Interleukin inhibitors for psoriatic arthritis

  • New
  • Protocol
  • Intervention

Authors


Abstract

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

To evaluate benefits and harms of interleukin inhibitors for the treatment of psoriatic arthritis (PsA).

Background

Description of the condition

Psoriatic arthritis (PsA) is an autoimmune disease which affects the musculoskeletal system (Gladman 1987). The prevalence of PsA based on a study from Olmsted County, Minnesota, USA was 101 per 100,000 or roughly 0.1% (Helmick 2008; Shbeeb 2000); and it affects approximately 30% of patients with psoriasis (Mease 2013). Studies have shown that prevalence of PsA varies according to age and geographical location, but not with gender (Parisi 2013). PsA is most prevalent in Europe (0.19%) and least in the Middle East (0.01%) (Stolwijk 2016).

PsA results in various musculoskeletal manifestations such as arthritis, enthesitis, distal interphalangeal joint and nail involvement, dactylitis (sausage digits), and axial involvement (Haroon 2012). PsA is diagnosed based on clinical and radiologic features. CASPAR criteria (Classification for PsoriaticArthritis) are used for PsA classification and diagnosis (Taylor 2006). To meet CASPAR criteria, patients must have an inflammatory articular disease with at least three of the following five: current psoriasis or personal or family history of psoriasis; psoriatic nail dystrophy; negative test for rheumatoid factor; dactylitis; radiological evidence of juxta-articular new bone formation.

Studies have shown that polyarticular disease (five or more swollen joints) predicts future deformities and erosion as compared to oligoarthritis (fewer than four joints) despite the use of multiple therapeutic modalities (Gladman 1990, Queiro-Silva 2003). PsA impacts on quality of life: Patients experience role limitation due to pain, stiffness, and emotional problems (Husted 2001; Zachariae 2002). Mortality in PsA is dependent on the severity of arthritis. Patients with severe PsA (high erythrocyte sedimentation rate and radiologic damage) are at increased risk of mortality, whereas patients with mild disease activity have the same risk as the general population (Gladman 2008). Studies have shown higher incidence and prevalence of cardiovascular disease in PsA as compared to the general population (Horreau 2013; Husted 2011). Indeed cardiovascular disease is the leading cause of mortality in patients with PsA (Arumugam 2012; Buckley 2010; Gladman 2008).

Description of the intervention

Treatment of patients with psoriatic arthritis is frequently done with non-steroidal anti-inflammatory drugs (NSAIDs) and disease-modifying anti-rheumatic drugs (DMARDs). NSAIDs are mainly used to treat pain and inflammation (Bruce 1998, Cuellar 1994). DMARDs are used when multiple joints are involved, when NSAIDs fail to relieve symptoms, or when the radiographic changes are evident. DMARDs improve signs and symptoms of psoriatic arthritis including mobility and function (Soriono 2012). DMARDs include sulphasalazine, methotrexate, leflunomide, and gold (Marguerie 2002). In one systematic review, it was found that high-dose methotrexate and sulphasalazine are the only two agents with demonstrated benefit in PsA (Jones 2000). Drugs such as retinoic acid derivative, photochemotherapy with psoralen plus ultraviolet A (PUVA), methotrexate, and cyclosporine A have shown improvement in both skin and joint involvement (Bruce 1998; Cuellar 1994; Goodfield 1994; Griffiths 1997; Willkens 1984). However, none of these medications have been shown to retard or prevent radiographic joint damage.

Another modality of treatment is tumor necrosis factor-alpha inhibitors. This class of drugs includes adalimumab, etanercept, infliximab, and also new agents like certolizumab pegol and golimumab (Mozaffari 2014). Several clinical trials and studies have proven the benefit of TNF-alpha inhibitors in treating PsA (Heiberg 2007; Saad 2008). Regarding the benefit and adverse effect profile, it seems that adalimumab, etanercept, golimumab, and infliximab are equally beneficial (Fénix-Caballero 2013). Despite their proven benefit in PsA, a number of patients with psoriatic arthritis treated with TNF-alpha inhibitors fail to respond, some develop resistance and some have serious adverse effects (Davari 2014), though they rarely cause the development of unusual side effects such as vasculitis (Gaudio 2013; Hemmati 2013). There is, therefore, a need for therapeutic agents with a different mechanism of action, a better adverse effects profile and an ability to prevent progression of joint damage.

How the intervention might work

New drugs for PsA are emerging which act on different pathways than tumor necrosis factor-alpha (TNF-alpha). Several review articles explain these pathways of inflammation and bone destruction and their significance in pathogenesis of psoriatic arthritis (Olivieri 2010; Sheane 2014). Recent reviews have provided details of the significance of interleukin-23 (IL-23) pathway and induction and regulation of type 17 T-helper cells in the pathogenesis of PsA (Kirkham 2014; Mease 2015; Nograles 2009). Hence, targeting IL-23 and other interleukin pathways can help in modifying the progression of the disease. One such drug is ustekinumab, a monoclonal antibody to the shared p40 subunit of interleukin-12 and interleukin-23, which interferes with receptor binding to immune cells (McKeage 2014). Ustekinumab is a recently developed agent belonging to the group of anti-interleukin-12p40 antibodies that is beneficial in the treatment of both skin and joint manifestation in patients with PsA (Gan 2013). There are several other interleukin inhibitors at various phases of development and some have already been proven beneficial in managing PsA by various trials (Mease 2015).

Why it is important to do this review

Interleukin inhibitors are new treatment options for PsA (Mease 2015). Several interleukin inhibitors have been approved for use in PsA by the US Food and Drug Administration (FDA) and by the European Medicines Agency (EMEA). Systematic reviews of therapies for PsA, including IL inhibitors, have been published but there is no systematic review using Cochrane methodology. This review will be conducted according to the guidelines recommended by the Cochrane Musculoskeletal Group Editorial Board (Ghogomu 2014).

Objectives

To evaluate benefits and harms of interleukin inhibitors for the treatment of psoriatic arthritis (PsA).

Methods

Criteria for considering studies for this review

Types of studies

We will include randomized clinical trials (RCTs) reported as full text, those published as abstracts only and unpublished data. There will be no language restrictions.

Types of participants

We will include patients older than 18 years with a confirmed clinical diagnosis of psoriatic arthritis (including Moll and Wright (Moll 1973) or CASPAR criteria (Taylor 2006)). We will different comparison for patients who are DMARDs- and biologics-experienced (i.e. unsuccessfully treated with DMARDs or unsuccessfully treated with biologics), or naive (i.e. have never been exposed to DMARDs or biologics).

Types of interventions

We will include trials comparing:

  • interleukin inhibitors approved by the FDA including (but not limited to) ustekinumab, secukinumab, and ixekizumab versus placebo;

  • interleukin inhibitors either in combination with or without other disease-modifying anti-rheumatic drugs (DMARDs) versus DMARDs or placebo;

  • interleukin inhibitors in combination with other biologics versus other biologics alone;

  • one interleukin inhibitor versus another interleukin inhibitor.

We will consider all doses for analysis but prioritize the recommended or approved dose. For analyses of different time points we will group analyses based on duration of treatment in three groups: short duration (six months or less), intermediate duration (more than six months to 12 months); or longer duration (longer than 12 months). We will not pool biologics in combinations with biologic monotherapy; for example, we will not pool a trial comparing interleukin inhibitor plus conventional DMARD versus placebo with interleukin inhibitor versus placebo.

Types of outcome measures

Major Outcomes

Major outcomes will be based on 2006 and 2016 Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)-Outcome Measures in Rheumatology (OMERACT) PsA Core Domain Sets (Ogdie 2017; Orbai 2016; Orbai 2017).

  • Criteria for Improvement: American College of Rheumatology 20% and 50% response criteria (ACR 20 and ACR 50) defined as 20% and 50% improvement in both tender and swollen joint counts, and improvement in three of the following five variables: patient global assessment; physician global assessments; pain scores; Health Assessment Questionnaire (HAQ) score; and acute phase reactants (Erythrocyte Sedimentation Rate (ESR) or C-Reactive Protein (CRP) (Chung 2006; Felson 1995)).

  • Function, measured by:

    • Health Assessment Questionnaire (HAQ) score or modified HAQ calculated as score changes (Fries 1980; Pincus 1983);

    • the proportion of people achieving minimally clinically important difference on HAQ ≥ 0.35 (Mease 2004).

  • Quality of Life: measured by validated instruments including Short Form 36 (SF-36) (i.e. continuous data, 8 domains; and two summary score, physical and mental component summary) (Ware 2001).

  • Psoriatic area and severity index: body is divided into four sections: head (H) (10%), arms (A) (20%), trunk (T) (30%), legs (L) (40%); for each, percentage of area involved is estimated and graded: 50% and 75% improvement in Psoriasis Area and Severity Index (PASI) is referred to as achievement of PASI50 & PASI75 respectively (Carlin 2004; Langley 2004).

  • Fatigue: measured by PsA-specific quality of life (PsAQoL) instrument (McKenna 2004); fatigue visual analogue scale (VAS ≥ 20 mm is clinically relevant and ≥ 50 mm is high); or by Short Form 36 (Pollard 2006).

  • Serious adverse events (SAEs): total number or the number of patients with ≥ 1 serious adverse event.

  • Withdrawals due to adverse events.

Minor Outcomes

  • Disease activity, measured by:

  • American College of Rheumatology 70% response criteria (ACR70) defined as 70% improvement in variables defined above under major outcome (Felson 1995).

  • Dactylitis defined as swelling within the digit that extends beyond the borders of joints. It is measured by dactylitis score sheet and by Leeds Dactylitis instrument (Helliwell 2005).

  • Enthesitis refers to inflammation localized to insertion of ligaments, tendon and other fibrous structure into bone, measured by Leeds Enthesitis Index, Newcastle Enthesitis Index (NEI), Massstricht AS Enthesitis Score (MASES), the Spondyloarthritis Research Consortium of Canada Index (SPARCC) or a simple enthesitis measure evaluating only the Achilles tendon and plantar fascia (Gladman 2004; Healy 2008; Heuft-Dorenbosch 2003; Mander 1987).

  • Dermatology Life quality index (DLQI) is a simple 10-question validated questionnaire used to evaluate the quality of life in dermatologic diseases. It is calculated by summing the score of each question. The score ranges from 0 to 30 (Finlay 1994).

  • Total adverse events.

  • Total withdrawals.

  • Withdrawals due to no benefit.

  • Death.

Search methods for identification of studies

Electronic searches

We will search the following databases from their inception to the present. We will impose no restriction on the language. The search strategy is provided in Appendix 1.

  • 1. The Cochrane Central Register of Controlled Trials (CENTRAL; in the Cochrane Library), Wiley InterScience (www.thecochranelibrary.com)

  • 2. MEDLINE Ovid

  • 3. Embase Ovid

  • 4. CINAHL (Cumulative Index to Nursing and Allied Health Literature)

  • 5. Science Citation Index (Web of Science)

  • 6. Current controlled trials (www.controlled-trials.com)

For the assessment of adverse effects, we will search the websites of regulatory agencies: US Food and Drug Administration-MedWatch, European Medicines Evaluation Agency, Australian Adverse Drug Reactions Bulletin, UK Medicines and Healthcare products Regulatory Agency (MHRA) pharmacovigilance and drug safety updates.

We will also conduct a search of www.ClinicalTrials.gov and the WHO trials portal (www.who.int/ictrp/en).

Searching other resources

We will check the reference lists of all primary studies and review articles for additional references. We will also contact authors of relevant papers regarding any unpublished work. We will search relevant manufacturers' web sites for trial information. We will also contact pharmaceutical companies regarding any published or unpublished data.

We will search for errata or retractions from included studies published in full text on PubMed (www.ncbi.nlm.nih.gov/pubmed) and report the date this was done within the review.

Data collection and analysis

Selection of studies

Two review authors (GS and ASM) will independently screen titles and abstracts for the inclusion of all the potential studies we identify as a result of the search and code them as 'retrieve' (eligible or potentially eligible/unclear) or 'do not retrieve'. We will retrieve the full-text study reports/publication and two review authors (GS and ASM) will independently screen the full text and identify studies for inclusion, and identify and record reasons for exclusion of the ineligible studies. We will resolve any disagreement through discussion and consultation with the senior author (JAS). We will identify and exclude duplicates and 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 record the selection process in sufficient detail to complete a PRISMA flow diagram (The PRISMA Group 2009).

Data extraction and management

We will use EndNote X7 software to manage the records retrieved from searches of electronic databases. We will use a data collection form for study characteristics and outcome data. One review author (GS) will extract study characteristics from included studies. A second review author (ASM) will spot-check study characteristics for accuracy against the trial report. We will extract the following study characteristics.

  • Methods: study design, the total duration of study, details of any 'run-in' period, the number of study centers and location, study setting, withdrawals, and date of study.

  • Participants: number, mean age, age range, sex, disease duration, severity of condition, diagnostic criteria, important baseline data, inclusion, and exclusion criteria.

  • Interventions: intervention, comparison, concomitant medications, and excluded medications.

  • Outcomes: primary and secondary outcomes specified and collected, and time points reported.

  • Characteristics of the design of the trial as outlined below in the 'Assessment of risk of bias in included studies' section.

  • Notes: funding for the trial, and notable declarations of interest of trial authors.

Two review authors (GS and ASM) will independently extract outcome data from included studies. We will extract the number of events, number of participants per treatment group for dichotomous outcomes' means and standard deviations, and number of participants per treatment group for continuous outcomes. We will note in the 'Characteristics of included studies' table if outcome data were not reported in a usable way and when data were transformed or estimated from a graph. We will resolve disagreements by consensus. If consensus is not reached, a third team member will make the final decision (JAS). One review author (GS) will transfer data to the Review Manager 5 (RevMan 5) file (Review Manager 2014). We will double-check that data is entered correctly by comparing the data presented in the systematic review with the study reports.

If the study provides more than one measure of the outcome including both final and change in values, based on a prior decision rule we will manage as follows.

  • If both final values and change from baseline values are reported for the same outcome we will extract the final value.

  • If both unadjusted and adjusted values for the same outcome are reported, we will extract unadjusted outcome, since most trials present unadjusted results and the randomized trials provide unbiased estimates.

  • If data are analysed based on an intention-to-treat (ITT) sample and another sample (e.g. per-protocol, as-treated), we will use ITT analyses, both for outcomes assessing benefits and outcomes assessing harms.

  • If multiple time points, we will give priority to the final time point in our analysis; we will also analyse additional time points that are reported in other studies and are clinically relevant. For example, if a trial presents 6-month, 12-month, 18-month and 24-month time point data, we will extract 24-month data as well as the time point which the other trials report and which are clinically relevant, such as 6-month and 12-month data, but priority will be given to the final time point (12 months in this case) and that will be included in the 'Summary of findings' table.

Assessment of risk of bias in included studies

Two review authors (GS and ASM) will independently assess the risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a). We will resolve any disagreements by discussion. We will assess the risk of bias according to the following domains.

  1. Random sequence generation.

  2. Allocation concealment.

  3. Blinding of participants and personnel.

  4. Blinding of outcome assessment.

  5. Incomplete outcome data.

  6. Selective outcome reporting.

We will grade each potential source of bias as high, low, or unclear and provide a quote from the study report together with a justification for our judgment in the 'Risk of bias' table. We will summarize the risk of bias judgments across different studies for each of the domains listed. We will consider blinding separately for different key outcomes where necessary (e.g. for unblinded outcome assessment, the risk of bias for all-cause mortality may be different than for a patient-reported outcome (HAQ)). We will consider the impact of missing data on key outcomes.

Where information on the risk of bias relates to unpublished data or correspondence with a trialist, we will note this in the 'Risk of bias' table. When considering treatment effects, we will take into account the risk of bias for the studies that contribute to that outcome. We will present the figures generated by the risk of bias tool to provide summary assessments of the risk of bias.

For cross-over trials we will try to assess the risk of bias based on the following additional questions.

  • Was the use of cross-over design appropriate?

  • Is it clear that order of receiving treatment was randomized?

  • Can it be assumed that the trial was not biased from carry-over effects?

  • Are unbiased data available?

Assessment of bias in conducting the systematic review

We will conduct the review according to this published protocol and report any deviations from it in the 'Differences between protocol and review' section of the systematic review.

Measures of treatment effect

We will analyse dichotomous data as risk ratios or Peto odds ratio when an outcome is a rare event (approximately less than 10%), and use 95% confidence intervals (CI). We will analyse continuous data as mean difference or standardized mean difference, depending on whether the same scale is used to measure an outcome, and 95% CI. We will enter data presented as a scale with a consistent direction of effect across studies.

When different scales are used to measure the same conceptual outcome (e.g. disability), we will calculate standardized mean differences (SMD) instead, with corresponding 95% CI. We will back-translate SMD to a typical scale (e.g. 0 to 10 for pain) by multiplying the SMD by a typical among-person standard deviation (e.g. the standard deviation of the control group at baseline from the most representative trial) (as per Chapter 12 of the Cochrane Handbook for Systematic Reviews of Interventions (Schünemann 2011b))

In the 'Effects of intervention' results section and the 'Comments' column of the 'Summary of findings' table, we will provide the absolute per cent difference, the relative per cent change from baseline, and the number needed to treat for an additional beneficial outcome (NNTB); (the last will be provided only when the outcome shows a statistically significant difference).

For dichotomous outcomes, such as serious adverse events, we will calculate the number needed to treat from the control group event rate and the relative risk using the Visual Rx NNT calculator (Cates 2008). We will calculate the NNTB for continuous measures using the Wells calculator (available at the CMSG Editorial office).

For dichotomous outcomes, we will calculate the absolute risk difference using the Risk Difference statistic in RevMan 5 and express the result as a percentage. For continuous outcomes, we will calculate the absolute benefit as the improvement in the intervention group minus the improvement in the control group, in the original units.

We will calculate the relative per cent change for dichotomous data as the Risk Ratio − 1 and express it as a percentage. For continuous outcomes, we will calculate the relative difference in the change from baseline as the absolute benefit divided by the baseline mean of the control group.

Unit of analysis issues

We will take the unit of analysis as the individual participant and will analyse each treatment group separately. Where multiple trial arms are reported in a single trial, we will include only the relevant arms. If two comparisons (e.g. drug A versus placebo and drug B versus placebo) are combined in the same meta-analysis, we will halve the control group to avoid double-counting.

Dealing with missing data

For any query in studies, we will contact the original investigators or study sponsors to retrieve missing data. If there is a discrepancy in the number randomized and number analysed in each treatment group, we will calculate the percentage lost to follow up in each group and report this information. If dropouts exceed 10% for any trial, we will assign the worse outcome to those lost to follow-up for the dichotomous outcome and assess the impact of this assumption in sensitivity analyses, comparing worse outcome result with the result of patients who completed follow-up. We will not make any assumption about loss of follow-up in continuous data, analysis will be based on those completing the follow-up.

If we make any assumptions and imputations to handle missing data, they will be clearly described and we will explore the effect of imputation by sensitivity analyses.

Assessment of heterogeneity

We will assess clinical and methodological diversity in terms of participants, interventions, outcomes, and study characteristics for the included studies to determine whether a meta-analysis is appropriate. We will conduct this by observing this data from the data extraction tables.

We will assess statistical heterogeneity by visual inspection of the forest plot, to assess for obvious differences in result between the studies and using the I² and Chi² statistical tests.

As recommended in the Cochrane Handbook for Systematic Reviews of Interventions (Deeks 2011), the interpretation of an I² value of 0% to 40% 'might not be important'; 30% to 60% may represent 'moderate' heterogeneity; 50% to 90% may represent 'substantial' heterogeneity; and 75% to 100% represents 'considerable' heterogeneity. As noted in the Cochrane Handbook for Systematic Reviews of Interventions, we will keep in mind that the importance of I² depends on (i) the magnitude and direction of effects and (ii) strength of evidence for heterogeneity.

We will interpret the Chi² test to indicate evidence of statistical heterogeneity where the P value is less than or equal to 0.10.

We will further explore reasons for statistical heterogeneity when I² is greater than 50% (Higgins 2003).

Assessment of reporting biases

We will create and examine a funnel plot to explore possible small-study biases. In interpreting funnel plots, we will examine the different possible reasons for funnel plot asymmetry like publication bias, heterogeneity and poor methodology, and relate this to the results of the review. If we are able to pool more than 10 trials, we will undertake formal statistical tests to investigate funnel plot asymmetry, and will follow the recommendations in section 10.4 of the Cochrane Handbook for Systematic Reviews of Interventions (Sterne 2011).

To assess outcome reporting bias, we will check trial protocols against published reports. For studies published after 1 July 2005, we will screen the Clinical Trials Register at the International Clinical Trials Registry Platform of the World Health Organization for the a priori trial protocol (apps.who.int/trialssearch). We will evaluate whether selective reporting of outcomes is present.

Data synthesis

We will undertake meta-analyses only where this is meaningful i.e. if the treatments, participants, and the underlying clinical question are similar enough for pooling to make sense. We will use a random-effects model over a fixed-effect model, because in the presence of heterogeneity a random-effects model weights the studies relatively more equally than a fixed-effect model. Random-effects meta-analysis produces more accurate effect estimates by avoiding small-study effects which can distort the results of meta-analyses (Higgins 2011b; Nüesch 2010).

We will present separate comparisons based on the following patient populations.

  • Patients who have been unsuccessfully treated with DMARDs (incomplete responders to DMARDs).

  • Patients who have been unsuccessfully treated with biologics (biologics-experienced).

  • Patients who have never been exposed to DMARDs or biologics (DMARDs or biologics naive).

We will restrict the primary analysis to studies with low risk of bias. Studies meeting low risk of bias for random sequence generation, allocation concealment, blinding of participants, outcomes assessors, and incomplete outcome data will be defined as studies with low risk of bias.

In trials with rescue treatment with an interleukin inhibitor in the control group, we will analyse data separately from the trials without rescue treatment. Usually, efficacy data are provided both before and after rescue treatment use, but in the case when a trial only provides data after rescue treatment we will analyse that data separately.

For cross-over trials, we will take all first period measurements from active intervention periods and control intervention periods, and will analyse them as if the trial was a parallel group trial of active intervention versus control.

'Summary of findings' table

We will create a 'Summary of findings' table, in a simple tabular format using the seven major outcomes listed earlier (Types of outcome measures). The comparison in the first 'Summary of findings' table will be interleukin inhibitors versus placebo/DMARDs, primary analysis for presentation will be of unsuccessfully treated with DMARDs with treatment duration between six months to 12 months.

Two people (GS and ASM) will independently assess the quality of the evidence. We will use the five GRADE considerations (study limitations, consistency of effect, imprecision, indirectness, and publication bias) to assess the quality of a body of evidence as it relates to the studies which contribute data to the meta-analyses for the prespecified outcomes. We will use methods and recommendations described in Chapter 8 Section 8.5, 8.7, Chapter 11, and chapter 13 section 13.5 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a; Schünemann 2011a), using GRADEpro software (GRADEproGDT). We will present the results using the table view 3.

We will justify all decisions to downgrade or upgrade the quality of studies using footnotes and we will make comments to aid the reader's understanding of the review where necessary. In the comments column of the 'Summary of findings' table, we will provide the number needed to treat for an additional beneficial outcome (NNTB); the NNTB will be provided only when the outcome shows a statistically significant difference. For dichotomous outcomes, such as serious adverse events, the number needed to treat will be calculated from the control group event rate and the relative risk using the Visual Rx NNT calculator (Cates 2008). The NNTB for continuous measures will be calculated using the Wells calculator (available at the CMSG Editorial office, musculoskeletal.cochrane.org). 

Subgroup analysis and investigation of heterogeneity

We plan to carry out the following subgroup analyses.

  1. By the type of interleukin inhibitor: IL-12/IL-23 inhibitor (ustekinumab) versus IL-17A inhibitor (secukinumab/ixekizumab).

  2. By the dose of interleukin inhibitor (standard dose approved by the regulatory agencies; low or high dose defined as a dose lower or higher than this approved dose).

  3. By length of treatment/trial: short (< 6 months), intermediate (≥ 6 to 12 months), or long duration (> 1 year).

  4. By previous disease-modifying anti-rheumatic drug (DMARD)-failure: DMARD-naive, DMARD-experienced, tumor necrosis factor-alpha inhibitors experienced.

We will use the following outcomes in subgroup analyses.

  1. American College of Rheumatology 50 response criteria (ACR50) defined as 50% improvement in both tender and swollen joint counts and 50% improvement in three of the five following variables: patient global assessment; physician global assessments; pain scores; Health Assessment Questionnaire (HAQ) score; and acute phase reactants (Erythrocyte Sedimentation Rate (ESR) or C-Reactive Protein (CRP) (Chung 2006; Felson 1995).

  2. Function: measured by Health Assessment Questionnaire (HAQ) score or modified HAQ. Score changes will be given priority as an outcome measure (Fries 1980; Pincus 1983) over the proportion achieving minimally clinically important difference on HAQ ≥ 0.22 (Wells 1993).

  3. Quality of Life: measured by validated instruments including Short Form 36 * (SF-36) (i.e. continuous data, 8 domains; and two summary score, physical and mental component summary) (Ware 2001).

  4. Psoriatic area and severity index: body is divided into four sections: head (H) (10%), arms (A) (20%), trunk (T) (30%), legs (L) (40%); for each, percentage of area involved is estimated and graded: 50% and 75% improvement in Psoriasis Area and Severity Index (PASI) is referred to as achievement of PASI50 & PASI75 respectively (Carlin 2004; Langley 2004).

  5. Fatigue: measured by PsA-specific quality of life (PsAQoL) instrument (McKenna 2004), fatigue visual analogue scale (VAS ≥ 20 mm is clinically relevant and ≥ 50 mm is high), or by Short Form 36 (Pollard 2006).

We will use the formal test for subgroup interactions in Review Manager 5 (Review Manager 2014); and will use caution in the interpretation of subgroup analyses as advised in section 9.6 of the Cochrane Handbook for Systematic Reviews of Interventions. The magnitude of the effects will be compared between the subgroups by means of assessing the overlap of the confidence intervals of the summary estimated. Non-overlap of the confidence intervals indicates statistical significance.

Sensitivity analysis

We will perform sensitivity analysis based on the risk of bias in the included studies by limiting analyses to studies with low risk of bias for allocation concealment, random sequence generation, blinding, and missing data (loss of follow-up < 20%). We will also perform sensitivity analysis based on industry funding/sponsorship of trials.

Interpreting results and reaching conclusions

We will follow the guidelines in the Cochrane Handbook for Systematic Reviews of Interventions, chapter 12 (Schünemann 2011b), for interpreting results and will be aware of distinguishing a lack of evidence of effect from a lack of effect. We will base our conclusions only on findings from the quantitative or narrative synthesis of included studies for this review. We will avoid making recommendations for practice and our implications for research will suggest priorities for future research and outline what the remaining uncertainties are in the area.

Acknowledgements

None

Appendices

Appendix 1. Search strategies

 MEDLINE(OVID) search strategy (from inception to the present):

1. exp. arthritis, psoriatic/

2. (arthr$ adj2 psoria$).tw.

3. Psoriasis, arthritis

4. psoriasis, arthritic

5. psoriasis, arthropathica

6. psoriatic, arthropathy

7. psoriatic, arthropathies

8. or/1-7

9. randomized controlled trial.pt.

10. controlled clinical trial.pt.

11. randomized.ab.

12. placebo.ab.

13. clinical trial as topic.sh.

14. randomly.ab.

15. trial.ti.

16. or/9-15

17. exp animals/not humans.sh.

18. 16 not 17

19. Antibodies, monoclonal/tu

20. Ustekinumab.mp.

21. Stelara.tw.

22. CNTO 1275.mp.

23. Antibody, monoclonal, humanized/tu

24. or/17-23

25. Antibody, monoclonal, humanized/tu

26. Secukinumab.mp.

27. AIN 457 .mp.

28. anti interleukin-17A.tw.

29. or/25-28

30. Antibody, monoclonal, humanized/tu

31. Brodalumab.m.p.

32. AMG-827.mp.

33. anti-interleukin-17-receptor.tw.

34. or/30-33

35. antibody, monoclonal, humanized/tu

36. ixekizumab.mp.

37. LY2439821.mp.

38. anti -interleukin-17.tw.

39. or/35-38

40. 8 and 18 and 24 and 29 and 34 and 39

Contributions of authors

  • Title registration: GS, JAS

  • Draft the protocol: GS, JAS

  • Review and edit the protocol: GS, ASM, JAS

Declarations of interest

Gaurav Sharma and Amy S Mudano have no conflicts.

Jasvinder A Singh has received research grants from Takeda and Savient and consultant fees from Savient, Takeda, Regeneron and Allergan. JAS is a member of the executive of OMERACT, an organization that develops outcome measures in rheumatology and receives arms-length funding from 36 companies; a member of the American College of Rheumatology's Guidelines Subcommittee of the Quality of Care Committee; and a member of the Veterans Affairs Rheumatology Field Advisory Committee

Sources of support

Internal sources

  • Birmingham VA Medical Center, USA.

    JAS is supported by resources and facilities at the Birmingham VA Medical Center, Birmingham, Alabama USA.

  • University of Alabama at Birmingham, USA.

    This work is supported by the Network Meta-analysis Cochrane Satellite of the Musculoskeletal Center that is supported by the Division of Rheumatology at the University of Alabama at Birmingham.

External sources

  • No sources of support supplied

Ancillary