plotProximalEffect {MRTSampleSize}

plot the graph for the proximal treatment effect

Description

plot of the graphs for the proximal treatment effect when the trend for the proximal treatment effect is constant, linear or quadractic.

Usage

plotProximalEffect(
  days,
  occ_per_day,
  beta_shape,
  beta_mean,
  beta_initial,
  beta_quadratic_max
)

Arguments

days

Duration of the study.

occ_per_day

Number of decision time points per day.

beta_shape

The trend for the proximal treatment effect, choices are constant, linear or quadratic. Note:

1. Constant The proximal treatment effect stays constant over the study.

2. Linear The linearly increasing form of a proximal treatment effect might be used if participants will get more enthusiastically engage in the apps and thus the proximal effect will increase as the study goes. The linearly decreasing form of a proximal treatment effect might be used if participants are likely to disengage the activity suggestionss and thus the proximal effect will decrease as the study goes.

3. Quadratic The quadratic form of a proximal treatment effect might be used if you expect that initially participants will enthusiastically engage in the apps and thus the proximal effect will get higher. Then, as the study goes on, some participants are likely to disengage or begin to ignore the activity suggestions and hence a downward trend.

beta_mean

Average of proximal treatment effect.

beta_initial

Initial value of proximal treatment effect when beta_shape is linear or quadratic.

beta_quadratic_max

Day of maximal proximal treatment effect when beta_shape is quadratic.

Value

A graph for the proximal treatment effect.

Examples

plotProximalEffect(days=42,
                   occ_per_day=5,
                   beta_shape="quadratic",
                   beta_mean=0.1,
                   beta_initial=0,
                   beta_quadratic_max=28)