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Simple Summary: Chordoma is a rare cancer that often occurs at the base of the skull. Treating skull base chordoma is challenging because the tumor is difficult to completely remove with surgery and has low radiosensitivity. This study compared two types of radiation modality: particle beam therapy (PT) and photon radiotherapy (RT). We found that PT provides better progression-free survival compared to photon RT. However, PT also has a higher risk of causing brain necrosis. Our findings suggest that PT is more effective for controlling skull base chordoma, but careful planning is needed to minimize side effects. [Objective] The aim of this study was to compare the efficacy of particle beam therapy (PT) with photon radiotherapy (RT) for treatment of skull base chordoma. [Methods] A systematic review was conducted for skull base chordoma treated with PT or photon RT reported from 1990 to 2022. Data were extracted for overall survival (OS) and progression-free survival (PFS), late adverse events, age, gender, gross total resection (GTR) rates, tumor volume, total irradiation dose, and treatment modality. Random-effects meta-regression analysis with the treatment modality as an explanatory variable was performed for each outcome to compare the modalities. [Results] A meta-analysis of 30 selected articles found 3- and 5-year OS rates for PT vs. photon RT or combined photon RT/proton beam therapy (PBT) of 90.8% (95% CI: 87.4–93.3%) vs. 89.5% (95% CI: 83.0–93.6%), p = 0.6543; 80.0% (95% CI: 75.7–83.6%) vs. 89.5% (95% CI: 83.0–93.6%), p = 0.6787. The 5-year PFS rates for PT vs. photon RT or photon RT/PBT were 67.8% (95% CI: 56.5–76.7%) vs. 40.2% (95% CI: 31.6–48.7%), p = 0.0004. A random-effects model revealed that the treatment modality (PT vs. photon RT or photon RT/PBT) was not a significant factor for 3-year OS (p = 0.42) and 5-year OS (p = 0.11), but was a significant factor for 5-year PFS (p < 0.0001). The rates of brain necrosis were 8–50% after PT and 0–4% after photon RT or photon RT/PBT. [Conclusion] This study shows that PT results in higher PFS compared to photon RT for skull base chordoma, but that there is a tendency for a higher incidence of brain necrosis with PT. Publication and analysis of further studies is needed to validate these findings. [ABSTRACT FROM AUTHOR]

Simple Summary: Radiation therapy for hepatocellular carcinoma is known to take time to shrink tumors. In recent years, there have been an increasing number of reports on proton beam therapy, but there are few reports on how tumors shrink after proton beam therapy. In this study, it was found that tumor shrinkage after proton beam therapy also takes time in hepatocellular carcinoma as in photon radiation therapy, with PR + CR rates of 57.5% at 1 year, 76.9% at 2 years, and 85.2% at 3 years. Moreover, in many cases, the tumor still shrinks after 1 year. Therefore, it is clear that at least one year of follow-up is necessary after irradiation to confirm the shrinkage effect of proton beam therapy for hepatocellular carcinoma. Background: Follow-up after treatment for hepatocellular carcinoma (HCC) can be mostly performed using dynamic CT or MRI, but there is no common evaluation method after radiation therapy. The purpose of this study is to examine factors involved in tumor reduction and local recurrence in patients with HCC treated with proton beam therapy (PBT) and to evaluate HCC shrinkage after PBT. Methods: Cases with only one irradiated lesion or those with two lesions irradiated simultaneously were included in this study. Pre- and post-treatment lesions were evaluated using Response Evaluation Criteria in Solid Tumors (RECIST) by measuring the largest diameter. Results: The 6-, 12-, and 24-month CR + PR rates after PBT were 33.1%, 57.5%, and 76.9%, respectively, and the reduction rates were 25.1% in the first 6 months, 23.3% at 6–12 months, and 14.5% at 13–24 months. Cases that reached CR/PR at 6 and 12 months had improved OS compared to non-CR/non-PR cases. Conclusions: It is possible that a lesion that reached SD may subsequently transition to PR; it is reasonable to monitor progress with periodic imaging evaluations even after 1 year of treatment. [ABSTRACT FROM AUTHOR]

Aim: Radio‐Taiso, a traditional exercise program in Japan, may serve as a coping strategy for older adults with frailty during the COVID‐19 pandemic. This phase II trial tested program adherence and safety and explored the potential effectiveness of a home‐based Radio‐Taiso. Methods: This assessor‐blind parallel randomized controlled trial included community‐dwelling Jolder Japanese adults with frailty and pre‐frailty. Fifty‐eight eligible participants were randomly allocated to intervention and control groups. Intervention participants performed 3–5‐min sessions of Radio‐Taiso one to four times per day for 12 weeks. Feasibility criteria were set at practice and retention rates of ≥75%. Safety was monitored by assessing all adverse events reported by participants during the intervention period, irrespective of causality. Potential effectiveness was exploratorily assessed using items that allow clinical interpretation of changes: mobility and health‐related quality of life (HR‐QoL), assessed using the modified short physical performance battery (SPPB) and the SF‐36, respectively. Results: Both practice (83%) and retention rates (100%) met the predetermined feasibility criteria. Eleven adverse events were reported but were supposedly unrelated to the intervention. In the intention‐to‐treat analysis, there was no clinically significant difference in the change in SPPB score between groups (−0.4 points, 95% confidence interval [CI], −1.2, 0.3); however, the intervention group scored higher in the mental component of HR‐QoL than did the control group (3.4 points, 95% CI: −1.1, 7.8). Conclusions: The preliminary data indicate that a phase III trial is feasible, focusing on the mental aspect of HR‐QoL as the primary outcome. Geriatr Gerontol Int 2023; 23: 32–37. [ABSTRACT FROM AUTHOR]

Summary: In randomised controlled trials (RCTs), the allocated treatment group and covariates are expected to be independent. In the framework of the normal model, omitting covariates affects precision of the treatment effect estimator but does not yield bias in the estimator. However, when covariates are omitted, the treatment effect estimator has a bias in non‐normal models such as logistic and Cox models even in RCTs. Additionally, covariate omission in the accelerated failure time model and Poisson model causes a bias in the standard error (SE) estimator and yields an inflation of Type‐I error rate for the treatment effect. In this study, we reviewed the literature regarding the effect of covariate omission from the aspect of the bias and precision of treatment effect estimator, bias of SE estimator, and Type‐I error rate for these models assumed in RCTs. Furthermore, we conducted a simulation study in a wide variety of scenarios to evaluate the effect of covariate omission. Our literature review and simulation study provide a simple guide for consideration of covariate adjustment in RCTs. We recommend that all important and measurement covariates be included in analysis models to reduce bias for the treatment effect and SE estimators and control the Type‐I error rate. [ABSTRACT FROM AUTHOR]

Summary: Likelihood‐based, mixed‐effects models for repeated measures (MMRMs) are occasionally used in primary analyses for group comparisons of incomplete continuous longitudinal data. Although MMRM analysis is generally valid under missing‐at‐random assumptions, it is invalid under not‐missing‐at‐random (NMAR) assumptions. We consider the possibility of bias of estimated treatment effect using standard MMRM analysis in a motivational case, and propose simple and easily implementable pattern mixture models within the framework of mixed‐effects modeling, to handle the NMAR data with differential missingness between treatment groups. The proposed models are a new form of pattern mixture model that employ a categorical time variable when modeling the outcome and a continuous time variable when modeling the missingness‐data patterns. The models can directly provide an overall estimate of the treatment effect of interest using the average of the distribution of the missingness indicator and a categorical time variable in the same manner as MMRM analysis. Our simulation results indicate that the bias of the treatment effect for MMRM analysis was considerably larger than that for the pattern mixture model analysis under NMAR assumptions. In the case study, it would be dangerous to interpret only the results of the MMRM analysis, and the proposed pattern mixture model would be useful as a sensitivity analysis for treatment effect evaluation. [ABSTRACT FROM AUTHOR]

The mixed effect models for repeated measures (MMRM) analysis is sometimes used as a primary analysis in longitudinal randomized clinical trials. The SE for the treatment effect in the MMRM analysis is usually estimated by assuming the orthogonality of the fixed effect and variance-covariance parameters, which is the orthogonality property of a multivariate normal distribution, because of default settings of most standard statistical software. However, this property might be lost when analysis models are misspecified and/or data include missing values with the mechanism of being missing at random. In this study, we investigated the effect of the assumption of the orthogonality property on the estimation of the SE for the MMRM analysis. From simulation and case studies, it was shown that the SE with the assumption of orthogonality property had nonnegligible bias, especially when the analysis models assuming heteroscedasticity between treatment groups were applied. We also introduce the SAS code for the MMRM analysis without assuming the orthogonality property. Assuming the orthogonality property in the MMRM analysis would lead to invalid statistical inference, and it is necessary to be careful when applying the MMRM analysis with most standard software. [ABSTRACT FROM AUTHOR]

We hypothesized that a single-leg version of the Hybrid Assistive Limb (HAL) system could improve the gait and physical function of patients with hemiparesis following a stroke. In this pilot study, we therefore compared the efficacy of HAL-based gait training with that of conventional gait training (CGT) in patients with acute stroke. Patients admitted to the participating university hospital were assigned to the HAL group, whereas those admitted to outside teaching hospitals under the same rehabilitation program who did not use the HAL were assigned to the control group. Over 3 weeks, all participants completed nine 20 min sessions of gait training, using either HAL (i.e., the single-leg version of HAL on the paretic side) or conventional methods (i.e., walking aids and gait orthoses). Outcome measures were evaluated before and after the nine training sessions. The Functional Ambulation Category (FAC) was the primary outcome measure, but the following secondary outcome measures were also assessed: National Institutes of Health Stroke Scale, Fugl–Meyer Assessment (Lower Extremity), comfortable walking speed, step length, cadence, 6-min walk distance, Barthel Index, and Functional Independence Measure. In total, 22 post-stroke participants completed the clinical trial: 12 in the HAL group and 10 in the CGT group. No serious adverse events occurred in either group. The HAL group showed significant improvement in FAC after nine sessions when compared with the CGT group (P = 0.014). However, secondary outcomes did not differ significantly between the groups. Our results demonstrate that HAL-based gait therapy may improve independent walking in patients with acute stroke hemiplegia who are dependent on ambulatory assistance. A larger-scale randomized controlled trial is needed to clarify the effectiveness of single-leg HAL therapy. Clinical Trial Registration: UMIN Clinical Trials Registry, identifier UMIN000022410. [ABSTRACT FROM AUTHOR]

Multiple imputation is a promising approach for handling of missing data. One uncertainty in applications of the multiple imputation to randomized controlled trials with longitudinal data is whether the imputation should be carried out across all subjects simultaneously or by treatment group separately, which leads to two different strategies for building imputation procedures and/or models. Indeed, it has not been sufficiently addressed and well-documented how the two imputation strategies work in the analysis of the longitudinal data. We consider situations in the presence of heteroscedasticity between treatment groups and conducted extensive simulation studies to examine how the choice of imputation strategy had impacts on the estimation of treatment effects under an assumption of missing at random mechanism. The choice of analysis model was also assessed. The simulation studies suggested that in the presence of heteroscedasticity, the separate imputation by treatment group was robust enough to provide unbiased and precise estimation of the treatment effects; in contrast, the simultaneous imputation, which is frequently used in applications, led to serious biases and poor coverage probabilities of 95% confidence interval for the treatment effects. The heteroscedasticity should be dealt with in more careful manners for the longitudinal data analysis, and if it could be the case in hand, we recommend using the separate imputation by treatment group, as well as applying unequal variance analysis methods for complete data with imputed values. The methods were illustrated with data from two real examples of pediatric research and mental health research. [ABSTRACT FROM AUTHOR]

Background: In a random effects meta-analysis model, true treatment effects for each study are routinely assumed to follow a normal distribution. However, normality is a restrictive assumption and the misspecification of the random effects distribution may result in a misleading estimate of overall mean for the treatment effect, an inappropriate quantification of heterogeneity across studies and a wrongly symmetric prediction interval.Methods: We focus on problems caused by an inappropriate normality assumption of the random effects distribution, and propose a novel random effects meta-analysis model where a Box-Cox transformation is applied to the observed treatment effect estimates. The proposed model aims to normalise an overall distribution of observed treatment effect estimates, which is sum of the within-study sampling distributions and the random effects distribution. When sampling distributions are approximately normal, non-normality in the overall distribution will be mainly due to the random effects distribution, especially when the between-study variation is large relative to the within-study variation. The Box-Cox transformation addresses this flexibly according to the observed departure from normality. We use a Bayesian approach for estimating parameters in the proposed model, and suggest summarising the meta-analysis results by an overall median, an interquartile range and a prediction interval. The model can be applied for any kind of variables once the treatment effect estimate is defined from the variable.Results: A simulation study suggested that when the overall distribution of treatment effect estimates are skewed, the overall mean and conventional I 2 from the normal random effects model could be inappropriate summaries, and the proposed model helped reduce this issue. We illustrated the proposed model using two examples, which revealed some important differences on summary results, heterogeneity measures and prediction intervals from the normal random effects model.Conclusions: The random effects meta-analysis with the Box-Cox transformation may be an important tool for examining robustness of traditional meta-analysis results against skewness on the observed treatment effect estimates. Further critical evaluation of the method is needed. [ABSTRACT FROM AUTHOR]

Background: It is clinically important to know who are likely to respond more or less to antidepressants. However, meaningful effect modifiers (variables associated with differential response depending on the treatment) are yet to be identified.Methods: We conducted individual participant data (IPD) meta-analysis and meta-regression to explore effect modifiers in placebo-controlled antidepressant trials conducted so far in Japan.Results: We obtained access to IPD from seven placebo-controlled trials comparing bupropion, duloxetine, escitalopram, mirtazapine, paroxetine or venlafaxine with placebo in the acute phase treatment of major depression (total n = 2803). The higher the guilt subscale score of the baseline Hamilton Rating Scale for Depression (HRSD), the greater the difference in reduction in depression severity between the antidepressants and placebo at week 6, while the older the current age or the age at onset, the smaller the difference. At week 8, the guilt subscale score of HRSD and presence of suicidal ideation at baseline predicted greater, and the anhedonia subscale and insomnia subscale scores of HRSD and early response at week 2 predicted smaller, difference in reduction.Limitations: Different studies measured different sets of baseline variables and we were able to analyze only a limited set of candidate variables for effect modification.Conclusion: Age, age at onset, several HRSD subscales including guilt, anhedonia and insomnia, presence of suicidal ideation at baseline and early response are potential effect modifiers for response to antidepressants in the acute phase antidepressant treatment of major depression. Future trials need to measure these and additional variables in concerted efforts to enable matching of treatments with individual characteristics in depression. [ABSTRACT FROM AUTHOR]