Non-factorial analyses of two-by-two factorial trial designs

Abstract

Background/aims

Factorial analyses of 2 × 2 trial designs are known to be problematic unless one can be sure that there is no interaction between the treatments (A and B). Instead, we consider non-factorial analyses of a factorial trial design that addresses clinically relevant questions of interest without any assumptions on the interaction. Primary questions of interest are as follows: (1) is A better than the control treatment C, (2) is B better than C, (3) is the combination of A and B (AB) better than C, and (4) is AB better than A, B, and C.

Methods

A simple three-step procedure that tests the first three primary questions of interest using a Bonferroni adjustment at the first step is proposed. A Hochberg procedure on the four primary questions is also considered. The two procedures are evaluated and compared in limited simulations. Published results from three completed trials with factorial designs are re-evaluated using the two procedures.

Results

Both suggested procedures (that answer multiple questions) require a 50%–60% increase in per arm sample size over a two-arm design asking a single question. The simulations suggest a slight advantage to the three-step procedure in terms of power (for the primary and secondary questions). The proposed procedures would have formally addressed the questions arising in the highlighted published trials arguably more simply than the pre-specified factorial analyses used.

Conclusion

Factorial trial designs are an efficient way to evaluate two treatments, alone and in combination. In situations where a statistical interaction between the treatment effects cannot be assumed to be 0, simple non-factorial analyses are possible that directly assess the questions of interest without the zero interaction assumption.