Michael Baiocchi

Instrumental variable methods for causal inference

A goal of many health studies is to determine the causal effect of a treatment or intervention on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly randomized experiment, and instead, an observational study must be used. A major challenge to the validity of observational studies is the possibility of unmeasured confounding (i.e., unmeasured ways in which the treatment and control groups differ before treatment administration, which also affect the outcome). Instrumental variables analysis is a method for controlling for unmeasured confounding. This type of analysis requires the measurement of a valid instrumental variable, which is a variable that (i) is independent of the unmeasured confounding; (ii) affects the treatment; and (iii) affects the outcome only indirectly through its effect on the treatment. This tutorial discusses the types of causal effects that can be estimated by instrumental variables analysis; the assumptions needed for instrumental variables analysis to provide valid estimates of causal effects and sensitivity analysis for those assumptions; methods of estimation of causal effects using instrumental variables; and sources of instrumental variables in health studies.

Mechanical or Biologic Prostheses for Aortic-Valve and Mitral-Valve Replacement

BACKGROUND In patients undergoing aortic‐valve or mitral‐valve replacement, either a mechanical or biologic prosthesis is used. Biologic prostheses have been increasingly favored despite limited evidence supporting this practice. METHODS We compared long‐term mortality and rates of reoperation, stroke, and bleeding between inverse‐probability‐weighted cohorts of patients who underwent primary aortic‐valve replacement or mitral‐valve replacement with a mechanical or biologic prosthesis in California in the period from 1996 through 2013. Patients were stratified into different age groups on the basis of valve position (aortic vs. mitral valve). RESULTS From 1996 through 2013, the use of biologic prostheses increased substantially for aortic‐valve and mitral‐valve replacement, from 11.5% to 51.6% for aortic‐valve replacement and from 16.8% to 53.7% for mitral‐valve replacement. Among patients who underwent aortic‐valve replacement, receipt of a biologic prosthesis was associated with significantly higher 15‐year mortality than receipt of a mechanical prosthesis among patients 45 to 54 years of age (30.6% vs. 26.4% at 15 years; hazard ratio, 1.23; 95% confidence interval [CI], 1.02 to 1.48; P=0.03) but not among patients 55 to 64 years of age. Among patients who underwent mitral‐valve replacement, receipt of a biologic prosthesis was associated with significantly higher mortality than receipt of a mechanical prosthesis among patients 40 to 49 years of age (44.1% vs. 27.1%; hazard ratio, 1.88; 95% CI, 1.35 to 2.63; P<0.001) and among those 50 to 69 years of age (50.0% vs. 45.3%; hazard ratio, 1.16; 95% CI, 1.04 to 1.30; P=0.01). The incidence of reoperation was significantly higher among recipients of a biologic prosthesis than among recipients of a mechanical prosthesis. Patients who received mechanical valves had a higher cumulative incidence of bleeding and, in some age groups, stroke than did recipients of a biologic prosthesis. CONCLUSIONS The long‐term mortality benefit that was associated with a mechanical prosthesis, as compared with a biologic prosthesis, persisted until 70 years of age among patients undergoing mitral‐valve replacement and until 55 years of age among those undergoing aortic‐valve replacement. (Funded by the National Institutes of Health and the Agency for Healthcare Research and Quality.)

The Role of Outpatient Facilities in Explaining Variations in Risk‐Adjusted Readmission Rates between Hospitals

OBJECTIVE Validate risk-adjusted readmission rates as a measure of inpatient quality of care after accounting for outpatient facilities, using premature infants as a test case. STUDY SETTING Surviving infants born between January 1, 1998 and December 12, 2001 at five Northern California Kaiser Permanente neonatal intensive care units (NICU) with 1-year follow-up at 32 outpatient facilities. STUDY DESIGN Using a retrospective cohort of premature infants (N=898), Poissons regression models determined the risk-adjusted variation in unplanned readmissions between 0-1 month, 0-3 months, 3-6 months, and 3-12 months after discharge attributable to patient factors, NICUs, and outpatient facilities. DATA COLLECTION Prospectively collected maternal and infant hospital data were linked to inpatient, outpatient, and pharmacy databases. PRINCIPAL RESULTS Medical and sociodemographic factors explained the largest amount of variation in risk-adjusted readmission rates. NICU facilities were significantly associated with readmission rates up to 1 year after discharge, but the outpatient facility where patients received outpatient care can explain much of this variation. Characteristics of outpatient facilities, not the NICUs, were associated with variations in readmission rates. CONCLUSION Ignoring outpatient facilities leads to an overstatement of the effect of NICUs on readmissions and ignores a significant cause of variations in readmissions.

Association of Playing High School Football With Cognition and Mental Health Later in Life

Importance American football is the largest participation sport in US high schools and is a leading cause of concussion among adolescents. Little is known about the long-term cognitive and mental health consequences of exposure to football-related head trauma at the high school level. Objective To estimate the association of playing high school football with cognitive impairment and depression at 65 years of age. Design, Setting, and Participants A representative sample of male high school students who graduated from high school in Wisconsin in 1957 was studied. In this cohort study using data from the Wisconsin Longitudinal Study, football players were matched between March 1 and July 1, 2017, with controls along several baseline covariates such as adolescent IQ, family background, and educational level. For robustness, 3 versions of the control condition were considered: all controls, those who played a noncollision sport, and those who did not play any sport. Exposures Athletic participation in high school football. Main Outcomes and Measures A composite cognition measure of verbal fluency and memory and attention constructed from results of cognitive assessments administered at 65 years of age. A modified Center for Epidemiological Studies’ Depression Scale score was used to measure depression. Secondary outcomes include results of individual cognitive tests, anger, anxiety, hostility, and heavy use of alcohol. Results Among the 3904 men (mean [SD] age, 64.4 [0.8] years at time of primary outcome measurement) in the study, after matching and model-based covariate adjustment, compared with each control condition, there was no statistically significant harmful association of playing football with a reduced composite cognition score (–0.04 reduction in cognition vs all controls; 97.5% CI, –0.14 to 0.05) or an increased modified Center for Epidemiological Studies’ Depression Scale depression score (–1.75 reduction vs all controls; 97.5% CI, –3.24 to –0.26). After adjustment for multiple testing, playing football did not have a significant adverse association with any of the secondary outcomes, such as the likelihood of heavy alcohol use at 65 years of age (odds ratio, 0.68; 95% CI, 0.32-1.43). Conclusions and Relevance Cognitive and depression outcomes later in life were found to be similar for high school football players and their nonplaying counterparts from mid-1950s in Wisconsin. The risks of playing football today might be different than in the 1950s, but for current athletes, this study provides information on the risk of playing sports today that have a similar risk of head trauma as high school football played in the 1950s.

Using Design Thinking to Differentiate Useful From Misleading Evidence in Observational Research

Few issues can be more important to physicians or patients than that treatment decisions are based on reliable information about benefits and harms. While randomized clinical trials (RCTs) are generally regarded as the most valid source of evidence about benefits and some harms, concerns about their generalizability, costs, and heterogeneity of treatment effects have led to the search for other sources of information to augment or possibly replace trials. This is embodied in the recently passed 21st Century Cures Act, which mandates that the US Food and Drug Administration develop rules for the use of “real world evidence” in drug approval, defined as “data…derived from sources other than randomized clinical trials.”1 A second push toward the use of nontrial evidence is based on the perception that the torrent of electronic health-related data—medical record, genomic, and lifestyle (ie, “Big Data”)—can be transformed into reliable evidence with the use of powerful modern analytic tools

Tutorial in Biostatistics: Instrumental Variable Methods for Causal Inference*

A goal of many health studies is to determine the causal effect of a treatment or intervention on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly randomized experiment, and instead, an observational study must be used. A major challenge to the validity of observational studies is the possibility of unmeasured confounding (i.e., unmeasured ways in which the treatment and control groups differ before treatment administration, which also affect the outcome). Instrumental variables analysis is a method for controlling for unmeasured confounding. This type of analysis requires the measurement of a valid instrumental variable, which is a variable that (i) is independent of the unmeasured confounding; (ii) affects the treatment; and (iii) affects the outcome only indirectly through its effect on the treatment. This tutorial discusses the types of causal effects that can be estimated by instrumental variables analysis; the assumptions needed for instrumental variables analysis to provide valid estimates of causal effects and sensitivity analysis for those assumptions; methods of estimation of causal effects using instrumental variables; and sources of instrumental variables in health studies.

The weaker sex? Vulnerable men and women’s resilience to socio-economic disadvantage

Sex differences in mortality vary over time and place as a function of social, health, and medical circumstances. The magnitude of these variations, and their response to large socioeconomic changes, suggest that biological differences cannot fully account for sex differences in survival. Drawing on a wide swath of mortality data across countries and over time, we develop a set of empiric observations with which any theory about excess male mortality and its correlates will have to contend. We show that as societies develop, M/F survival first declines and then increases, a “sex difference in mortality transition” embedded within the demographic and epidemiologic transitions. After the onset of this transition, cross-sectional variation in excess male mortality exhibits a consistent pattern of greater female resilience to mortality under socio-economic adversity. The causal mechanisms underlying these associations merit further research.

Likelihood of Unemployed Smokers vs Nonsmokers Attaining Reemployment in a One-Year Observational Study

IMPORTANCE Studies in the United States and Europe have found higher smoking prevalence among unemployed job seekers relative to employed workers. While consistent, the extant epidemiologic investigations of smoking and work status have been cross-sectional, leaving it underdetermined whether tobacco use is a cause or effect of unemployment. OBJECTIVE To examine differences in reemployment by smoking status in a 12-month period. DESIGN, SETTING, AND PARTICIPANTS An observational 2-group study was conducted from September 10, 2013, to August 15, 2015, in employment service settings in the San Francisco Bay Area (California). Participants were 131 daily smokers and 120 nonsmokers, all of whom were unemployed job seekers. Owing to the studys observational design, a propensity score analysis was conducted using inverse probability weighting with trimmed observations. Including covariates of time out of work, age, education, race/ethnicity, and perceived health status as predictors of smoking status. MAIN OUTCOMES AND MEASURES Reemployment at 12-month follow-up. RESULTS Of the 251 study participants, 165 (65.7) were men, with a mean (SD) age of 48 (11) years; 96 participants were white (38.2%), 90 were black (35.9%), 24 were Hispanic (9.6%), 18 were Asian (7.2%), and 23 were multiracial or other race (9.2%); 78 had a college degree (31.1%), 99 were unstably housed (39.4%), 70 lacked reliable transportation (27.9%), 52 had a criminal history (20.7%), and 72 had received prior treatment for alcohol or drug use (28.7%). Smokers consumed a mean (SD) of 13.5 (8.2) cigarettes per day at baseline. At 12-month follow-up (217 participants retained [86.5%]), 60 of 108 nonsmokers (55.6%) were reemployed compared with 29 of 109 smokers (26.6%) (unadjusted risk difference, 0.29; 95% CI, 0.15-0.42). With 6% of analysis sample observations trimmed, the estimated risk difference indicated that nonsmokers were 30% (95% CI, 12%-48%) more likely on average to be reemployed at 1 year relative to smokers. Results of a sensitivity analysis with additional covariates of sex, stable housing, reliable transportation, criminal history, and prior treatment for alcohol or drug use (25.3% of observations trimmed) reduced the difference in employment attributed to smoking status to 24% (95% CI, 7%-39%), which was still a significant difference. Among those reemployed at 1 year, the average hourly wage for smokers was significantly lower (mean [SD],

Peer Assessment Enhances Student Learning: The Results of a Matched Randomized Crossover Experiment in a College Statistics Class.

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Evidence That Classroom-Based Behavioral Interventions Reduce Pregnancy-Related School Dropout Among Nairobi Adolescents

4.68]) than for nonsmokers (mean [SD],