 Protocol
 Open Access
 Published:
Predicting the treatment response of certolizumab for individual adult patients with rheumatoid arthritis: protocol for an individual participant data metaanalysis
Systematic Reviews volume 9, Article number: 140 (2020)
Abstract
Background
A model that can predict treatment response for a patient with specific baseline characteristics would help decisionmaking in personalized medicine. The aim of the study is to develop such a model in the treatment of rheumatoid arthritis (RA) patients who receive certolizumab (CTZ) plus methotrexate (MTX) therapy, using individual participant data metaanalysis (IPDMA).
Methods
We will search Cochrane CENTRAL, PubMed, and Scopus as well as clinical trial registries, drug regulatory agency reports, and the pharmaceutical company websites from their inception onwards to obtain randomized controlled trials (RCTs) investigating CTZ plus MTX compared with MTX alone in treating RA. We will request the individuallevel data of these trials from an independent platform (http://vivli.org). The primary outcome is efficacy defined as achieving either remission (based on ACREULAR Boolean or indexbased remission definition) or low disease activity (based on either of the validated composite disease activity measures). The secondary outcomes include ACR50 (50% improvement based on ACR core set variables) and adverse events. We will use a twostage approach to develop the prediction model. First, we will construct a risk model for the outcomes via logistic regression to estimate the baseline risk scores. We will include baseline demographic, clinical, and biochemical features as covariates for this model. Next, we will develop a metaregression model for treatment effects, in which the stage 1 risk score will be used both as a prognostic factor and as an effect modifier. We will calculate the probability of having the outcome for a new patient based on the model, which will allow estimation of the absolute and relative treatment effect. We will use R for our analyses, except for the second stage which will be performed in a Bayesian setting using R2Jags.
Discussion
This is a study protocol for developing a model to predict treatment response for RA patients receiving CTZ plus MTX in comparison with MTX alone, using a twostage approach based on IPDMA. The study will use a new modeling approach, which aims at retaining the statistical power. The model may help clinicians individualize treatment for particular patients.
Systematic review registration
PROSPERO registration number pending (ID#157595).
Background
Rheumatoid arthritis (RA) is a chronic inflammatory disease, for which we cannot currently expect complete cure. The drugs that can delay disease progression are known as diseasemodifying antirheumatic drugs (DMARDs). There are 3 categories: conventional synthetic DMARDs (csDMARDs), biologic DMARDs (bDMARDs), and targeted synthetic DMARDs (tsDMARDs). bDMARDs can be further divided into several subtypes according to the target, among which the tumor necrosis factor (TNF) α inhibitors are the most classic and widely used.
Most RA patients undergo longterm treatment. According to the treattotarget strategy proposed by the EULAR (European League Against Rheumatism) practice guideline [1], repeated assessment of disease activity should be performed every 3 to 6 months after a treatment is given, to evaluate the response and decide the nextstep strategy: switching drugs, maintenance, tapering, or discontinuation. Hence, the disease course of RA is composed of many shortterm (3 to 6 months) interventionresponse loops. For the purpose of improving longterm prognosis, such as delaying the progression of bone fusion or functional deficiency, shortterm interventionresponse loops need to have beneficial outcomes [2].
To find the optimal treatment for a particular patient, it is necessary to personalize the treatment. It would be helpful if we could predict the probability of treatment response based on the patient’s genetic, biologic, and clinical features. However, common evidence in the form of randomized controlled trials (RCTs) or their metaanalyses (MAs) at the aggregate level only reports average results. The drug that works for the average patients might not work or even be harmful for a particular patient. Consequently, it is desirable to identify subgroups of patients associated with different treatment effects.
Individual participant data metaanalysis (IPDMA) has been previously employed to develop prediction models for treatment effects [3,4,5,6]. Previous treatment response prediction models for RA were mainly based on observational studies [7,8,9,10,11]. Observational studies seem suited for predicting the absolute risk of an outcome, but it may be less satisfactory in estimating the relative risk between different drugs, because unknown confounders may persist even when we try to adjust for known confounders. On the other hand, though the population in RCTs is highly restricted hence may be less representative, data from RCTs are more rigorously collected and more likely to provide an unbiased estimate of the relative treatment effects [12]. The synthesis of RCT data via IPDMA can increase the statistical power [13] and have been used to predict treatment response [6, 14,15,16,17]. To the best of the authors’ knowledge, such an approach has not been taken to predict treatment response in RA to date.
Our aim is to develop a prediction model of treatment effects based on individual characteristics of RA patients through IPDMA. Since TNFα inhibitors are the most classic and widely used bDMARDs for RA, we will build a model for certolizumab (CTZ), a TNFα inhibitor with sufficient IPD data, in this study. We will first estimate the pooled average effect sizes for the primary and secondary outcomes using onestage Bayesian hierarchical IPDMA. The main objective of the study is to use a twostage risk modeling approach to predict the individualized treatment effects interest [12]. The first stage is to build a multivariable model aiming to predict the baseline risk for a particular patient blinded to treatment. In the second stage, this baseline risk score will be used as a prognostic factor and an effect modifier in an IPD metaregression model to estimate the individualized treatment effects of CTZ. We consider to validate and optimize the modeling approach in the present study, and plan eventually to expand it to an IPD network metaanalysis to compare several drug types (e.g., interleukin6 inhibitors, antiCD20 antibodies) as our future research perspective.
Methods
The present protocol has been registered within the PROSPERO database (provisional registration number ID#157595) and is being reported in accordance with the reporting guidance provided in the Preferred Reporting Items for Systematic Reviews and MetaAnalyses Protocols (PRISMAP) statement [18] (see the checklist in Additional file 1). The proposed IPDMA will be reported in accordance with the reporting guidance provided in the Preferred Reporting Items for Systematic Reviews and Metaanalyses of Individual Participant Data (PRISMAIPD) statement [19]. Any amendments made to this protocol when conducting the study will be outlined and reported in the final manuscript.
Eligibility criteria
Studies will be selected according to the following criteria: patients, interventions, outcomes, and study design.
Patients
We will include adults (18 years or older) who are diagnosed with either early RA (2010 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) classification criteria) [20, 21] or established RA (1987 classification criteria) [22]. Patients with inner organ involvement (such as interstitial lung diseases), vasculitis, or concomitant other systemic autoimmune diseases will be excluded. We will include both treatmentnaïve patients and patients who have insufficient response to previous treatments. We will include patients with moderate to severe disease activity based on any validated composite disease activity measures. Patients who have already achieved remission or at low disease activity at baseline will be excluded. Patients who have used certolizumab (CTZ) within 6 months before randomization will be excluded.
Interventions
We will include RCTs which compare certolizumab (CTZ) plus methotrexate (MTX) with MTX monotherapy, regardless of doses. If a study compares CTZ + any csDMARDs with any csDMARDs, we will only include patients on CTZ + MTX or MTX from that study. Trials investigating the tapering or discontinuation strategy of CTZ will be excluded.
Outcomes
Our primary outcome is efficacy defined by disease states, which is achieving either remission (based on ACREULAR Boolean or indexbased remission definition [23]) or low disease activity (based on either of the validated composite disease activity measures [24]: DAS28 (Disease Activity Score based on the evaluation of 28 joints) ≤3.2 [25], CDAI (Clinical Disease Activity Index) ≤ 10 [26], SDAI (Simplified Disease Activity Index) ≤ 11 [27]) at 3 months (allowance 2–4 months) after treatment, as a binary outcome. We choose it as our primary outcome because it is suggested as the indicator of the treatment target in both the practice guideline [1] and the guideline for conducting clinical trials in RA [2], and it has been shown to provide more information for future joint damage and functional outcomes compared to relative response (change from baseline) [28].
We have two secondary outcomes. One is efficacy defined by response (improvement from baseline), for which we will use the ACR response criteria ACR50 (50% improvement based on ACR core set variables) [29]. Another is adverse events (AEs). We will perform an IPDMA separately for patients with all kinds of infectious AEs within 3 months since it is one of the most important AEs for biologic agents. We will also describe other noticeable AEs within 3 months reported in the trials. We will not make predictions models for the secondary outcomes.
Study design
We will include doubleblind RCTs only. If there are crossover RCTs, only the data of the first phase will be used for analysis. Cluster RCTs, quasirandomized trials, and observational studies will be excluded.
Information source and literature search
We will conduct an electronic search of Cochrane CENTRAL, PubMed, and Scopus from inception onwards, with the keywords: “rheumatoid arthritis,” “certolizumab” or “CDP870” “Cimzia”, “methotrexate” or “MTX,” without language restrictions. A draft search strategy is provided in Additional file 2. We will search WHO International Clinical Trials Registry Platform to find the registered studies. We will search the US Food and Drug Administration (FDA) reports to see if there are any unpublished reports from the pharmaceutical companies. For IPD, we will contact the company which markets certolizumab and request IPD through http://vivli.org. We will assess the representativeness of the IPD among all the eligible studies by investigating the potential differences between trials with IPD and those without IPD.
Screening and selection procedure
Two independent reviewers will screen the titles and abstracts retrieved from the electronic searches to assess for inclusion. If both reviewers agree that a trial does not meet eligibility criteria, it will be excluded. The full text of all the remaining articles will be obtained for further reading, and the same eligibility criteria will be applied to determine which to exclude. Any disagreements will be resolved through discussion with a third member of the review team.
Data collection
At aggregate level
Two reviewers will independently extract the information for all the included studies at aggregate level. A detailed data extraction template will be developed and piloted on 3 articles; after finalizing the items on the data extraction form, the 3 articles will be reextracted. The main information includes intervention/control details, trial implementation features (e.g., completion year, randomized numbers, dropouts, followup length), baseline demographic and diseasespecific characteristics, and outcomes of interested. The above information will be used for: (1) exploring the representativeness of the trials with IPD among all the eligible trials and (2) confirming if the IPD is consistent with the reported results.
At IPD level: for studies with IPD available
When the IPD is ready to be used, we will identify the variables of interest before the analysis. The variables regarding intervention, control, and outcomes are defined as the above in the “Eligibility criteria” section. With regard to patient or trial characteristics to be used as potential covariates in the prognostic model, based on the literature [30,31,32] and our clinical practice, we propose the following factors as candidates of potential prognostic factors (PFs, baseline factors that may affect the response regardless of the treatment) (Table 1), which will be used for baseline risk model development (see the “Predicting treatment effect for patients with particular characteristics: a twostage model” section below). We will try to collect all the information listed in Table 1 from the data, but only available factors that have been recorded in the trials will be added into the model. We will decide in which type (e.g., continuous, categorical, binary, etc.) a covariate will be put into the model according to the distribution of that covariate after we obtain the data.
Risk of bias assessment
Two independent reviewers will assess the risk of bias (RoB) for each included RCT according to “RoB 2 tool” proposed by the Cochrane group [34]. For the efficacy primary outcome, RCTs will be graded as “low risk of bias,” “high risk of bias,” or “some concerns” in the following five domains: risk of bias arising from the randomization process, risk of bias due to deviations from the intended interventions, missing outcome data, risk of bias in measurement of the outcome, and risk of bias in selection of the reported result. The assessment will be adapted for IPDMA, i.e., as per the obtained data and not the conducted and reported analyses in the original publications. Finally, they will be summarized as an overall risk of bias according to the RoB 2 algorithm.
Data analysis
Since our primary aim is to develop a prediction model and not to get a precise estimation of the treatment effects, all the analyses will be based on IPD only. Therefore, we will neither analyze aggregate data together nor investigate the robustness of the IPDMA including aggregate data, for they are beyond the perspectives of the present study.
Average relative treatment effect: IPDMA
We first synthesize the data using onestage Bayesian hierarchical IPDMA [35]. We will estimate the average relative treatment effect in terms of odds ratio (OR) for efficacy.
Let y_{ij} denote the dichotomous outcome of interest (y_{ij} = 1 for remission or low disease activity), for patient i where i = 1, 2, …, n_{j} in trial j out of N trials, t_{ij} be 0/1 for patient in control/intervention group, and p_{ij} is the probability of having the outcome.
where α_{j} is the log odds of the outcome for the control group, in trial j, which is independent across trials; δ_{j} is the treatment effect (log OR), which we assume to be exchangeable across trials; δ is the summary estimate of the logodds ratios for the intervention versus the control arm; and τ^{2} is the heterogeneity of δ across trials and normally distributed across trials.
Predicting treatment effect for patients with particular characteristics: a twostage model
Data preprocessing
Within each study, the outcomes and the covariates will be evaluated for missing data, and we will further look at their distributional characteristics and correlations between the covariates (listed in the “At IPD level: for studies with IPD available” section). We will use multiple imputation methods for handling missing data [36]. We will consider data transformation for continuous variables to resolve skewness and recategorization for categorical variables if necessary. If two or more variables are highly correlated, we will only retain the variable that is most commonly reported across studies and in the literature or the variable that has the least missing values.
Stage 1: Developing a baseline risk model
In this step, we will build a multivariable model to predict the probability that a patient, given her or his baseline characteristics, is likely to achieve remission or low disease activity irrespective of treatment; we will refer to this model as the baseline risk model. The risk model can be built using the patients from the control group only, or from both intervention and control group. The former is more intuitive; however, a simulation study indicated that models based on the whole participants produced estimates with narrower distribution of bias and were less prone to overfitting [37]. We will fit a multivariable logistic regression model:
r_{ij} is the probability of the outcome for patient i from trial j at the baseline. b_{0j} is the intercept, which is exchangeable across studies. PF_{ijk} denotes the k prognostic factor (in total, there are p prognostic factors) in study j for patient i, and b_{kj} is the regression coefficient for k prognostic factor in study j and is exchangeable across studies.
In order to select the most appropriate model, we propose two approaches: (1) use previously identified prognostic factors and through discussions with rheumatologists to decide the subset of the most clinically relevant factors and estimate the coefficients using penalized maximum likelihood estimation shrinkage method and (2) use LASSO penalization methods for variable selection and coefficient shrinkage [38].
For each possible model, we will examine the sample size first, in order to assess the reliability of the model. We will calculate the events per variable (EPV) for our model, using all the categories of categorical variables and the degrees of freedom of continuous outcomes [39]. We will calculate efficient sample size for developing a logistic regression model [40]. Validation is essential in prediction model development. Since no external data is available, we can only use internal validation. Via resampling methods like bootstrap or crossvalidation, we can estimate the calibration slope and cstatistic for each model, to indicate the ability of calibration and discrimination.
Stage 2: Developing a metaregression model for treatment effects
We use the same notation system as that in the “Average relative treatment effect: IPDMA” section. The logit(r_{ij}) from stage 1 will be used as a covariate in the metaregression model, both as a prognostic factor and as an effect modifier. Let \( {\overline{\mathrm{logit}\left({r}_{ij}\right)}}^j \) denote the average of logitrisk for all the individuals in study j. The regression equation will be:
a_{j} is the log odds in the control group when a patient has a risk equal to the mean risk, which is assumed to be independent across trials. g_{0j} is the coefficient of the risk score, while g_{j} is the treatment effect modification of the risk score for the intervention group versus the control group; both are assumed to be exchangeable cross trials and normally distributed about a summary estimate γ_{0} and γ respectively.
Predicting the probability of having the outcome for a new patient
Assume a new patient i who is not from any trial j has a baseline risk score \( \overset{\sim }{\mathrm{logit}\left({r}_i\right)} \) calculated from stageone. In order to predict the absolute logitprobability to achieve the outcome, we use:
We would have estimated δ, γ_{0}, and γ in the metaregression stage. We will estimate \( \overline{\mathrm{logit}(r)} \) as the mean of logit(r_{ij}) across all the individuals and studies. For a, we will estimate it by synthesizing all the control arms. Then, we can calculate the individual probability of the outcome both for the control and the intervention and estimate the predicted absolute and relative treatment effect.
To evaluate the performance of the twostage prediction model, we will use internally validation methods via both the traditional measures, like cstatistic, and measures relevant to clinical usefulness.
Publication bias
Considering that we will probably not be able to include all the relevant research works, as some studies or their results were likely not published owing to nonsignificant results (study publication bias and outcome reporting bias) [41, 42], we will evaluate this issue by comparing the search and screening results (as we will try to retrieve possibly unpublished reports) with the IPD we can get. If necessary, we will address it by adding the study’s variance as an extra covariate in the final IPD metaregression model (see the section “Predicting treatment effect for patients with particular characteristics: a twostage model”—“Stage 2: Developing a metaregression model for treatment effects”).
Statistical software
We will use R for our analyses. Stage 2 will be performed in a Bayesian setting using R2Jags. For the development of the baseline risk model, we will use the pmsampsize command to estimate if the available sample size is enough. We will examine the linear relationship between each one of the prognostic factors and the outcome via rcs and anova commands. The LASSO model will be developed using the cv.glmnet command. We will use the lrm command for the predefined model based on prior knowledge, and then for the penalized maximum likelihood estimation, we will use the pentrace command. For the bootstrap internal validation (both for the baseline risk score and for the twostage prediction model), we will use selfprogrammed Rroutines.
Discussion
We have presented the study protocol for a prediction model of treatment effects for RA patients receiving CTZ plus MTX, using a twostage approach based on IPDMA.
Though there are many optional drugs in treating RA, as treatment failure is relatively high, individualizing the treatment is imperative. Many prognostic models for RA have been proposed, but no one is sufficiently satisfactory [31]. We have discovered several problems.
Most previous models focused on longterm radiographic or functional prognosis. Although they are certainly the critical outcomes that both clinicians and patients care about, the complex therapeutic changes during the long treatment process are extremely difficult to handle in developing prediction models. Thus, it usually ends up with a simplified strategy, such as taking only the initial treatment into account, which compromises the clinical interpretation and relevancy of the model. On the other hand, a good shortterm treatment response is always positively associated with good longterm prognosis [43, 44]. Predicting shortterm treatment effect is instructive in clinical practice; however, research is lacking. A few established “shortterm” diseaseactivityoriented prediction models used an outcome measured at 6 months or 12 months. The problem is, unless in activecontrolled studies, there would be considerable dropouts after 3–4 months; furthermore, due to ethnical issues, many trials would offer the nonresponders other active treatments after 3–4 months. Under the ITT principle, patients were commonly analyzed as originally allocated; when dropouts were not negligible, imputation methods were usually used, but mostly single imputation such as nonresponder imputation or last observation carried forward (LOCF) [45]. One may argue that these estimates were conservative to the intervention group though not precise. But in fact it is not always conservative for a relative effect estimate, while unbiased relative estimates are of critical interest in building personalized prediction models. As a result, in order to be methodologically rigorous, we choose the outcome measured at 3 months, when the randomization is likely kept, and which is consistent with the assessment time recommended by the guideline [1]. Additionally, thanks to the IPD, we will be able to use multiple imputation to handle missing data, rather than the single imputations used in primary RCTs.
We will use a twostage approach to construct the prediction model using IPDMA. Unlike the usual approach, which includes baseline features as prognostic factors and effect modifiers (through interaction terms) simultaneously, we first build a risk model for baseline factors, then treating the risk score as both a prognostic factor and an effect modifier. By doing so, overfitting problem caused by too many covariates and interaction terms can be alleviated. Moreover, since penalization will only be used in common regression during risk modeling stage but not in metaregression, the compromised penalization in metaregression can be avoided. For stage 1, generally there are two types of risk models. One is an externally developed model, which is derived based on data independent from the data used at stage 2, such as established models from previous studies, or using some other studies. The other is an internally developed risk model, for which the same data will be used to build both the risk model and the treatment effects model [37]. Because there is no wellestablished risk model to predict the shortterm disease activity for RA patients and also because we will very probably not have sufficient sample size to divide the entire data into two parts, we will use the internal risk model for our study.
We acknowledge several limitations in our study. First, we handle effect modification at the level of risk scores, instead of particular covariates. That is, we will not try to identify specific effect modifiers. This may cause some problems in interpretation, as the concept of distinguishing prognostic factors and effect modifiers is well recognized. However, our approach assures the statistical power. Second, due to the restricted sample size, only internal validation is planned while external validation is lacking. It needs to be validated on an external dataset in the future. Third, we only focus on shortterm treatment response for RA patients receiving two kinds of treatment, CTZ and MTX. Future studies may extend the scope to compare several kinds of therapies and treatment strategies and finally model for the longterm prognosis taking into consideration all the treatment processes.
Availability of data and materials
The data that support the findings of this study are available from http://vivli.org but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from http://vivli.org upon reasonable request and application, after their permission.
Abbreviations
 ACR:

American College of Rheumatology
 AE:

Adverse event
 CDAI:

Clinical Disease Activity Index
 CTZ:

Certolizumab
 DAS28:

Disease Activity Score based on the evaluation of 28 joints
 DMARD:

Diseasemodifying antirheumatic drug (bDMARD, biologic DMARD; csDMARD, conventional synthetic DMARD; tsDMARDs, targeted synthetic DMARD)
 EPV:

Events per variable
 EULAR:

European League Against Rheumatism
 FDA:

Food and Drug Administration
 IPD:

Individual participant data
 LOCF:

Last observation carried forward
 MA:

Metaanalysis
 MTX:

Methotrexate
 OR:

Odds ratio
 PF:

Prognostic factor
 RA:

Rheumatoid arthritis
 RCT:

Randomized controlled trial
 RoB:

Risk of bias
 SDAI:

Simplified Disease Activity Index
 TNF:

Tumor necrosis factor
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Acknowledgements
GS has received funding from the Swiss National Science Foundation (Grant No. 179158). KC and GS acknowledged HTx project funded by the European Union’s Horizon 2020 research and innovation program under grant agreement (No. 825162). SF has received a research grant from JSPS KAKENHI Grant Number JP 20 K18964.
Funding
This study was supported by the intramural support to the Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health. The funder has no role in the study design, data collection, data analysis, data interpretation, writing of the report, or in the decision to submit for publication.
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YL and TAF conceived the study. KC and GS designed the modeling strategy. RY and SF provided substantial contribution to the design of the study during its development. YL drafted the manuscript, and all the authors critically revised it. All authors gave final approval of the version to be published.
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This study does not require institutional review board approval and participant consent.
Competing interests
TAF reports personal fees from MitsubishiTanabe, MSD, and Shionogi and a grant from MitsubishiTanabe, outside the submitted work; TAF has a patent 2018177688. GS was invited to participate in two methodological meetings about the use of realworld data, organized by Biogen and by Merck. All the other authors report no competing interests to declare.
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Luo, Y., Chalkou, K., Yamada, R. et al. Predicting the treatment response of certolizumab for individual adult patients with rheumatoid arthritis: protocol for an individual participant data metaanalysis. Syst Rev 9, 140 (2020). https://doi.org/10.1186/s1364302001401x
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Keywords
 Rheumatoid arthritis
 Certolizumab
 Individual participant data metaanalysis
 Prediction model
 Treatment response