Dataset Information

Robust analyses for radiographic progression in rheumatoid arthritis.

ABSTRACT: Demonstrating inhibition of the structural damage to joints as a statistically significant difference in radiographic progression as measured by the van der Heijde modified Total Sharp Score (mTSS) is a common objective in trials for rheumatoid arthritis treatments. The frequently used analysis of the covariance model with missing data imputed using linear extrapolation (analyses of covariance, ANCOVA+LE) may not be ideal for long-term extension studies or for paediatric studies. The random coefficient (RC) model may represent a better alternative.A two-arm (active treatment and placebo) setting with a week 44 study period was considered. RC model, ANCOVA+LE and ANCOVA with last observation carried forward imputation were compared under different scenarios in bias, root mean square error (RMSE), power and type I error rate.The RC model outperformed ANCOVA+LE in metrics measuring bias, RMSE, power and type I error rate under the evaluated scenarios. ANCOVA and RC provide similar performance when there are no missing data. With missing data, RC+observed (OBS) provides similar or better results than ANCOVA+LE in power and bias.Our simulations support that RC is both a more sensitive and a more precise alternative to the commonly used ANCOVA+LE as a primary method for analysing mTSS in long-term extension and paediatric studies with a higher likelihood of missing data. The RC model can provide a reference at time points with missing data by estimating a slope; mTSS change by one unit change in time. ANCOVA+LE is recommended as a sensitivity analysis.

SUBMITTER: Landewe R

PROVIDER: S-EPMC10083852 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature

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Robust analyses for radiographic progression in rheumatoid arthritis.

Landewé Robert R Sun Luna L Chen Yun-Fei YF Daojun Mo M van der Heijde Desirée D

RMD open 20230401 2

Demonstrating inhibition of the structural damage to joints as a statistically significant difference in radiographic progression as measured by the van der Heijde modified Total Sharp Score (mTSS) is a common objective in trials for rheumatoid arthritis treatments. The frequently used analysis of the covariance model with missing data imputed using linear extrapolation (analyses of covariance, ANCOVA+LE) may not be ideal for long-term extension studies or for paediatric studies. The random coef ...[more]

PMID: 37015757

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Project description:BackgroundThe detection of statistically significant reductions in radiographic progression during clinical studies in patients with rheumatoid arthritis (RA) has become increasingly difficult over the past decade due to early-escape study designs and declining rates of progression in control-group patients. We investigated the impact of extremes of radiographic data (outliers) and baseline prognostic factors on detection of treatment effects, to provide guidance on future analysis of joint structural data in RA clinical trials.MethodsData were from two, phase 3, randomized, double-blind, placebo-controlled trials of tofacitinib in adult patients with moderate to severe RA: ORAL Scan (NCT00847613) and ORAL Start (NCT01039688). These studies detected significant reductions in radiographic progression with tofacitinib 10 mg twice daily (BID) plus background methotrexate (ORAL Scan), and with tofacitinib 5 or 10 mg BID as monotherapy (ORAL Start). We evaluated mean changes from baseline in van der Heijde modified total Sharp score (mTSS) at month 6 and month 12, using analysis of covariance (ANCOVA). A trimmed analysis was used to deal with extremes of data. The impact of baseline prognostic factors on radiographic progression was evaluated using ANCOVA to analyze the mean change from baseline in mTSS for each factor in turn.ResultsThe analysis included data from 720 patients from ORAL Scan and 880 patients from ORAL Start. Trimmed analyses were unbiased for the true mean estimate and enabled us to remove the effect of influential extreme observations in the data set. Almost all patients had at least one poor prognostic factor at baseline (e.g., high level of disease activity, or positive for rheumatoid factor). The strongest predictor of treatment effect was the severity of radiographic damage at baseline.ConclusionsA trimmed analysis can establish whether any significant inhibition of structural damage is being driven by extremes of data, and should be one of the sensitivity analyses of choice for structural data in RA clinical trials. Furthermore, analysis of radiographic data based on baseline prognostic factors may reveal increased treatment effects. Application of these methods to analysis of radiographic data from clinical trials in patients with RA, allows a more complete interpretation of data.Trial registrationClinicaltrials.gov NCT00847613 (registered 17 February 2009) and NCT01039688 (registered 23 December 2009).

Project description:Semaphorin 3A (Sema3A) and semaphorin 4D (Sema4D) are molecules which regulate immune responses as well as bone remodeling process. The aim of this study was to evaluate the serum levels of Sema3A and Sema4D and to investigate their clinical significance in rheumatoid arthritis (RA). The serum levels of Sema3A and Sema4D were measured in 130 patients with RA and 65 sex- and age-matched healthy individuals. Circulating levels of biomarkers of RA-related inflammation and bone turnover such as tumor necrosis factor- (TNF-) α, interleukin- (IL-) 6, IL-22, IL-34, osteopontin, Dkk-1, and sclerostin were also measured. Disease activity was determined by the 28-joint disease activity score (DAS28), and radiographic joint damage was assessed by the modified Sharp van der Heijde score (SHS). The serum levels of Sema3A were significantly higher in patients with RA than those in healthy controls (p < 0.001), whereas serum4D levels did not differ between the two groups. The levels of Sema4D showed a positive correlation with C-reactive protein (p = 0.001) and IL-6 (p < 0.001) levels, whereas the levels of Sema3A showed a negative correlation with Dkk-1 (p = 0.007) and TNF-α (p = 0.001). Even though Sema3A and Sema4D levels were comparable between RA patients with DAS28> 3.2 and with DAS28 ≤ 3.2, RA patients with radiographic progression (ΔSHS change/year ≥ 1) had significantly higher baseline levels of Sema4D than those without progression (p = 0.029). Additionally, when RA patients were divided into 3 groups using tertiles of Sema4D levels, the percentage of progressors was significantly increased (p = 0.045). In multivariate logistic regression analysis, serum Sema4D levels were an independent risk factor for radiographic progression. Our results suggest that the baseline levels of Sema4D might be a useful marker to identify RA patients with subsequent radiographic progression and that Sema4D may be an active mediator involved in RA-induced joint damage.

Project description:ObjectivePhase III clinical trials of the tumour necrosis factor inhibitors SB4, SB2, and SB5 (biosimilars to etanercept, infliximab, and adalimumab, respectively) have demonstrated efficacy in moderate-to-severe rheumatoid arthritis (RA). Data from these trials were used to identify baseline characteristics associated with radiographic progression and to build a matrix risk model for its prediction.MethodsPatients with radiographic progression and baseline demographic and disease characteristic data were pooled across the 3 phase III studies of each biosimilar and its reference product. Baseline demographics and disease characteristics were evaluated for their relationship with radiographic progression (1-year mean change in mTSS > 0); 3 factors were selected based on strongest Pearson's correlation coefficient with the change in modified Total Sharp Score. Univariate logistic regression was performed to assess the association between each baseline factor and the rate of radiographic progression, with subsequent matrix model development performed using multivariate logistic regression.ResultsA total of 1371 patients were included in the analysis, with a radiographic progression rate of 27.4%. The 3 baseline predictors of radiographic progression, based on Pearson's correlation coefficient, were 28 swollen joint count (SJC28), C-reactive protein (CRP), and physician global assessment (PhGA). A matrix model showed that the predicted risk of radiographic progression was higher with the increased level of SJC28, CRP, and PhGA (P < 0.001).ConclusionsIn this pooled analysis of phase III clinical trial data of biosimilars for RA, identifiable baseline factors (SJC28, CRP, and PhGA) associated with radiographic progression were similar to those described in prior studies. Even though radiographic progression was minimal, a small number of patients who have increased SJC28, CRP, and PhGA at baseline should be closely monitored and follow treat-to-target approach.Clinical trial registration numbersEudraCT 2012-005026-30. Registered 30 April 2013, https://www.clinicaltrialsregister.eu/ctr-search/trial/2012-005026-30/results EudraCT 2012-005733-37. Registered 10 July 2013, https://www.clinicaltrialsregister.eu/ctr-search/trial/2012-005733-37/results EudraCT 2013-005013-13. Registered 01 April 2014, https://www.clinicaltrialsregister.eu/ctr-search/trial/2013-005013-13/results.

Project description:BackgroundRadiographic progression is reported to be highly heritable in rheumatoid arthritis (RA). However, previous study using genetic loci showed an insufficient accuracy of prediction for radiographic progression. The aim of this study is to identify a biologically relevant prediction model of radiographic progression in patients with RA using a genome-wide association study (GWAS) combined with bioinformatics analysis.MethodsWe obtained genome-wide single nucleotide polymorphism (SNP) data for 374 Korean patients with RA using Illumina HumanOmni2.5Exome-8 arrays. Radiographic progression was measured using the yearly Sharp/van der Heijde modified score rate, and categorized in no or severe progression. Significant SNPs for severe radiographic progression from GWAS were mapped on the functional genes and reprioritized by post-GWAS analysis. For robust prediction of radiographic progression, tenfold cross-validation using a support vector machine (SVM) classifier was conducted. Accuracy was used for selection of optimal SNPs set in the Hanyang Bae RA cohort. The performance of our final model was compared with that of other models based on GWAS results and SPOT (one of the post-GWAS analyses) using receiver operating characteristic (ROC) curves. The reliability of our model was confirmed using GWAS data of Caucasian patients with RA.ResultsA total of 36,091 significant SNPs with a p value <0.05 from GWAS were reprioritized using post-GWAS analysis and approximately 2700 were identified as SNPs related to RA biological features. The best average accuracy of ten groups was 0.6015 with 85 SNPs, and this increased to 0.7481 when combined with clinical information. In comparisons of the performance of the model, the 0.7872 area under the curve (AUC) in our model was superior to that obtained with GWAS (AUC 0.6586, p value 8.97 × 10-5) or SPOT (AUC 0.7449, p value 0.0423). Our model strategy also showed superior prediction accuracy in Caucasian patients with RA compared with GWAS (p value 0.0049) and SPOT (p value 0.0151).ConclusionsUsing various biological functions of SNPs and repeated machine learning, our model could predict severe radiographic progression relevantly and robustly in patients with RA compared with models using only GWAS results or other post-GWAS tools.

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Robust analyses for radiographic progression in rheumatoid arthritis.

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Robust analyses for radiographic progression in rheumatoid arthritis.

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