Types of studies

We included double‐blind studies of single dose oral diclofenac compared with placebo for the treatment of moderate to severe postoperative pain in adults, with at least 10 participants randomly allocated to each treatment group. We included multiple dose studies if appropriate data from the first dose were available, and cross‐over studies provided that data from the first arm were presented separately.

We excluded:

• review articles, case reports, and clinical observations;

• studies of experimental pain;

• studies where pain relief was assessed only by clinicians, nurses, or carers (not participant‐reported);

• studies of less than four hours' duration or studies that failed to present data over four to six hours postdose.

For postpartum pain, we included studies if the pain investigated was due to episiotomy or Caesarean section irrespective of the presence of uterine cramps; we excluded studies investigating pain due to uterine cramps alone.

Types of participants

We included studies of adult participants (15 years or older) with established postoperative pain of moderate to severe intensity following day surgery or in‐patient surgery. For studies using a visual analogue scale (VAS), we considered that pain intensity of greater than 30 mm equated to pain of at least moderate intensity (Collins 1997).

Types of interventions

Orally administered diclofenac sodium or potassium with matched placebo administered as a single oral dose for postoperative pain.

Types of outcome measures

We collected the following data where available.

• Participant characteristics.

• Participant‐reported pain at baseline (we did not include physician‐, nurse‐, or carer‐reported pain in the analysis).

• Participant‐reported pain relief expressed at least hourly over four to six hours using validated pain scales (pain intensity or pain relief in the form of VAS or categorical scales, or both).

• Patient global assessment of efficacy (PGE), using a standard categorical scale.

• Time to use of rescue medication.

• Number of participants using rescue medication.

• Number of participants with one or more adverse events.

• Number of participants with serious adverse events.

• Number of withdrawals (all‐cause, adverse events).

Electronic searches

We searched the following databases.

• Cochrane Central Register of Controlled Trials (CENTRAL), the Cochrane Library Issue 4, 2008 for the earlier version, and via Cochrane Register of Studies Online (CRSO) to 9 March 2015 for this update.

• MEDLINE (via Ovid) from inception to December 2008 for the earlier versions, and 2008 to 9 March 2015 for this update.

• EMBASE (via Ovid) from inception to December 2008 for the earlier versions, and 2008 to 9 March 2015 for this update.

• The Oxford Pain Relief Database (Jadad 1996a) for the earlier version. This database is no longer being updated.

See Appendix 1 for the CENTRAL search strategy, Appendix 2 for the MEDLINE search strategy, and Appendix 3 for the EMBASE search strategy.

Searching other resources

We searched for additional studies in reference lists of retrieved articles and reviews. We also searched the ClinicalTrials database (www.clinicaltrials.gov) and the WHO International Clinical Trials Registry Platform (apps.who.int/trialsearch/) for otherwise unpublished trial results and information about ongoing studies.

Merck provided details of two unpublished studies for the original review, but we did not contact manufacturers for this update.

Selection of studies

Two review authors independently assessed the search results and agreed on the studies to be included in the review. Disagreements would have been resolved by consensus or referral to a third review author, but this was not necessary.

Data extraction and management

Two review authors extracted data and recorded them on a standard data extraction form. One review author entered data suitable for pooling into RevMan 5.3 (RevMan 2014).

Assessment of risk of bias in included studies

We used the Oxford Quality Score as the basis for study inclusion, limiting inclusion to studies that were randomised and double‐blind as a minimum (Jadad 1996b).

We also completed a 'Risk of bias' table using methods adapted from those described by the Cochrane Pregnancy and Childbirth Group. Two review authors independently assessed risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions, resolving any disagreements by discussion (Higgins 2011). We assessed the following for each study.

1. Random sequence generation (checking for possible selection bias). We assessed the method used to generate the allocation sequence as: low risk of bias (any truly random process: random number table; computer random number generator); unclear risk of bias (method used to generate sequence not clearly stated). We excluded studies using a non‐random process (odd or even date of birth; hospital or clinic record number), which were therefore at high risk of bias.

2. Allocation concealment (checking for possible selection bias). The method used to conceal allocation to interventions before assignment determines whether the intervention allocation could have been foreseen in advance of, or during recruitment, or changed after assignment. We assessed the methods as: low risk of bias (telephone or central randomisation; consecutively numbered, sealed, opaque envelopes); unclear risk of bias (method not clearly stated). We excluded studies that did not conceal allocation (open list), which were therefore at high risk of bias.

3. Blinding of outcome assessment (checking for possible detection bias). We assessed the methods used to blind study participants and outcome assessors from knowledge of which intervention a participant received. We assessed the methods as: low risk of bias (study stated that it was blinded and described the method used to achieve blinding: identical tablets; matched in appearance and smell); unclear risk of bias (study stated that it was blinded but did not provide an adequate description of how blinding was achieved). We excluded studies that were not double‐blind and therefore at high risk of bias.

4. Size (checking for possible biases confounded by small size). Small studies have been shown to overestimate treatment effects, probably because the conduct of small studies is more likely to be less rigorous, allowing critical criteria to be compromised (Dechartres 2013; Nüesch 2010). We considered studies to be at low risk of bias if they had 200 participants or more, at unclear risk if they had 50 to 200 participants, and at high risk of bias if they had fewer than 50 participants.

Measures of treatment effect

We used risk ratio (or relative risk, RR) to establish statistical difference, and numbers needed to treat to benefit (NNT) and pooled percentages as absolute measures of benefit or harm.

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

• When significantly fewer adverse outcomes occurred with treatment than with control (placebo or active), we used the term the number needed to treat to prevent one event (NNTp).

• When significantly more adverse outcomes occurred with treatment compared with control (placebo or active), we used the term the number needed to treat to harm or cause one event (NNH).

Unit of analysis issues

We accepted only randomisation of the individual participant.

Dealing with missing data

The only likely issue with missing data in these studies was from imputation using last observation carried forward when a patient requested rescue medication. We have previously shown that this does not affect results for up to six hours after taking study medication (Moore 2005).

Assessment of heterogeneity

We examined heterogeneity using L'Abbé plots (L'Abbé 1987), a visual method for assessing differences in results of individual studies.

Assessment of reporting biases

We assessed the number of trials of average size amongst the included studies, with a RR of 1 (no effect), that would be needed to reduce any statistically significant result to one that fails to meet statistical significance (following Moore 2008).

Data synthesis

For efficacy analyses we used the number of participants in each treatment group who were randomised, received medication, and provided at least one post‐baseline assessment. For safety analyses we used the number of participants randomised to each treatment group who took the study medication. We analysed results for different doses separately.

For each study we converted the mean total pain relief (TOTPAR), summed pain intensity difference (SPID), VAS TOTPAR, or VAS SPID values for the active and placebo groups to %maxTOTPAR or %maxSPID by division into the calculated maximum value (see Appendix 4; Cooper 1991). We then calculated the proportion of participants in each treatment group who achieved at least 50%maxTOTPAR using verified equations (Moore 1997a; Moore 1997b; Moore 1997b). We converted these proportions into the number of participants achieving at least 50%maxTOTPAR by multiplying by the total number of participants in the treatment group. We used this information on the number of participants with at least 50%maxTOTPAR for active and placebo groups to calculate RR and NNT.

We accepted the following pain measures for the calculation of TOTPAR or SPID (in order of priority; see Appendix 4).

• Five‐point categorical pain relief scales with comparable wording to 'none, slight, moderate, good, or complete'.

• Four‐point categorical pain intensity scales with comparable wording to 'none, mild, moderate, severe'.

• VAS for pain relief.

• VAS for pain intensity.

If none of these measures was available, we used the number of participants reporting 'very good or excellent' on a five‐point categorical global scale with the wording 'poor, fair, good, very good, excellent' for the number of participants achieving at least 50% pain relief (Collins 2001).

For each treatment group we extracted the number of participants reporting treatment‐emergent adverse effects and calculated relative benefit and risk estimates with 95% confidence intervals (CI) using a fixed‐effect model (Morris 1995). We calculated NNT and NNH with 95% CIs from the pooled number of events using the method of Cook and Sackett (Cook 1995). We assumed a statistically significant difference from control when the 95% CI of the RR did not include the number one.

Subgroup analysis and investigation of heterogeneity

We planned to report results according to formulation, and within a formulation the dose of diclofenac. The three formulations used were:

1. any fast‐acting formulation (gel capsule, effervescent or other solution, or other formulation declared to be fast‐acting). Both salts were allowed for this analysis;

2. diclofenac potassium; and

3. diclofenac sodium.

In this update, we include any formulation tested.

A minimum of two studies and 200 participants had to be available in any subgroup analysis (Moore 1998), which was restricted to the primary outcome (50% pain relief over 4 to 6 hours) and the dose with the greatest amount of data. We determined significant differences between NNT, NNTp, or NNH for different groups in subgroup and sensitivity analyses using the z test (Tramèr 1997).

Sensitivity analysis

No sensitivity analyses were planned.