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6. Multiple Imputation for Non-Monotone Missing Not at Random Binary Data using the No Self-Censoring Model

Author

Ren, Boyu, Lipsitz, Stuart R., Weiss, Roger D., and Fitzmaurice, Garrett M.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Although approaches for handling missing data from longitudinal studies are well-developed when the patterns of missingness are monotone, fewer methods are available for non-monotone missingness. Moreover, the conventional missing at random (MAR) assumption -- a natural benchmark for monotone missingness -- does not model realistic beliefs about non-monotone missingness processes (Robins and Gill, 1997). This has provided the impetus for alternative non-monotone missing not at random (MNAR) mechanisms. The "no self-censoring" (NSC) model is such a mechanism and assumes the probability an outcome variable is missing is independent of its value when conditioning on all other possibly missing outcome variables and their missingness indicators. As an alternative to "weighting" methods that become computationally demanding with increasing number of outcome variables, we propose a multiple imputation approach under NSC. We focus on the case of binary outcomes and present results of simulation and asymptotic studies to investigate the performance of the proposed imputation approach. We describe a related approach to sensitivity analysis to departure from NSC. Finally, we discuss the relationship between MAR and NSC and prove that one is not a special case of the other. The proposed methods are illustrated with application to a substance use disorder clinical trial.

Published
2023

7. Permutation tests for assessing potential non-linear associations between treatment use and multivariate clinical outcomes

Author

Ren, Boyu, Lipsitz, Stuart R., Fitzmaurice, Garrett M., and Weiss, Roger D.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

In many psychometric applications, the relationship between the mean of an outcome and a quantitative covariate is too complex to be described by simple parametric functions; instead, flexible nonlinear relationships can be incorporated using penalized splines. Penalized splines can be conveniently represented as a linear mixed effects model (LMM), where the coefficients of the spline basis functions are random effects. The LMM representation of penalized splines makes the extension to multivariate outcomes relatively straightforward. In the LMM, no effect of the quantitative covariate on the outcome corresponds to the null hypothesis that a fixed effect and a variance component are both zero. Under the null, the usual asymptotic chi-square distribution of the likelihood ratio test for the variance component does not hold. Therefore, we propose three permutation tests for the likelihood ratio test statistic: one based on permuting the quantitative covariate, the other two based on permuting residuals. We compare via simulation the Type I error rate and power of the three permutation tests obtained from joint models for multiple outcomes, as well as a commonly used parametric test. The tests are illustrated using data from a stimulant use disorder psychosocial clinical trial.

Published
2023

9. Patient Perceptions of a Personal Health Record: A Test of the Diffusion of Innovation Model

Author

Emani, Srinivas, Yamin, Cyrus K, Peters, Ellen, Karson, Andrew S, Lipsitz, Stuart R, Wald, Jonathan S, Williams, Deborah H, and Bates, David W

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundPersonal health records (PHRs) have emerged as an important tool with which patients can electronically communicate with their doctors and doctor’s offices. However, there is a lack of theoretical and empirical research on how patients perceive the PHR and the differences in perceptions between users and non-users of the PHR. ObjectiveTo apply a theoretical model, the diffusion of innovation model, to the study of PHRs and conduct an exploratory empirical study on the applicability of the model to the study of perceptions of PHRs. A secondary objective was to assess whether perceptions of PHRs predict the perceived value of the PHR for communicating with the doctor’s office. MethodsWe first developed a survey capturing perceptions of PHR use and other factors such as sociodemographic characteristics, access and use of technology, perceived innovativeness in the domain of information technology, and perceptions of privacy and security. We then conducted a cross-sectional survey (N = 1500). Patients were grouped into five groups of 300: PHR users (innovators, other users, and laggards), rejecters, and non-adopters. We applied univariate statistical analysis (Pearson chi-square and one-way ANOVA) to assess differences among groups and used multivariate statistical techniques (factor analysis and multiple regression analysis) to assess the presence of factors identified by the diffusion of innovation model and the predictors of our dependent variable (value of PHR for communicating with the doctor’s office). ResultsOf the 1500 surveys, 760 surveys were returned for an overall response rate of 51%. Computer use among non-adopters (75%) was lower than that among PHR users (99%) and rejecters (92%) (P < .001). Non-adopters also reported a lower score on personal innovativeness in information technology (mean = 2.8) compared to 3.6 and 3.1, respectively, for users and rejecters (P < .001). Four factors identified by the diffusion of innovation model emerged in the factor analysis: ease of use, relative advantage, observability, and trialability. PHR users perceived greater ease of use and relative advantage of the PHR than rejecters and non-adopters (P

Published
2012
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19. Reply by Authors

Author

Lucas, Mayra I., Qian, Zhiyu, Lipsitz, Stuart R., Chen, Xi, Alkhatib, Khalid, Kibel, Adam S., Cole, Alexander P., Iyer, Hari S., and Trinh, Quoc-Dien

Published
2024
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22. Semiparametric analysis of clustered interval-censored survival data using Soft Bayesian Additive Regression Trees (SBART)

Author

Basak, Piyali, Linero, Antonio R., SInha, Debajyoti, and Lipsitz, Stuart

Subjects
Statistics - Applications
Abstract

Popular parametric and semiparametric hazards regression models for clustered survival data are inappropriate and inadequate when the unknown effects of different covariates and clustering are complex. This calls for a flexible modeling framework to yield efficient survival prediction. Moreover, for some survival studies involving time to occurrence of some asymptomatic events, survival times are typically interval censored between consecutive clinical inspections. In this article, we propose a robust semiparametric model for clustered interval-censored survival data under a paradigm of Bayesian ensemble learning, called Soft Bayesian Additive Regression Trees or SBART (Linero and Yang, 2018), which combines multiple sparse (soft) decision trees to attain excellent predictive accuracy. We develop a novel semiparametric hazards regression model by modeling the hazard function as a product of a parametric baseline hazard function and a nonparametric component that uses SBART to incorporate clustering, unknown functional forms of the main effects, and interaction effects of various covariates. In addition to being applicable for left-censored, right-censored, and interval-censored survival data, our methodology is implemented using a data augmentation scheme which allows for existing Bayesian backfitting algorithms to be used. We illustrate the practical implementation and advantages of our method via simulation studies and an analysis of a prostate cancer surgery study where dependence on the experience and skill level of the physicians leads to clustering of survival times. We conclude by discussing our method's applicability in studies involving high dimensional data with complex underlying associations.

Published
2020

35. Estimating the impact of enhanced care at minority‐serving hospitals on disparities in the treatment of breast, prostate, lung, and colon cancers

Author

Beatrici, Edoardo, primary, Paciotti, Marco, additional, Nguyen, David‐Dan, additional, Filipas, Dejan K., additional, Qian, Zhiyu, additional, Lughezzani, Giovanni, additional, Daniels, Danesha, additional, Lipsitz, Stuart R., additional, Kibel, Adam S., additional, Cole, Alexander P., additional, and Trinh, Quoc‐Dien, additional

Published
2024
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36. Virtual Scribes and Physician Time Spent on Electronic Health Records

Author

Rotenstein, Lisa, primary, Melnick, Edward R., additional, Iannaccone, Christine, additional, Zhang, Jianyi, additional, Mugal, Aqsa, additional, Lipsitz, Stuart R., additional, Healey, Michael J., additional, Holland, Christopher, additional, Snyder, Richard, additional, Sinsky, Christine A., additional, Ting, David, additional, and Bates, David W., additional

Published
2024
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40. PD19-02 THE ASSOCIATION OF COUNTY-LEVEL PROSTATE-SPECIFIC ANTIGEN SCREENING WITH METASTATIC PROSTATE CANCER AND PROSTATE CANCER MORTALITY

Author

Stone, Benjamin J., primary, Labban, Muhieddine, additional, Beatrici, Edoardo, additional, Filipas, Dejan K., additional, D'Amico, Anthony V., additional, Lipsitz, Stuart R., additional, Choueiri, Toni K., additional, Kibel, Adam S., additional, Cole, Alexander P., additional, Iyer, Hari S., additional, and Trinh, Quoc-Dien, additional

Published
2024
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41. MP35-04 SMOKING CHARACTERISTICS AND YEARS SINCE QUITTING SMOKING OF US ADULTS WITH LUNG AND BLADDER CANCER

Author

Beatrici, Edoardo, primary, D'Agnino, Filippo, additional, Labban, Muhieddine, additional, Filipas, Dejan K., additional, Stone, Benjamin J., additional, Reis, Leonardo O., additional, Lughezzani, Giovanni, additional, Buffi, Nicolò M., additional, Lipsitz, Stuart R., additional, Clinton, Timothy N., additional, Matulewicz, Richard S., additional, Trinh, Quoc-Dien, additional, and Cole, Alexander P., additional

Published
2024
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42. MP68-04 ESTIMATING THE IMPACT OF ENHANCED CARE AT MINORITY-SERVING HOSPITALS ON DISPARITIES IN THE TREATMENT OF PROSTATE CANCER

Author

Beatrici, Edoardo, primary, Paciotti, Marco, additional, Nguyen, David-Dan, additional, Filipas, Dejan K., additional, Qian, Zhiyu, additional, D'Agnino, Filippo, additional, Lughezzani, Giovanni, additional, Lipsitz, Stuart R., additional, Kibel, Adam S., additional, Cole, Alexander P., additional, and Trinh, Quoc-Dien, additional

Published
2024
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43. Semiparametric Mixed-Scale Models Using Shared Bayesian Forests

Author

Linero, Antonio R., Sinha, Debajyoti, and Lipsitz, Stuart R.

Subjects
Statistics - Methodology
Abstract

This paper demonstrates the advantages of sharing information about unknown features of covariates across multiple model components in various nonparametric regression problems including multivariate, heteroscedastic, and semi-continuous responses. In this paper, we present methodology which allows for information to be shared nonparametrically across various model components using Bayesian sum-of-tree models. Our simulation results demonstrate that sharing of information across related model components is often very beneficial, particularly in sparse high-dimensional problems in which variable selection must be conducted. We illustrate our methodology by analyzing medical expenditure data from the Medical Expenditure Panel Survey (MEPS). To facilitate the Bayesian nonparametric regression analysis, we develop two novel models for analyzing the MEPS data using Bayesian additive regression trees - a heteroskedastic log-normal hurdle model with a "shrink-towards-homoskedasticity" prior, and a gamma hurdle model.

Published
2018

47. Testing a Novel Inpatient Respiratory Depression Electronic Clinical Quality Measure (eCQM) for Orthopedic Practice in Two Large U.S. Health Systems

Author

Syrowatka, Ania, primary, Li, Troy, additional, Curtin-Bowen, Mica, additional, Pullman, Avery, additional, Lipsitz, Stuart R., additional, Sainlaire, Michael, additional, Song, Wenyu, additional, Thai, Tien, additional, Businger, Alexandra, additional, Bozic, Kevin J., additional, Jiranek, William A., additional, Lieberman, Jay R., additional, Bates, David W., additional, and Dykes, Patricia C., additional

Published
2022
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49. Generative artificial intelligence as a source of breast cancer information for patients: Proceed with caution.

Author

Park, Ko Un, Lipsitz, Stuart, Dominici, Laura S., Lynce, Filipa, Minami, Christina A., Nakhlis, Faina, Waks, Adrienne G., Warren, Laura E., Eidman, Nadine, Frazier, Jeannie, Hernandez, Lourdes, Leslie, Carla, Rafte, Susan, Stroud, Delia, Weissman, Joel S., King, Tari A., and Mittendorf, Elizabeth A.

Subjects
*GENERATIVE artificial intelligence, *CHATGPT, *INTRACLASS correlation, *CHATBOTS, *WORD frequency
Abstract

Background Methods Results Conclusions This study evaluated the accuracy, clinical concordance, and readability of the chatbot interface generative pretrained transformer (ChatGPT) 3.5 as a source of breast cancer information for patients.Twenty questions that patients are likely to ask ChatGPT were identified by breast cancer advocates. These were posed to ChatGPT 3.5 in July 2023 and were repeated three times. Responses were graded in two domains: accuracy (4‐point Likert scale, 4 = worst) and clinical concordance (information is clinically similar to physician response; 5‐point Likert scale, 5 = not similar at all). The concordance of responses with repetition was estimated using intraclass correlation coefficient (ICC) of word counts. Response readability was calculated using the Flesch Kincaid readability scale. References were requested and verified.The overall average accuracy was 1.88 (range 1.0–3.0; 95% confidence interval [CI], 1.42–1.94), and clinical concordance was 2.79 (range 1.0–5.0; 95% CI, 1.94–3.64). The average word count was 310 words per response (range, 146–441 words per response) with high concordance (ICC, 0.75; 95% CI, 0.59–0.91; p < .001). The average readability was poor at 37.9 (range, 18.0–60.5) with high concordance (ICC, 0.73; 95% CI, 0.57–0.90; p < .001). There was a weak correlation between ease of readability and better clinical concordance (−0.15; p = .025). Accuracy did not correlate with readability (0.05; p = .079). The average number of references was 1.97 (range, 1–4; total, 119). ChatGPT cited peer‐reviewed articles only once and often referenced nonexistent websites (41%).Because ChatGPT 3.5 responses were incorrect 24% of the time and did not provide real references 41% of the time, patients should be cautioned about using ChatGPT for medical information. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Patient Safety Indicators During the Initial COVID-19 Pandemic Surge in the United States

Author

Rodriguez, Jorge A., primary, Samal, Lipika, additional, Ganesan, Sandya, additional, Yuan, Nina H., additional, Wien, Matthew, additional, Ng, Kenney, additional, Huang, Hu, additional, Park, Yoonyoung, additional, Rajmane, Amol, additional, Jackson, Gretchen Purcell, additional, Lipsitz, Stuart R., additional, Bates, David W., additional, and Levine, David M., additional

Published
2024
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