Builds a two-by-two factorial design in which assignments to each factor are independent of each other.

two_by_two_designer(N = 100, prob_A = 0.5, prob_B = 0.5,
  weight_A = 0.5, weight_B = 0.5, outcome_means = rep(0, 4),
  mean_A0B0 = outcome_means[1], mean_A0B1 = outcome_means[2],
  mean_A1B0 = outcome_means[3], mean_A1B1 = outcome_means[4],
  sd_i = 1, outcome_sds = rep(0, 4))

Arguments

N

An integer. Size of sample.

prob_A

A number in [0,1]. Probability of assignment to treatment A.

prob_B

A number in [0,1]. Probability of assignment to treatment B.

weight_A

A number. Weight placed on A=1 condition in definition of "average effect of B" estimand.

weight_B

A number. Weight placed on B=1 condition in definition of "average effect of A" estimand.

outcome_means

A vector of length 4. Average outcome in each A,B condition, in order AB = 00, 01, 10, 11. Values overridden by mean_A0B0, mean_A0B1, mean_A1B0, mean_A1B1, if provided.

mean_A0B0

A number. Mean outcome in A=0, B=0 condition.

mean_A0B1

A number. Mean outcome in A=0, B=1 condition.

mean_A1B0

A number. Mean outcome in A=1, B=0 condition.

mean_A1B1

A number. Mean outcome in A=1, B=1 condition.

sd_i

A nonnegative scalar. Standard deviation of individual-level shock (common across arms).

outcome_sds

A nonnegative vector of length 4. Standard deviation of (additional) unit level shock in each condition, in order AB = 00, 01, 10, 11.

Value

A two-by-two factorial design.

Details

Three types of estimand are declared. First, weighted averages of the average treatment effects of each treatment, given the two conditions of the other treatments. Second and third, the difference in treatment effects of each treatment, given the conditions of the other treatment.

Units are assigned to treatment using complete random assignment. Potential outcomes follow a normal distribution.

Treatment A is assigned first and then Treatment B within blocks defined by treatment A. Thus, if there are 6 units 3 are guaranteed to receive treatment A but the number receiving treatment B is stochastic.

See multi_arm_designer for a factorial design with non independent assignments.

Examples

design <- two_by_two_designer(outcome_means = c(0,0,0,1)) # A design biased for the specified estimands: design <- two_by_two_designer(outcome_means = c(0,0,0,1), prob_A = .8, prob_B = .2)
# NOT RUN { diagnose_design(design) # }
# A design with estimands that "match" the assignment: design <- two_by_two_designer(outcome_means = c(0,0,0,1), prob_A = .8, prob_B = .2, weight_A = .8, weight_B = .2)
# NOT RUN { diagnose_design(design) # }
# Compare power with and without interactions, given same average effects in each arm designs <- redesign(two_by_two_designer(), outcome_means = list(c(0,0,0,1), c(0,.5,.5,1)))
# NOT RUN { diagnose_design(designs) # }

Author

DeclareDesign Team