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Intervenciones farmacológicas para la colangitis esclerosante primaria

Appendices

Appendix 1. Methods for network meta‐analysis if we find this is possible in the future

Measures of treatment effect

Relative treatment effects

For dichotomous variables (e.g. proportion of participants with serious adverse events or any adverse events), we will calculate the odds ratio with 95% credible interval (or Bayesian confidence interval) (Severini 1993). For continuous variables (e.g. quality of life reported on the same scale), we will calculate the mean difference with 95% credible interval. We will use standardised mean difference values with 95% credible interval for quality of life if included trials use different scales. For count outcomes (e.g. numbers of adverse events and serious adverse events), we will calculate the rate ratio with 95% credible interval. For time‐to‐event data (e.g. mortality at maximal follow‐up), we will calculate hazard ratio with 95% credible interval.

Relative ranking

We will estimate ranking probabilities for all treatments of being at each possible rank for each intervention. Then, we will obtain the surface under the cumulative ranking curve (SUCRA) (cumulative probability) and rankogram (Salanti 2011; Chaimani 2013).

Unit of analysis issues

We will collect data for all trial treatment groups that meet the inclusion criteria. The codes for analysis that we will use account for the correlation between effect sizes from trials with more than two groups.

Assessment of heterogeneity

We will assess clinical and methodological heterogeneity by carefully examining the characteristics and design of included trials. We will assess the presence of clinical heterogeneity by comparing effect estimates under different categories of potential effect modifiers. Different study designs and risk of bias may contribute to methodological heterogeneity.

We will assess the statistical heterogeneity by comparing results of the fixed‐effect model meta‐analysis and the random‐effects model meta‐analysis, and between‐study standard deviation (tau2 and comparing this with values reported in the study of the distribution of between‐study heterogeneity (Turner 2012)), and by calculating I2 (using Stata/SE 14.2). If we identify substantial heterogeneity ‐ clinical, methodological, or statistical ‐ we will explore and address heterogeneity in a subgroup analysis (see ‘Subgroup analysis and investigation of heterogeneity for network meta‐analysis’ section).

Assessment of transitivity across treatment comparisons

We will evaluate the plausibility of the transitivity assumption (the assumption that participants included in different studies with different immunosuppressive regimens can be considered part of a multi‐arm randomised clinical trial and could potentially have been randomised to any treatment) (Salanti 2012). In other words, any participant who meets the inclusion criteria is, in principle, equally likely to be randomised to any of the above eligible interventions. If we have any concern that clinical safety and effectiveness are dependent upon effect modifiers, we will continue to do traditional Cochrane pair‐wise comparisons and will not perform a network meta‐analysis on all participant subgroups.

Assessment of reporting biases

For the network meta‐analysis, we will judge reporting bias by completeness of the search (i.e. searching various databases and including conference abstracts), as we do not currently find any meaningful order to performing a comparison‐adjusted funnel plot, as suggested by Chaimani 2012. However, if we find any meaningful order, for example, the control group used depended upon the year of conduct of the trial, we will use a comparison‐adjusted funnel plot, as suggested by Chaimani 2012.

Data synthesis

Methods for indirect and mixed comparisons

We will conduct network meta‐analyses to compare multiple interventions simultaneously for each of the primary and secondary outcomes. Network meta‐analysis combines direct evidence within trials and indirect evidence across trials (Mills 2012). We will obtain a network plot to ensure that trials were connected by treatments using Stata/SE 14.2 (Chaimani 2013). The network plot for mortality at maximal follow‐up for this review is presented in Figure 5. We will exclude any trials that were not connected to the network. We will conduct a Bayesian network meta‐analysis using the Markov chain Monte Carlo method in OpenBUGS 3.2.3, as per guidance from the National Institute for Health and Care Excellence (NICE) Decision Support Unit (DSU) documents (Dias 2014a). We will model treatment contrast (i.e. log odds ratio for binary outcomes, mean difference or standardised mean difference for continuous outcomes, log rate ratio for count outcomes, and log hazard ratio for time‐to‐event outcomes) for any two interventions ('functional parameters') as a function of comparisons between each individual intervention and an arbitrarily selected reference group ('basic parameters') (Lu 2006) using appropriate likelihood functions and links. We will use binomial likelihood and logit link for binary outcomes, Poisson likelihood and log link for count outcomes, binomial likelihood and complementary log‐log link for time‐to‐event outcomes, and normal likelihood and identity link for continuous outcomes. We will apply a fixed‐effect model and a random‐effects model for the network meta‐analysis. We will report both models for comparison with the reference group in a forest plot. For pair‐wise comparison, we will report the fixed‐effect model if the two models reported similar results; otherwise, we will report the more conservative model.


Network plot for mortality at maximal follow‐up. The size of the node (circle) provides a measure of the number of trials in which the particular treatment was included in one of the arms. The thickness of the line provides a measure of the number of direct comparisons between two nodes (treatments).

Network plot for mortality at maximal follow‐up. The size of the node (circle) provides a measure of the number of trials in which the particular treatment was included in one of the arms. The thickness of the line provides a measure of the number of direct comparisons between two nodes (treatments).

We will use a hierarchical Bayesian model using three different initial values and codes provided by NICE DSU (Dias 2014a). We will use a normal distribution with large variance (10,000) for treatment effect priors (vague or flat priors). For the random‐effects model, we will use a prior distributed uniformly (limits: 0 to 5) for between‐trial standard deviation but assumed similar between‐trial standard deviation across treatment comparisons (Dias 2014a). We will use a 'burn‐in' of 5000 simulations, check for convergence visually, and run models for another 10,000 simulations to obtain effect estimates. If we did not obtain convergence, we will increase the number of simulations for 'burn‐in'. If we do not obtain convergence still, we will use alternate initial values and priors according to methods suggested by van Valkenhoef 2012. We will also estimate the probability that each intervention ranks at one of the possible positions using the NICE DSU codes (Dias 2014a).

Assessment of inconsistency

We will assess inconsistency (statistical evidence of violation of the transitivity assumption) by fitting both an inconsistency model and a consistency model. We will use inconsistency models described in the NICE DSU manual, as we plan to use a common between‐study deviation for comparisons (Dias 2014b). In addition, we will use the design‐by‐treatment full interaction model (Higgins 2012) and IF (inconsistency factor) plots (Chaimani 2013) to assess inconsistency. In the presence of inconsistency, we will assess whether it is due to clinical or methodological heterogeneity by performing separate analyses for each of the different subgroups mentioned in the ‘Subgroup analysis and investigation of heterogeneity for network meta‐analysis’ section below.

If we find evidence of inconsistency, we will identify areas in the network where substantial inconsistency might be present in terms of clinical and methodological diversities between trials and, when appropriate, will limit network meta‐analysis to a more compatible subset of trials.

Direct comparison

We will perform direct comparisons using the same codes and the same technical details.

Sample size calculations

To control for risk of random errors, we will interpret information with caution when the accrued sample size in the network meta‐analysis (i.e. across all treatment comparisons) was less than the required sample size (required information size). For calculation of the required information size, see Appendix 3.

Subgroup analysis and investigation of heterogeneity for network meta‐analysis

We will assess differences in effect estimates between subgroups listed in subgroup analysis and investigation of heterogeneity using meta‐regression with the help of the OpenBUGS code (Dias 2012a) if we include a sufficient number of trials. We will use potential modifiers as study level co‐variates for meta‐regression. We will calculate a single common interaction term (Dias 2012a). If 95% credible intervals of the interaction term do not overlap zero, we will consider this as evidence of difference in subgroups.

Presentation of results

We will present effect estimates with 95% CrI for each pair‐wise comparison calculated from direct comparisons and network meta‐analysis. We will present the cumulative probability of treatment ranks (i.e. the probability that the treatment is within the top two, the probability that the treatment is within the top three, etc.) in graphs (surface under the cumulative ranking curve, or SUCRA) (Salanti 2011). We will plot the probability that each treatment is best, second best, third best, etc., for each of the different outcomes (rankograms), which generally are considered more informative (Salanti 2011; Dias 2012b).

We will present 'Summary of findings' tables for mortality. In summary of findings Table for the main comparison, we will follow the approach suggested by Puhan et al. (Puhan 2014). First, we will calculate direct and indirect effect estimates and 95% credible intervals using the node‐splitting approach (Dias 2010) (i.e. calculate the direct estimate for each comparison by including only trials that performed direct comparisons of treatments, and the indirect estimate for each comparison by excluding trials that performed direct comparisons of treatments). Then we will rate the quality of direct and indirect effect estimates using GRADE, which takes into account risk of bias, inconsistency, directness of evidence, imprecision, and publication bias (Guyatt 2011). We will present estimates of the network meta‐analysis and will rate the quality of network meta‐analysis effect estimates as the best quality of evidence between direct and indirect estimates (Puhan 2014). In addition, in the same table, we will present illustrations and information on numbers of trials and participants, as per the standard 'Summary of findings' table.

Appendix 2. Search strategies

Database

Time span

Search strategy

Central Register of Controlled Trials (CENTRAL) (Wiley).

Issue 2, 2017.

#1 MeSH descriptor: [Cholangitis, Sclerosing] explode all trees

#2 primary sclerosing cholangitis or PSC

#3 #1 or #2

MEDLINE (OvidSP).

January 1947 to February 2017.

1. exp Cholangitis, Sclerosing/

2. (primary sclerosing cholangitis or PSC).mp. [mp=title, abstract, original title, name of substance word, subject heading word, keyword heading word, protocol supplementary concept word, rare disease supplementary concept word, unique identifier]

3. 1 or 2

4. randomised controlled trial.pt.

5. controlled clinical trial.pt.

6. randomised.ab.

7. placebo.ab.

8. drug therapy.fs.

9. randomly.ab.

10. trial.ab.

11. groups.ab.

12. 4 or 5 or 6 or 7 or 8 or 9 or 10 or 11

13. exp animals/ not humans.sh.

14. 12 not 13

15. 3 and 14

Embase (OvidSP).

January 1974 to February 2017.

1. exp primary sclerosing cholangitis/

2. (primary sclerosing cholangitis or PSC).mp. [mp=title, abstract, subject headings, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword]

3. 1 or 2

4. exp crossover‐procedure/ or exp double‐blind procedure/ or exp randomised controlled trial/ or single‐blind procedure/

5. (((((random* or factorial* or crossover* or cross over* or cross‐over* or placebo* or double*) adj blind*) or single*) adj blind*) or assign* or allocat* or volunteer*).af.

6. 4 or 5

7. 3 and 6

Science Citation Index ‐ Expanded (Web of Knowledge)

January 1945 to February 2017.

#1 TS=(primary sclerosing cholangitis or PSC)

#2 TS=(random* OR rct* OR crossover OR masked OR blind* OR placebo* OR meta‐analysis OR systematic review* OR meta‐analys*)

#3 #1 AND #2

World Health Organization International Clinical Trials Registry Platform Search Portal (apps.who.int/trialsearch/Default.aspx)

February 2017.

Condition: primary sclerosing cholangitis

ClinicalTrials.gov

February 2017.

Interventional Studies | primary sclerosing cholangitis | Phase 2, 3, 4

Appendix 3. Sample size calculation

Five‐year mortality in patients with primary sclerosing cholangitis is 18% (Talwalkar 2001). The required information size is based on a control group proportion of 18%, a relative risk reduction of 20% in the experimental group, type I error of 5%, and type II error of 20% in 3396 participants. Network analyses may be more prone to risk of random error than direct comparisons (Del Re 2013). Accordingly, a larger sample size is required in indirect comparisons than in direct comparisons (Thorlund 2012). Power and precision in indirect comparisons depend upon various factors, such as the number of participants included under each comparison and heterogeneity between the trials (Thorlund 2012). If no heterogeneity is evident across trials, the sample size in indirect comparisons would be equivalent to the sample size in direct comparisons. The effective indirect sample size can be calculated using the number of participants included in each direct comparison (Thorlund 2012). For example, a sample size of 2500 participants in the direct comparison A versus C (nAC) and a sample size of 7500 participants in the direct comparison B versus C (nBC) result in an effective indirect sample size of 1876 participants. However, in the presence of heterogeneity within comparisons, the sample size required is greater. In the above scenario, for an I2 statistic for each of the comparisons A versus C (IAC2) and B versus C (IBC2) of 25%, the effective indirect sample size is 1407 participants. For an I2 statistic for each of the comparisons A versus C and B versus C of 50%, the effective indirect sample size is 938 participants (Thorlund 2012). If the study includes only three groups and sample size is greater than required information size, we will calculate the effective indirect sample size using the following generic formula (Thorlund 2012):

((nAC × (1 ‐ IAC2)) × (nBC × (1 ‐ IBC2))/((nAC × (1 ‐ IAC2)) + (nBC × (1 ‐ IBC2)).

No method is currently known to calculate the effective indirect sample size for a network analysis involving more than three intervention groups.

Study flow diagram.
Figures and Tables -
Figure 1

Study flow diagram.

Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.
Figures and Tables -
Figure 2

Risk of bias graph: review authors' judgements about each risk of bias item presented as percentages across all included studies.

Risk of bias summary: review authors' judgements about each risk of bias item for each included study.
Figures and Tables -
Figure 3

Risk of bias summary: review authors' judgements about each risk of bias item for each included study.

Based on an alpha error of 2.5%, power of 90% (beta error of 10%), relative risk reduction (RRR) of 20%, control group proportion observed in the trials (Pc), and heterogeneity observed in the analyses, only a small fraction of the diversity‐adjusted required information size (DARIS) has been reached (required information size = 348; DARIS = 14,509 for mortality at maximal follow‐up; required information size = 348; DARIS = 35,846 for liver transplantation; required information size = 348; DARIS = 29,191 for cholangiocarcinoma), and trial sequential monitoring boundaries were not drawn. The Z‐curves (blue lines) do not cross conventional boundaries (dotted green lines). This indicates high risk of random errors for all outcomes included in this review.
Figures and Tables -
Figure 4

Based on an alpha error of 2.5%, power of 90% (beta error of 10%), relative risk reduction (RRR) of 20%, control group proportion observed in the trials (Pc), and heterogeneity observed in the analyses, only a small fraction of the diversity‐adjusted required information size (DARIS) has been reached (required information size = 348; DARIS = 14,509 for mortality at maximal follow‐up; required information size = 348; DARIS = 35,846 for liver transplantation; required information size = 348; DARIS = 29,191 for cholangiocarcinoma), and trial sequential monitoring boundaries were not drawn. The Z‐curves (blue lines) do not cross conventional boundaries (dotted green lines). This indicates high risk of random errors for all outcomes included in this review.

Network plot for mortality at maximal follow‐up. The size of the node (circle) provides a measure of the number of trials in which the particular treatment was included in one of the arms. The thickness of the line provides a measure of the number of direct comparisons between two nodes (treatments).
Figures and Tables -
Figure 5

Network plot for mortality at maximal follow‐up. The size of the node (circle) provides a measure of the number of trials in which the particular treatment was included in one of the arms. The thickness of the line provides a measure of the number of direct comparisons between two nodes (treatments).

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 1 Mortality at maximal follow‐up.
Figures and Tables -
Analysis 1.1

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 1 Mortality at maximal follow‐up.

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 2 Serious adverse events proportion.
Figures and Tables -
Analysis 1.2

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 2 Serious adverse events proportion.

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 3 Serious adverse events number.
Figures and Tables -
Analysis 1.3

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 3 Serious adverse events number.

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 4 Adverse events proportion.
Figures and Tables -
Analysis 1.4

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 4 Adverse events proportion.

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 5 Adverse events number.
Figures and Tables -
Analysis 1.5

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 5 Adverse events number.

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 6 Quality of life.
Figures and Tables -
Analysis 1.6

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 6 Quality of life.

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 7 Liver transplantation.
Figures and Tables -
Analysis 1.7

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 7 Liver transplantation.

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 8 Cholangiocarcinoma.
Figures and Tables -
Analysis 1.8

Comparison 1 Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose, Outcome 8 Cholangiocarcinoma.

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 1 Mortality at maximal follow‐up.
Figures and Tables -
Analysis 2.1

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 1 Mortality at maximal follow‐up.

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 2 Serious adverse events proportion.
Figures and Tables -
Analysis 2.2

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 2 Serious adverse events proportion.

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 3 Serious adverse events number.
Figures and Tables -
Analysis 2.3

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 3 Serious adverse events number.

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 4 Adverse events proportion.
Figures and Tables -
Analysis 2.4

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 4 Adverse events proportion.

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 5 Adverse events number.
Figures and Tables -
Analysis 2.5

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 5 Adverse events number.

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 6 Quality of life.
Figures and Tables -
Analysis 2.6

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 6 Quality of life.

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 7 Liver transplantation.
Figures and Tables -
Analysis 2.7

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 7 Liver transplantation.

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 8 Cholangiocarcinoma.
Figures and Tables -
Analysis 2.8

Comparison 2 Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose, Outcome 8 Cholangiocarcinoma.

Summary of findings for the main comparison. Ursodeoxycholic acid versus placebo for primary sclerosing cholangitis

Ursodeoxycholic acid versus placebo for primary sclerosing cholangitis

Patient or population: people with primary sclerosing cholangitis
Settings: secondary or tertiary care
Intervention: ursodeoxycholic acid
Comparison: placebo

Outcomes

Illustrative comparative risks* (95% CI)

Relative effect
(95% CI)

Number of participants
(trials)

Quality of the evidence
(GRADE)

Assumed risk

Corresponding risk

Placebo

Ursodeoxycholic acid

Mortality

Follow‐up: 60 months

72 per 1000

105 per 1000
(47 to 220)

OR 1.51
(0.63 to 3.63)

348
(2 trials)

⊕⊝⊝⊝
very low1,2,3

Serious adverse events

No trials reported the number of participants with serious adverse events or numbers of serious adverse events.

Proportion of people with adverse events

Follow‐up: 60 months

337 per 1000

358 per 1000
(237 to 498)

OR 1.22
(0.68 to 2.17)

198
(1 trial)

⊕⊝⊝⊝
very low1,2,3

Number of adverse events

No trials reported the number of adverse events.

Health‐related quality of life

Follow‐up: 5 years

Scale: SF‐36 General Health Scale (Limits: 0 to 100; higher = better)

Mean in the placebo group was 61.10.

Mean in the ursodeoxycholic acid group was 1.30 higher (5.61 lower or 8.21 higher).

198

(1 trial)

⊕⊝⊝⊝
very low1,2,3

Liver transplantation

Follow‐up: 60 months

123 per 1000

120 per 1000
(68 to 202)

OR 0.97
(0.52 to 1.81)

348
(2 trials)

⊕⊝⊝⊝
very low1,2,3,4

Any malignancy

No trials reported this outcome.

Cholangiocarcinoma

Follow‐up: 60 months

43 per 1000

57 per 1000
(21 to 142)

OR 1.34
(0.48 to 3.68)

348
(2 trials)

⊕⊝⊝⊝
very low1,2,3

Colorectal cancer

No trials reported this outcome.

Cholecystectomy

No trials reported this outcome.

*The basis for the assumed risk is the mean control group proportion. The corresponding risk (and its 95% confidence interval) is based on assumed risk in the comparison group and the relative effect of the intervention (and its 95% CI).
CI: confidence interval; OR: odds ratio.

GRADE Working Group grades of evidence
High quality: Further research is very unlikely to change our confidence in the estimate of effect.
Moderate quality: Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate.
Low quality: Further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate.
Very low quality: We are very uncertain about the estimate.

1 Downgraded one level for risk of bias: the trial(s) were at high risk of bias.
2 Downgraded one level for imprecision: the sample size was small.
3 Downgraded one level for imprecision: the confidence intervals were wide and overlapped a clinically significant reduction or increase (25% reduction or increase) and no effect.
4 Downgraded two levels for inconsistency: I2 was high and overlap of confidence intervals was poor.

Figures and Tables -
Summary of findings for the main comparison. Ursodeoxycholic acid versus placebo for primary sclerosing cholangitis
Table 1. Characteristics table (according to comparisons)

Study name

Number of people in intervention group

Number of people in control group

Risk of bias

Overall risk of bias

Random sequence generation

Allocation concealment

Blinding of participants and personnel

Blinding of outcome assessment

Incomplete outcome data

Selective reporting

Vested interest bias

Colchicine vs placebo

Olsson 1995

44

40

Unclear

Unclear

Low

Low

Unclear

High

Unclear

High

Cyclosporin vs placebo

Sandborn 1993

16

10

Unclear

Unclear

Low

Low

High

High

High

High

Infliximab vs placebo

Hommes 2008

4

3

Unclear

Unclear

Low

Low

High

High

High

High

Methotrexate vs placebo

Knox 1994

11

10

Unclear

Unclear

Low

Low

High

High

High

High

Rasmussen 1998

5 (crossed over after 1 year)

8

(crossed over after 1 year)

Unclear

Unclear

Unclear

Unclear

Unclear

High

Unclear

High

NorUrsodeoxycholic acid vs placebo

Trauner 2016

Not stated

Not stated

Unclear

Unclear

Unclear

Unclear

Unclear

High

High

High

Penicillamine vs placebo

LaRusso 1988

39

31

Unclear

Unclear

Low

Low

Unclear

Low

High

High

Steroids vs placebo

Allison 1986

6

5

Unclear

Low

Low

Low

High

High

Low

High

UDCA (high) vs placebo

Lindor 2009

76

74

Low

Low

Low

Low

Low

High

High

High

UDCA (moderate) vs placebo

Bansi 1996

11

11

Unclear

Unclear

Unclear

Unclear

High

High

Unclear

High

Mitchell 2001

13

13

Unclear

Unclear

Low

Low

Low

High

Unclear

High

Olsson 2005

97

101

Unclear

Low

Low

Low

High

Low

High

High

UDCA (low) vs placebo

Beuers 1992

6

8

Low

Unclear

Low

Low

Unclear

High

High

High

Lindor 1997

51

51

Low

Unclear

Low

Low

High

High

High

High

Lo 1992

7

7

Unclear

Unclear

Unclear

Unclear

High

High

Unclear

High

Stiehl 1989

6

6

Unclear

Unclear

Unclear

Unclear

High

High

Unclear

High

UDCA (low) vs UDCA (moderate) vs UDCA (high)

Cullen 2008

11

11 (UDCA (moderate)) and 9 (UDCA (high))

Low

Low

Low

Low

High

High

High

High

UDCA (low) vs colchicine vs placebo

De Maria 1996

20

19 (colchicine) and 20 (placebo)

Unclear

Unclear

High

Unclear

Unclear

High

Unclear

High

UDCA (low) plus metronidazole vs UDCA (low)

Farkkila 2004

37

34

Low

Low

Low

Low

High

High

High

High

UDCA (low) plus mycophenolate vs UDCA (low)

Sterling 2004

6

10

Unclear

Unclear

High

High

Unclear

High

High

High

Vancomycin vs metronidazole

Tabibian 2013

16

13

Unclear

Unclear

Low

Low

High

High

Low

High

Vancomycin vs placebo

Rahimpour 2016

18

11

Low

Low

Low

Low

Low

High

Low

High

Figures and Tables -
Table 1. Characteristics table (according to comparisons)
Comparison 1. Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

1 Mortality at maximal follow‐up Show forest plot

6

Odds Ratio (M‐H, Fixed, 95% CI)

Subtotals only

1.1 Colchicine vs placebo

1

84

Odds Ratio (M‐H, Fixed, 95% CI)

0.44 [0.04, 5.07]

1.2 Penicillamine vs placebo

1

70

Odds Ratio (M‐H, Fixed, 95% CI)

1.18 [0.39, 3.58]

1.3 Steroids vs placebo

1

11

Odds Ratio (M‐H, Fixed, 95% CI)

3.0 [0.10, 90.96]

1.4 Ursodeoxycholic acid vs placebo

2

348

Odds Ratio (M‐H, Fixed, 95% CI)

1.51 [0.63, 3.63]

1.5 Vancomycin vs placebo

1

29

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

2 Serious adverse events proportion Show forest plot

3

Odds Ratio (M‐H, Fixed, 95% CI)

Totals not selected

2.1 Infliximab vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

2.2 Steroids vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

2.3 Vancomycin vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

3 Serious adverse events number Show forest plot

3

rate ratio (Fixed, 95% CI)

Totals not selected

3.1 Infliximab vs placebo

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

3.2 Penicillamine vs placebo

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

3.3 Steroids vs placebo

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

4 Adverse events proportion Show forest plot

3

Odds Ratio (M‐H, Fixed, 95% CI)

Totals not selected

4.1 Steroids vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

4.2 Ursodeoxycholic acid vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

4.3 Vancomycin vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

5 Adverse events number Show forest plot

5

rate ratio (Fixed, 95% CI)

Totals not selected

5.1 Cyclosporin vs placebo

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

5.2 Penicillamine vs placebo

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

5.3 Steroids vs placebo

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

5.4 Ursodeoxycholic acid plus metronidazole vs ursodeoxycholic acid

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

5.5 Vancomycin vs metronidazole

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

6 Quality of life Show forest plot

1

Mean Difference (IV, Fixed, 95% CI)

Totals not selected

6.1 Ursodeoxycholic acid vs placebo

1

Mean Difference (IV, Fixed, 95% CI)

0.0 [0.0, 0.0]

7 Liver transplantation Show forest plot

7

Odds Ratio (M‐H, Fixed, 95% CI)

Subtotals only

7.1 Colchicine vs placebo

1

84

Odds Ratio (M‐H, Fixed, 95% CI)

0.59 [0.09, 3.71]

7.2 Penicillamine vs placebo

1

70

Odds Ratio (M‐H, Fixed, 95% CI)

1.14 [0.32, 4.01]

7.3 Steroids vs placebo

1

11

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

7.4 Ursodeoxycholic acid vs placebo

2

348

Odds Ratio (M‐H, Fixed, 95% CI)

0.97 [0.52, 1.81]

7.5 Vancomycin vs placebo

1

29

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

7.6 Ursodeoxycholic acid plus metronidazole vs ursodeoxycholic acid

1

71

Odds Ratio (M‐H, Fixed, 95% CI)

0.29 [0.03, 2.90]

8 Cholangiocarcinoma Show forest plot

4

Odds Ratio (M‐H, Fixed, 95% CI)

Subtotals only

8.1 Cyclosporin vs placebo

1

26

Odds Ratio (M‐H, Fixed, 95% CI)

0.19 [0.01, 5.20]

8.2 Ursodeoxycholic acid vs placebo

2

348

Odds Ratio (M‐H, Fixed, 95% CI)

1.34 [0.48, 3.68]

8.3 Vancomycin vs placebo

1

29

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

Figures and Tables -
Comparison 1. Pharmacological treatments for primary sclerosing cholangitis: not stratified by ursodeoxycholic acid dose
Comparison 2. Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose

Outcome or subgroup title

No. of studies

No. of participants

Statistical method

Effect size

1 Mortality at maximal follow‐up Show forest plot

6

Odds Ratio (M‐H, Fixed, 95% CI)

Totals not selected

1.1 Colchicine vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

1.2 Penicillamine vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

1.3 Steroids vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

1.4 Ursodeoxycholic acid (high) vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

1.5 Ursodeoxycholic acid (moderate) vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

1.6 Vancomycin vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

2 Serious adverse events proportion Show forest plot

3

Odds Ratio (M‐H, Fixed, 95% CI)

Totals not selected

2.1 Infliximab vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

2.2 Steroids vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

2.3 Vancomycin vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

3 Serious adverse events number Show forest plot

4

rate ratio (Fixed, 95% CI)

Totals not selected

3.1 Infliximab vs placebo

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

3.2 Penicillamine vs placebo

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

3.3 Steroids vs placebo

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

3.4 Ursodeoxycholic acid (high) vs ursodeoxycholic acid (moderate)

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

3.5 Ursodeoxycholic acid (high) vs ursodeoxycholic acid (low)

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

3.6 Ursodeoxycholic acid (moderate) vs ursodeoxycholic acid (low)

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

4 Adverse events proportion Show forest plot

3

Odds Ratio (M‐H, Fixed, 95% CI)

Totals not selected

4.1 Steroids vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

4.2 Ursodeoxycholic acid (moderate) vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

4.3 Vancomycin vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

5 Adverse events number Show forest plot

6

rate ratio (Fixed, 95% CI)

Totals not selected

5.1 Cyclosporin vs placebo

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

5.2 Penicillamine vs placebo

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

5.3 Steroids vs placebo

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

5.4 Ursodeoxycholic acid (high) vs ursodeoxycholic acid (low)

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

5.5 Ursodeoxycholic acid (high) vs ursodeoxycholic acid (moderate)

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

5.6 Ursodeoxycholic acid (low) plus metronidazole vs ursodeoxycholic acid (low)

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

5.7 Ursodeoxycholic acid (moderate) vs ursodeoxycholic acid (low)

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

5.8 Vancomycin vs metronidazole

1

rate ratio (Fixed, 95% CI)

0.0 [0.0, 0.0]

6 Quality of life Show forest plot

1

Mean Difference (IV, Fixed, 95% CI)

Totals not selected

6.1 Ursodeoxycholic acid (moderate) vs placebo

1

Mean Difference (IV, Fixed, 95% CI)

0.0 [0.0, 0.0]

7 Liver transplantation Show forest plot

8

Odds Ratio (M‐H, Fixed, 95% CI)

Totals not selected

7.1 Colchicine vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

7.2 Penicillamine vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

7.3 Steroids vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

7.4 Ursodeoxycholic acid (high) vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

7.5 Ursodeoxycholic acid (moderate) vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

7.6 Vancomycin vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

7.7 Ursodeoxycholic acid (moderate) vs ursodeoxycholic acid (low)

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

7.8 Ursodeoxycholic acid (high) vs ursodeoxycholic acid (low)

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

7.9 Ursodeoxycholic acid (high) vs ursodeoxycholic acid (moderate)

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

7.10 Ursodeoxycholic acid (low) plus metronidazole vs ursodeoxycholic acid (low)

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

8 Cholangiocarcinoma Show forest plot

4

Odds Ratio (M‐H, Fixed, 95% CI)

Totals not selected

8.1 Cyclosporin vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

8.2 Ursodeoxycholic acid (high) vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

8.3 Ursodeoxycholic acid (moderate) vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

8.4 Vancomycin vs placebo

1

Odds Ratio (M‐H, Fixed, 95% CI)

0.0 [0.0, 0.0]

Figures and Tables -
Comparison 2. Pharmacological treatments for primary sclerosing cholangitis: stratified by ursodeoxycholic acid dose