Model selection in historical research using approximate Bayesian computation

Formal Models and History Computational models are increasingly being used to study historical dynamics. This new trend, which could be named Model-Based History, makes use of recently published datasets and innovative quantitative methods to improve our understanding of past societies based on their written sources. The extensive use of formal models allows historians to reevaluate hypotheses formulated decades ago and still subject to debate due to the lack of an adequate quantitative framework. The initiative has the potential to transform the discipline if it solves the challenges posed by the study of historical dynamics. These difficulties are based on the complexities of modelling social interaction, and the methodological issues raised by the evaluation of formal models against data with low sample size, high variance and strong fragmentation. This work examines an alternate approach to this evaluation based on a Bayesian-inspired model selection method. The validity of the classical Lanchester's laws of combat is examined against a dataset comprising over a thousand battles spanning 300 years. Four variations of the basic equations are discussed, including the three most common formulations (linear, squared, and logarithmic) and a new variant introducing fatigue. Approximate Bayesian Computation is then used to infer both parameter values and model selection via Bayes Factors. Results indicate decisive evidence favouring the new fatigue model. The interpretation of both parameter estimations and model selection provides new insights into the factors guiding the evolution of warfare. At a methodological level, the case study shows how model selection methods can be used to guide historical research through the comparison between existing hypotheses and empirical evidence.Funding for this work was provided by the SimulPast Consolider Ingenio project (CSD2010-00034) of the former Ministry for Science and Innovation of the Spanish Government and the European Research Council Advanced Grant EPNet (340828).Peer ReviewedPostprint (published version

Efficacy and safety of the oral direct factor Xa inhibitor apixaban for symptomatic deep vein thrombosis. The Botticelli DVT dose-ranging study.

International audienceBACKGROUND: Apixaban, an oral potent reversible direct inhibitor of activated factor X, has shown promise in the prevention of venous thromboembolism following major orthopedic surgery. We conducted a dose-ranging study in patients with deep vein thrombosis. METHODS: Consecutive patients with symptomatic deep vein thrombosis were included and randomized to receive 84-91 days of apixaban 5 mg twice-daily, 10 mg twice-daily, or 20 mg once-daily, or low molecular weight heparin (LMWH) followed by a vitamin K antagonist (VKA). The primary efficacy outcome was the composite of symptomatic recurrent venous thromboembolism and asymptomatic deterioration of bilateral compression ultrasound or perfusion lung scan. The principal safety outcome was the composite of major and clinically relevant, non-major bleeding. RESULTS: The mean age of the 520 included patients was 59 years, and 62% were male. The primary outcome occurred in 17 of the 358 apixaban-treated patients [4.7%, 95% confidence interval (CI) 2.8-7.5%] and in five of the 118 LMWH/VKA-treated patients (4.2%, 95% CI 1.4-9.6%) who were evaluable. The incidence in all three apixaban groups was low and comparable without evidence of a dose response. The principal safety outcome occurred in 28 (7.3%) of the 385 apixaban-treated patients and in 10 (7.9%) of the 126 LMWH/VKA-treated patients. No dose response for apixaban was observed. CONCLUSION: These observations warrant further evaluation of apixaban in phase III studies. The attractive fixed-dose regimen of this compound may meet the demand to simplify anticoagulant treatment in patients with established venous thromboembolism