1
|
Ye T, Small DS, Rosenbaum PR. Dimensions, power and factors in an observational study of behavioral problems after physical abuse of children. Ann Appl Stat 2022. [DOI: 10.1214/22-aoas1611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Ting Ye
- Department of Biostatistics, University of Washington
| | - Dylan S. Small
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania
| | - Paul R. Rosenbaum
- Department of Statistics and Data Science, Wharton School, University of Pennsylvania
| |
Collapse
|
2
|
Zhao A, Lee Y, Small DS, Karmakar B. Evidence factors from multiple, possibly invalid, instrumental variables. Ann Stat 2022. [DOI: 10.1214/21-aos2148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Anqi Zhao
- Department of Statistics and Data Science, National University of Singapore
| | - Youjin Lee
- Department of Biostatistics, Brown University
| | - Dylan S. Small
- Department of Statistics and Data Science, University of Pennsylvania
| | | |
Collapse
|
3
|
Maindonald JH. Replication and Evidence Factors in Observational StudiesPaul R.RosenbaumChapman & Hall/CRC, 2021, xviii + 254 pages, $120, hardback ISBN: 978‐036748‐388‐3. Int Stat Rev 2022. [DOI: 10.1111/insr.12495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- John H. Maindonald
- Statistics Research Associates Limited PO Box 12‐649, Thorndon Wellington 6144 New Zealand
| |
Collapse
|
4
|
Karmakar B, Small DS, Rosenbaum PR. Reinforced Designs: Multiple Instruments Plus Control Groups as Evidence Factors in an Observational Study of the Effectiveness of Catholic Schools. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2020.1745811] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Bikram Karmakar
- Department of Statistics, University of Florida, Gainesville, FL
| | - Dylan S. Small
- Statistics Department, University of Pennsylvania, Philadelphia, PA
| | | |
Collapse
|
5
|
Cohen PL, Olson MA, Fogarty CB. Multivariate one-sided testing in matched observational studies as an adversarial game. Biometrika 2020. [DOI: 10.1093/biomet/asaa024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Summary
We present a multivariate one-sided sensitivity analysis for matched observational studies, appropriate when the researcher has specified that a given causal mechanism should manifest itself in effects on multiple outcome variables in a known direction. The test statistic can be thought of as the solution to an adversarial game, where the researcher determines the best linear combination of test statistics to combat nature’s presentation of the worst-case pattern of hidden bias. The corresponding optimization problem is convex, and can be solved efficiently even for reasonably sized observational studies. Asymptotically, the test statistic converges to a chi-bar-squared distribution under the null, a common distribution in order-restricted statistical inference. The test attains the largest possible design sensitivity over a class of coherent test statistics, and facilitates one-sided sensitivity analyses for individual outcome variables while maintaining familywise error control through its incorporation into closed testing procedures.
Collapse
Affiliation(s)
- P L Cohen
- Operations Research and Statistics Group, Massachusetts Institute of Technology, 100 Main Street, Cambridge, Massachusetts 02142, U.S.A
| | - M A Olson
- The Voleon Group, 2170 Dwight Way, Berkeley, California 94704, U.S.A
| | - C B Fogarty
- Operations Research and Statistics Group, Massachusetts Institute of Technology, 100 Main Street, Cambridge, Massachusetts 02142, U.S.A
| |
Collapse
|
6
|
Karmakar B, Small DS. Assessment of the extent of corroboration of an elaborate theory of a causal hypothesis using partial conjunctions of evidence factors. Ann Stat 2020. [DOI: 10.1214/19-aos1929] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
7
|
Karmakar B, Doubeni CA, Small DS. EVIDENCE FACTORS IN A CASE-CONTROL STUDY WITH APPLICATION TO THE EFFECT OF FLEXIBLE SIGMOIDOSCOPY SCREENING ON COLORECTAL CANCER. Ann Appl Stat 2020; 14:829-849. [PMID: 38465229 PMCID: PMC10924422 DOI: 10.1214/20-aoas1329] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
As in any observational study, in a case-control study a primary concern is potential unmeasured confounders. Bias, due to unmeasured confounders, can result in a false discovery of an apparent treatment effect when there is none. Replication of an observational study, which tries to provide multiple analyses of the data where the biases affecting each analysis are thought to be different, is one way to strengthen the evidence from an observational study. Evidence factors allow for internal replication by testing a hypothesis using multiple comparisons in a way that the comparisons yield independent evidence and differ in the sources of potential bias. We construct evidence factors in a case-control study in which there are two types of cases, "narrow" cases which are thought to be potentially more affected by the exposure and "marginal" cases which are thought to have more heterogeneous causes. We develop and study an inference procedure for using such evidence factors and apply it to a study of the effect of sigmoidoscopy screening on colorectal cancer.
Collapse
Affiliation(s)
- Bikram Karmakar
- Department of Statistics, College of Liberal Arts and Sciences, University of Florida
| | - Chyke A Doubeni
- Center for Health Equity and Community Engagement Research, Mayo Clinic
| | - Dylan S Small
- Department of Statistics, The Wharton School, University of Pennsylvania
| |
Collapse
|
8
|
Karmakar B, Small DS, Rosenbaum PR. Using Evidence Factors to Clarify Exposure Biomarkers. Am J Epidemiol 2020; 189:243-249. [PMID: 31912138 DOI: 10.1093/aje/kwz263] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 08/15/2019] [Accepted: 08/26/2019] [Indexed: 11/14/2022] Open
Abstract
A study has 2 evidence factors if it permits 2 statistically independent inferences about 1 treatment effect such that each factor is immune to some bias that would invalidate the other factor. Because the 2 factors are statistically independent, the evidence they provide can be combined using methods associated with meta-analysis for independent studies, despite using the same data twice in different ways. We illustrate evidence factors, applying them in a new way in investigations that have both an exposure biomarker and a coarse external measure of exposure to a treatment. To illustrate, we consider the possible effects of cigarette smoking on homocysteine levels, with self-reported smoking and a cotinine biomarker. We examine joint sensitivity of 2 factors to bias from confounding, a central aspect of any observational study.
Collapse
Affiliation(s)
- Bikram Karmakar
- Department of Statistics, College of Liberal Arts and Sciences, University of Florida, Gainesville, Florida
| | - Dylan S Small
- Department of Statistics, the Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Paul R Rosenbaum
- Department of Statistics, the Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania
| |
Collapse
|
9
|
Harris RA, Kranzler HR, Chang KM, Doubeni CA, Gross R. Long-term use of hydrocodone vs. oxycodone in primary care. Drug Alcohol Depend 2019; 205:107524. [PMID: 31707268 PMCID: PMC9338763 DOI: 10.1016/j.drugalcdep.2019.06.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 05/20/2019] [Accepted: 06/19/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND Hydrocodone and oxycodone are the Schedule II opioids most often prescribed in primary care. Notwithstanding the dangers of prescription opioid use, the likelihood of long-term use with either drug is presently unknown. METHODS Using a retrospective cohort design and data from a commerical healthcare claims repository, we compared the likelihood of long-term use of hydrocodone and oxycodone in primary care patients presenting with acute back pain. Treatment was categorized as long-term if the prescription dates spanned ≥90 days from initial prescription to the run-out date of the last prescription, and included ≥120 days' supply or ≥10 fills. Instrumental variable methods and probit regression were used to model the effect of drug choice on long-term use, estimate the average treatment effect, and correct for confounding by indication. RESULTS A total of 3,983 patients who were prescribed only hydrocodone or only oxycodone were followed for 270 days in 2016. Long-term opioid use was observed in 320 patients (8%). Controlling for potential confounders including morphine milligram equivalents and dosage, an estimated 12% (95 CI, 10%-14%) treated with hydrocodone transitioned to long-term use vs. 2% (95 CI, 1%-3%) on oxycodone. Among patients who received more than one prescription (n = 1,866), an estimated 23% (95 CI, 19%-26%) treated with hydrocodone transitioned to long-term use vs. 5% (95 CI, 3%-7%) on oxycodone. The difference between drugs was supported in sensitivity and subgroup analyses. Sample selection bias was not detected. CONCLUSIONS Long-term use was substantially greater for patients treated with hydrocodone than oxycodone, despite equianalgesia.
Collapse
Affiliation(s)
- Rebecca Arden Harris
- Department of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
| | - Henry R Kranzler
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; VISN 4 Mental Illness Research, Education and Clinical Center, The Corporal Michael Crescenz VA Medical Center, United States
| | - Kyong-Mi Chang
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; The Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, United States
| | - Chyke A Doubeni
- Department of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Robert Gross
- Department of Medicine, Infectious Diseases, Department of Epidemiology, Biostatistics, Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| |
Collapse
|
10
|
Karmakar B, Small DS, Rosenbaum PR. Using Approximation Algorithms to Build Evidence Factors and Related Designs for Observational Studies. J Comput Graph Stat 2019. [DOI: 10.1080/10618600.2019.1584900] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Bikram Karmakar
- Wharton School, Department of Statistics, University of Pennsylvania, Philadelphia, PA
| | - Dylan S. Small
- Wharton School, Department of Statistics, University of Pennsylvania, Philadelphia, PA
| | - Paul R. Rosenbaum
- Wharton School, Department of Statistics, University of Pennsylvania, Philadelphia, PA
| |
Collapse
|
11
|
Rosenbaum PR. Sensitivity analysis for stratified comparisons in an observational study of the effect of smoking on homocysteine levels. Ann Appl Stat 2018. [DOI: 10.1214/18-aoas1153] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
12
|
Karmakar B, Heller R, Small DS. False discovery rate control for effect modification in observational studies. Electron J Stat 2018. [DOI: 10.1214/18-ejs1476] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|