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60 Mary CARDOSA, Phoon Ping CHEN
Madan I, Reading I, Palmer KT, et al. Cultural differences in musculoskeletal symptoms and disability. Int J Epidemiol
2008;37:1181–9.
MacGregor AJ, Griffiths GO, Baker J, et al. Determinants of pressure pain threshold in adult twins: Evidence that
shared environmental influences predominate. Pain 1997;73:253–7.
Malmivaara A, Hakkinen U, Aro T, et al. e treatment of acute low back pain — bed rest, exercises, or ordinary
activity? N Engl J Med 1995;332:351–5.
Maniadakis N, Gray A. e economic burden of back pain in the UK. Pain 2000;84:95–103.
McKenzie KJ, Crowcroft NS. Race, ethnicity, culture, and science. BMJ 1994;309:286–7.
McMillan AS, Wong MC, Zheng J, et al. Prevalence of orofacial pain and treatment seeking in Hong Kong Chinese. J
Orofac Pain 2006;20:218–25.
McRoberts JA, Li J, Ennes HS, et al. Sex-dependent differences in the activity and modulation of N-methyl-d-aspartic
acid receptors in rat dorsal root ganglia neurons. Neuroscience 2007;148:1015–20.
Meyer-Rosberg K, Kvarnström A, Kinnman E, et al. Peripheral neuropathic pain — a multidimensional burden for
patients. Eur J Pain 2001;5:379–89.
Miu DKY, Chan TY, Chan MH. Pain and disability in a group of Chinese elderly out-patients in Hong Kong. Hong
Kong Med J 2004;10:160–5.
Mogil JS, Yu L, Basbaum AI. Pain genes?: Natural variation and transgenic mutants. Ann Rev Neurosci 2000;23:777–
811.
Montagna P. Recent advances in the pharmacogenomics of pain and headache. Neurol Sci 2007; 28(Suppl 2):S208–
12.
Moulin DE, Clark AJ, Speechley M, et al. Chronic pain in Canada — prevalence, treatment, impact and the role of
opioid analgesia. Pain Res Manage 2002;7:179–84.
Ng B, Dinsdale JE, Rollnik JD, et al. e effect of ethnicity on prescriptions for patient-controlled analgesia for post-
operative pain. Pain 1996;66:9–12.
Ng KFJ, Tsui SL, Chan WS. Prevalence of common chronic pain in Hong Kong adults. Clin J Pain 2002;18:275–81.
Nicholas MK, Cardosa MS, Chen PP. Developing multidisciplinary cognitive behavioural pain management programs
in Asia. In Flor H, Kalso E, Dostrovsky JP, eds. Proceedings of the World Congress in Pain. Seattle: IASP Press, 2006:773–
87.
Nilsen CV, Lande SA, Malterud K. Is headache a women’s disease? [in Swedish] Tidsskr Nor Laegeforen 1998;118:1404–
7.
Palmer B, Macfarlane G, Afzal C, et al. Acculturation and the prevalence of pain amongst South Asian minority ethnic
groups in the UK. Rheumatology 2007;46:1009–14.
Perkins FM, Kehlet H. Chronic pain as an outcome of surgery. A review of predictive factors. Anesthesiology
2000;93:1123–33.
Perquin CW, Hazebroek-Kampschreur AA, Hunfeld JA, et al. Pain in children and adolescents: A common experience.
Pain 2000;87:51–8.
Poulsen L, Brøsen K, Arendt-Nielsen L, et al. Codeine and morphine in extensive and poor metabolizers of sparteine:
Pharmacokinetics, analgesic effect and side effects. Eur J Clin Pharmacol 1996;51:289–95.
Riley JL, Robinson ME, Wise EA, et al. Sex differences in the perception of noxious experimental stimuli: A meta-
analysis. Pain 1998;74:181–7.
Riley JL, Wade JB, Myers CD, et al. Racial/ethnic differences in the experience of chronic pain. Pain 2002;100:291–
8.
Robinson ME, Gagnon CM, Riley 3rd JL, et al. Altering gender role expectations: Effects on pain tolerance, pain
threshold, and pain ratings. J Pain 2003;4:284–8.
Rosmus C, Johnston CC, Chan-Yip A, et al. Pain response in Chinese and non-Chinese Canadian infants: Is there a
difference? Soc Sci Med 2000;51:175–84.
Roth-Isigkeit A, yen U, Stoven H, et al. Pain among children and adolescents: Restriction in daily living and triggering
factors. Pediatrics 2005;115:152–62.
Rustoen T, Wahl AK, Hanestad BR, et al. Prevalence and characteristics of chronic pain in the general Norwegian
population. Eur J Pain 2004;8:555–65.
Sartain JB, Barry JJ. e impact of an acute pain service on postoperative pain management. Anaesth Intensive Care
1999;27:375–80.
Scher AI, Stewart WF, Lipton RB. Migraine and headache: A meta-analytical approach. In Crombie IK, Croft PR,
Linton SJ, LeResche L, Von Korff M. Epidemiology of Pain. Seattle: IASP Press, 1999.
Scott NW, Fayers PM, Aaronson NK, et al. e relationship between overall quality of life and its subdimensions was
influenced by culture: analysis of an international database. J Clin Epidemiol 2008;61:788–95.
Selim AJ, Fincke G, Ren XS, et al. Racial differences in the use of lumbar spine radiographs: Results from the veterans
health study. Spine 2001;26:1364–9.
72.
73.
74.
75.
76.
77.
78.
79.
80.
81.
82.
83.
84.
85.
86.
87.
88.
89.
90.
91.
92.
93.
94.
95.
96.
97.
98.
99.
100.
101.
https://t.me/medicina_free
61
Epidemiology in Chronic Pain, Gender and Cultural Aspects
Sheffield D, Kirby DS, Biles PL, et al. Comparison of perception of angina pectoris during exercise testing in African-
Americans versus Caucasians. Am J Cardiol 1999;83:106–8, A8
Sheffield D, Biles PL, Orom H, et al. Race and sex differences in cutaneous pain perception. Psychosom Med 2000:62:517–
23.
Siddall PJ, Cousins MJ. Persistent pain as a disease entity: Implications for clinical management. Anesth Analg
2004;99:510–20.
Smedley BD, Stith AY, Nelson AR, eds. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care.
Washington DC: National Academies Press, 2003.
Smith BH, Elliott AM, Chambers WA, et al. e impact of chronic pain in the community. Fam Pract 2001;18:292–
9.
Smith BH, Torrance N, Bennett MI, et al. Health and quality of life associated with chronic pain of predominantly
neuropathic origin in the community. Clin J Pain 2007;23:143–9.
Soetanto AL, Chung JW, Wong TK. Are there gender differences in pain perception? J Neurosci Nurs 2006;38:172–6.
Soetanto AL, Chung JW, Wong TK. Gender differences in pain perception: A signal detection theory approach. Acta
Anaesthesiol Sin 2004;42:15–22.
Streltzer J, Wade TC. e influence of cultural group on the under-treatment of postoperative pain. Psychosom Med
1981;43:397–403.
Sutton-Tyrrell K. Assessing bias in case-controlled studies. Stroke 1991;22:938–42.
Tan EC, Lim Y, Teo YY, et al. Ethnic differences in pain perception and patient-controlled analgesia usage for
postoperative pain. J Pain 2008;9:849–55.
omas E, Peat G, Harris L, et al. e prevalence of pain and pain interference in a general population of older adults:
Cross-sectional findings from the North Staffordshire Osteoarthritis Project (NorStOP). Pain 2004;110:361–8.
Todd KH, Deaton C, D’Adamo AP, et al. Ethnicity and analgesic practice. Ann Emerg Med 2000;35:11–6.
Todd KH, Lee T, Hoffman JR. e effect of ethnicity on physician estimates of pain severity in patients with isolated
extremity trauma. JAMA 1994;271:925–8.
Torrance N, Smith BH, Bennett MI, et al. e epidemiology of chronic pain of predominantly neuropathic origin.
Results from a general population survey. J Pain 2006;7:281–9.
Tsai YF. Gender differences in pain and depressive tendency among Chinese elders with knee osteoarthritis Pain
2007;130(1-2):188–94.
Tsui SL, Lo RJ, Tong WN, et al. A clinical audit for postoperative pain control on 1,443 surgical patients. Acta
Anaesthesiol Sin 1995;33:137–48.
Unruh AM. Gender variations in clinical pain experience. Pain 1996; 65:123–67.
Unruh AM, Ritchie J, Merskey H. Does gender affect appraisal of pain and pain coping strategies? Clin J Pain
1999;15:31–40.
US Census Bureau, International Database. Global population profile 2002, http://www.census.gov/ipc/www/wp02.
html.
van Den Kerkhof EG, Hopman WM, Towheed TE, et al. for Canadian Multicentre Osteoporosis Study Research
Group. e impact of sampling and measurement on the prevalence of self-reported pain in Canada. Pain Res Manag
2003;8:157–63.
van Leeuwen MT, Blyth FM, March LM, et al. Chronic pain and reduced work effectiveness: e hidden cost to
Australian employers. Eur J Pain 2006;10:161–6.
Veerapen K, Wigley RD, Valkenburg H. Musculoskeletal pain in Malaysia: A COPCORD survey. J Rheumatol
2007;34:207–13.
Verhaak PF, Kerssens JJ, Dekker J, et al. Prevalence of chronic benign pain disorder among adults: A review of the
literature. Pain 1998;77:231–9.
Weisse CS, Sorum PC, Sanders KN, et al. Do gender and race affect decisions about pain management. J Gen Intern
Med 2001;16:211–7.
White SF, Asher, MA, et al. Patients’ perceptions of overall function, pain, and appearance after primary posterior
instrumentation and fusion for idiopathic scoliosis. Spine 1999;24:1693–9.
Wiesenfeld-Hallin Z. Sex differences in pain perception. Gend Med 2005;2:137–45.
Wolfe F, Ross K, Anderson J,et al. e prevalence and characteristics of fibromyalgia in the general population. Arthritis
Rheum 1995;38(1):19–28.
Wood AJ, Zhou HH. Ethnic differences in drug disposition and responsiveness. Clin Pharmacokinet 1991;20:350–73.
Yunus MB. Gender differences in fibromyalgia and other related syndromes. J Gend Specif Med 2002;5:42–7.
Zalinawati AH, Teng CL, Kamil MA, et al. Pain morbidity in primary care. Med J Malaysia 2006;61:162–7.
Zhang Y, Sun Z, Liu J, et al. Advances in susceptibility genetics of intervertebral degenerative disc disease. Int J Biol Sci
2008;4:283–90.
102.
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120.
121.
122.
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124.
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132.
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Introduction
e objective of this chapter is to increase the understanding
of evidence-based medicine and clinical trials as applied to
pain medicine. Practical concerns specific to pain medicine
will be addressed and some guidance will be provided for the
interpretation of results. Full details of statistical analysis are
not within the scope of the chapter since there are numerous
resources such as textbooks in biostatistics that are readily
available.
Special features of the study of pain
e experience of pain is a biopsychosocial phenomenon. us,
a traditional biological approach is often not sufficient for
research studies in the field of pain medicine. is fact affects
the methodology, assessment and interpretation of results
of research in acute and chronic pain. For example, in the
context of pain assessment, many factors other than a patient’s
experience of pain could potentially influence the report of
pain. Such factors include other subjective experiences and
feelings (e.g. anxiety), and motivational factors (e.g. appearing
stoic). Outcome measures that are important in pain studies
include not only pain measures but also measures of physical
and emotional functioning because common psychosocial
factors (e.g. sleep disturbance, anger, depression) will exert an
influence on a patient’s response.
It is also well known that reported changes in pain do not
always match behaviour and function. Fordyce et al. [15]
found that positive patient statements about severity of
chronic pain and functional impairment after an intervention
showed few correlations with medication consumption, health
care utilisation, or recorded activity level. It has been suggested
that the analysis of patient behaviour should also be included
in the evaluation of chronic pain.
Biostatistics in Pain Medicine
6
Jacqueline YAP, Tony GIN
INTRODUCTION
Special features of the study of
pain
EVIDENCE-BASED MEDICINE IN
PAIN MEDICINE
Lev
els of evidence
Systematic reviews and meta-
analysis
Presentation of results from meta-
analysis
Application of evidence-based
medicine in pain medicine
CLINICAL TRIALS IN PAIN
RESEARCH
General principles
Randomisation
Control group
P
articipant selection
Blinding
Sample size
Outcome measurement
Analysis
Presentation of results
Limitations of r
andomised
controlled trials
OUTCOME MEASUREMENT AND
QUESTIONN
AIRES IN STUDIES OF
P
AIN
Reliability
Validity
Responsiveness
Issues in outcome measurement
for clinical trials in pain
research
CONCLUSION
REFERENCES
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64 Jacqueline YAP, Tony GIN
Finally, not all pain is the same. One needs to be
aware that acute pain is not a single entity. For
example, the efficacy of different analgesic agents
varies according to the acute pain model and
severity of the noxious stimuli. ere are significant
differences between acute and chronic pain in terms
of quality, duration and mechanism. Studies of
acute and chronic pain must be designed differently.
For example, unlike acute pain, the measurement
of pain has not been subject to standardisation in
chronic pain. is is because standardisation may
not be possible across all chronic pain conditions,
which are often heterogeneous in terms of patient
groups, mechanism and type of pain. In addition,
the ethics of using placebo controls in chronic pain
have been debated. In single dose analgesic studies
in acute pain, there are rarely ethical objections to
the use of placebos, because patients understand that
they can terminate the study and take additional
analgesics at any time. Chronic pain studies are a
different matter. Although a placebo control has
been considered appropriate in studies of chronic
pain syndromes in patients who have already failed
all standard treatments, chronic administration of a
placebo cannot be justified in pain syndromes that
generally respond to therapy.
Evidence-Based Medicine in Pain
Medicine
Evidence-based medicine (EBM) has been described
as “the conscientious, explicit and judicious use of
current best evidence in making decisions about
the care of individual patients” [35]. Large amounts
of information currently exist in the medical
literature. Evidence-based medicine can be utilised
to distil this information and assist the clinician
to use this knowledge with wisdom. Resources for
EBM relevant to pain medicine include:
• Cochrane Pain, Palliative and Supportive Care
Group (http://www.cochrane.org/index.htm)
• Bandolier (http://www.jr2.ox.ac.uk/
Bandolier/index.html)
• e Oxford Pain Internet Site (http://www.
jr2.ox.ac.uk/bandolier/booth/painpag/)
• e Centre for Evidence Based Medicine
(http://www.cebm.net/)
• Database — MEDLINE, EMBASE,
PsychINFO, Cochrane Central Register of
Controlled Trials (CENTRAL)
• Journals — Bandolier Journal, Pain, Clinical
Journal of Pain, Pain Medicine and other pain-
related journals.
Although EBM is popular, it does have potentially
significant and persistent problems [18, 25]. It is
interesting to note there is no evidence that the
practice of EBM as originally defined improves
patient outcome, and EBM recommendations
may turn out to be incorrect at times. EBM is
not a new paradigm, and many physicians were
practising medicine based on available evidence
before the advent of EBM. A pragmatic appraisal
of EBM is that it can provide good evidence with
the assistance of modern information technology,
but it must be carefully interpreted by experts and
individuals before it can be applied to patients.
Levels of evidence
e strength of evidence can be presented according
to various classifications. A typical classification in
descending order of credibility is:
• Strong evidence from at least one systematic
review of multiple well-designed randomised
controlled trials
• Strong evidence from at least one properly
designed randomised controlled trial of
appropriate size
• Evidence from well-designed trials such as
non-randomised trials, cohort studies, time
series or matched case-controlled studies
• Evidence from well-designed non-
experimental studies from more than one
centre or research group
• Opinions of respected authorities, based
on clinical evidence, descriptive studies, or
reports of expert committees.
Systematic reviews and meta-
analysis
A systematic review is a summary of the medical
literature that uses explicit methods to perform
a thorough literature search, critically appraises
individual studies and employs appropriate
statistical techniques to combine valid results. A
meta-analysis is basically the quantitative statistical
methods to combine and summarise the results from
independent studies. It also assesses the presence and
effect of heterogeneity, and explores the robustness
of the results using sensitivity analysis.
The process of systematic reviews and meta-
analysis
e process of systematic review involves the
following steps:
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Biostatistics in Pain Medicine 65
1. Define the question
e intervention of interest, relevant patient groups
and outcomes should be clearly stated.
2. Literature search
Published and unpublished literature from English
and non-English sources, studies reported at
conferences, expert opinions should be sought
and identified. Publication bias is of concern here.
For example, studies that report positive outcomes
are more likely to be reported in journals than
negative studies. Although the negative studies
may eventually be published, there can be a delay
of several years and it can take this long before a
systematic review reports an accurate estimate of
the treatment effect.
3. Study appraisal
Each study identified will then be reviewed and
critically appraised for quality and eligibility to be
included into the systematic review. is process
should involve two independent reviewers. ere is
no mandatory provision for an expert in the field
to assess the relevance of the studies for inclusion or
exclusion, but this should be a crucial step because
studies may be methodologically perfect for
inclusion yet flawed in the way they try to answer
the question posed.
4. Pooling of results
Once the individual studies have been assessed to
be eligible for inclusion into the systematic review,
the findings from each study need to be combined
to produce an overall conclusion. Qualitative and
quantitative techniques may be used to pool the
results. Further information on these techniques
is discussed in the next section on presentation of
results.
5. Discussion
Issues such as heterogeneity of the studies, bias
and the applicability of the findings should be
addressed.
Limitations of systematic reviews and meta-
analysis
Meta-analysis extracts additional information
through calculation from existing data. Hence, the
findings from a meta-analysis need to be validated
with the results from subsequent large-scale, well-
conducted randomised controlled trials. In general,
there has been agreement between meta-analysis
and subsequent large-scale high quality trials but
discrepancies also exist [24]. A comparison by
Ioanndis and colleagues found that the results
of subsequent large trials have disagreed with
conclusions from a systematic review and meta-
analysis 10–23% of the time [22]. In view of this
limitation, the results of a meta-analysis should
not be considered as additional evidence over the
original raw data.
Systematic reviews have replaced traditional
narrative reviews in summarising research evidence.
Some deficiencies of narrative reviews are overcome
by applying the same rigorous standards to
secondary research (where the unit of study is other
research studies) as should be applied to primary
research (the original empirical study).
During the literature search, selection bias is
of concern in any systematic review or meta-
analysis. Funnel plots can be used to assess the
likely presence of selection bias. e effect size of
the studies is compared against the sample size
in a funnel plot. In the absence of bias, the plot
should resemble an inverted symmetrical funnel
as smaller studies have more chance of variability
and will scatter more widely at the bottom of the
graph. If selection bias exists, then there will be an
asymmetrical appearance of the funnel plot with a
gap at the bottom of the funnel.
Finally, systematic reviews are not all equal, and
quality issues are important. ere is clear guidance
on the process of developing systematic reviews
[8, 11]. A recommendation on the reporting of
systematic reviews is provided by the Quality of
Reporting of Meta-analysis (QUOROM) statement
in an effort to improve the quality of systematic
reviews [29].
Another major concern in meta-analysis is the
extent to which different studies are combined.
In order to get a precise answer in a meta-analysis,
only studies that match exactly should be included.
Unfortunately, research differs in conditions
such as patient group, setting, intervention and
outcome. Hence, heterogeneity is constantly
present in a meta-analysis. One then needs to
ascertain whether it will significantly affect the
conclusion of the meta-analysis. In order to be more
transparent with heterogeneity, its presence can
be more explicitly shown by a graphical or tabled
presentation of the results from individual studies,
grouped by important variables such as patient and
intervention. Statistical tests of heterogeneity exist
but may lack power, so that while positive results
suggest that there may be a problem, a negative
result does not exclude heterogeneity [16].
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66 Jacqueline YAP, Tony GIN
Sensitivity analysis explores the robustness of the
results of the meta-analysis by varying the approach
to combining the studies. e effect of excluding
various categories of studies and the consistency
of the results across different subgroups may be
explored. If sensitivity analysis is not performed, the
results of the meta-analysis should be interpreted
with caution as consistency is not guaranteed.
In a meta-analysis, common data from different
studies are combined while others are discarded. For
example, meta-analysis of pain relief typically uses
50% reduction in pain as a dichotomous outcome.
is results in the information on the exact amount
of pain relieved being lost, as the result is classified
into successes and failures. is problem may
affect the applicability of the meta-analysis. Loss of
information may also occur as a result of inadequate
presentation of results. A pooled number needed to
treat (NNT) may be the only result presented by a
meta-analysis, but the NNT changes significantly
with a change in baseline risk.
Presentation of results from
meta-analysis
Data from a meta-analysis are displayed by pictorial
representations and summary measures of the effect
(Fig. 6.01).
e proportion of patients achieving the outcome
with the experimental intervention is plotted (Y
axis) against the proportion achieving the outcome
in the control (X axis). Each point on the L’Abbé
Plot is one trial in a systematic review. Trials in
which the experimental treatment is better than the
control will lie in the upper left of the plot between
the Y axis and the line of equality. If the points are
mainly focused on the line of equality, then there is
no difference between the intervention and control.
e L’Abbé Plot also gives a quick indication of the
heterogeneity and the level of agreement among
trials. Points representing a consistent cloud imply
a homogenous effect. But if the points are scattered
all over the graph, and particularly if they cross
the line of equality, then this is an indication of
heterogeneity and the reader should be cautious
when interpreting the results.
e summary measure used in the Forest Plot is
usually the odds ratio. e scale is a logarithmic
scale. e line of equality is where the odds ratio
equals 1. e findings from each individual study
are represented as a circle or square (the measured
effect), with a horizontal line (the 95% confidence
interval) around the main finding. e size of the
circle or square may vary to reflect the amount of
information (or weighting) in that individual study.
e width of the horizontal line or 95% confidence
interval represents the uncertainty of the estimate
of treatment effect for that study, hence a narrower
width implies greater precision. e overall size and
direction of the effect is displayed at the bottom of
the figure together with the statistical significance.
Summary measure of eect
e treatment effect can be summarised and
presented in different ways depending on the type
of data available. Sometimes this is a difference in
the mean or median, or in the case of data that
are binary, this may be a difference in proportions,
risk, odds or number needed to treat. Table 6.01
represents the results of a hypothetical pain trial
with summary measures derived from the results.
Risks
e risk (or probability) is the likelihood of an
event happening. If the event in the paracetamol
group is 90 and its non-event is 10, then the risk
Figure 6.01 An idealised L’Abbé Plot [4].
Copyright permission granted from Bandolier (http://www.
jr2.ox.ac.uk/bandolier/booth/glossary/labbe.html).
Pictorial representation
L’Abbé Plot
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Biostatistics in Pain Medicine 67
is 90/(90+10)=0.9 or 90%. If the risk had doubled
(relative risk=2) or halved (relative risk=0.5), then
the event would be twice as likely, or half as likely
to occur.
e relative risk (RR) is the risk of an event in the
treated group divided by the risk of an event in
the control group, i.e. the ratio of EER to CER.
It is usually expressed as a decimal proportion.
If the risks in the two groups are the same, then
the relative risk is 1. Referring to the example in
Table 6.01, a RR=1.8 means that the event in the
treatment group is 1.8 times more likely to occur
when compared with those in the control group.
Relative risks are interpretated and used as follows;
• A RR greater than 1 means that the treatment
effect is better than placebo.
• If the 95% confidence interval of a RR does
not include 1, then the result is statistically
significant. If the 95% confidence interval
of a RR includes 1, then the result is not
statistically significant.
• e RR is frequently reported in prospective
cohort studies, meta-analysis and can also
be used as a summary measure in clinical
trials. In these study designs, it is advisable to
measure the relative risk rather than the odds
ratio as it is easier to intuitively interpret the
relative risk. However, in retrospective case
control studies, it is not possible to calculate
the relative risk since subjects are chosen based
Figure 6.02 Hypothetical Forest Plot.
on the presence or absence of the disease. In
addition, the incidence rate and risk cannot
be determined because accurate information
about the total number of patients at risk
is not available. In this situation, one may
approximate the relative risk using the odds
ratio if the event is rare when compared with
the total.
e relative risk reduction (RRR) is the proportion
of the risk removed by the treatment. It is the
absolute risk reduction (ARR) divided by the initial
risk in control group, i.e. (EER-CER)/CER. It is
usually expressed as a percentage.
Relative risk measures do not reflect the baseline
risk of individuals with regard to the outcome being
measured. erefore, relative risk measures cannot
discriminate between large and small effects, and
do not give a true reflection of how much benefit
the individual would derive from the intervention.
Relative risk measures tend to overestimate the
benefits of an intervention and for this reason are
popular with the health industries.
e absolute risk reduction (ARR) is the absolute
change in risk, where the effect is due solely the
treatment and nothing else. It is the risk of an event
in the treatment group minus the risk of an event
in the control group (EER-CER). It is usually
expressed as a percentage.
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68 Jacqueline YAP, Tony GIN
Absolute risk measures overcome the problems
of relative risk measures because they reflect the
baseline risk and are better at discriminating
between small and large treatment effects.
However, since absolute risk measures are
dependent on the baseline risk, they have limited
generalisability. Hence, it is inappropriate to
extrapolate published absolute risk reductions
from one population to another population with
different baseline risks.
While relative and absolute risk can show that an
intervention works compared with the control,
they are of limited use in informing clinicians how
well an intervention works (i.e. the magnitude of
the effect). e NNT is a summary measure that
provides this information.
Odds
e odds are the ratio of events to non-events. For
example, if the event in the paracetamol group is 90
and its non-event rate is 10, then the experimental
event odds are 90/10=9.
e odds ratio (OR) is the odds of an event in the
treated group divided by the odds of an event in the
control group. It is usually expressed as a decimal
proportion. Odds ratios are not usually as intuitively
interpreted by clinicians as relative risks.
Odds ratios are interpretated and used as follows;
• An OR that is greater than 1 means that
the experimental intervention is better than
control.
• If a 95% confidence interval is calculated,
statistical significance is assumed if the interval
does not include 1.
• Despite the difficulty in interpreting the
odds ratio, it is useful in a few specific
situations. In retrospective case control
studies where the relative risk cannot
be directly calculated, the odds ratio
approximates the relative risk when the
event is rare. In addition, logistic regression
models work in terms of odds and its
coefficients are reported as odds ratios.
Number needed to treat (NNT)
e NNT is the number of people who have to
be treated to achieve the desired outcome in one
person. When interpreting the NNT, it is always
treatment specific, relative to the comparator,
and applies to a particular outcome and duration
of treatment. For example, if the NNT=3 for an
analgesic, this means that for every three patients
given the active treatment over a certain duration
when compared with the control, one patient
experienced the desired outcome. e NNT has
clinical relevance because it shows immediately the
Table 6.01 Results of a hypothetical acute pain trial
Treatment Number who achieved at
least 50% pain relief
Number who did not achieve
at least 50% pain relief
Total number of patients
treated
Paracetamol 1 g 90 10 100
Placebo 50 50 100
Summary measures:
Experimental event rate (EER) 90/100=0.90 or 90%
Control event rate (CER) 50/100=0.50 or 50%
Relative risk (EER/CER) 0.9/0.5=1.8
Relative risk reduction ((EER-CER)/CER) ((0.9–0.5)/0.5)=0.8 or 80%
Absolute risk reduction (EER-CER) 0.9–0.5=0.4 or 40%
Experimental event odds 90/10=9
Control event odds 50/50=1
Odds ratio 9/1=9
NNT (1/ARR) 1/0.4=2.5
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Biostatistics in Pain Medicine 69
clinical effect and effort required to produce one
result with a particular intervention.
e NNT can be calculated by a number of ways,
such as by using the absolute risk reduction (ARR),
a table of odds ratios with corresponding NNTs
[34], or a nomogram of NNT based on the relative
risk reduction [9]. e commonest method would
be to use the absolute risk reduction, i.e. NNT is
1/ARR or 1/(EER-CER). In other words,
NNT =
where IMP
act
= number of patients given
active treatment
achieving target
TOT
act
= total number of patients given
active treatment
IMP
con
= number of patients given
control treatment
achieving target
TOT
con
= total number of patients given
control treatment
An NNT=1 means that a favourable outcome
occurs in every patient given the treatment but
in no patient in the control group. is “perfect
result” is very difficult to achieve in the clinical
setting. Hence, NNTs of 2 or 3 are generally
accepted as an indication of an effective treatment.
Higher NNTs represent less effective outcomes
from a treatment. A negative NNT occurs when
there are more favourable events with the control
than with the treatment. e concept of NNT
can also be applied to harm. e number needed
to harm (NNH) should be high because it implies
that adverse events are rare.
It must be reinforced that the interpretation of
NNTs are by definition relative. ere is no rule
that says that an NNT of 2 is always good and one
of 100 is always bad: it depends on the context. For
example, a NNT of 20 in a fatal condition could be
regarded as reasonable — one person in 20 could
be saved. e same NNT would not be regarded
in the same manner in a different context, such
as for the prevention of postoperative vomiting if
the treatment is associated with significant adverse
effects. In addition, low values of NNH are not
desirable because this means adverse events are
common, but this is influenced by the severity of
the adverse event. For example, one may accept
a lower NNH for a mild adverse event when the
treatment is very effective.
e benefits of NNT include;
• Provides a single numerical reminder of
effectiveness.
• Gives a measure of the therapeutic effort
required to obtain one beneficial event.
• NNTs are better at discriminating between
large and small treatment effects and more
useful than relative risk measures in describing
outcomes in clinical trials.
• NNTs are useful in comparing the utility
of treatments in comparable trials with
comparable outcomes. NNT league tables
(e.g. Oxford league table of analgesic efficacy)
can rank treatments relative to one another
and may be helpful in making a choice
of treatment based on the comparative
effectiveness.
However, NNT also has its limitations and they
include the followings;
• Calculation of the NNT requires dichotomous
data.
• e comparison of NNTs across a disease
condition can only be done if conditions,
outcomes and interventions are similar. For
example, most of the NNTs published in
analgesic league tables relate to the acute
pain setting, with a single drug dose and
assessment over a 6-hour period. ese league
tables should not be used to make treatment
decisions for patients with chronic pain who
require repeated and continuous treatment.
• e NNT is sensitive to trials with high CER,
i.e. the proportion of patients in the control
group that achieved the outcome. Increases
in CER results in a higher NNT result and
hence, apparently, less effective NNTs . is
can be easily worked out from the formula
NNT=1/(EER-CER).
• NNTs for a specified intervention in an
individual patient depend not only on the
nature of the treatment but also on the
baseline risk. If a patient’s expected event rate
is high, then he will benefit more.
• e NNT is only a part of any patient
assessment — one should also consider
other factors including the consequences of
the disease, costs, availability of alternative
1
(IMP
act
/TOT
act
) - (IMP
con
/TOT
con
)
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