtaining evidence by a single well-powered trial or several
modestly powered trials. SMMR 2012: 1 – 15.
21
Table 1. Baseline characteristics in the randomised comparison of metformin versus placebo in the
CIMT trial.
Metformin
+ insulin
(n=206)
Placebo
+ insulin
(n=206)
Age (years)
No (%) male
Weight (kg)
Body-mass index*
Waist-hip ratio
No (%) smokers
Median (IQR) alcohol consumption
(units/week)
No (%) caucasians
Duration of type 2 diabetes (years)
No (%) GAD65 antibodies ≥25U/ml
HbA1c (%)
HbA1c (mmol/mol)
No (%) prior cardiovascular disease
Systolic blood pressure (mmHg)
Diastolic blood pressure (mmHg)
Total cholesterol (mmol/L)
LDL cholesterol (mmol/L)
Median (IQR) VLDL cholesterol
(mmol/L)
HDL cholesterol (mmol/L)
Median (IQR) triglycerides (mmol/L)
Mean carotid IMT (mm)
Medication
No (%) metformin‡
No (%) insulin‡
No (%) sulphonylurea‡
No (%) other antihyperglycaemic
drug‡
No (%) RAS blockade
No (%) other antihypertensive drug
No (%) statin
No (%) aspirin
GAD=glutamic acid decarboxylase; HbA1c=haemoglobin A1c; LDL=lowdensity lipoprotein; VLDL=very low-density lipoprotein; HDL=high-density
lipoprotein; IMT=intima-media thickness; RAS=Renin angiotensin system.
* Body mass index is calculated as weight (kg) divided by height (m) 2.
‡ All antihyperglycaemic drugs were terminated at randomisation.
22
Table 2. Clinical outcomes in the randomised comparison of metformin versus placebo in the CIMT trial.
Outcomes
Time to death,
myocardial infarction,
stroke, peripheral
amputation, coronary
revascularisation or
peripheral
revascularization
Time to death,
myocardial infarction,
stroke or peripheral
amputation
Time to
cardiovascular death
All-cause mortality
Myocardial infarction
Coronary
revascularization
Percutaneous
coronary intervention
(PCI)
Stroke
Peripheral
revascularization
Peripheral amputation
Number of outcomes per
intervention group by 18
month
Placebo
Metformin
Hazard ratio
With 95% confidence interval
(P values)
23
Table 3. Clinical outcomes in the randomised comparison of metformin versus placebo in the CIMT trial.
Outcomes
Number of outcomes per
intervention group by 18 month
Insulin A
Time to death,
myocardial infarction,
stroke, peripheral
amputation, coronary
revascularisation or
peripheral
revascularisation
Time to death,
myocardial infarction,
stroke or peripheral
amputation
Time to cardiovascular
death
All-cause mortality
Myocardial infarction
Coronary
revascularisation
Percutaneous coronary
intervention (PCI)
Stroke
Peripheral
revascularisation
Peripheral amputation
Insulin B
Insulin C
Hazard ratio with 95 %
confidence intervals
Comparison of insulin
regimen A versus B
Hazard ratio with 95 %
confidence intervals
Comparison of insulin
regimen A versus C