title: Evaluating dynamic treatment strategies: does it have to be more costly?
abstract: Dynamic treatment strategies are designed to change treatments over time in response to intermediate outcomes. They can be deployed for primary treatment as well as for the introduction of adjuvant treatment or other treatment-enhancing interventions. When treatment interventions are delayed until needed, more cost-efficient strategies will result. Sequential multiple assignment randomized (SMAR) trials allow for unbiased estimation of the marginal effects of different sequences of history-dependent treatment decisions. Because a single SMAR trial enables evaluation of many different dynamic regimes at once, it is naturally thought to require larger sample sizes than the parallel randomized trial. In this paper, we compare power between SMAR trials studying a regime, where treatment boosting enters when triggered by an observed event, versus the parallel design, where a treatment boost is consistently prescribed over the entire study period. In some settings, we found that the dynamic design yields the more efficient trial for the detection of treatment activity. We develop one particular trial to compare a dynamic nursing intervention with telemonitoring for the enhancement of medication adherence in epilepsy patients. To this end, we derive from the SMAR trial data either an average of conditional treatment effects ('conditional estimator') or the population-averaged ('marginal') estimator of the dynamic regimes. Analytical sample size calculations for the parallel design and the conditional estimator are compared with simulated results for the population-averaged estimator. We conclude that in specific settings, well-chosen SMAR designs may require fewer data for the development of more cost-efficient treatment strategies than parallel designs.
authors.affiliation: Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281-S9, Gent, Oost-Vlaanderen, Belgium. Jozefien.Buyze@UGent.be
authors.forename: Jozefien
authors.initials: J
authors.lastname: Buyze
authors.forename: Els
authors.initials: E
authors.lastname: Goetghebeur
mesh_terms: D000927:Anticonvulsants / Q000191:economics / Q000627:therapeutic use; D003362:Cost-Benefit Analysis; D003627:Data Interpretation, Statistical; D016527:Drug Costs; D004827:Epilepsy / Q000175:diagnosis / Q000188:drug therapy / Q000191:economics; D017048:Health Care Costs; D006801:Humans; D055118:Medication Adherence; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000191:economics* / Q000706:statistics & numerical data; D012017:Referral and Consultation / Q000191:economics; D012107:Research Design / Q000706:statistics & numerical data; D018401:Sample Size; D017216:Telemedicine / Q000191:economics; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D000927:Anticonvulsants