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Censoring Survival Data01:09

Censoring Survival Data

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Continuous-Time Causal Inference With Marked Point Process Weights: An Example on Sodium-Glucose Co-Transporters 2

Sumeet Kalia1,2, Olli Saarela3, Tao Chen2

  • 1Department of Statistics, University of Manitoba, Winnipeg, Canada.

Statistics in Medicine
|May 19, 2025
PubMed
Summary
This summary is machine-generated.

Sodium-Glucose co-Transporters 2 Inhibitor (SGLT-2i) medications did not increase the risk of recurrent urinary tract infections (UTIs) in patients with type II diabetes and chronic kidney disease. Continuous-time marginal structural models were used to analyze the causal effect.

Keywords:
causal inferenceconstrained optimizationelectronic health recordsmarginal structural modelsmarked‐point process weightsprimary caretime‐to‐recurrent event analysis

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Pharmacology

Background:

  • Treatment-confounder feedback in longitudinal studies can lead to biased causal inference.
  • Conventional models struggle with irregularly timed events and complex treatment histories.
  • Marginal structural models (MSMs) offer a way to estimate causal effects by adjusting for time-dependent confounding.

Purpose of the Study:

  • To develop and apply a continuous-time marginal structural model (MSM) to estimate the causal effect of Sodium-Glucose co-Transporters 2 Inhibitor (SGLT-2i) exposure on time-to-recurrent urinary tract infection (UTI).
  • To address challenges in primary care data with irregular visit times and detailed treatment information.

Main Methods:

  • Utilized a continuous-time marked point process to model recurrent UTI episodes and SGLT-2i dosage (none, low, high).
  • Applied stabilized and optimal treatment weights within the MSM framework.
  • Analyzed a cohort of type II diabetes patients with chronic kidney disease.

Main Results:

  • The study found no increase in recurrent urinary tract infections (UTIs) with SGLT-2i medication use.
  • Both low-dose and high-dose SGLT-2i prescriptions were associated with similar rates of recurrent UTIs.

Conclusions:

  • Continuous-time MSMs are effective for analyzing recurrent events with complex treatment exposures in irregularly timed data.
  • SGLT-2i medications appear safe regarding the risk of recurrent UTIs in this patient population.