Here is the design of a clinical trial:

Official Title: Fruit and Vegetable Intervention in Lactating Women to Reduce Breast Cancer Risk: Effects on Breast Cell DNA Methylation, Breast Inflammation,and Weight

Included conditions:
- Breast Cancer Female
- Inflammation
- Postpartum Weight Retention
- Diet, Healthy
- Risk Reduction

Study Armgroups:
- {'label': 'Dietary Intervention', 'type': 'EXPERIMENTAL', 'description': 'Intensive dietary counseling and fruit and vegetable box delivery.', 'interventionNames': ['Behavioral: Dietary Counseling']}
- {'label': 'Information', 'type': 'NO_INTERVENTION', 'description': 'Control condition of information on healthy eating during breastfeeding'}

Interventions:
- {'type': 'BEHAVIORAL', 'name': 'Dietary Counseling', 'description': 'The counseling approach will employ strategies shown to be successful in previous and ongoing dietary modification studies including supportive and motivational interviewing techniques. Each woman in the diet intervention group will be assigned a trained nutrition coach/counselor. Counselors will focus on helping participants identify and address barriers to achieving the goal of consuming at least 8 to 10 servings of nutrient dense fruits and vegetables each day (e.g., modifying recipes and food preparation). Participants will also receive weekly boxes of fruits and vegetables .', 'armGroupLabels': ['Dietary Intervention']}

Primary Outcomes:
- {'measure': 'DNA methylation', 'description': '% methylation in 300,000 CpG sites in DNA from sloughed breast epithelial cells from milk', 'timeFrame': '20 weeks'}
- {'measure': 'Inflammatory markers in milk', 'description': 'Adiponectin, CRP, IFN-y, IL1-beta, Leptin, IL-6, IL-8, TNF-alpha, b-FGF, FLT-1, PLGF, VegF-D', 'timeFrame': '20 weeks'}
- {'measure': 'Early weight', 'description': 'maternal weight', 'timeFrame': '20 weeks'}
- {'measure': 'Early waist circumference', 'description': 'maternal waist circumference', 'timeFrame': '20 weeks'}
- {'measure': 'One-year Weight', 'description': 'maternal weight', 'timeFrame': 'One-year'}
- {'measure': 'One-year Waist Circumference', 'description': 'maternal weight circumference', 'timeFrame': 'one year'}

Please estimate the sample size based on the assumption: 
Bonferroni correction to maintain overall Type I error at 0.05. Significance level set at p = 0.05/300000 for CpG measurements. Power of 99% (82%) for CpG measurements with ρ = 0.5 (0.3). Power of 94% (84%) for inflammatory markers with ρ = 0.5 (0.3). Power of 98% for body weight, waist circumference, and fruit/vegetable consumption with a type I error of 0.05.
Power and sample size calculations
        Power calculations for linear mixed models with adjustment for multiple testing were carried out through simulation studies. Due to computational considerations, adjustment for multiple testing was carried out through a conservative Bonferroni correction procedure to maintain the overall type I error at 0.05. CpG measurements (1a): In each dataset, we simulated repeated measurements of CpG levels according to a multivariate normal distribution with unit variance, assuming a within-subject correlation Ï, a mean difference in levels in the control group comparing baseline to the 20-week (T2) visit equal to 0.1 SD units and a mean difference in the intervention group between the 20-week (T2) and baseline (T0) of Î SD units. The threshold for statistical significance was set at pâ=â0.05/300000, corresponding to a Bonferroni correction to maintain the overall Type I error at 0.05. When the correlation between the repeated CpG measurements is set to Ïâ=â0.5 (Ïâ=â0.3), a sample size of 200 per group results in 99% (82%) power to detect a mean difference in the intervention arm between baseline and T2 of Îâ=â0.75 SD units, obtained by averaging over 100 simulated datasets. For the analysis of the 12 inflammatory markers, we simulated repeated measurements of inflammatory marker levels according to a multivariate normal distribution as described above. Assuming a Bonferroni correction to maintain the overall Type I error at 0.05 and when the correlation between the repeated inflammatory measurements is set to Ïâ=â0.5 (Ïâ=â0.3), a sample size of 200 per group results in 94% (84%) power to detect a mean difference in the intervention arm between baseline and T2 of Îâ=â0.45 SD units, obtained by averaging over 100 simulated datasets.
        Power calculations for the analyses of body weight, waist circumference and fruit and vegetable consumption at 1Â year were based on a generalized linear model, using the R package pwr [32]. A sample size of 400 subjects results in 98% power to detect an effect size of 0.05 or larger, assuming a two-sided test and a type I error 0.05. We assumed the same variability at the two time points so the power is the same for 20Â weeks and one year.
        Previous studies found a reduction of 3.90Â kg in the intervention arm compared to the control group in weight change from baseline, with an associated SD of approximately 4.8Â kg [33]. A 98% power to detect an effect size of 0.05 or larger would correspond to a difference in weight change of 0.24Â kg or larger between the two randomized groups. Previous studies found a reduction of 7.4Â cm in the diet-intervention arm compared to the control group in waist circumference change from baseline, with an associated SD of approximately 15Â cm [34]. A 98% power to detect an effect size of 0.05 or larger would correspond to a difference in waist circumference change of 0.75Â cm between the two randomized groups. Our preliminary data showed a mean increase in fruit/vegetable consumption in the diet intervention arm of 7.3 servings with an associated SD of approximately 3 servings [35]. A 98% power to detect an effect size of 0.05 or larger would correspond to a difference in fruit/vegetable consumption change of 0.15 servings between the two randomized groups.