Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT01718561 |
| Other study ID # |
DIFFICAIR |
| Secondary ID |
|
| Status |
Completed |
| Phase |
N/A
|
| First received |
October 29, 2012 |
| Last updated |
February 7, 2014 |
| Start date |
October 2012 |
| Est. completion date |
January 2014 |
Study information
| Verified date |
February 2014 |
| Source |
Hillerod Hospital, Denmark |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
Denmark: The Danish National Committee on Biomedical Research Ethics |
| Study type |
Interventional
|
Clinical Trial Summary
In general anesthetic the patient is deprived of his awareness and ability to breathe. It is
therefore one of the most important tasks in anesthesia to ensure the patient's airway and
breathing. It has been shown both in Denmark and internationally that failed management of
the patient's airway is the main anesthesia-related cause of death and brain damage.
Therefore, it is very important and highly prioritized among anesthesia personal, to be able
to identify patients with a difficult airway. The aim of "The DIFFICAIR Trial" is to reduce
the incidence of UNEXPECTED difficult airway management by optimizing assessment of the
patient's airway before anesthesia. There is an international consensus on the importance of
proper identification of patients with a difficult airway prior to anesthesia. Enabling
optimal preparation and thus reducing mortality and complications. The DIFFICAIR Trial is a
nationwide multicentre trial with approx. 70,000 patients. 28 of the country's anesthesia
departments is randomized either to airway assessment based on the physicians' clinical
judgment (current practice) or to use an objective risk score for airway evaluations
including anatomical conditions known to be associated with difficult airway management.
Data from The Danish Anesthesia Database is used to compare the success rates of the two
methods. We hope that by using a systematic airway assessment we may reduce the number of
unexpected difficult airway managements and thereby reducing the associated complications
and death. Based on data we will contribute to a national recommendation for airway
assessment before anesthesia.
Description:
The full trial protocol is published in Trials (2013) Volume: 14, Issue: 1, Pages: 347 ISSN:
1745-6215 DOI: 10.1186/1745-6215-14-347 PubMed: 24152537
http://www.trialsjournal.com/content/14/1/347/abstract
Detailed statistical analysis plan for the difficult airway management (DIFFICAIR) trial:
Introduction The difficult airway management trial (DIFFICAIR) is a stratified, parallel
group, cluster (cluster = department) randomized and multicentre trial involving 28
departments of anaesthesia in Denmark. The DIFFICAIR trial compares the effect of two
regimens of preoperative airway assessment on the frequency of unanticipated difficult
airway management.
Prediction of difficult airway management remains a pivotal challenge in anaesthesia.
Difficult tracheal intubation and difficult mask ventilation may cause serious patient
complications [1-6]. By allocating experienced personnel and relevant equipment, better
prediction of difficult airway management may reduce complications and thereby associated
morbidity and mortality. There is no single predictor that is sufficiently valid in
predicting difficult tracheal intubation[7-12]. However, several studies show that by
combining multiple predictors of difficult tracheal intubation, the positive and the
negative predictive value of the assessment increases[12]. In Denmark as well as
internationally, there is no clear recommendation on how to perform airway assessment.
Consequently, airway assessment in Denmark is based exclusively on the individual
anaesthesiologist's preoperative clinical assessment. It is poorly documented how accurately
this preoperative clinical assessment predicts actual airway management conditions.
The "Simplified Airway Risk Index" (SARI)[13] is based on a multivariate model for airway
assessment described by El-Ganzouri and colleagues enabling an estimation of the likelihood
of a difficult direct laryngoscopy. The SARI contains seven individual predictors for a
difficult direct laryngoscopy, each given a weighted score of 0-1 or 0-2. A summarized value
of the SARI score > 3 indicate that a future direct laryngoscopy will be difficult. It is
unknown, whether the SARI score predicts difficult intubation better or worse than a
clinical assessment. The rationale for this trial was to prospectively compare the effect of
the SARI with an unspecified clinical airway assessment on the frequency of unanticipated
difficult airway management.
The target population was adult patients undergoing anaesthesia. Twenty-eight departments of
anaesthesia were randomized to one of two groups. Intervention departments used the SARI
score for preoperative airway assessment. The intervention group additionally did an
assessment of risk factors for difficult mask ventilation as described by Kheterpal and
colleagues[14-16]. Departments in the control group continued normal practice of
preoperative airway assessment. All data were registered in the DAD. A more detailed trial
protocol describing background, design and rationale has been published in TRIALS[17].
In order to prevent outcome reporting bias and results based on data driven analysis it has
been increasingly encouraged to prospectively publish a trial protocol [18, 19]. The same
argument applies for a prospective publication of a statistical analysis plan. Concordantly,
the International Conference on Harmonisation (ICH) of Good Clinical Practice (GCP)
recommends that clinical trials are analysed according to a pre-specified plan[19].
This analysis plan has been written while the data collection from the DIFFICAIR trial was
still on-going and trial data not accessible. The data analysis of the main publication will
follow this plan. The statistical analysis was approved by the DIFFICAIR steering committee
on 29 December 2013. The last day of data collection was 31 December 2013. The involved
departments were given one additional month to ensure registration of all patients in the
Danish Anaesthesia Database. On 31 January 2014 the database was locked and data extracted.
The statistical analysis plan was published on www.clinicaltrials.gov before the last data
entry and before data was extracted and data management commenced.
The DIFFICAIR trial protocol has been written according to the SPIRIT guidelines and has
been public on www.difficair.com since the beginning of the trial and is registered at
www.clinicaltrials.gov (NCT01718561). The Danish Anaesthesia Database and the Danish Society
of Anaesthesiology and Intensive care Medicine (DASAIM) endorsed the trial.
The trial is carried out in accordance with the Helsinki declaration. The Scientific Ethics
Committee of Copenhagen County has declared that it is regarded as a quality assurance
project and thus should not be reported to the committee system (Journal No.:
H-3-2012-FSP2). The trial is approved by The Danish Data Protection Agency (J.No.: 2007-58-
0015/HIH-2011-10, I-Suite nr: 02079)
Objective The primary aim of the DIFFICAIR trial is to compare the effect of using a
systematic airway assessment with a standard clinical airway assessment on the frequency of
unanticipated difficult airway management. Both are registered in the database. The primary
null hypothesis is • There is no difference in the proportion of unanticipated difficult
intubations when the preoperative airway assessment is based on the SARI score compared with
a preoperative airway assessment based on the individual anaesthesiologist's assessment.
The alternative hypothesis is
• The use of a systematic SARI airway assessment, registration of the SARI and risk factors
for difficult mask ventilation, and continuous education in airway assessment will decrease
the relative risk of a difficult intubation with 30%, corresponding to a NNT of 180
patients.
Methods Randomisation and sample size Our sample size calculation was based on an adjustment
for the stratification and the cluster randomized design [20, 21]. Since there are no
previous records of the trial's primary outcome measure, "unanticipated difficult
intubation" a baseline study was conducted based on data from the Danish Anaesthesia
Database (DAD). In order to reject or detect a 30 % relative risk reduction in the
proportions of unanticipated difficult intubation between the intervention group and the
control group approximately 30 departments were required in a 15 months period. Calculations
were based on a maximum risk of type 1 error of 5% and risk of type 2 error of maximum 20 %
(80 % power).
A total of 28 departments were included and randomized 1:1 using a computer generated list.
The sample size calculation was based on an average cluster size of 1,611 patients. We
estimated the average cluster size in the DIFFICAIR trial to approximately 2,500 patients,
giving a total of 70,000 included patients during the trial period. The enhanced sample size
allows for a potentially slight loss of clusters according to the power calculation, from 30
to potentially 26. Our sample size estimation might be of a conservative nature, calling for
more clusters than necessary[22].
Populations The DIFFICAIR trial focuses on two essential elements of airway management i.e.
tracheal intubation by direct laryngoscopy and mask ventilation. This statistical analysis
plan will address analysis of the data regarding tracheal intubation. Data analysis
regarding prediction of difficult mask ventilation will be handled in an analogous way, but
will not be further elaborated in the present paper.
The part of the DIFFICAIR trial regarding prediction of difficult intubation comprises two
populations; 1) patients that were primarily attempted intubated by direct laryngoscopy; 2)
patients that were primarily attempted intubated by direct laryngoscopy (population 1) plus
patients anticipated to be difficult to intubate and therefore scheduled for and intubated
with an advanced method (e.g. video laryngoscopic or fibre optic intubation).
The results of population 1 and 2 will be presented in one publication. Due to the extent of
data, further publications presenting data from the DIFFICAIR trial will follow, but further
elaboration on data analysis exceeds the content frame of this paper.
Adjusting and stratification variables Each cluster (department) was randomized to a control
or intervention group, making this the intervention group indicator. The trial site may
account for further intervention heterogeneity and will be used for adjustment in the
analysis of the intervention effect. Further, a stratification variable that grouped the
departments according to whether the proportion of unanticipated difficult intubation at
baseline was ≥ or < 2% will be used for adjustment according to recent evidence of increased
power in the analysis of stratified trials[21].
Assumed confounding covariates We define age; gender; ASA classification; emergency/elective
procedure; Body Mass Index (BMI); and use of neuromuscular blocking agents as covariates
that are possible confounders, necessitating adjusted analyses of the primary outcome and
pre-defined subgroup analyses.
Primary outcomes
The primary outcome measures are:
1. Fraction of unanticipated difficult intubations = all intubations with unanticipated
difficulties [False negative] / all patients primarily (attempted) intubated by direct
laryngoscopy
2. Fraction of unanticipated easy intubations = all intubations with anticipated
difficulties that were easy [False Positive] / all patients primarily (attempted)
intubated by direct laryngoscopy
2. The two primary outcomes are linked and simultaneous low fractions are desirable for the
optimal prediction of a difficult intubation.
Secondary outcomes
1. 48 hour mortality
2. 30 day mortality
3. Fraction = anticipated difficult intubations planned for, and intubated by an advanced
method / all patients (attempted) intubated.
4. Fraction = unanticipated difficult intubations [False Negative] / all difficult
intubations ([False negative] + [True Positive])
5. Sensitivity
6. Specificity
7. Positive predictive value
8. Negative predictive value
9. Positive Likelihood Ratio = (Sensitivity / (1-Specificity))
10. Negative Likelihood Ratio = ((1-Sensitivity) / Specificity)
11. The Receiver Operating Characteristic (ROC) curve. A graphical representation of
sensitivity as a function of (1-Specificity).
Outcomes 5-11 are measured for prediction of difficult intubation for both intervention
groups.
Datapoints Baseline covariates
Individual level:
1. Sex
2. Age
3. Height
4. Weight
5. BMI
6. American Society of Anesthesiologists (ASA) Classification
7. Use of neuromuscular blocking agents
8. Hospital unit
9. Region
10. Anticipated difficult tracheal intubation
11. Anticipated difficult mask ventilation
12. Scheduled airway
13. Priority; Acute/Elective
14. Surgical procedure codes
15. Intubation score
16. Mask ventilation score
Intervention covariates
1. Mouth opening
2. Thyro-mental distance
3. Modified Mallampati classification
4. Jaw protrusion
5. Neck mobility
6. Previous difficult airway management
7. Number of completed risk factors
8. The calculated SARI score
9. Dichotomised SARI score (< or ≥ 4)
10. Snoring
11. Sleep apnoea
12. Presence of beard
13. Changes in the neck due to radiation
Cluster level summaries
1. Mean cluster size
2. Mean number of intubated patients
3. Fraction of private hospitals
4. Mean fraction of unanticipated difficult intubation
5. Age
6. BMI
7. ASA classification
Definition of difficult intubation In the DAD an intubation score is programmed based on
numbers of intubation attempts and use of equipment.
1. A maximum of two intubation attempts - Only by direct laryngoscopy
2. A maximum of two intubation attempts in which other intubation equipment or assistive
devices for direct laryngoscopy is used (e.g. videolaryngoscope)
3. Three intubation attempts or more - Regardless of intubation method
4. Intubation failed despite attempts
Tracheal intubation by direct laryngoscopy is pre-defined in the DAD as unproblematic by a
score = 1 and difficult by a score ≥ 2. In our primary analyses and sample size calculation
we will use the same definition as DAD.
General analysis principles
1. Unless otherwise stated, all main analyses will compare the two intervention groups
using intention-to-treat (ITT) [23].
2. In order to ensure a correct type 1 error rate, all main analyses will account for the
clustered design of the trial and the stratification variable [24-26]. Analyses will be
based on individual patient level data but clustering of patients and the
stratification variable will be accounted for in a mixed effects model.
3. In all analyses a maximum level of 5 % (two sided) type 1 error will be regarded as
statistically significant unless otherwise stated
4. Main analyses will be according to ITT adjusted for cluster and stratification
variables. Sensitivity analyses will be performed adjusted and unadjusted for the prior
listed potential confounding covariates. We will discuss if results differ from the
main analyses. The conclusion of the trial will be based on the primary analyses.
5. Test of interaction will be applied for subgroup analyses.
6. Risks are reported as relative risks. When relative risks are calculated from odds
ratios with 95% confidence interval (CI) it will be done according to Zhang et Yu[27].
7. For missing data exceeding a rate of 5 %, and with a statistical significant Little's
test, indicating that the missing data is not a completely random sample of the total
data, point estimates with 95 % CI will be calculated using a worst/best case scenario
imputation on the missing values. If the imputation of a worst/best case scenario
implies different conclusions, multiple imputations will be performed on the missing
values assuming missingness at random[28]. Unadjusted and complete case analyses will
also be presented.
8. In order to avoid rejecting a true null hypothesis we will address the problem of
multiplicity by Bonferroni adjustments on the secondary outcome measures. If unadjusted
analyses are insignificant (P>0,05), Bonferroni adjustments will not be applied. In
case the adjustment changes an unadjusted significant P-value to a non-significant
P-value, this will be discussed.
9. To ensure complete objectivity, the statistician will be blinded for the intervention
group in the primary outcome analysis. After data collection, a third party data
manager will generate a complete data set with blinded coding of the intervention
groups and other variables possibly revealing the intervention. The statistician
performs the primary outcome analysis on this data set. If the primary outcome differs
between groups we will construct different conclusions reflecting the results,
considering that significant differences of the intervention could be both of benefit
or harm. After writing the conclusions, we will uncover the code of the blinding, and
subsequently the correct conclusion will be employed[29].
Statistical analyses Trial profile The flow of study participants will be displayed in a
Consolidated Standards of Reporting Trials (CONSORT) diagram at a cluster level and at
individual level. The number of clusters fulfilling the inclusion criterion and the number
of clusters included in primary and secondary analyses will be presented. The number of
patients who fulfilled study inclusion criteria as well as the number included in the
primary and secondary analyses will be reported. Reasons for exclusions of clusters and
patients in the primary and secondary analyses will be reported.
Primary outcome Frequencies and percentages per group will be reported with 95 % CI. The
primary outcome is presented as odds ratios and relative risk ratios.
The first analysis of the primary outcome will be adjusted for the stratification- and the
cluster-variable performed according to the ITT principle including patients that met the
inclusion- and not the exclusion-criteria. A logistic regression including a mixed effect
model will be used. Intervention group and stratification variable are regarded as fixed
effects and trial site is regarded as random effects in the model.
The second analysis of the primary outcome will be adjusted for the stratification- and
cluster-variables as well as baseline covariates assumed as confounders incorporated in a
mixed effect model.
The third analysis of the primary outcome will compare the patients in the control group
that met the inclusion- and not the exclusion-criteria with patients in the intervention
group who received the protocoled intervention. That is, a per protocol analysis of control
group vs. the subgroup in the intervention group that had a sufficiently registered SARI.
Interaction test will be performed in the intervention group between patients receiving
sufficient/insufficient SARI registration.
In an alternative sensitivity analysis of the first analysis of the primary outcome we will
employ a different cut-off value for difficult intubation using ≥ 3 instead of ≥ 2 as the
definition of difficult intubation.
Secondary outcomes Frequencies, proportions, percentages, odds and risk ratios are presented
with 95 % CI for each group. A chi- squared test is used to assess the effect of the
intervention on binary outcomes. For categorical outcomes and the adjusted analyses logistic
regression analysis will be performed.
Baseline comparisons of patient characteristics Baseline characteristics are presented for
each intervention group. Frequencies, proportions and percentages will be used to summarize
discrete variables. In case of missing values, percentages are presented with the actual
denominator and otherwise calculated according to the number of participating patients.
Continuous variables are summarized 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.
Baseline comparisons of cluster characteristics Cluster characteristics are presented for
each group, control and intervention. Unless otherwise stated, data will be presented as
means with 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 on individual
patient level and cluster level. The second figure will illustrate the SARI score and
tutorial instruments. The third figure will demonstrate the registration in the DAD,
including the intubation score. The fourth figure will present baseline data from each
intervention group on individual and cluster level and the fifth figure will be outlining
the main outcome results for each intervention group.
Discussion In order to avoid outcome reporting bias and data driven results this paper
presents the detailed statistical analysis plan for the main publication of the DIFFICAIR
trial. The DIFFICAIR trial raises two important questions i.e. is it possible via the
intervention to reduce the frequency of difficult intubation and/or difficult mask
ventilation? This plan only addresses the statistical analysis of the population of
intubated patients. This is because our sample size calculations were based on the same
population; the SARI was developed as a prediction tool for difficult intubation; and due to
the extent of data.
By adjusting our primary outcome analysis for different design variables, such as clustering
and stratification we strive to eliminate inflated type 1 error rates as a consequence of
the trial design. A mixed effect model is applied based on an evaluation of each variable as
having random or fixed effects[30, 31].
When multiple comparisons are performed between two groups, you may risk accepting an
intervention effect erroneously (Type 1 error). There are several approaches that deal with
multiple testing. We chose to employ a Bonferroni adjustment on the secondary outcome
measures in order to evaluate, identify and discuss dubious significant outcomes that may be
due to statistical multiplicity.
The value of a diagnostic test is usually presented as sensitivity and specificity. We have
chosen (1 - total accuracy) i.e. the proportion of unanticipated difficult intubations
(False Negative, FN) and the proportion of unanticipated easy intubations (False Positive,
FP). Both scenarios are of clinical relevance since the FN are at risk of hypoxia, increased
morbidity and even death, while the FP are at risk of being imposed unnecessary discomfort
by e.g. awake intubation. At the same time, both the FN and FP can take up unnecessary
resources. Sensitivity and specificity are more difficult to interpret intuitively.
Consequently, we chose to present more transparent primary outcomes. Using proportions of
unanticipated difficult intubation allowed us to perform a baseline cohort study, on which
we based our sample size and power calculations.
By publishing this paper where we pre-specify our methods and analyses, it is our hope that
the results from the DIFFICAIR trial will be as transparent and robust as possible.
Conclusion This paper presents the principles of analyses used in the DIFFICAIR trial for
the first publication of the main outcomes for the population of intubated patients. Our
approach aims to minimize the risk of data-driven results and outcome reporting bias.
References
1. Cooper GM, McClure JH: Anaesthesia chapter from Saving mothers' lives; reviewing
maternal deaths to make pregnancy safer. Br J Anaesth 2008, 100:17-22.
2. Hove LD, Steinmetz J, Christoffersen JK, Møller A, Nielsen J, Schmidt H, Moller A:
Analysis of deaths related to anesthesia in the period 1996-2004 from closed claims
registered by the Danish Patient Insurance Association. Anesthesiology 2007,
106:675-680.
3. Peterson GN, Domino KB, Caplan RA, Posner KL, Lee LA, Cheney FW: Management of the
difficult airway - A closed claims analysis. Anesthesiology 2005, 103:33-39.
4. Rosenstock C, Møller J, Hauberg A: Complaints related to respiratory events in
anaesthesia and intensive care medicine from 1994 to 1998 in Denmark. Acta Anaesthesiol
Scand 2001, 45:53-58.
5. McClure JH, Cooper GM, Clutton-Brock TH: Saving mothers' lives: reviewing maternal
deaths to make motherhood safer: 2006-8: a review. Br J Anaesth 2011, 107:127-32.
6. Cook T, Woodall N, Frerk C: Major complications of airway management in the United
Kingdom. Natl Audit Proj 2011.
7. Lundstrøm LH, Vester-Andersen M, Møller AM, Charuluxananan S, L'hermite J, Wetterslev
J: Poor prognostic value of the modified Mallampati score: a meta-analysis involving
177 088 patients. Br J Anaesth 2011, 107:659-67.
8. Lee A, Fan LTY, Gin T, Karmakar MK, Ngan Kee WD: A systematic review (meta-analysis) of
the accuracy of the Mallampati tests to predict the difficult airway. Anesth Analg
2006, 102:1867-1878.
9. Lundstrøm LH, Møller AM, Rosenstock C, Astrup G, Wetterslev J: High body mass index is
a weak predictor for difficult and failed tracheal intubation: a cohort study of 91,332
consecutive patients scheduled for direct laryngoscopy registered in the Danish
Anesthesia Database. Anesthesiology 2009, 107:266-274.
10. Lundstrøm LH, Møller AM, Rosenstock C, Astrup G, Gätke MR, Wetterslev J: Avoidance of
neuromuscular blocking agents may increase the risk of difficult tracheal intubation: a
cohort study of 103,812 consecutive adult patients recorded in the Danish Anaesthesia
Database. Br J Anaesth 2009, 103:283-90.
11. Lundstrøm LH, Møller AM, Rosenstock C, Astrup G, Gätke MR, Wetterslev J: A documented
previous difficult tracheal intubation as a prognostic test for a subsequent difficult
tracheal intubation in adults. Anaesthesia 2009, 64:1081-8.
12. Shiga T, Wajima Z, Inoue T, Sakamoto S, Sakamoto A: Predicting difficult intubation in
apparently normal patients. Anesthesiology 2005, 103:429-437.
13. El-Ganzouri AR, McCarthy RJ, Tuman KJ, Tanck EN, Ivankovich AD: Preoperative airway
assessment: predictive value of a multivariate risk index. Anesth Analg 1996,
82:1197-1204.
14. Kheterpal S, Han R, Tremper KK, Shanks AM, Tait AR, O'Reilly M, Ludwig TA, Martin L:
Incidence and predictors of difficult and impossible mask ventilation. Anesthesiology
2006, 105:885-891.
15. Kheterpal S, Martin L, Shanks AM, Tremper KK: Prediction and outcomes of impossible
mask ventilation: a review of 50,000 anesthetics. Anesthesiology 2009, 110:891-897.
16. Kheterpal S, Healy D, Aziz M: Incidence, Predictors, and Outcome of Difficult Mask
Ventilation Combined with Difficult Laryngoscopy: A Report from the Multicenter
Perioperative Outcomes Group. Anesthesiology 2013:1-10.
17. Nørskov AK, Rosenstock CV, Wetterslev J, Lundstrøm LH: Incidence of unanticipated
difficult airway using an objective airway score versus a standard clinical airway
assessment: the DIFFICAIR trial -- trial protocol for a cluster randomized clinical
trial. Trials 2013, 14:347.
18. Chan A-W, Tetzlaff JM, Altman DG, Laupacis A, Gøtzsche PC, Hro A, Mann H, Dickersin K,
Berlin JA, Dore CJ, Parulekar WR, Summerskill WSM, Groves T, Schulz KF, Sox HC,
Rockhold FW, Rennie D, Moher D, Krleža-Jerić K, Hróbjartsson A, Doré CJ: Research and
Reporting Methods SPIRIT 2013 Statement : Defining Standard Protocol Items for Clinical
Trials. Ann Intern Med 2013, 158:200-7.
19. Dwan K, Gamble C, Williamson PR, Altman DG: Reporting of clinical trials: a review of
research funders' guidelines. Trials 2008, 9:66.
20. Hayes RJ, Bennett S: Simple sample size calculation for cluster-randomized trials. Int
J Epidemiol 1999, 28:319-26.
21. Kahan B, Morris T: Reporting and analysis of trials using stratified randomisation in
leading medical journals: review and reanalysis. BMJ 2012, 345:e5840.
22. Crespi CM, Wong WK, Wu S: A new dependence parameter approach to improve the design of
cluster randomized trials with binary outcomes. Clin Trials 2011, 8:687-98.
23. Fergusson D, Aaron SD, Guyatt G, Hébert P: Post-randomisation exclusions: the intention
to treat principle and excluding patients from analysis. BMJ 2002, 325:652-4.
24. Klar N, Donner A: Current and future challenges in the design and analysis of cluster
randomization trials. Stat Med 2001, 20:3729-3740.
25. Hayes RJ, Moulton LH: Cluster Randomised Trials (Chapman & Hall/CRC Interdisciplinary
Statistics). Chapman and Hall/CRC; 2009:338.
26. Kahan BC, Morris TP: Assessing potential sources of clustering in individually
randomised trials. BMC Med Res Methodol 2013, 13:58.
27. Zhang J, Yu KF: What's the relative risk? A method of correcting the odds ratio in
cohort studies of common outcomes. JAMA 1998, 280:1690-1.
28. Schafer JL: Multiple imputation: a primer. Stat Methods Med Res 1999, 8:3-15.
29. Gøtzsche PC: Blinding during data analysis and writing of manuscripts. Control Clin
Trials 1996, 17:285-90.
30. Kahan BC, Morris TP: Adjusting for multiple prognostic factors in the analysis of
randomised trials. BMC Med Res Methodol 2013, 13:99.
31. Kahan BC, Morris TP: Analysis of multicentre trials with continuous outcomes: when and
how should we account for centre effects? Stat Med 2013, 32:1136-49.
