Publications

August 2020: Pharmacoepidemiology & Drug Safety

• Pardiss Kaviani, Suzanne N Landi, Aaron McKethan, M Alan Brookhart,  Leah J McGrath

Research using healthcare databases often includes patients frequently excluded from clinical trials; yet it is not known whether commonly used data represents the overall population or specific sub-populations of interest. We aimed to examine population representativeness from data sources in recent research studies in the United States (US).

August 2020: AIDS and Behavior

• Sara N Levintow, Brian W Pence, Kimberly A Powers, Teerada Sripaipan, Tran Viet Ha, Viet Anh Chu, Vu Minh Quan, Carl A Latkin, Vivian F Go

The burden of depression and HIV is high among people who inject drugs (PWID), yet the effect of depression on transmission risk behaviors is not well understood in this population. Using causal inference methods, we analyzed data from 455 PWID living with HIV in Vietnam 2009-2013. Study visits every 6 months over 2 years measured depressive symptoms in the past week and injecting and sexual behaviors in the prior 3 months. Severe depressive symptoms (vs. mild/no symptoms) increased injection equipment sharing (risk difference [RD] = 3.9 percentage points, 95% CI -1.7, 9.6) but not condomless sex (RD = -1.8, 95% CI -6.4, 2.8) as reported 6 months later. The cross-sectional association with injection equipment sharing at the same visit (RD = 6.2, 95% CI 1.4, 11.0) was stronger than the longitudinal effect. Interventions on depression among PWID may decrease sharing of injection equipment and the corresponding risk of HIV transmission.

August 2020: JAMA Health Forum

• Aaron McKethan

NoviSci CEO Aaron McKethan discusses the need for “a more robust conversation about reform” this campaign season. 

August 2020: Clinical Epidemiology

• Sara N Levintow, Stephanie R Reading, Bradley Saul, Ying Yu, Diane Reams, Leah J McGrath, Kiran Philip, Paul J Dluzniewski, M Alan Brookhart

The 2013 ACC/AHA cholesterol treatment guidelines removed the recommendation to treat adults at risk of cardiovascular disease to goal levels of low-density lipoprotein cholesterol (LDL-C). We anticipated that the frequency of LDL-C testing in clinical practice would decline as a result. To test this hypothesis, we evaluated the frequency of LDL-C testing before and after the guideline release.

June 2020: Pharmacoepidemiology & Drug Safety

• Leah J McGrath, Leslie Spangler, Jeffrey R Curtis, Vera Ehrenstein, Henrik T Sørensen, Bradley Saul, Sara N Levintow, Diane Reams, Brian D Bradbury, M Alan Brookhart

In contrast to randomized clinical trials, comparative safety and effectiveness assessments of osteoporosis medications in clinical practice may be subject to confounding by indication. We used negative control outcomes to detect residual confounding when comparing osteoporosis medications.

May 2020: Clinical Epidemiology

• Leah J McGrath, Karynsa Kilpatrick, Robert A Overman, Diane Reams, Anjali Sharma, Ivy Altomare, Jeffrey Wasser, M Alan Brookhart

Patients with immune thrombocytopenia (ITP) have low platelet counts and an increased risk of bleeding. We described treatment patterns and clinical outcomes in routine practice in the United States (US).

November 2019: Pharmacoepidemiology & Drug Safety

• Suzanne N Landi, Sarah Radke, Kim Boggess, Stephanie M Engel, Til Stürmer, Anna S Howe, Michele Jonsson Funk

To measure the comparative effectiveness of metformin versus insulin for initial pharmacological management of gestational diabetes mellitus (GDM).

August 2019: American Journal of Kidney Diseases

• Anne M Butler, J Bradley Layton, Vikas R Dharnidharka, John M Sahrmann, Marissa J Seamans, David J Weber, Leah J McGrath

Studies of patients on maintenance dialysis therapy suggest that standard-dose influenza vaccine (SDV) may not prevent influenza-related outcomes. Little is known about the comparative effectiveness of SDV versus high-dose influenza vaccine (HDV) in this population.

July 2019: American Journal of Epidemiology

• Alexander Breskin, Daniel Westreich, Stephen R Cole, Jessie K Edwards

In the absence of strong assumptions (e.g., exchangeability), only bounds for causal effects can be identified. Here we describe bounds for the risk difference for an effect of a binary exposure on a binary outcome in 4 common study settings: observational studies and randomized studies, each with and without simple random selection from the target population. Through these scenarios, we introduce randomizations for selection and treatment, and the widths of the bounds are narrowed from 2 (the width of the range of the risk difference) to 0 (point identification). We then assess the strength of the assumptions of exchangeability for internal and external validity by comparing their contributions to the widths of the bounds in the setting of an observational study without random selection from the target population. We find that when less than two-thirds of the target population is selected into the study, the assumption of exchangeability for external validity of the risk difference is stronger than that for internal validity. The relative strength of these assumptions should be considered when designing, analyzing, and interpreting observational studies and will aid in determining the best methods for estimating the causal effects of interest.

May 2019: JAMA Guide to Statistics and Methods

• Matthew Maciejewski, M Alan Brookhart

Randomized clinical trials are considered the most reliable source of evidence for the effects of medical interventions, but nonexperimental studies are often used to assess the effectiveness of treatments as they are used in actual clinical practice. In nonexperimental studies, treatment groups may differ by important patient characteristics, such as disease severity, frailty, cognitive function, vulnerability to adverse effects, and ability to pay. While statistical adjustment can account for imbalances in observed characteristics between groups, observed imbalances are concerning because they suggest that unobserved differences may also exist. Unobserved patient characteristics that influence both treatment and the outcomes result in “unobserved confounding,” a bias that cannot be removed using standard statistical adjustment.

May 2019: Pharmacoepidemiology Drug Safety

• Anne M Butler, Jonathan V Todd, John M Sahrmann, Catherine R Lesko, M Alan Brookhart

Health plan disenrollment occurs frequently in commercial insurance claims databases. If individuals who disenroll are different from those who remain enrolled, informative censoring may bias descriptive statistics as well as estimates of causal effect. We explored whether patterns of disenrollment varied by patient or health plan characteristics.

April 2019: American Journal of Epidemiology

• Stephen R Cole, Michael G Hudgens, Jessie K Edwards, M Alan Brookhart, David B Richardson, Daniel Westreich, Adaora A Adimora

Nonparametric bounds for the risk difference are straightforward to calculate and make no untestable assumptions about unmeasured confounding or selection bias due to missing data (e.g., dropout). These bounds are often wide and communicate uncertainty due to possible systemic errors. An illustrative example is provided.

January 2019: Cancer Medicine

• Leah J McGrath, Rohini K Hernandez, Robert Overman, Diane Reams, Alexander Liede, M Alan Brookhart, Elizabeth O’Donnell

Prior to 2018, intravenous bisphosphonates (IV BPs) were the only therapies recommended to prevent skeletal-related events for patients diagnosed with multiple myeloma (MM). We examined patterns of IV BP initiation and interruption among patients with newly diagnosed MM (NDMM) in the United States.

September 2018: Clinical Epidemiology

• Leah J McGrath, Robert Overman, Diane Reams, Alexander Liede, Steven A Narod, M Alan Brookhart, Rohini K Hernandez

Bone-modifying agents (BMAs) are recommended for women with bone metastasis from breast cancer to prevent skeletal-related events. We examined the usage patterns and identified the factors associated with the use of BMAs (denosumab and intravenous bisphosphonates) among women in the US.

May 2018: Epidemiology

• Alexander Breskin, Stephen R Cole, Michael G Hudgens

Causal diagrams have become widespread in epidemiologic research. Recently developed single-world intervention graphs explicitly connect the potential outcomes framework of causal inference with causal diagrams. Here, we provide a practical example demonstrating how single-world intervention graphs can supplement traditional causal diagrams.