Here is the design of a clinical trial:

Official Title: Effects of Infrared Laser Moxibustion on Cancer-related Fatigue in Breast Cancer Patients

Included conditions:
- Cancer-related Fatigue

Study Armgroups:
- {'label': 'Infrared Laser Moxibustion Therapy', 'type': 'EXPERIMENTAL', 'description': 'Each patient will receive this treatment twice per week for six weeks (12 sessions total).', 'interventionNames': ['Device: Infrared Laser Moxibustion']}
- {'label': 'Sham Infrared Laser Moxibustion Therapy', 'type': 'SHAM_COMPARATOR', 'description': 'The patients will receive treatment from sham laser moxibustion instrument.', 'interventionNames': ['Device: Sham Infrared Laser Moxibustion']}
- {'label': 'Waitlist Controls', 'type': 'NO_INTERVENTION', 'description': 'The patients maintain their usual treatment and self-care,'}

Interventions:
- {'type': 'DEVICE', 'name': 'Infrared Laser Moxibustion', 'description': 'We will use SX10-C1 laser moxibustion devices (Shanghai Wonderful Opto-Electrics Tech Co Ltd, Shanghai, China) for the ILM and SILM groups. Four laser probes will be synchronously aligned with 4 points (tST36 (bilateral), CV4, and CV6 acupoints) and we will irradiate each acupoint 2 cm away from the skin surface for 20 minutes. Each patient will receive this treatment twice per week for six weeks (12 sessions total).', 'armGroupLabels': ['Infrared Laser Moxibustion Therapy']}
- {'type': 'DEVICE', 'name': 'Sham Infrared Laser Moxibustion', 'description': 'The sham laser moxibustion instrument appears to be identical as the real one.', 'armGroupLabels': ['Sham Infrared Laser Moxibustion Therapy']}

Primary Outcomes:
- {'measure': 'Chinese version of the Brief Fatigue Inventory (BFI-C)', 'description': 'The Chinese version of the Brief Fatigue Inventory (BFI) : It uses 10-point numeric descriptions: scores of 1 to 3 represent mild levels of fatigue, scores of 4 to 6 represent moderate levels of fatigue, and scores of 7 to 10 represent severe levels of fatigue.', 'timeFrame': 'Change from Baseline BFI-C at 6 weeks'}

Please estimate the sample size based on the assumption: 
Alpha of 5%, power of 80%, and a 20% attrition rate.
3.15
        Statistical analysis and sample size calculation
        Standard descriptive statistics will be used to analyze baseline participant characteristics. We will produce summary statistics such as means, medians, standard deviations, and ranges for measured variables. We will tabulate frequencies for categorical and ordinal variables. We will use graphical methods extensively to examine distributions, identify potentially influential points, and guide data transformations if warranted. For continuous variables with markedly non-normal or skewed distributions, appropriate transformation may be required, such as natural logarithms, which we will apply as necessary and appropriate.[43] In the sections below, we will assume, when discussing particular outcomes, that the appropriately transformed variables will be used. We will perform analysis according to the intention-to-treat (ITT) principle (i.e., subjects will be analyzed according to the treatment group to which they will be randomly allocated regardless of drop-out).
        To determine the effects of ILM on fatigue, we will use mixed effects models.[44] This statistical procedure takes into account within-subject correlations from repeated measurements in the same subjects and allows estimation of between-group difference without necessitating that the last observation is carried forward or exclusion of participants with missing data. Tests of ITT differences between intervention arms with respect to change in BFI-C will be based on time-intervention interactions in the mixed-effects models. Although randomization theoretically balances the potential confounders between treatment groups, occasional unequal distribution can be seen. Should this happen, the effect of confounding will be evaluated by including the potential confounders as co-variates in the models.[43] For secondary outcomes, we will use similar analytical strategies for co-morbid symptoms. To evaluate the long-term durability of treatment effect, we will display long-term data descriptively. It is possible that the long-term outcome may display different patterns from short-term therapeutic effect. If this occurs, we may need to create piece-wise linear mixed-effects models to fit the data and test for statistical significance on outcome change among treatment groups.
        We have based our sample size calculation on our preliminary study. We plan to enroll and randomize 140 participants (2:2:1) to ILM, SILM, and WLC. Assuming a 20% attrition rate, we will have 45 participants in each of the ILM and SILM groups and 22 participants in the WLC group who will provide evaluable outcomes. In our previous study, we found that ILM produced a greater reduction in BFI-C intensity at the end of intervention (Week 4 from baseline) at a magnitude of 0.60 SD (Standard Deviation) as compared to SILM. Given an alpha of 5%, with 45 subjects in each of the ILM and SILM groups, we will be able to detect a between-group difference of reduction in BFI-C of 0.60 SD with 80% power. With 45 subjects each in the ILM and SILM groups and 22 participants in the WLC group, a power of 80% and two-sided alpha of 0.05, we will be able to detect an effect size of 0.71 SD (i.e., difference of reduction in BFI-C) between ILM and WLC or SILM and WLC groups. This is a conservative way to estimate sample size since the longitudinal analysis using all repeated measures with the mixed-effects model specified in the analysis plan will provide higher power than a t test.