Knee Cartilage Defect Patients Enrolled in Randomized Controlled Trials Are Not Representative of Patients in Orthopedic Practice

AbstractVarious techniques have proven to be effective for treating articular cartilage defect of the knee joint, but knowledge regarding which method is best still remains uncertain. Osteochondral autologous transplantation (OAT) provides hyaline or hyaline-like repair for articular defects, whereas microfracture (MF) provides fibrocartilage repair tissue. To compare the OAT with MF procedure for the treatment of articular cartilage defect, we present an update of previous meta-analysis of randomized controlled trials.We searched for the published results of relevant trials. Then, we pooled the outcome measures of the included trials for analysis. The outcome measures assessed in this study included the number of patients who reached excellent or good results, the patients returning to the activity, the International Cartilage Repair Society (ICRS) scores, the failure rate, and the osteoarthritis rate. Six studies and 294 patients were identified as eligible for data extraction and meta-analysis. The pooled result showed that there was no significant difference in the excellent or good results (relative risks [RRs], 1.27; 95% confidence intervals [CIs], 0.95 to 1.70; p = 0.11) and the rate of osteoarthritis (RRs, 0.64; 95% CIs, 0.37 to 1.13; p = 0.12). There were significant differences between the two groups in the scores of ICRS (mean differences [MDs], 12.51; 95% CIs, 10.55 to 14.47; p = 0.00001), the patients returning to activity (RRs, 2.04; 95% CIs, 1.36 to 3.07; p = 0.0006), and in the failure rate (RRs, 0.23; 95% CIs, 0.11 to 0.49; p = 0.0001). OAT has more advantages compared with MF procedure in referring to the index of return to activity, the scores of ICRS, and the rate of failure. However, the limitations restrict the generalizability of this study, and larger, sufficiently powered studies are necessary to evaluate the efficiency of OAT compared with MF procedure in the future.

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Surgical Techniques for Knee Cartilage Repair: An Updated Large-Scale Systematic Review and Network Meta-analysis of Randomized Controlled Trials

Arthroscopy The Journal of Arthroscopic and Related Surgery ◽

10.1016/j.arthro.2019.11.096 ◽

2020 ◽

Vol 36 (3) ◽

pp. 845-858 ◽

Cited By ~ 5

Author(s):

Radoslav Zamborsky ◽

Lubos Danisovic

Keyword(s):

Systematic Review ◽

Randomized Controlled Trials ◽

Cartilage Repair ◽

Large Scale ◽

Meta Analysis ◽

Surgical Techniques ◽

Controlled Trials ◽

Knee Cartilage ◽

Randomized Controlled

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Abstracts #300082: Current Therapies in Nonalcoholic Fatty Liver Disease (NAFLD). A Meta-analysis of Randomized Controlled Trials

Endocrine Practice ◽

10.1016/s1530-891x(20)48249-7 ◽

2020 ◽

Vol 26 ◽

pp. 5

Author(s):

Emily Wood

Keyword(s):

Liver Disease ◽

Fatty Liver ◽

Randomized Controlled Trials ◽

Nonalcoholic Fatty Liver Disease ◽

Fatty Liver Disease ◽

Meta Analysis ◽

Controlled Trials ◽

Nonalcoholic Fatty Liver ◽

Randomized Controlled ◽

Current Therapies

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258 Ambulatory monitoring for heart failure patients: it is possible to improve survival and quality of life: a metanalysis of randomized controlled trials

European Journal of Heart Failure Supplements ◽

10.1016/s1567-4215(07)60137-4 ◽

2007 ◽

Vol 6 (1) ◽

pp. 51-51

Author(s):

F CANARIOALMEIDA ◽

L AZEVEDO ◽

A COSTAPEREIRA

Keyword(s):

Quality Of Life ◽

Heart Failure ◽

Randomized Controlled Trials ◽

Ambulatory Monitoring ◽

Controlled Trials ◽

Heart Failure Patients ◽

Randomized Controlled

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The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽

10.1027/1614-2241/a000121 ◽

2017 ◽

Vol 13 (2) ◽

pp. 41-60

Author(s):

Shahab Jolani ◽

Maryam Safarkhani

Keyword(s):

Randomized Controlled Trials ◽

Discrete Time ◽

Treatment Effect ◽

Survival Data ◽

Controlled Trials ◽

Missing Covariates ◽

Indicator Method ◽

Imputation Methods ◽

Randomized Controlled ◽

Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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