Bayesian Modeling Framework for Causal Inference and Assessing Sensitivity to Unmeasured Confounding with Multiple Treatments [Methods Study], United States, 2020-2022 (ICPSR 39721)
The research team based their new method on an existing method called Bayesian Additive Regression Trees, or BART. To test the new method, the team used data created by a computer program to look like real patient data. Then they compared the new method with current methods under different scenarios. Each scenario included three treatments. The team changed the total number of patients, the number of patients who took each treatment, and how alike or different the patients were who took each treatment. Across all scenarios, the team predicted the average treatment effect for all patients and for only patients who received a treatment.
Next, the research team used the new method with real data from patients with lung cancer who were receiving care in New York City hospitals. The team compared three types of surgery: open chest, robotic assisted, and video assisted. The team looked at the effects of each type of surgery on four health outcomes: breathing problems; length of hospital stay after surgery; stay in an intensive care unit, or ICU; and the need to return to the hospital.
Patients, doctors, and researchers helped design the study.
Comparison of Outcomes of Antibiotic Drugs and Appendectomy (CODA), United States, 2016-2020 (ICPSR 38541)
Antibiotics are considered a feasible treatment for appendicitis, yet appendectomy remains the treatment standard in the United States. Previous randomized trials comparing these treatments excluded important subgroups and recruited small sample sizes but questions remain about the applicability of these previous findings. This study conducted the Comparison of Outcomes of antibiotic Drugs and Appendectomy (CODA) randomized clinical trial to compare antibiotics with appendectomy among adults with appendicitis, including those with appendicolith. Those recruited comprised a diverse population, compared an overall measure of health status as the primary outcome, and included several secondary clinical and patient-reported outcomes, complications, and measures of healthcare utilization.
Computer-Administered Animation as a New Method for Measuring Young Children's Health Outcomes [Methods Study], Orange County, California, 2013-2018 (ICPSR 39517)
Patients often take surveys about their health or quality of life. Results from these surveys can help doctors meet patients' needs. Young children can't fill out surveys by themselves. They may not be able to read or understand the questions. Most often, parents or hospital staff read the questions aloud, or parents answer the questions for their children. But this method may not give accurate results.
In this study, the research team tested three surveys for children ages 4 to 12 who are going to have or who recently had surgery. The first survey asks about general health. The second survey asks about feeling worried before surgery. The third survey asks about pain after surgery. A computer program reads the survey questions aloud. The surveys are animated and choices for the answers appear as cartoons.
The team wanted to learn if the surveys were
- Accurate, or correctly capturing how the children were feeling
- Reliable, or if children answered in a consistent way when asked similar questions
National Health Interview Survey, 1977: Hearing Supplement (ICPSR 9228)
National Health Interview Survey, 1990: Hearing Supplement (ICPSR 9910)
National Health Interview Survey, 1991: Hearing Supplement (ICPSR 6433)
National Hospital Ambulatory Medical Care Survey, 1992 (ICPSR 6585)
National Hospital Ambulatory Medical Care Survey, 1993 (ICPSR 6915)
National Hospital Ambulatory Medical Care Survey, 1994 (ICPSR 6824)
National Hospital Ambulatory Medical Care Survey, 1995 (ICPSR 2422)
National Hospital Ambulatory Medical Care Survey, 1996 (ICPSR 2365)
National Hospital Ambulatory Medical Care Survey, 1997 (ICPSR 2740)
National Hospital Ambulatory Medical Care Survey, 1998 (ICPSR 2916)
National Hospital Ambulatory Medical Care Survey, 1999 (ICPSR 3156)
National Hospital Ambulatory Medical Care Survey, 2000 (ICPSR 3551)
National Hospital Ambulatory Medical Care Survey, 2001 (ICPSR 3813)
National Hospital Ambulatory Medical Care Survey, 2002 (ICPSR 4405)
National Hospital Ambulatory Medical Care Survey, 2003 (ICPSR 4406)
National Hospital Ambulatory Medical Care Survey, 2004 (ICPSR 4530)
National Hospital Ambulatory Medical Care Survey, 2005 (ICPSR 28261)
National Hospital Ambulatory Medical Care Survey, 2006 (ICPSR 28321)
National Hospital Ambulatory Medical Care Survey, 2007 (ICPSR 28442)
National Hospital Ambulatory Medical Care Survey, 2008 (ICPSR 29922)
National Neighborhood Data Archive (NaNDA): Healthcare Services by Census Tract and ZCTA, United States, 1990-2022 (ICPSR 209050)
This dataset contains measures of the number and density of health care services per United States Census Tract or ZIP Code Tabulation Area (ZCTA) from 1990 through 2022. The dataset includes four separate files for four different geographic areas (GIS shapefiles from the United States Census Bureau).