Sentence examples for sequential risk from inspiring English sources

Time dependent propensity score matching is a sequential risk set matching that uses time dependent propensity scores.

The implications of these studies relate to whether simultaneous or sequential risk reduction interventions are more effective in populations with several behavioral risk factors.

Design Controlled propensity matched retrospective cohort study, in which pairs of patients with or without (control) adrenaline were created with a sequential risk set matching based on time dependent propensity score.

The findings from our study suggest that for underserved populations, sequential risk reduction interventions may be more appropriate than simultaneous interventions because the physical activity and nutrition readiness to change scores were discrepant stages of change.

Our study was sufficiently powered and successfully adjusted for time dependent severity of cardiac arrest, using sequential risk set matching with time dependent propensity scores (in other words, it adjusted for the varying duration of resuscitation procedures).

Most prediction rules use sequential numerical risk scoring to quantify prognosis and are an advanced form of audit.

Because of the heterogeneity, we did subgroup analyses according to country of origin, use of tinidazole (instead of metronidazole) in the sequential therapy, risk of bias, type of publication, and proton pump inhibitor used (supplementary table C).

In the model, the disease is subdivided into seven sequential stages: high-risk HPV infection (low-risk HPV infections are not simulated in the model); three pre-invasive stages (CIN 1, 2 and 3), and three invasive stages (International Federation of Obstetrics and Gynecology staging, FIGO 1A, 1B and 2+).

The trial sequential analysis adjusted risk ratio was 1.29 (95% confidence interval 1.06 to 1.56).

In conclusion, this proof-of-concept study in invasive candidiasis opens the way to a new sequential approach for risk stratification in critically ill patients.

We compared AFs from the unadjusted approach using Levin's formula, from Levin's formula using adjusted OR estimates, from logistic regression according to Bruzzi's approach, from logistic regression with sequential removal of risk factors ('sequential and) and from logistic regression with all possible removal sequences and subsequent averaging ('average AF'average