Li Yin

Li Yin

Statistician
E-mail: li.yin@ki.se
Telephone: +46852486187
Visiting address: Nobels väg 12a, 17165 Solna
Postal address: C8 Medicinsk epidemiologi och biostatistik, C8 Applied Biostatistics ABS, 171 77 Stockholm
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About me

  • I work as applied statistician at Department of Medical Epidemiology and
    Biostatistics.
    1982 BSc in Chemistry from Sichuan University, Chengdu, China
    1989 PhD in Quantum Chemistry from Uppsala University, Sweden
    1992 Post doctor in Quantum Chemistry, University of Minnesota, USA

Research

  • *CAUSAL INFERENCE FROM COMPLEX LONGITUDINAL DATA: CHALLENGE AND BREAKTHROUGH
    *
    In many medical practices, one treatment is hardly a done deal to influence a
    certain outcome, for example, the survival of a patient. Instead, treatments
    are assigned in a sequence to influence the outcome. Statistically, we should
    infer the causal effect from a treatment sequence rather than a single
    treatment.
    *Causal effects of interest:*
    *The net effects of individual treatments in the sequence.* The net effect
    has the following medical significance:
    * The net effect of treatment distinguishes the effects of earlier
    treatments from later treatments on a certain outcome.
    * The net effects allow us to find optimized treatment: given a certain
    condition of the patient, say, age and prognosis, we could know which
    treatment would be optimal for a certain outcome.
    * It also allows us to find factors relevant to the net effects, say, if
    social economic factors such as income are important in the treatment
    under Swedish health care system.
    *The causal effect of a treatment sequence.* The sequential causal effect has
    the following medical significance
    * The sequential causal effect compares the effects of different treatment
    regimes on the outcome.
    * It may give an optimized treatment regime for a sub population, for
    instance, a subpopulation of young patients.
    * It may also give an optimized treatment regime for the whole patient
    population.
    *Estimation of the Causal effects*
    The well-known G-formula identifies the causal effects from longitudinal data
    such as the clinical data in which the time-dependent factors such as
    prognostic factors and side effects are outcomes of the earlier treatments as
    well as confounders of the subsequent treatments. Consequently, we have the
    well-known problem: a high (even infinite) dimensional and saturated model is
    needed, so it is extremely difficult to estimate and test these causal
    effects.
    In our work (to appear in Annals of Statistics [1]), we derived the new
    G-formula in which the time-dependent factors are only confounders for the
    subsequent treatments, so we only need a low dimensional and unsaturated
    model to estimate and test these causal effects.
    *Reference*
    Wang, X. and *Yin, L.* (2019). New G-Formula for the Sequential Causal Effect
    and Blip Effect of Treatment in Sequential Causal Inference. To appear in
    *Annals of Statistics*. [2]
    Wang, X. and *Yin, L.* (2015). Identifying and Estimating Net Effects of
    Treatments in Sequential Causal Inference. *Electronic Journal of
    Statistics*, 9: 1608–1643 [3]
    [1] http://hig.diva-portal.org/smash/get/diva2:1294432/FULLTEXT01.pdf
    [2] http://hig.diva-portal.org/smash/get/diva2:1294432/FULLTEXT01.pdf
    [3] https://projecteuclid.org/download/pdfview_1/euclid.ejs/1438883470

Articles

All other publications

Employments

  • Statistician, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 2014-

Supervisor

  • Yun Du, Ph.D., Karolinska Institute, Sweden, 2023

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