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. 2017 Apr 25;2017(4):CD012637. doi: 10.1002/14651858.CD012637

Oral paracetamol (acetaminophen) for cancer pain

Philip J Wiffen 1, Sheena Derry 1, R Andrew Moore 1,✉, Ewan D McNicol 2, Rae F Bell 3, Daniel B Carr 4, Mairead McIntyre 5, Bee Wee 6
PMCID: PMC6478112

Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

To assess the efficacy of oral paracetamol (acetaminophen) for cancer pain in adults and children, and the adverse events associated with its use in clinical trials.

Background

A previous Cochrane review that examined the evidence for nonsteroidal anti‐inflammatory drugs (NSAIDs) or paracetamol, alone or combined with opioids, for cancer pain was withdrawn in 2015 because it was out of date (McNicol 2015); the date of the last search was 2005.

This protocol is for one of three reviews on the efficacy and safety of oral non‐opioid medicines to treat cancer pain, in this case focusing on paracetamol in adults and children. Another review will examine oral non‐steroidal anti‐inflammatory drugs (NSAIDs) in adults. A separate review will examine the efficacy of NSAIDs for cancer pain in children.

Description of the condition

Cancer is estimated to cause over eight million deaths per annum ‐ approximately 13% of deaths worldwide (IARC 2012). Globally, 32 million people are living with cancer, and detailed information for individual countries is available on the WHO website for the International Agency for Research on Cancer (http://globocan.iarc.fr/Pages/fact_sheets_cancer.aspx). In the UK alone in 2014, there were around 350,000 new cases of cancer annually, with around 50% of people surviving for 10 years or more after diagnosis (Cancer Research UK 2016).

Cancer pain is perhaps one of the most feared symptoms associated with the disease. Pain may be the first symptom to cause someone to seek medical advice that leads to a diagnosis of cancer, and 30% to 50% of all people with cancer will experience moderate to severe pain (Portenoy 1999). Pain can occur at any time as the disease progresses, but the frequency and intensity of pain tends to increase as the cancer advances (Portenoy 1999; Van den Beuken‐van Everdingen 2016). For people with advanced cancer, some 75% to 90% will experience pain having a major impact on daily living (Wiffen 2016). Pain had a significant negative correlation with quality of life in people with cancer in China, Japan, and Palestine, for example (Deng 2012; Dreidi 2016; Mikan 2016). A recent systematic review has shown that approximately 40% of patients suffered pain after curative treatment, 55% during cancer treatment and 66% in advanced disease. Pain related to cancer is frequently described as distressing or intolerable by more than one‐third of patients (Breivik 2009; Van den Beuken‐van Everdingen 2016).

Cancer pain can be the result of the cancer itself, interventions to treat the cancer, and sometimes other underlying pains. Prevalence of pain is also linked to cancer type, with head and neck cancer showing the highest prevalence. Age also has an impact, with younger patients experiencing more pain (Prommer 2015). For this review, we will not consider postsurgical pain related to surgery or neuropathic pain related specifically to chemotherapy or nerve pressure.

The current World Health Organisation (WHO) cancer pain ladder for adults recommends the use of non‐opioid analgesics, including paracetamol, as the first step on the ladder, with or without an adjuvant (WHO 2016). Non‐opioid analgesics are also to be used on the second and third steps, together with weak or strong opioids. The current National Institute for Health and Care Excellence (NICE) in the UK advises that non‐opioid analgesics alone be used for treating mild pain (0 to 3 on a 0 to 10 pain scale), together with a weak opioid or such as codeine or tramadol for mild to moderate pain (3 to 6), and with a strong opioid such as morphine for severe pain (6 to 10) (NICE 2016). Some authorities have suggested that the second step on the ladder could be removed, and replaced with low doses of strong opioids such as morphine (Twycross 2014).

Children with cancer consider pain as the most prevalent symptom, usually resulting from treatment side effects or painful procedures (Twycross 2015). The amount and quality of research on pharmacological treatments for children tends to be poor (Mercadante 2014).

Description of the intervention

Paracetamol (acetaminophen) is available in a very large number of formats and products with different names, including Panadol and Tylenol. Paracetamol plus codeine often comes with the generic name co‐codamol, and paracetamol plus dihydrocodeine as co‐dydramol. A listing of brand names by country is available but the list is too long to be given here (Drugs.com 2016). Paracetamol use is measured in thousands of tonnes, and questions have been asked about the benefits and risk of such extensive use (Moore 2016).

Paracetamol was first identified as the active metabolite of two older antipyretic drugs, acetanilide and phenacetin, in the late nineteenth century (Axelrod 2003). Since then, it has become one of the most popular antipyretic and analgesic drugs worldwide, and is often also used in combination with other drugs. It became available in the UK on prescription in 1956, and without prescription (over‐the‐counter) in 1963 (PIC 2015). Nonprescription medications are less expensive, more accessible, and have favourable safety profiles relative to many prescription treatments. There have been calls for the position of paracetamol as a widely‐available analgesic to be re‐evaluated, based on an assessment of evidence over the past 130 years (Brune 2015).

Despite a low incidence of adverse effects, paracetamol has a recognised potential for hepatotoxicity and is thought to be responsible for approximately half of all cases of liver failure in the UK (Hawton 2001), and about 40% in the US (Norris 2008). One study, evaluating all cases of acute liver failure leading to registration for transplantation (ALFT) across seven European countries for a three‐year period, showed that paracetamol overdose was responsible for one‐sixth of cases of ALFT, though this varied considerably between each country (Gulmez 2015). Acute paracetamol hepatotoxicity at therapeutic doses has been judged to be extremely unlikely, despite reports of so‐called 'therapeutic misadventure' (Prescott 2000). However, it has been observed that non‐overdose ALFT is more likely to follow therapeutic‐dose paracetamol exposure than similar NSAID exposure (Gulmez 2013). Legislative changes have been introduced in the UK to restrict pack sizes and the maximum number of tablets permitted in nonprescription sales (CSM 1997) on the basis of evidence that poisoning is lower in countries that restrict availability (Gunnell 1997; Hawton 2001). The contribution of these changes, which were inconvenient and costly (particularly to people with chronic pain), to any observed reductions in incidence of liver failure or death, remains uncertain (Bateman 2014a; Bateman 2014b; Hawkins 2007; Hawton 2013). Changes have also been made in the USA, and professionals have been advised not to dispense prescription combination drugs with more than 325 mg paracetamol.

There is a substantial public health impact of paracetamol toxicity. In the USA, incidence proportions peaked at age two years and ages 16 to 18 years (Altyar 2015). There have been concerns over the safety of paracetamol in people with compromised hepatic function (people with severe alcoholism, cirrhosis, or hepatitis), but these have not been substantiated (Dart 2000; PIC 2015).

The use of paracetamol during pregnancy has been questioned following reports that it is linked to behavioural problems and hyperkinetic disorders in children whose mothers took it during pregnancy (Liew 2014), and suggestions that it can interfere with sex hormones (Mazaud‐Guittot 2013).

In an analysis of single‐dose studies in migraine, there was no evidence that adverse events were more common with paracetamol 1000 mg than with placebo, and no serious adverse events occurred with paracetamol alone (Derry 2013b).

Oral paracetamol has long been used as a first‐line analgesic for a variety of acute and chronic conditions. It has modest efficacy in acute pain and migraine (Derry 2013b; Toms 2008), although some randomised trials, systematic reviews, and meta‐analyses have suggested that there is no good evidence for a clinically relevant benefit of paracetamol (as monotherapy) in many chronic pain conditions, such as osteoarthritis and back pain (Machado 2015; Moore 2014a; Williams 2014). There are few or no data for a range of other common painful conditions, including dysmenorrhoea and neck pain. Moreover, accumulating evidence from observational studies indicates a considerable degree of paracetamol toxicity, especially at the upper end of standard analgesic doses (Roberts 2016).

How the intervention might work

The lack of significant anti‐inflammatory activity of paracetamol implies a mode of action distinct from that of NSAIDs; yet, despite years of use and research, the mechanisms of action of paracetamol are not fully understood. NSAIDs act by inhibiting the activity of cyclo‐oxygenase (COX), now recognised to consist of two isoforms (COX‐1 and COX‐2), which catalyses the production of prostaglandins responsible for pain and inflammation. Paracetamol has previously been shown to have no significant effects on COX‐1 or COX‐2 (Schwab 2003), but is now being considered as a selective COX‐2 inhibitor (Hinz 2008). Significant paracetamol‐induced inhibition of prostaglandin production has been demonstrated in tissues in the brain, spleen, and lung (Botting 2000; Flower 1972). A 'COX‐3 hypothesis', wherein the efficacy of paracetamol is attributed to its specific inhibition of a third COX isoform enzyme, COX‐3 (Botting 2000; Chandrasekharan 2002), now has little credibility, and a central mode action of paracetamol is thought to be likely (Graham 2005). Paracetamol metabolism is subject to genetic variation (Zhao 2011).

There is some experimental research in rats to suggest that paracetamol may have an effect in neuropathic pain via cannabinoid receptors (Curros‐Criado 2009; Dani 2007). One single case report suggested intravenous paracetamol was effective for phantom limb pain (Gulcu 2007).

Why it is important to do this review

A previous Cochrane review examined the evidence for NSAIDs or paracetamol, alone or combined with opioids, for cancer pain (McNicol 2015), with the last search date in 2005. There have been few subsequent systematic reviews of the evidence. Nabal (Nabal 2011) examined the evidence for combinations with opioids, finding little evidence. A review of paracetamol and NSAIDs concluded that while the role of these non‐opioid drugs remains controversial, the evidence for paracetamol alone was limited (Mercadante 2013).

The evidence of effectiveness of the WHO pain ladder for cancer has been examined several times in the past two decades. These studies reported varying degrees of success, typically between 20% and 100% of people with cancer pain achieving good relief (Azevedo São Leão Ferreira 2006; Carlson 2016; Jadad 1995), with some suggesting that as many as 50% of people with cancer pain are undertreated (Deandrea 2008).

In many countries, opioids are severely restricted, if available at all. This leaves many people with cancer at the risk of severe life‐limiting pain unless non‐opioid analgesics can be used. This review will inform policy makers such as the WHO on the possible utility of paracetamol to treat cancer‐related pain. It is hoped that the review will inform patients and carers on the value or otherwise of paracetamol in this context.

Other relevant Cochrane reviews include an assessment of codeine alone and with paracetamol (Straube 2014), and an evaluation of tramadol alone and with paracetamol (Wiffen 2017). A number of other reviews have evaluated the evidence for opioids, including buprenorphine (Schmidt‐Hansen 2015a), transdermal fentanyl (Hadley 2013), hydromorphone (Bao 2016), morphine (Wiffen 2016), oxycodone (Schmidt‐Hansen 2015b), and tapentadol (Wiffen 2015).

The standards used to assess evidence in pain trials have changed substantially in recent years, with particular attention being paid to trial duration, withdrawals, and statistical imputation following withdrawal, all of which can substantially alter estimates of efficacy (Moore 2014b). The most important change is the move away from using mean pain scores, or mean change in pain scores, to the number of people who have a large decrease in pain (by at least 50%) (Dworkin 2008; Moore 2013a). Pain intensity reduction of 50% or more correlates with improvements in comorbid symptoms, function, and quality of life generally (Moore 2013b). These standards are set out in the PaPaS author and referee guidance for pain studies of the Cochrane Pain, Palliative and Supportive Care Group (PaPaS 2012).

Three additional issues potentially affect how evidence is evaluated.

The first issue is study size and the overall amount of information available for analysis. There are issues over both random chance effects with small amounts of data, and potential bias in small studies, especially in pain (Dechartes 2013; Dechartres 2014; Fanelli 2017; Moore 1998; Nguyen 2017; Nüesch 2010; Thorlund 2011). Cochrane reviews have been criticised for perhaps overemphasising results of underpowered studies or analyses (AlBalawi 2013; Turner 2013). On the other hand, it may be unethical to ignore potentially important information from small studies or to randomise more participants if a meta‐analysis including small, existing studies provided conclusive evidence. In this review, we have therefore chosen to limit analyses to studies with a minimum of 25 participants randomised per treatment group, which we believe has not been done previously.

The second issue is that of study duration. Previous reviews have examined studies of any duration, even in some cases single‐dose studies, or studies lasting one day or less, often with intravenous or intramuscular formulations (McNicol 2015; Mercadante 2013). While short term studies and non‐oral formulations may have some relevance in some circumstances, they have little relevance to the vast majority of people with cancer pain who will be treated with oral paracetamol over weeks, months, or even years. We have therefore chosen to include only studies with five days' duration or longer.

The third issue is that of comparators. Many cancer pain studies involve direct comparisons of one or more formulations of the same drug, as particularly noted for oral morphine (Wiffen 2016). This type of design has limited importance in evaluating the analgesic contribution of a drug, if that is not already well established (McQuay 2005). For that reason, we have limited this review to the two comparators that speak to the efficacy of paracetamol in cancer pain, namely the comparison of paracetamol versus placebo, and paracetamol plus opioid versus the same dose of opioid alone. The latter comparison would be similar to methods used for determining dose‐response of analgesics in acute pain (McQuay 2007), or caffeine as an analgesic adjuvant in acute pain (Derry 2014).

Objectives

To assess the efficacy of oral paracetamol (acetaminophen) for cancer pain in adults and children, and the adverse events associated with its use in clinical trials.

Methods

Criteria for considering studies for this review

Types of studies

To be included, studies must:

  • be randomised (described as 'randomised' anywhere in the manuscript);

  • ideally be double‐blind, but we will include single‐blind or open studies because we expect there to be a limited literature on this important topic, and we desire to be as inclusive as possible;

  • ideally include a minimum of 25 participants randomised per treatment arm; for crossover studies this will mean a minimum of 25 participants at the initial randomisation.

  • have a study duration of at least five days of continuous treatment, with outcomes reported at the end of that period.

We will exclude non‐randomised studies, studies of experimental pain, case reports and clinical observations. Studies have to be fully published or available as extended abstracts (e.g. from clinical trial websites); we will exclude short (usually conference) abstracts as these are often unreliable (PaPaS 2012).

Types of participants

Studies will include adults or children who experience cancer pain.

Types of interventions

Orally administered paracetamol for cancer pain where paracetamol is the only analgesic intervention, or where oral paracetamol is tested in combination with any opioid compared with the same dose of opioid alone.

Types of outcome measures

Pain must be measured using a validated assessment tool. For pain intensity, for example, this could be a 100 mm visual analogue scale (VAS) or 11‐point numerical rating scale (no pain to worst pain imaginable), or a 4‐point categorical scale (none, mild, moderate, severe), and for pain relief, for example, this could be a 100 mm VAS (no relief to complete relief), or 5‐point categorical scale (none, a little, some, a lot, complete or words to that effect). Measures of 30% or greater (moderate) and 50% or greater (substantial) reduction of pain over baseline are recommended outcomes for chronic pain studies from the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) (Dworkin 2008).

When considering Patient Global Impression of Change (PGIC), 30% or greater reduction of pain over baseline equates to much improved and very much improved, and 50% or greater reduction of pain over baseline equates to very much improved. We will also use results equivalent to no pain or mild pain, because these are also outcomes acceptable to people with various types of pain (Moore 2013a).

Primary outcomes

  • Number of participants with pain reduction of 50% or greater from baseline.

  • Number of participants with pain reduction of 30% or greater from baseline.

  • Number of participants with pain no worse than mild (Moore 2013a).

  • Number of participants with PGIC much improved or very much improved (or equivalent wording).

Secondary outcomes

  • Quality of life.

  • Use of rescue medication.

  • Participant satisfaction or preference.

  • Serious adverse events, including death.

  • Other adverse events, particularly reports of effects of treatment on somnolence, appetite, or thirst (Wiffen 2014).

  • Withdrawals due to lack of efficacy, adverse events, or any cause.

Search methods for identification of studies

Electronic searches

We will search the following databases without language or date restrictions.

  • The Cochrane Central Register of Controlled Trials (CENTRAL) (via CRSO).

  • MEDLINE (via Ovid).

  • Embase (via Ovid).

We will use a combination of MeSH or equivalent and text word terms and tailor search strategies to individual databases. The search strategy for MEDLINE is in Appendix 1.

Searching other resources

We will search the metaRegister of controlled trials in ClinicalTrials.gov (www.clinicaltrials.gov) and the WHO International Clinical Trials Registry Platform (ICTRP) (http://apps.who.int/trialsearch/) for ongoing trials. In addition, we will check reference lists of reviews and retrieved articles for additional studies and perform citation searches on key articles. We do not plan to contact authors for additional information.

Data collection and analysis

Selection of studies

Two review authors (RAM, SD) will independently read the abstract of each study identified by the search, eliminate studies that clearly do not satisfy inclusion criteria, and obtain full copies of the remaining studies. Two review authors (RAM, SD) will read these studies independently to select relevant studies for inclusion. In the event of disagreement, a third review author (PW) will adjudicate. We will not anonymise the studies before assessment. We will include a PRISMA flow chart in the review to show the status of identified studies as recommended in Section 11.2.1 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will include studies in the review, irrespective of whether measured outcome data are reported in a 'usable' way.

Data extraction and management

Two review authors (RAM, SD) will independently extract data using a standard form and check for agreement before entry into Review Manager (RevMan) (RevMan 2014). We will include information about the number of participants treated and demographic details, type of cancer, drug and dosing regimen, study design (placebo or active control) and methods, study duration and follow‐up, analgesic outcome measures and results, withdrawals and adverse events. We will collate multiple reports of the same study, so that each study rather than each report is the unit of interest in the review. We will collect characteristics of the included studies in sufficient detail to complete a 'Characteristics of included studies' table.

Assessment of risk of bias in included studies

Two review authors (RAM, SD) will independently assess risk of bias for each study, using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Chapter 8, Higgins 2011) and adapted from those used by the Cochrane Pregnancy and Childbirth Group, with any disagreements resolved by discussion. We will complete a 'risk of bias' table for each included study using the 'risk of bias' tool in RevMan (RevMan 2014).

We will assess the following for each study.

  • Random sequence generation (checking for possible selection bias). We will assess the method used to generate the allocation sequence as: low risk of bias (any truly random process, e.g. random number table; computer random number generator); unclear risk of bias (method used to generate sequence not clearly stated). We will exclude studies using a non‐random process (e.g. odd or even date of birth; hospital or clinic record number).

  • Allocation concealment (checking for possible selection bias). The method used to conceal allocation to interventions prior to assignment determines whether intervention allocation could have been foreseen in advance of, or during, recruitment or changed after assignment. We will assess the methods as: low risk of bias (e.g. telephone or central randomisation; consecutively numbered sealed opaque envelopes); unclear risk of bias (method not clearly stated); high risk of bias (studies that do not conceal allocation (e.g. open list).

  • Blinding of participants and personnel (checking for possible performance bias). We will assess the methods used to blind study participants and personnel from knowledge of which intervention a participant received. We will assess methods as: low risk of bias (study states that it was blinded and describes the method used to achieve blinding, such as identical tablets matched in appearance or smell, or a double‐dummy technique); unclear risk of bias (study states that it was blinded but does not provide an adequate description of how it was achieved); high risk of bias (study participants or personnel, or both, not blinded).

  • Blinding of outcome assessment (checking for possible detection bias). We will assess the methods used to blind study participants and outcome assessors from knowledge of which intervention a participant received. We will assess the methods as: low risk of bias (study has a clear statement that outcome assessors were unaware of treatment allocation, and ideally describes how this was achieved); unclear risk of bias (study states that outcome assessors were blind to treatment allocation but lacks a clear statement on how it was achieved); high risk of bias (outcome assessment not blinded).

  • Incomplete outcome data (checking for possible attrition bias due to the amount, nature and handling of incomplete outcome data). We will assess the methods used to deal with incomplete data as: low risk (fewer than 10% of participants did not complete the study or used ‘baseline observation carried forward’ analysis, or both); unclear risk of bias (used 'last observation carried forward' analysis); high risk of bias (used 'completer' analysis).

  • Size of study (checking for possible biases confounded by small size (Dechartes 2013; Dechartres 2014; Moore 1998; Nüesch 2010; Thorlund 2011)). We will assess studies as being at low risk of bias (200 participants or more per treatment arm); unclear risk of bias (50 to 199 participants per treatment arm); high risk of bias (fewer than 50 participants per treatment arm).

Measures of treatment effect

We will use dichotomous data to calculate risk difference (RD) or risk ratio (RR) with 95% confidence intervals (CIs) using a fixed‐effect model, and calculate numbers needed to treat for one additional beneficial outcome (NNT) as the reciprocal of the absolute risk reduction (McQuay 1998). For unwanted effects, the number needed to treat becomes the number needed to treat for one additional harmful outcome (NNH), and is calculated in the same manner.

We will use the following terms to describe adverse outcomes in terms of harm or prevention of harm.

  • When significantly fewer adverse outcomes occur with paracetamol than with control (placebo or active control), we will use the term number needed to treat to prevent one event (NNTp).

  • When significantly more adverse outcomes occur with paracetamol compared with control (placebo or active control) we will use the term number needed to treat for an additional harmful outcome or cause one event (NNH).

We do not plan to use continuous data for the primary outcome because it is inappropriate where there is an underlying skewed distribution, as is usually the case with analgesic response.

Unit of analysis issues

The unit of randomisation will be the individual participant.

Dealing with missing data

We plan to use intention‐to‐treat (ITT) analyses: participants who were randomised, took the study medication, and gave a minimum of one post‐baseline assessment. We will report per‐protocol data in the absence of ITT data.

Assessment of heterogeneity

We will assess statistical heterogeneity using L'Abbé plots, a visual method for assessing differences in results of individual studies (L'Abbé 1987), and by use of the I2 statistic. We anticipate that there may be an effect of differences between participants, environment (inpatient versus outpatient), and outcome measures. We plan to explore these with subgroup and sensitivity analyses where there are sufficient data, but recognise the difficulties of assessing heterogeneity with small numbers of small studies (Gavaghan 2000; IntHout 2015).

Assessment of reporting biases

We aim to use dichotomous data of known utility (Moore 2010; Moore 2013a). The review will not depend on what authors of the original studies chose to report or not.

We will undertake an assessment of publication bias if there are sufficient data for meta‐analysis, using a method designed to detect the amount of unpublished data with a null effect required to make any result clinically irrelevant (usually taken to mean an NNT of 10 or higher) (Moore 2008).

Data synthesis

If data are sufficient, we will undertake a quantitative synthesis and present data in forest plots. In the event of substantial clinical heterogeneity, we will switch off the totals in the forest plots.

  • We will undertake a meta‐analysis only if participants, interventions, comparisons, and outcomes are judged to be sufficiently similar to ensure an answer that is clinically meaningful.

  • We will undertake a meta‐analysis only where there are data from at least two studies and 200 participants for analysis.

  • We will use RevMan for meta‐analysis (RevMan 2014) and Excel for NNT and NNH.

Quality of the evidence

We will use the GRADE system to assess the quality of the evidence related to the key outcomes listed in Types of outcome measures, as appropriate (Appendix 2). Two review authors (RAM, SD) will independently rate the quality of each outcome.

We will pay particular attention to inconsistency, where point estimates vary widely across studies or confidence intervals (CIs) of studies show minimal or no overlap (Guyatt 2011), and to the potential for publication bias, based on the amount of unpublished data required to make the result clinically irrelevant (Moore 2008).

In addition, there may be circumstances where the overall rating for a particular outcome needs to be adjusted as recommended by GRADE guidelines (Guyatt 2013a). For example, if there are so few data that the results are highly susceptible to the random play of chance, or if a study uses last observation carried forward (LOCF) imputation in circumstances where there are substantial differences in adverse event withdrawals, one would have no confidence in the result, and would need to downgrade the quality of the evidence by three levels, to very low quality. In circumstances where there are no data reported for an outcome, we will report the level of evidence as very low quality (Guyatt 2013b).

In addition, we are aware that many Cochrane reviews are based largely or wholly on small underpowered studies, and that there is a danger of making conclusive assessments of evidence based on inadequate information (AlBalawi 2013; Brok 2009; Roberts 2015; Turner 2013).

'Summary of findings' table

We will include a 'Summary of findings' table as set out in the Pain, Palliative and Supportive Care Review Group author guide (PaPaS 2012) and recommended in Chapter 4.6.6 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We plan to include 'Summary of findings' table(s) to present the main findings in a transparent and simple tabular format. We will include key information concerning the quality of evidence, the magnitude of effect of the interventions examined, the sum of available data on the outcomes of at least 30% and at least 50% pain relief, and adverse events.

For the 'Summary of findings' table we will use the following descriptors for levels of evidence (EPOC 2015):

High:

This research provides a very good indication of the likely effect. The likelihood that the effect will be substantially different† is low.

Moderate:

This research provides a good indication of the likely effect. The likelihood that the effect will be substantially different† is moderate.

Low:

This research provides some indication of the likely effect. However, the likelihood that it will be substantially different† is high.

Very low:

This research does not provide a reliable indication of the likely effect. The likelihood that the effect will be substantially different† is very high.

† Substantially different: a large enough difference that it might affect a decision.

Subgroup analysis and investigation of heterogeneity

We plan several possible subgroup analyses, depending on the availability of data.

  1. Because we expect that many studies will have a cross‐over design that can impede meta‐analysis (Elbourne 2002), we plan to examine cross‐over and parallel‐group designs separately.

  2. We will investigate whether subgroup analysis by dose of paracetamol (up to 2000 mg/day, 2001 to 4000 mg/day) is possible.

  3. We will analyse separately studies with paracetamol alone, and paracetamol combined with opioid. These studies may also reflect different levels of initial pain intensity.

We plan no other subgroup analyses because the data are expected to be sparse, with small numbers of small trials.

Sensitivity analysis

We do not plan any sensitivity analyses because the data are expected to be sparse, with small numbers of small trials.

Acknowledgements

The National Institute for Health Research (NIHR) is the largest single funder of the Cochrane Pain, Palliative and Supportive Care Review Group. Disclaimer: The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the NIHR, National Health Service (NHS), or the Department of Health.

Institutional support was provided by the Oxford Pain Relief Trust.

Appendices

Appendix 1. Search strategy for MEDLINE (via Ovid)

  1. Acetaminophen/

  2. (acetaminophen or paracetamol or Panadol or Tylenol).mp.

  3. 1 or 2

  4. exp Neoplasms/

  5. (neoplasm* or malignan* or tumour* or tumor* or cancer* or carcinoma*).mp.

  6. 4 or 5

  7. exp Pain/

  8. (pain* or nocicept* or neuropath*).mp.

  9. 7 or 8

  10. randomized controlled trial.pt.

  11. controlled clinical trial.pt.

  12. randomized.ab.

  13. placebo.ab.

  14. drug therapy.fs.

  15. randomly.ab.

  16. trial.ab.

  17. 10 or 11 or 12 or 13 or 14 or 15 or 16

  18. 3 and 6 and 9 and 17

Appendix 2. GRADE: criteria for assigning grade of evidence

The GRADE system uses the following criteria for assigning a quality level to a body of evidence (Chapter 12, Higgins 2011).

  1. High: randomised trials; or double‐upgraded observational studies.

  2. Moderate: downgraded randomised trials; or upgraded observational studies.

  3. Low: double‐downgraded randomised trials; or observational studies.

  4. Very low: triple‐downgraded randomised trials; or downgraded observational studies; or case series/case reports.

Factors that may decrease the quality level of a body of evidence are:

  1. limitations in the design and implementation of available studies suggesting high likelihood of bias;

  2. indirectness of evidence (indirect population, intervention, control, outcomes);

  3. unexplained heterogeneity or inconsistency of results (including problems with subgroup analyses);

  4. imprecision of results (wide confidence intervals);

  5. high probability of publication bias.

Factors that may increase the quality level of a body of evidence are:

  1. large magnitude of effect;

  2. all plausible confounding would reduce a demonstrated effect or suggest a spurious effect when results show no effect;

  3. dose‐response gradient.

Contributions of authors

RAM, SD, and PW drafted the initial protocol, and other authors commented on that draft to produce the final version.

Sources of support

Internal sources

  • Oxford Pain Relief Trust, UK.

    General institutional support

External sources

  • The National Institute for Health Research (NIHR), UK.

    NIHR Cochrane Programme Grant: 13/89/29 ‐ Addressing the unmet need of chronic pain: providing the evidence for treatments of pain

Declarations of interest

PW: none known.

SD: none known.

RAM has received grant support from Grünenthal relating to individual patient‐level analyses of trial data regarding tapentadol in osteoarthritis and back pain (2015). He has received honoraria for attending boards with Menarini concerning methods of analgesic trial design (2014), with Novartis (2014) about the design of network meta‐analyses, and RB on understanding pharmacokinetics of drug uptake (2015). He has received honoraria from Omega Pharma (2016) and Futura Pharma (2016) for providing advice on trial and data analysis methods.

EDM: none known.

RFB: none known.

DBC: none known; DBC is a specialised pain physician and has managed patients with cancer pain.

MM: none known.

BW: none known. BW is a specialist Palliative Medicine Consultant physician and manages patients with advanced life‐threatening illnesses, including cancer.

New

References

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