Here is the design of a clinical trial:

Official Title: Intermittent Fasting Accompanying Chemotherapy in Gynecological Cancers - a Randomized, Controlled, Two-armed Intervention Study

Included conditions:
- Breast Cancer
- Ovarian Cancer

Study Armgroups:
- {'label': 'Fasting', 'type': 'ACTIVE_COMPARATOR', 'description': '60-72 h-modified fasting (36-48 h before and 24 h after chemotherapy)', 'interventionNames': ['Other: Fasting']}
- {'label': 'Vegan', 'type': 'ACTIVE_COMPARATOR', 'description': '60-72 h-vegan diet (36-48 h before and 24 h after chemotherapy)', 'interventionNames': ['Other: Vegan']}

Interventions:
- {'type': 'OTHER', 'name': 'Fasting', 'description': 'Patients follow a modified fasting regime of 60-72 h (36-48 h before and 24 h after CT) with a dietary energy supply of 350-400kcal per day with vegetable juices during the first four cycles of CT. During the rest of the CT cycles they will observe two days of caloric restriction (24 h before and after CT). Between CTs a mainly vegetarian diet will be performed and the patients are encouraged to follow a pattern of time restricted feeding with 14 h fasting over night at least for six days a week. The patients will receive an individual nutrition training by trained nutritionists.', 'armGroupLabels': ['Fasting']}
- {'type': 'OTHER', 'name': 'Vegan', 'description': 'Patients follow a 60-72 h vegan diet with sugar restriction (36-48 h before and 24 h after CT) during the first four cycles of CT. During the rest of the CT cycles they will observe two days of vegan and sugar-restricted diet (24 h before and after CT). Between CTs a mainly vegetarian diet will be performed. The patients will receive an individual nutrition training by trained nutritionists.', 'armGroupLabels': ['Vegan']}

Primary Outcomes:
- {'measure': 'FACT-G', 'description': 'Summarized change of FACT-G score', 'timeFrame': 'Date of inclusion (baseline), day -2 and +7 at each chemotherapy (CT) in triweekly cycles/-2 days at each CT in weekly cycles and +7 after the last weekly CT, 4 months after inclusion, 3 weeks after end of CT and 1, 2 and 3 years after inclusion'}

Please estimate the sample size based on the assumption: 
The significance level is set at 0.05, the power is 80%, and a 10% dropout rate is assumed. The minimal important differences (MIDs) are over 3-7 (mean 5) for FACT-G©, over 3-4 for the fatigue subscale, and 6 for total FACT-F. Normality will be tested with the Shapiro-Wilks test. Various statistical adjustments will be applied, including change score analysis, analysis of covariance, logistic regression, and Mantel-Haenszel test. Statistical analysis will be performed using R version 3.5.1 and IBM SPSS Statistics version 26.0.
Data analysis (sample size and statistical analysis)
        The sample size was calculated using a 2-sided Wilcoxon-Mann-Whitney test. The sample size with a fasting group of 67 patients and a plant-based group of 67 patients reaches a power of 80% at a significance level of pâ=â0.05 to detect an effect size of 0.5 for FACT-GÂ© between these two groups. If an additional 10% dropout rate is assumed, we need a minimum of 148 patients. With a ratio of 4:1 between breast cancer and ovarian cancer patients, we need a total of 150 patients (120 breast cancer and 30 ovarian cancer patients), divided into 75 patients each (60 breast cancer and 15 ovarian cancer patients) for the STF group and the plant-based group. All FACITÂ© scales are designed with a higher score indicating better well-being. Accordingly, we reversed response scores on negatively phrased questions. The scores will be obtained in accordance with the formula that was previously established by the FACITÂ© system. In cases where individual questions are skipped, scores will be prorated using the average of the other answers in the subscale (prorated subscale scoreâ=â[sum of the item scores]âÃâ[N of items in subscale]/[N of items answered]) as long as more than 50% of the items are answered (minimum of 4 items for the subscales). The FACT-G score is considered appropriate as long as at least 22 of 27 FACT-G items are completed (â¥â80%). Inter-subscale correlations will be computed using Pearson correlation, and the reliability of the internal consistency for all scales will be assessed by computing Cronbachâs alpha. When Cronbachâs alpha exceeded 0.90, the scale is considered to have sufficient precision for individual classification or diagnosis. The minimal important differences (MIDs) of STF and plant-based groups, i.e., the âsmallest change in the score that patients perceive as important, either beneficial or harmful, and that would lead the clinician to consider a change in the patientâs managementâ will be used to find clinically meaningful improvements [54]. MID values over 3â7 (mean 5) for FACT-GÂ© and over 3â4 for the fatigue subscale and 6 for total FACT-F will be considered significant. Normality will be tested with the Shapiro-Wilks test. Furthermore, various methods for adjustment at the statistical analysis stage will be applied. We will use âchange score analysisâ that determines group effect, based on the difference between the baseline and the post-treatment score (basic adjustment) and analysis of covariance. This is a model-based adjustment that includes the baseline of the outcome variable in the model. Statistical adjustment can also be performed by the use of logistic regression or by pooling the stratified analyses, using, for example, a Mantel-Haenszel test. We will simultaneously use these various design methods that reduce covariate imbalance and statistical adjustment during analysis. Statistical analysis will be performed using R version 3.5.1 (R Foundation) and IBM SPSS Statistics, version 26.0 (IBM Corp., Armonk, NY, USA).