A simple method for calculating power based on a prior trial

A Protocol for Simultaneous In Vivo Imaging of Cardiac and Neuroinflammation in Dystrophin-Deficient MDX Mice Using [18F]FEPPA PET

Duchenne muscular dystrophy (DMD) is a neuromuscular disorder caused by dystrophin loss-notably within muscles and the central neurons system. DMD presents as cognitive weakness, progressive skeletal and cardiac muscle degeneration until pre-mature death from cardiac or respiratory failure. Innovative therapies have improved life expectancy; however, this is accompanied by increased late-onset heart failure and emergent cognitive degeneration. Thus, better assessment of dystrophic heart and brain pathophysiology is needed. Chronic inflammation is strongly associated with skeletal and cardiac muscle degeneration; however, neuroinflammation's role is largely unknown in DMD despite being prevalent in other neurodegenerative diseases. Here, we present an inflammatory marker translocator protein (TSPO) positron emission tomography (PET) protocol for in vivo concomitant assessment of immune cell response in hearts and brains of a dystrophin-deficient mouse model [mdx:utrn(+/-)]. Preliminary analysis of whole-body PET imaging using the TSPO radiotracer, [¹⁸F]FEPPA in four mdx:utrn(+/-) and six wildtype mice are presented with ex vivo TSPO-immunofluorescence tissue staining. The mdx:utrn(+/-) mice showed significant elevations in heart and brain [¹⁸F]FEPPA activity, which correlated with increased ex vivo fluorescence intensity, highlighting the potential of TSPO-PET to simultaneously assess presence of cardiac and neuroinflammation in dystrophic heart and brain, as well as in several organs within a DMD model.

Time between diagnosis and surgical treatment on pathological and clinical outcomes in prostate cancer: does it matter?

INTRODUCTION

Prostate cancer (PC) most of the time presents with an indolent course. Thus, delays in treatment due to any causes might not affect long-term survival and may not affect cancer cure rates.

PURPOSE

In this study, we evaluated the effect of delay-time between PC diagnosis and radical prostatectomy regarding oncological outcomes: Gleason score upgrade on surgical specimen, pathologic extracapsular extension (ECE) on surgical specimen, and postoperative biochemical recurrence (BCR) on follow-up.

METHODS

We evaluated PC patients who underwent radical prostatectomy (RP) regarding clinical and pathological findings and theirs respective interval between diagnosis and surgical treatment measured in days and months. We used univariate and multivariate logistic regression to evaluate the impact of interval-time.

RESULTS

A total of 908 PC patients underwent RP between 2006 and 2014. Mean age was 61.5 years, the mean time-to-surgery was 191 days (> 6 months) and 187 (20.5%) patients had BCR, with a mean follow-up of 44 months. According to our analysis, no statistically significant maximum cut-off time interval between diagnostic biopsy and surgery could be established (p = 0.215). Regardless of interval-time: ≤ 6 months (56.5%), 6-12 months (38.5%), and > 12 months (5.1%) after biopsy, we found no time interval correlated with poor oncological outcomes. This study has several limitations. It was retrospective and had a mean follow-up of 4 years. Additional follow-up is necessary to determine whether these findings will be maintained over time.

CONCLUSIONS

We showed that the time between diagnosis and surgical treatment did not affect the oncological outcomes in our study.

A simulation-based sample size calculation method for pre-clinical tumor xenograft experiments

Pre-clinical tumor xenograft experiments usually require a small sample size that is rarely greater than 20, and data generated from such experiments very often do not have censored observations. Many statistical tests can be used for analyzing such data, but most of them were developed based on large sample approximation. We demonstrate that the type-I error rates of these tests can substantially deviate from the designated rate, especially when the data to be analyzed has a skewed distribution. Consequently, the sample size calculated based on these tests can be erroneous. We propose a modified signed log-likelihood ratio test (MSLRT) to meet the type-I error rate requirement for analyzing pre-clinical tumor xenograft data. The MSLRT has a consistent and symmetric type-I error rate that is very close to the designated rate for a wide range of sample sizes. By simulation, we generated a series of sample size tables based on scenarios commonly expected in tumor xenograft experiments, and we expect that these tables can be used as guidelines for making decisions on the numbers of mice used in tumor xenograft experiments.

Studies with group treatments required special power calculations, allocation methods, and statistical analyses

OBJECTIVE

In some trials, the intervention is delivered to individuals in groups, for example, groups that exercise together. The group structure of such trials has to be taken into consideration in the analysis and has an impact on the power of the trial. Our aim was to provide optimal methods for the design and analysis of such trials.

STUDY DESIGN AND SETTING

We described various treatment allocation methods and presented a new allocation algorithm: optimal batchwise minimization (OBM). We carried out a simulation study to evaluate the performance of unrestricted randomization, stratification, permuted block randomization, deterministic minimization, and OBM. Furthermore, we described appropriate analysis methods and derived a formula to calculate the study size.

RESULTS

Stratification, deterministic minimization, and OBM had considerably less risk of imbalance than unrestricted randomization and permuted block randomization. Furthermore, OBM led to unpredictable treatment allocation. The sample size calculation and the analysis of the study must be based on a multilevel model that takes the group structure of the trial into account.

CONCLUSION

Trials evaluating interventions that are carried out in subsequent groups require adapted treatment allocation, power calculation, and analysis methods. From the perspective of obtaining overall balance, we conclude that minimization is the method of choice. When the number of prognostic factors is low, stratification is an excellent alternative. OBM leads to better balance within the batches, but it is more complicated. It is probably most worthwhile in trials with many prognostic factors. From the perspective of predictability, a treatment allocation method, such as OBM, that allocates several subjects at the same time, is superior to other methods because it leads to the lowest possible predictability.