Are sample sizes of randomized clinical trials in rheumatoid arthritis too large?

Çelik, Selda; Yazıcı, Yusuf; Yazıcı, Hasan

doi:10.1111/eci.12337

Cited by 7 publications

(5 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Follow-up variability data (standard error of the mean) are derived from the generated delta and baseline variability measures, using the inverse formula, setting hypotheses on pre-post correlation (ρ) and considering, in each scenario, a sequence of ρ values from 0.3 to 0.95 by in increments of 0.05. The sample size n i is obtained by sampling from a uniform distribution bounded 50-100, reflecting the general framework of the sample size in rheumatological trials [30].…”

Section: Simulation Frameworkmentioning

confidence: 99%

Handling missing continuous outcome data in a Bayesian network meta-analysis

Azzolina

Baldi

Minto

et al. 2022

ebph

View full text Add to dashboard Cite

Background: A Bayesian network meta-analysis (NMA) model is a statistical method aimed at estimating the relative effects of multiple interventions against the same disease. The method has recently gained prominence, leading to the synthesis of the evidence regarding rank probabilities for each treatment. In several cases, an NMA is performed excluding incomplete data of studies retrieved through a systematic review, resulting in a loss of precision and power. Methods: There are several methods for handling missing or incomplete data in an NMA framework, especially for continuous outcomes. In certain cases, only baseline and follow-up measurements are available; in this framework, to obtain data regarding mean changes, it is necessary to consider the pre-post study correlation. In this context, in a Bayesian setting, several authors suggest imputation strategies for pre-post correlation. In other cases, a variability measure associated with a mean change score might be unavailable. Different imputation methods have been suggested, such as those based on maximum standard deviation imputation. The purpose of this study is to verify the robustness of Bayesian NMA models concerning different imputation strategies through simulations. Results: Simulation results show that the bias is notably small for every scenario, confirming that rankings provided by models are robust concerning different imputation methods in several heterogeneity-correlation settings. Conclusions: This NMA method seems to be more robust to missing data imputation when data reported in different studies are generated in a low-heterogeneity scenario. The NMA method seems to be more robust to missing value imputation if the expectation of the prior distribution, defined on the heterogeneity parameter, approaches the true value of the variability across studies.

show abstract

Section: Simulation Frameworkmentioning

confidence: 99%

Handling missing continuous outcome data in a Bayesian network meta-analysis

Azzolina

Baldi

Minto

et al. 2022

ebph

View full text Add to dashboard Cite

show abstract

“…Celik et al . [ 35 ] found that most randomized controlled trials (RCTs) on rheumatoid arthritis enroll more patients than needed. This is a needless exposure of patients to a regimen that is under trial.…”

Section: Large Nmentioning

confidence: 99%

The importance of small samples in medical research

Indrayan

Mishra

2021

Journal of Postgraduate Medicine

View full text Add to dashboard Cite

Almost all bio-statisticians and medical researchers believe that a large sample is always helpful in providing more reliable results. Whereas this is true for some specific cases, a large sample may not be helpful in more situations than we contemplate because of the higher possibility of errors and reduced validity. Many medical breakthroughs have occurred with self-experimentation and single experiments. Studies, particularly analytical studies, may provide more truthful results with a small sample because intensive efforts can be made to control all the confounders, wherever they operate, and sophisticated equipment can be used to obtain more accurate data. A large sample may be required only for the studies with highly variable outcomes, where an estimate of the effect size with high precision is required, or when the effect size to be detected is small. This communication underscores the importance of small samples in reaching a valid conclusion in certain situations and describes the situations where a large sample is not only unnecessary but may even compromise the validity by not being able to exercise full care in the assessments. What sample size is small depends on the context.

show abstract

“…Since this is a retrospective study, the sample size was not calculated. It has been reported that at least 40 patients should be included in each group with 90% potency to evaluate the efficacy in biological drug studies used in RA [16].…”

Section: Sample Sizementioning

confidence: 99%

Which anti-TNF is most effective for my patient? Which one should I choose?

Pamukçu¹,

Baykara²

2021

Journal of Surgery and Medicine

View full text Add to dashboard Cite

Background/Aim: Multicenter controlled studies were conducted on the effect of anti-Tumor Necrosis Factor (TNF) agents in rheumatoid arthritis (RA) and varying effectiveness rates were reported. These agents have different advantages over each other. We aimed to compare the disease activation parameters in patients with RA at the beginning and the 52 nd week of therapy in patients who were followed up in our center and started on anti-TNF (etanercept, adalimumab, and golimumab), and examine the effects of the drugs that are used by comparing them with each other. Methods: This retrospective cohort study included 187 patients with RA who were started on anti-TNF therapy because the disease activity could not be controlled by the concomitant use of at least three different conventional Disease-Modifying Anti-Rheumatic drugs, and whose adequate response to anti-TNF were observed at the 12th-week follow-up. RA disease activity was measured using the 28-joint Disease Activity Score incorporating erythrocyte sedimentation rate (DAS-28 ESR) and the patients were evaluated by a Health Assessment Questionnaire (HAQ). For each drug group, disease activation and laboratory parameters were compared before treatment initiation and at 52 weeks of treatment. These values were then compared between the drug groups. Results: The mean age of 187 patients included in the study was 52.70 (10.17) years, 119 (63.6%) were female and 68 (36.4%) were male. Of the patients, 63 (33.7%) were using adalimumab, 62 (33.2%) were using etanercept and 62 (33.2%) were using golimumab. In all patients, there was a significant improvement in all parameters except mean corpuscular hemoglobin, gamma-glutamyl transferase, and creatinine. There were significant changes in hemoglobin, leukocyte and platelet count, erythrocyte sedimentation rate, C reactive protein, neutrophil count, serum albumin, DAS-28 ESR, and HAQ levels in all three groups (P<0.05). Conclusion: There were no differences in efficacy between adalimumab, etanercept and golimumab therapies, which were planned considering the comorbidities and drug preferences of the patients. In addition to controlled studies, real-life data to be reported by rheumatology centers will help us obtain more accurate information about the therapy results of anti-TNF agents.

show abstract

Are sample sizes of randomized clinical trials in rheumatoid arthritis too large?

Abstract: Most RCTs in RA enrol more patients than needed. This is costly and has the immediate consequence of exposing needless number of patients to potential harm.

Cited by 7 publications

References 52 publications

Handling missing continuous outcome data in a Bayesian network meta-analysis

Handling missing continuous outcome data in a Bayesian network meta-analysis

The importance of small samples in medical research

Which anti-TNF is most effective for my patient? Which one should I choose?

Contact Info

Product

Resources

About