Study type

Study topic

DiseaseĀ /health condition

Study type

Non-interventional study

Scope of the study

Disease epidemiology
Non-interventional study

Non-interventional study design

Cohort
Study drug and medical condition

Name of medicine

PAXLOVID

Medical condition to be studied

SARS-CoV-2 test positive
Population studied

Age groups

Children (2 to < 12 years)
Adolescents (12 to < 18 years)
Adults (18 to < 46 years)
Adults (46 to < 65 years)
Adults (65 to < 75 years)
Adults (75 to < 85 years)
Adults (85 years and over)
Study design details

Main study objective

This study aims to understand the characteristics of patients with COVID-19 and contextualize the COVID-19 complication and safety events of interests using real world data.

Outcomes

Distribution of demographics, medical history, biomarkers, health-equity, healthcare utilization and time to clinical events at baseline for COVID-19 patients. Background incidence of COVID-19 manifestations and safety outcomes for COVID-19 patients. Incidence of long COVID-19, Incidence of oxygen supplementation among COVID-19 patients hospitalized with mechanical ventilation/ECMO Predictors of COVID-19 severe outcomes/manifestations and long COVID Real world effectiveness of Paxlovid in reducing the COVID-19 related outcomes.

Data analysis plan

Baseline characteristics will be summarized for COVID 19 patients in the study. Means with standard deviations, medians with interquartile will be provided for continuous variables. Numbers and percentages will be provided for dichotomous variables or categorical variables. For dichotomous endpoints such as COVID-19 complications and outcomes, the crude cumulative incidence and incidence rate of each endpoint will be estimated. To assess the feasibility of real-world effectiveness of Paxlovid, we will apply propensity score matching (PSM) method to control confounding and prescription time distribution method (PTDM) to handle immortal bias. For exploratory analysis, machine learning models (eg, LASSO Cox regression, Random Survival Forest (RSF), and XGBoosting (XGB) models) will be used to identify important predictors associated with progression to COVID-19 severe outcomes/manifestations as well as long COVID.
Documents
Study, other information
English (1.93 MB - PDF)View document
English (1.93 MB - PDF)View document