Critical Period After Stroke Study (CPASS): A phase II clinical trial testing an optimal time for motor recovery after stroke in humans.

Restoration of human brain function after injury is a signal challenge for translational neuroscience. Rodent stroke recovery studies identify an optimal or sensitive period for intensive motor training after stroke: near-full recovery is attained if task-specific motor training occurs during this sensitive window. We extended these findings to adult humans with stroke in a randomized controlled trial applying the essential elements of rodent motor training paradigms to humans. Stroke patients were adaptively randomized to begin 20 extra hours of self-selected, task-specific motor therapy at ≤30 d (acute), 2 to 3 mo (subacute), or ≥6 mo (chronic) after stroke, compared with controls receiving standard motor rehabilitation. Upper extremity (UE) impairment assessed by the Action Research Arm Test (ARAT) was measured at up to five time points. The primary outcome measure was ARAT recovery over 1 y after stroke. By 1 y we found significantly increased UE motor function in the subacute group compared with controls (ARAT difference = +6.87 ± 2.63, P = 0.009). The acute group compared with controls showed smaller but significant improvement (ARAT difference = +5.25 ± 2.59 points, P = 0.043). The chronic group showed no significant improvement compared with controls (ARAT = +2.41 ± 2.25, P = 0.29). Thus task-specific motor intervention was most effective within the first 2 to 3 mo after stroke. The similarity to rodent model treatment outcomes suggests that other rodent findings may be translatable to human brain recovery. These results provide empirical evidence of a sensitive period for motor recovery in humans.

Design and modeling for drug combination experiments with order effects.

Combinations of drugs are now ubiquitous in treating complex diseases such as cancer and HIV due to their potential for enhanced efficacy and reduced side effects. The traditional combination experiments of drugs focus primarily on the dose effects of the constituent drugs. However, with the doses of drugs remaining unchanged, different sequences of drug administration may also affect the efficacy endpoint. Such drug effects shall be called as order effects. The common order-effect linear models are usually inadequate for analyzing combination experiments due to the nonlinear relationships and complex interactions among drugs. In this article, we propose a random field model for order-effect modeling. This model is flexible, allowing nonlinearities, and interaction effects to be incorporated with a small number of model parameters. Moreover, we propose a subtle experimental design that will collect good quality data for modeling the order effects of drugs with a reasonable run size. A real-data analysis and simulation studies are given to demonstrate that the proposed design and model are effective in predicting the optimal drug sequences in administration.

Set-regression with applications to subgroup analysis.

Regression is a commonly used statistical model. It is the conditional mean of the response given covariates µ(x)=E(Y|X=x) . However, in some practical problems, the interest is the conditional mean of the response given the covariates belonging to some set A. Notably, in precision medicine and subgroup analysis in clinical trials, the aim is to identify subjects who benefit the most from the treatment, or identify an optimal set in the covariate space which manifests treatment favoritism if a subject's covariates fall in this set and the subject is classified to the favorable treatment subgroup. Existing methods for subgroup analysis achieve this indirectly by using classical regression. This motivates us to develop a new type of regression: set-regression, defined as µ(A)=E(Y|X∈A) which directly addresses the subgroup analysis problem. This extends not only the classical regression model but also improves recursive partitioning and support vector machine approaches, and is particularly suitable for objectives involving optimization of the regression over sets, such as subgroup analysis. We show that the new versatile set-regression identifies the subgroup with increased accuracy. It is easy to use. Simulation studies also show superior performance of the proposed method in finite samples.

Robust estimates of regional treatment effects in multiregional randomized clinical trials with ordinal responses.

Global clinical trials involving multiple regions are common in current drug development processes. Determining the regional treatment effects of a new therapy over an existing therapy is important to both the sponsors and the regulatory agencies in the regions. Existing methods are mainly for continuous primary endpoints and use subjectively specified models, which may deviate from the true model. Here, we consider trials that have ordinal responses as the primary endpoint. This article extends the recently developed robust semiparametric ordinal regression model to estimate regional treatment effects, in which the regression coefficients and regional effects are modeled parametrically for ease of interpretation, and the regression link function is specified nonparametrically for robustness. The model parameters are estimated by semiparametric maximum likelihood estimation, and the null hypothesis of no regional effect is tested by the Wald test. Simulation studies are conducted to evaluate the performance of the proposed method and compare it with the commonly used parametric model. The results of the former show an improved overall performance over the latter. In particular, the model yields much higher precision in estimation and prediction than the fixed-link model. This result is especially appealing since our interest is to estimate the treatment effect more efficiently and the estimand is of particular interest in multiregional clinical trials. We then apply the method by analyzing real multiregional clinical trials with ordinal responses as their primary endpoint.

Robust estimates of regional treatment effects in multiregional randomized clinical trials with semiparametric logistic model.

In multiregional randomized clinical trials (MRCTs), determining the regional treatment effect of a new treatment over an existing one is important to both the sponsor and related regulatory agencies. Also of particular interest is to test the null hypothesis that the treatment benefit is the same among all the regions. Existing methods are mainly for continuous endpoint and use parametric models, which are not robust. MRCTs are known for facing increased variation and heterogeneity and a robust model for its design and analysis would be desirable. We consider clinical trials with a binary primary endpoint and propose a robust semiparametric logistic model which has a known parametric and an unknown nonparametric component. The parametric component represents our prior knowledge about the model, and the nonparametric part reflects uncertainty. Compared to the classic logistic model for this problem, the proposed model has the following advantages: robust to model assumption, more flexible and accurate to model the relationship between the response and covariates, and possibly more accurate parameter estimates. The model parameters are estimated by profile maximum likelihood approach, and the null hypothesis of regional treatment difference being the same is tested by the profile likelihood ratio statistic. Asymptotic properties of the estimates are derived. Simulation studies are conducted to evaluate the performance of the proposed model, which demonstrated clear advantages over the classic logistic model. The method is then applied to analyzing a real MRCT.

Identification of subgroups via partial linear regression modeling approach.

In clinical trials, treatment effects often vary from subject to subject. Some subjects may benefit more than others from a specific treatment. One of the aims of subgroup analysis is to identify if there are subgroups of subjects with differential treatment effects. As in standard analysis, we first test if subgroups with differential treatment effects exist; if they do, we classify the subjects into different subgroups based on their covariate profiles; otherwise, we conclude no subgroups have differential treatment effects in this population. Existing methods utilize regression models, particularly linear models, for such analysis. However, in practice, not all effects of covariates on responses are linear. To address this issue, the article proposes a more flexible model, the partial linear model with a nonlinear monotone function to describe some specific effects of covariates and with a linear component to describe the effects of other covariates, develops model-fitting algorithm and derives model asymptotics. We then utilize the Wald statistic to test the existence of subgroups and the Neyman-Pearson rule to classify subjects into the subgroups. Simulation studies are conducted to evaluate the finite sample performance of the proposed method by comparing it with the commonly used linear models. Finally, we apply the methods to analyzing a real clinical trial.

Comparison of the effects of 10.6-µm infrared laser and traditional moxibustion in the treatment of knee osteoarthritis.

Based on two separate randomized controlled trials (RCTs) on traditional Chinese medicine (TCM) moxibustion and 10.6-µm infrared laser moxibustion in treating knee osteoarthritis (OA), we did an indirect and preliminary comparison of the effects of the 10.6-µm laser moxibustion with the traditional moxibustion for knee osteoarthritis. The objective was to see whether the laser moxibustion is non-inferior to the traditional moxibustion in alleviating symptoms of knee osteoarthritis such as pain, stiffness, and joint dysfunction as well as improving quality of life for the patients with knee osteoarthritis, and whether a further RCT directly comparing the laser and traditional moxibustion is necessary. Pooled data from two RCTs in patients with knee osteoarthritis, trial ISRCTN68475405 and trial ISRCTN26065334, were used. In the two RCTs, the eligibility criteria were almost identical, the treatment procedure (i.e., sessions, duration, and points) were similar, and the outcome measurements (i.e., WOMAC for symptoms and SF-36 for quality of life) were the same. The double robustness method was used for the WOMAC scale and the SF-36 endpoints to detect the difference between traditional and laser moxibustion. The analysis comprised 55 patients from ISRCTN68475405 in real moxibustion arm (moxibustion group) and 88 patients from ISRCTN26065334 in real laser moxibustion arm (laser group). Demographic characteristics and course of disease were similar between the two groups. Causal inference, using the doubly robust estimating approach to correct for bias due to baseline differences, showed that there was no statistically significant difference in the WOMAC pain, stiffness, and physical function between the two treatments at midterm, end of treatment, and 4 weeks after the end of treatment (P > 0.05). The exception was that there was statistically significantly more benefit associated with laser moxibustion compared with traditional moxibustion in physical function at the follow-up of 4 weeks after the end of treatment (P=0.006). There was no statistically significant difference in most SF-36 endpoints (P > 0.05) except that physical functioning (PF), mental health (MH), and bodily pain (BP) were statistically significantly better in the laser group than in the traditional moxibustion group at the follow-up of 4 weeks after the end of treatment (P = 0.005, 0.034, 0.002). The benefits of 10.6-µm infrared laser moxibustion and the traditional moxibustion for knee osteoarthritis were comparable in pain, stiffness, physical dysfunction, and in most of the quality of life subdimensions. The laser moxibustion might be more beneficial in terms of physical function, body pain, and mental health in the long term. RCTs directly comparing 10.6-µm laser moxibustion with traditional moxibustion are warranted.

Pembrolizumab in patients with thymic carcinoma: a single-arm, single-centre, phase 2 study.

BACKGROUND: Treatment options are limited for patients with thymic carcinoma. These aggressive tumours are not typically associated with paraneoplastic autoimmune disorders, and strong PD-L1 expression has been reported in thymic epithelial tumours. We aimed to assess the activity of pembrolizumab, a monoclonal antibody that targets PD-1, in patients with advanced thymic carcinoma. METHODS: We completed a single-arm phase 2 study of pembrolizumab in patients with recurrent thymic carcinoma who had progressed after at least one line of chemotherapy. This was a single-centre study performed at Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA. Key inclusion criteria were an Eastern Cooperative Oncology Group performance status of 0-2, no history of autoimmune disease or other malignancy requiring treatment or laboratory abnormality, and adequate organ function. Patients received 200 mg of pembrolizumab every 3 weeks for up to 2 years. The primary objective of the study was the proportion of patients who had achieved a response assessed with Response Evaluation Criteria in Solid Tumors version 1.1. Analysis was per protocol, in all eligible patients. The study is registered with ClinicalTrials.gov, number NCT02364076, and is closed to accrual; we report the final analysis. FINDINGS: 41 patients were enrolled from March 12, 2015, to Dec 16, 2016, of whom 40 were eligible and evaluable and one was excluded because of elevated liver enzymes at screening. The median follow-up was 20 months (IQR 14-26). The proportion of patients who achieved a response was 22·5% (95% CI 10·8-38·5); one (3%) patient achieved a complete response, eight (20%) patients achieved partial responses, and 21 (53%) patients achieved stable disease. The most common grade 3 or 4 adverse events were increased aspartate aminotransferase and alanine aminotransferase (five [13%] patients each). Six (15%) patients developed severe autoimmune toxicity, including two (5%) patients with myocarditis. There were 17 deaths at the time of analysis, but no deaths due to toxicity. INTERPRETATION: Pembrolizumab is a promising treatment option in patients with thymic carcinoma. Because severe autoimmune disorders are more frequent in thymic carcinoma than in other tumour types, careful monitoring is essential. FUNDING: Merck & Co.

Experimental design for multi-drug combination studies using signaling networks.

Combinations of multiple drugs are an important approach to maximize the chance for therapeutic success by inhibiting multiple pathways/targets. Analytic methods for studying drug combinations have received increasing attention because major advances in biomedical research have made available large number of potential agents for testing. The preclinical experiment on multi-drug combinations plays a key role in (especially cancer) drug development because of the complex nature of the disease, the need to reduce development time and costs. Despite recent progresses in statistical methods for assessing drug interaction, there is an acute lack of methods for designing experiments on multi-drug combinations. The number of combinations grows exponentially with the number of drugs and dose-levels and it quickly precludes laboratory testing. Utilizing experimental dose-response data of single drugs and a few combinations along with pathway/network information to obtain an estimate of the functional structure of the dose-response relationship in silico, we propose an optimal design that allows exploration of the dose-effect surface with the smallest possible sample size in this article. The simulation studies show our proposed methods perform well.

Subgroup analysis with semiparametric models toward precision medicine.

In analyzing clinical trials, one important objective is to classify the patients into treatment-favorable and nonfavorable subgroups. Existing parametric methods are not robust, and the commonly used classification rules ignore the fact that the implications of treatment-favorable and nonfavorable subgroups can be different. To address these issues, we propose a semiparametric model, incorporating both our knowledge and uncertainty about the true model. The Wald statistics is used to test the existence of subgroups, while the Neyman-Pearson rule to classify each subject. Asymptotic properties are derived, simulation studies are conducted to evaluate the performance of the method, and then method is used to analyze a real-world trial data.

SAFE-HEaRt: Rationale and Design of a Pilot Study Investigating Cardiac Safety of HER2 Targeted Therapy in Patients with HER2-Positive Breast Cancer and Reduced Left Ventricular Function.

BACKGROUND: Human epidermal growth receptor 2 (HER2) targeted therapies have survival benefit in adjuvant and metastatic HER2 positive breast cancer but are associated with cardiac dysfunction. Current U.S. Food and Drug Administration recommendations limit the use of HER2 targeted agents to patients with normal left ventricular (LV) systolic function. METHODS: The objective of the SAFE-HEaRt study is to evaluate the cardiac safety of HER2 targeted therapy in patients with HER2 positive breast cancer and mildly reduced left ventricular ejection fraction (LVEF) with optimized cardiac therapy. Thirty patients with histologically confirmed HER2 positive breast cancer (stage I-IV) and reduced LVEF (40% to 49%) who plan to receive HER2 targeted therapy for ≥3 months will be enrolled. Prior to initiation on study, optimization of heart function with beta-blockers and angiotensin converting enzyme inhibitors will be initiated. Patients will be followed by serial echocardiograms and cardiac visits during and 6 months after completion of HER2 targeted therapy. Myocardial strain and blood biomarkers, including cardiac troponin I and high-sensitivity cardiac troponin T, will be examined at baseline and during the study. DISCUSSION: LV dysfunction in patients with breast cancer poses cardiac and oncological challenges and limits the use of HER2 targeted therapies and its oncological benefits. Strategies to prevent cardiac dysfunction associated with HER2 targeted therapy have been limited to patients with normal LVEF, thus excluding patients who may receive the highest benefit from those strategies. SAFE-HEaRt is the first prospective pilot study of HER2 targeted therapies in patients with reduced LV function while on optimized cardiac treatment that can provide the basis for clinical practice changes. The Oncologist 2017;22:518-525 IMPLICATIONS FOR PRACTICE: Human epidermal growth receptor 2 (HER2) targeted therapies have survival benefit in adjuvant and metastatic HER2 positive breast cancer but are associated with cardiac dysfunction. To our knowledge, SAFE-HEaRt is the first clinical trial that prospectively tests the hypothesis that HER2 targeted therapies may be safely administered in patients with mildly reduced cardiac function in the setting of ongoing cardiac treatment and monitoring. The results of this study will provide cardiac safety data and inform consideration of clinical practice changes in patients with HER2 positive breast cancer and reduced cardiac function, as well as provide information regarding cardiovascular monitoring and treatment in this population.

Efficient statistical tests to compare Youden index: accounting for contingency correlation.

Youden index is widely utilized in studies evaluating accuracy of diagnostic tests and performance of predictive, prognostic, or risk models. However, both one and two independent sample tests on Youden index have been derived ignoring the dependence (association) between sensitivity and specificity, resulting in potentially misleading findings. Besides, paired sample test on Youden index is currently unavailable. This article develops efficient statistical inference procedures for one sample, independent, and paired sample tests on Youden index by accounting for contingency correlation, namely associations between sensitivity and specificity and paired samples typically represented in contingency tables. For one and two independent sample tests, the variances are estimated by Delta method, and the statistical inference is based on the central limit theory, which are then verified by bootstrap estimates. For paired samples test, we show that the estimated covariance of the two sensitivities and specificities can be represented as a function of kappa statistic so the test can be readily carried out. We then show the remarkable accuracy of the estimated variance using a constrained optimization approach. Simulation is performed to evaluate the statistical properties of the derived tests. The proposed approaches yield more stable type I errors at the nominal level and substantially higher power (efficiency) than does the original Youden's approach. Therefore, the simple explicit large sample solution performs very well. Because we can readily implement the asymptotic and exact bootstrap computation with common software like R, the method is broadly applicable to the evaluation of diagnostic tests and model performance.

Combination immunotherapy using adoptive T-cell transfer and tumor antigen vaccination on the basis of hTERT and survivin after ASCT for myeloma.

In a phase 1/2 two-arm trial, 54 patients with myeloma received autografts followed by ex vivo anti-CD3/anti-CD28 costimulated autologous T cells at day 2 after transplantation. Study patients positive for human leukocyte antigen A2 (arm A, n = 28) also received pneumococcal conjugate vaccine immunizations before and after transplantation and a multipeptide tumor antigen vaccine derived from the human telomerase reverse transcriptase and the antiapoptotic protein survivin. Patients negative for human leukocyte antigen A2 (arm B, n = 26) received the pneumococcal conjugate vaccine only. Patients exhibited robust T-cell recoveries by day 14 with supraphysiologic T-cell counts accompanied by a sustained reduction in regulatory T cells. The median event-free survival (EFS) for all patients is 20 months (95% confidence interval, 14.6-24.7 months); the projected 3-year overall survival is 83%. A subset of patients in arm A (36%) developed immune responses to the tumor antigen vaccine by tetramer assays, but this cohort did not exhibit better EFS. Higher posttransplantation CD4(+) T-cell counts and a lower percentage of FOXP3(+) T cells were associated with improved EFS. Patients exhibited accelerated polyclonal immunoglobulin recovery compared with patients without T-cell transfers. Adoptive transfer of tumor antigen vaccine-primed and costimulated T cells leads to augmented and accelerated cellular and humoral immune reconstitution, including antitumor immunity, after autologous stem cell transplantation for myeloma. This study was registered at www.clinicaltrials.gov as NCT00499577.

Interpreting the CPASS Trial: Do Not Shift Motor Therapy to the Subacute Phase.

The Critical Periods After Stroke Study (CPASS, n = 72) showed that, compared to controls, an additional 20 hours of intensive upper limb therapy led to variable gains on the Action Research Arm Test depending on when therapy was started post-stroke: the subacute group (2-3 months) improved beyond the minimal clinically important difference and the acute group (0-1 month) showed smaller but statistically significant improvement, but the chronic group (6-9 months) did not demonstrate improvement that reached significance. Some have misinterpreted CPASS results to indicate that all inpatient motor therapy should be shifted to outpatient therapy delivered 2 to 3 months post-stroke. Instead, however, CPASS argues for a large dose of motor therapy delivered continuously and cumulatively during the acute and subacute phases. When interpreting trials like CPASS, one must consider the substantial dose of early usual customary care (UCC) motor therapy that all participants received. CPASS participants averaged 27.9 hours of UCC occupational therapy (OT) during the first 2 months and 9.8 hours of UCC OT during the third and fourth months post-stroke. Any recovery experienced would therefore result not just from CPASS intensive motor therapy but the combined effects of experimental therapy plus UCC. Statistical limitations also did not allow direct comparisons of the acute and subacute group outcomes in CPASS. Instead of shifting inpatient therapy hours to the subacute phase, CPASS argues for preserving inpatient UCC. We also recommend conducting multi-site dosing trials to determine whether additional intensive motor therapy delivered in the first 2 to 3 months following inpatient rehabilitation can further improve outcomes.

Evaluating traditional Chinese medicine using modern clinical trial design and statistical methodology: application to a randomized controlled acupuncture trial.

Traditional Chinese medicine (TCM), used in China and other Asian counties for thousands of years, is increasingly utilized in Western countries. However, due to inherent differences in how Western medicine and this ancient modality are practiced, employing the so-called Western medicine-based gold standard research methods to evaluate TCM is challenging. This paper is a discussion of the obstacles inherent in the design and statistical analysis of clinical trials of TCM. It is based on our experience in designing and conducting a randomized controlled clinical trial of acupuncture for post-operative dental pain control in which acupuncture was shown to be statistically and significantly better than placebo in lengthening the median survival time to rescue drug. We demonstrate here that PH assumptions in the common Cox model did not hold in that trial and that TCM trials warrant more thoughtful modeling and more sophisticated models of statistical analysis. TCM study design entails all the challenges encountered in trials of drugs, devices, and surgical procedures in the Western medicine. We present possible solutions to some but leave many issues unresolved.

[Adequate application of quantitative and qualitative statistic analytic methods in acupuncture clinical trials].

Recently, proper use of the statistical methods in traditional Chinese medicine (TCM) randomized controlled trials (RCTs) has received increased attention. Statistical inference based on hypothesis testing is the foundation of clinical trials and evidence-based medicine. In this article, the authors described the methodological differences between literature published in Chinese and Western journals in the design and analysis of acupuncture RCTs and the application of basic statistical principles. In China, qualitative analysis method has been widely used in acupuncture and TCM clinical trials, while the between-group quantitative analysis methods on clinical symptom scores are commonly used in the West. The evidence for and against these analytical differences were discussed based on the data of RCTs assessing acupuncture for pain relief. The authors concluded that although both methods have their unique advantages, quantitative analysis should be used as the primary analysis while qualitative analysis can be a secondary criterion for analysis. The purpose of this paper is to inspire further discussion of such special issues in clinical research design and thus contribute to the increased scientific rigor of TCM research.

Highly robust causal semiparametric U-statistic with applications in biomedical studies.

With our increased ability to capture large data, causal inference has received renewed attention and is playing an ever-important role in biomedicine and economics. However, one major methodological hurdle is that existing methods rely on many unverifiable model assumptions. Thus robust modeling is a critically important approach complementary to sensitivity analysis, where it compares results under various model assumptions. The more robust a method is with respect to model assumptions, the more worthy it is. The doubly robust estimator (DRE) is a significant advance in this direction. However, in practice, many outcome measures are functionals of multiple distributions, and so are the associated estimands, which can only be estimated via U-statistics. Thus most existing DREs do not apply. This article proposes a broad class of highly robust U-statistic estimators (HREs), which use semiparametric specifications for both the propensity score and outcome models in constructing the U-statistic. Thus, the HRE is more robust than the existing DREs. We derive comprehensive asymptotic properties of the proposed estimators and perform extensive simulation studies to evaluate their finite sample performance and compare them with the corresponding parametric U-statistics and the naive estimators, which show significant advantages. Then we apply the method to analyze a clinical trial from the AIDS Clinical Trials Group.

A flexible multi-stage design for phase II oncology trials.

Phase II trials evaluate whether a new drug or a new therapy is worth further pursuing or certain treatments are feasible or not. A typical phase II is a single arm (open label) trial with a binary clinical endpoint (response to therapy). Although many oncology Phase II clinical trials are designed with a two-stage procedure, multi-stage design for phase II cancer clinical trials are now feasible due to increased capability of data capture. Such design adjusts for multiple analyses and variations in analysis time, and provides greater flexibility such as minimizing the number of patients treated on an ineffective therapy and identifying the minimum number of patients needed to evaluate whether the trial would warrant further development. In most of the NIH sponsored studies, the early stopping rule is determined so that the number of patients treated on an ineffective therapy is minimized. In pharmaceutical trials, it is also of importance to know as early as possible if the trial is highly promising and what is the likelihood the early conclusion can sustain. Although various methods are available to address these issues, practitioners often use disparate methods for addressing different issues and do not realize a single unified method exists. This article shows how to utilize a unified approach via a fully sequential procedure, the sequential conditional probability ratio test, to address the multiple needs of a phase II trial. We show the fully sequential program can be used to derive an optimized efficient multi-stage design for either a low activity or a high activity, to identify the minimum number of patients required to assess whether a new drug warrants further study and to adjust for unplanned interim analyses. In addition, we calculate a probability of discordance that the statistical test will conclude otherwise should the trial continue to the planned end that is usually at the sample size of a fixed sample design. This probability can be used to aid in decision making in a drug development program. All computations are based on exact binomial distribution.

Effect of Laser Moxibustion for Knee Osteoarthritis: A Multisite, Double-blind Randomized Controlled Trial.

OBJECTIVE: To examine the effects of laser moxibustion on pain and function in patients with knee osteoarthritis (OA). METHODS: A double-blind randomized clinical trial (4-week treatment, 20-week follow-up) was conducted. A total of 392 symptomatic knee OA patients with moderate to severe clinically significant knee pain were randomly assigned to laser treatment or sham laser control group (1:1). Twelve sessions of laser moxibustion or sham laser treatments on the acupuncture points at the affected knee(s) were performed 3 times a week for 4 weeks. The primary outcome measurement was change in Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain score from baseline to Week 4. RESULTS: Among the 392 randomized participants, 364 (92.86%) completed the trial. The median WOMAC pain score decreased significantly at Week 4 in the active group than in the sham group (2.1, 95% CI 1.6-2.6, P < 0.01). At Week 24, compared to the sham laser, active laser treatment resulted in significant pain reduction and function improvement (3.0, 95% CI 2.5-3.6, P < 0.01, and 14.8, 95% CI 11.9-17.6, P < 0.01, respectively). The physical component of the quality of life significantly improved in the active group vs the sham controls at Week 4 (3.2, 95% CI 1.3-5.0, P = 0.001) up to Week 24 (5.1, 95% CI 3.3-7.0, P < 0.001). No serious adverse effects were reported. CONCLUSION: Laser moxibustion resulted in statistically and clinically significant pain reduction and function improvement following a 4-week treatment in patients with knee OA.