The structural complexity of crossover studies for bioequivalence test confuses analysts and leaves them a hard choice among various programs. Our study reviews PROC GLM and PROC MIXED in SAS and compares widely used SAS codes for crossover studies. PROC MIXED based on REML is more recommended since it provides best linear unbiased estimator of the random between-subject effects and its variance. Our study also considers the covariance structure within subject over period which most PK/PD studies and crossover studies ignore. The QT interval data after the administration of moxifloxacin for a fixed time point are analyzed for the comparison of representative SAS codes for crossover studies.
Cross-Over Studies* ; Therapeutic Equivalency

Background: The impact of persistent coronavirus disease 2019 (COVID-19) symptoms on quality of life remains unclear. This study aimed to describe such persistent symptoms and their relationships with quality of life, including clinical frailty and subjective health status. Methods: A prospective longitudinal 3-month follow-up survey monitored symptoms, health quality, support needs, frailty, and employment. Results: A total of 82 patients with a mean age of 52 years (ranging from 23–84 years) were enrolled, including 48 (58.6%) men, and 34 (41.5%) women. The fully active status decreased from 87.8% before admission to 78.1% post discharge. Two patients (2.4%) were ambulatory and capable of all self-care but unable to carry out any work-related activities 12 weeks after discharge. Clinical frailty scale (CFS) levels 1, 2, 3 and 4 changed drastically between admission and 12 weeks later after discharge. Just after admission, the median EuroQol visual analogue scales (EQ-VAS) was 82.23 (± 14.38), and it decreased to 78.10 (± 16.02) 12 weeks after discharge; 62 (75.6%) of patients reported at least one symptom 12 weeks after discharge. The most frequent symptom was fatigue followed by smell disorder, anxiety, sleep disorder, headache, depressive mood, dyspnea, and taste disorder. CFS was definitively associated with fatigue. Decreased EQ-VAS was associated with fatigue and palpitation, cough, taste disorder, and chest pain. EQ-VAS was worse in women (28%) than in men. Compared with regular outpatient clinic visits before admission, 21 patients (25.6%) reported increased outpatient clinic visits, one (1.4%) reported readmission, and one (1.4%) reported emergency room visits. Six of the 54 (77.1%) patients who were employed before admission lost their jobs. And most vulnerable type was self-employed, because three selfemployed job workers were not working at 12 weeks after discharge. Conclusion COVID-19 sequelae should not be underestimated. We find a decrease in health quality and increase in psychological problems in discharged COVID-19 patients, and some patients experience unemployment. The number of patients suffering from COVID-19 sequelae would not be negligible considering there are more than one million COVID-19 infection cases in Korea. Hence, the government should start a systematic monitoring system for discharged patients and prepare timely medical and social interventions accordingly.

Statistical analysts engaged in typical clinical trials often have to confront a tight schedule to finish massive statistical analyses specified in a Standard Operation Procedure (SOP). Thus, statisticians or not, most analysts would want to reuse or slightly modify existing programs. Since even a slight misapplication of statistical methods or techniques can easily drive a whole conclusion to a wrong direction, analysts should arm themselves with well organized statistical concepts in advance. This paper will review basic statistical concepts related to typical clinical trials. The number of variables and their measurement scales determine an appropriate method. Since most of the explanatory variables in clinical trials are designed beforehand, the main statistics we review for clinical trials include univariate data analysis, design of experiments, and categorical data analysis. Especially, if the response variable is binary or observations collected from a subject are correlated, the analysts should pay special attention to selecting an appropriate method. McNemar's test and multiple McNemar's test are respectively recommended for comparisons of proportions between correlated two samples or proportions among correlated multi-samples.
Appointments and Schedules ; Arm ; Chi-Square Distribution ; Cross-Over Studies ; Statistics as Topic ; Weights and Measures