Simulation and minimization: Technical advances for factorial experiments designed to optimize clinical interventions
| Autor: | Andrea Chu, Radley C Sheldrick, Dana Rubin, Emily Feinberg, Sarabeth Broder-Fingert, Lisa R. Fortuna, Jocelyn Kuhn, Megan Jordan |
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| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Factorial Epidemiology Computer science Randomized experiment Health Informatics Context (language use) Sample (statistics) Randomization 01 natural sciences 010104 statistics & probability 03 medical and health sciences Clinical trials 0302 clinical medicine Covariate Humans Factorial design Computer Simulation 030212 general & internal medicine 0101 mathematics Multi-phase optimization strategy Equivalence (measure theory) Randomized Controlled Trials as Topic lcsh:R5-920 Subject allocation Clinical intervention studies Factorial experiment Minimization Technical Advance Research Design Sample size determination lcsh:Medicine (General) Algorithms |
| Zdroj: | BMC Medical Research Methodology BMC Medical Research Methodology, Vol 19, Iss 1, Pp 1-9 (2019) |
| DOI: | 10.21203/rs.2.12285/v1 |
| Popis: | Background The Multiphase Optimization Strategy (MOST) is designed to maximize the impact of clinical healthcare interventions, which are typically multicomponent and increasingly complex. MOST often relies on factorial experiments to identify which components of an intervention are most effective, efficient, and scalable. When assigning participants to conditions in factorial experiments, researchers must be careful to select the assignment procedure that will result in balanced sample sizes and equivalence of covariates across conditions while maintaining unpredictability. Methods In the context of a MOST optimization trial with a 2x2x2x2 factorial design, we used computer simulation to empirically test five subject allocation procedures: simple randomization, stratified randomization with permuted blocks, maximum tolerated imbalance (MTI), minimal sufficient balance (MSB), and minimization. We compared these methods across the 16 study cells with respect to sample size balance, equivalence on key covariates, and unpredictability. Leveraging an existing dataset to compare these procedures, we conducted 250 computerized simulations using bootstrap samples of 304 participants. Results Simple randomization, the most unpredictable procedure, generated poor sample balance and equivalence of covariates across the 16 study cells. Stratified randomization with permuted blocks performed well on stratified variables but resulted in poor equivalence on other covariates and poor balance. MTI, MSB, and minimization had higher complexity and cost. MTI resulted in balance close to pre-specified thresholds and a higher degree of unpredictability, but poor equivalence of covariates. MSB had 19.7% deterministic allocations, poor sample balance and improved equivalence on only a few covariates. Minimization was most successful in achieving balanced sample sizes and equivalence across a large number of covariates, but resulted in 34% deterministic allocations. Small differences in proportion of correct guesses were found across the procedures. Conclusions Based on the computer simulation results and priorities within the study context, minimization with a random element was selected for the planned research study. Minimization with a random element, as well as computer simulation to make an informed randomization procedure choice, are utilized infrequently in randomized experiments but represent important technical advances that researchers implementing multi-arm and factorial studies should consider. |
| Databáze: | OpenAIRE |
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