Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Causal inference with multiple versions of treatment and application to
  personalized medicine   The development of high-throughput sequencing and targeted therapies has led
to the emergence of personalized medicine: a patient's molecular profile or the
presence of a specific biomarker of drug response will correspond to a
treatment recommendation made either by a physician or by a treatment
assignment algorithm. The growing number of such algorithms raises the question
of how to quantify their clinical impact knowing that a personalized medicine
strategy will inherently include different versions of treatment.
  We thus specify an appropriate causal framework with multiple versions of
treatment to define the causal effects of interest for precision medicine
strategies and estimate them emulating clinical trials with observational data.
Therefore, we determine whether the treatment assignment algorithm is more
efficient than different control arms: gold standard treatment, observed
treatments or random assignment of targeted treatments.
  Causal estimates of the precision medicine effects are first evaluated on
simulated data and they demonstrate a lower biases and variances compared with
naive estimation of the difference in expected outcome between treatment arms.
The various simulations scenarios also point out the different bias sources
depending on the clinical situation (heterogeneity of response, assignment of
observed treatments etc.). A RShiny interactive application is also provided to
further explore other user-defined scenarios. The method is then applied to
data from patient-derived xenografts (PDX): each patient tumour is implanted in
several immunodeficient cloned mice later treated with different drugs, thus
providing access to all corresponding drug sensitivities for all patients.
Access to these unique pre-clinical data emulating counterfactual outcomes
allows to validate the reliability of causal estimates obtained with the
proposed method.

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The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
1
The categories of this paper are: cs
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
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The categories of this paper are: comp-gas
5
The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: supr-con
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The categories of this paper are: q-fin
9
The categories of this paper are: nlin