11 research outputs found
Statistical Methods for Dealing with Outcome Misclassification in Studies with Competing Risks Survival Outcomes
Indiana University-Purdue University Indianapolis (IUPUI)In studies with competing risks outcomes, misidentifying the event-type responsible
for the observed failure is, by definition, an act of misclassification. Several authors have
established that such misclassification can bias competing risks statistical analyses, and have
proposed statistical remedies to aid correct modeling. Generally, these rely on adjusting
the estimation process using information about outcome misclassification, but invariably
assume that outcome misclassification is non-differential among study subjects regardless
of their individual characteristics. In addition, current methods tend to adjust for the
misclassification within a semi-parametric framework of modeling competing risks data.
Building on the existing literature, in this dissertation, we explore the parametric modeling
of competing risks data in the presence of outcome misclassification, be it differential or
non-differential. Specifically, we develop parametric pseudo-likelihood-based approaches
for modeling cause-specific hazards while adjusting for misclassification information that is
obtained either through data internal or external to the current study (respectively, internal
or external-validation sampling). Data from either type of validation sampling are used
to model predictive values or misclassification probabilities, which, in turn, are used to
adjust the cause-specific hazard models. We show that the resulting pseudo-likelihood
estimates are consistent and asymptotically normal, and verify these theoretical properties
using simulation studies. Lastly, we illustrate the proposed methods using data from a
study involving people living with HIV/AIDS (PLWH)in the East-African consortium of the International Epidemiologic Databases for the Evaluation of HIV/AIDS (IeDEA EA). In
this example, death is frequently misclassified as disengagement from care as many deaths
go unreported to health facilities caring for these patients. In this application, we model
the cause-specific hazards of death and disengagement from care among PLWH after they
initiate anti-retroviral treatment, while adjusting for death misclassification.2021-03-1
Trends in typologies of concurrent alcohol, marijuana, and cigarette use among US adolescents: An ecological examination by sex and race/ethnicity
Substance use during adolescence is a public health concern due to associated physical and behavioral health consequences. Such consequences are amplified among concurrent substance users. Although sex and racial/ethnic differences in single-substance use have been observed, the current literature is inconclusive as to whether differences exist in the prevalence of concurrent use. The current study used data from the 2011–2014 National Survey on Drug Use and Health to examine typologies (single and concurrent patterns) of alcohol, marijuana, and cigarette use among current adolescent users age 12–18 by sex and race/ethnicity. Participants were 14,667 White, Hispanic, African American, Asian, and Native American adolescents. The most common typology was alcohol only, followed by concurrent use of alcohol and marijuana. Weighted prevalence estimates indicated that adolescent females were more likely to be current users of alcohol only, whereas male adolescents were more likely to belong to all other typologies. Compared to Whites, racial/ethnic minorities had larger proportions of marijuana only users and were generally less likely than or equally likely to be concurrent users. One exception was for African American adolescents, who were more likely to be alcohol and marijuana users than their White counterparts. Results suggest that concurrent substance use is common among U.S. adolescents, making up over 40% of past-month use, but typologies of use vary by sex and race/ethnicity. Preventive interventions should consider all typologies of use rather than only single substance exposures and address patterns of use that are most pertinent to adolescents based on sex and race/ethnicity
A pseudo-likelihood method for estimating misclassification probabilities in competing-risks settings when true event data are partially observed
Outcome misclassification occurs frequently in binary-outcome studies and can result in biased estimation of quantities such as the incidence, prevalence, cause-specific hazards, cumulative incidence functions etc. A number of remedies have been proposed to address the potential misclassification of the outcomes in such data. The majority of these remedies lie in the estimation of misclassification probabilities, which are in turn used to adjust analyses for outcome misclassification. A number of authors advocate using a gold-standard procedure on a sample internal to the study to learn about the extent of the misclassification. With this type of internal validation, the problem of quantifying the misclassification also becomes a missing data problem as, by design, the true outcomes are only ascertained on a subset of the entire study sample. Although, the process of estimating misclassification probabilities appears simple conceptually, the estimation methods proposed so far have several methodological and practical shortcomings. Most methods rely on missing outcome data to be missing completely at random (MCAR), a rather stringent assumption which is unlikely to hold in practice. Some of the existing methods also tend to be computationally-intensive. To address these issues, we propose a computationally-efficient, easy-to-implement, pseudo-likelihood estimator of the misclassification probabilities under a missing at random (MAR) assumption, in studies with an available internal validation sample. We present the estimator through the lens of studies with competing-risks outcomes, though the estimator extends beyond this setting. We describe the consistency and asymptotic distributional properties of the resulting estimator, and derive a closed-form estimator of its variance. The finite-sample performance of this estimator is evaluated via simulations. Using data from a real-world study with competing risks outcomes, we illustrate how the proposed method can be used to estimate misclassification probabilities. We also show how the estimated misclassification probabilities can be used in an external study to adjust for possible misclassification bias when modeling cumulative incidence functions
Hospital outcomes in non-surgical patients identified at risk for OSA
Background: In-hospital respiratory outcomes of non-surgical patients with undiagnosed obstructive sleep apnea (OSA), particularly those with significant comorbidities are not well defined. Undiagnosed and untreated OSA may be associated with increased cardiopulmonary morbidity.
Study objectives: Evaluate respiratory failure outcomes in patients identified as at-risk for OSA by the Berlin Questionnaire (BQ).
Methods: This was a retrospective study conducted using electronic health records at a large health system. The BQ was administered at admission to screen for OSA to medical-service patients under the age of 80 years old meeting the following health system criteria: (1) BMI greater than 30; (2) any of the following comorbid diagnoses: hypertension, heart failure, acute coronary syndrome, pulmonary hypertension, arrhythmia, cerebrovascular event/stroke, or diabetes. Patients with known OSA or undergoing surgery were excluded. Patients were classified as high-risk or low-risk for OSA based on the BQ score as follows: low-risk (0 or 1 category with a positive score on the BQ); high-risk (2 or more categories with a positive score on BQ). The primary outcome was respiratory failure during index hospital stay defined by any of the following: orders for conventional ventilation or intubation; at least two instances of oxygen saturation less than 88% by pulse oximetry; at least two instances of respiratory rate over 30 breaths per minute; and any orders placed for non-invasive mechanical ventilation without a previous diagnosis of sleep apnea. Propensity scores were used to control for patient characteristics.
Results: Records of 15,253 patients were assessed. There were no significant differences in the composite outcome of respiratory failure by risk of OSA (high risk: 11%, low risk: 10%, p = 0.55). When respiratory failure was defined as need for ventilation, more patients in the low-risk group experienced invasive mechanical ventilation (high-risk: 1.8% vs. low-risk: 2.3%, p = 0.041). Mortality was decreased in patients at high-risk for OSA (0.86%) vs. low risk for OSA (1.53%, p < 0.001).
Conclusions: Further prospective studies are needed to understand the contribution of undiagnosed OSA to in-hospital respiratory outcomes
Declining Tuberculosis Incidence Among People Receiving HIV Care and Treatment Services in East Africa, 2007–2012
Background: Antiretroviral therapy (ART) reduces the risk of Tuberculosis (TB) among people living with HIV (PLWH). With ART scale-up in sub-Saharan Africa over the past decade, incidence of TB among PLWH engaged in HIV care is predicted to decline.
Methods: We conducted a retrospective analysis of routine clinical data from 168,330 PLWH receiving care at 35 facilities in Kenya, Tanzania, and Uganda during 2003–2012, participating in the East African region of the International Epidemiologic Databases to Evaluate AIDS. Temporal trends in facility-based annual TB incidence rates (per 100,000 person years) among PLWH and country-specific standardized TB incidence ratios using annual population-level TB incidence data from the World Health Organization were computed between 2007 and 2012. We examined patient-level and facility-level factors associated with incident TB using multivariable Cox models.
Results: Overall, TB incidence rates among PLWH in care declined 5-fold between 2007 and 2012 from 5960 to 985 per 100,000 person years [P = 0.0003] (Kenya: 7552 to 1115 [P = 0.0007]; Tanzania: 7153 to 635 [P = 0.0025]; Uganda: 3204 to 242 [P = 0.018]). Standardized TB incidence ratios significantly decreased in the 3 countries, indicating a narrowing gap between incidence rates among PLWH and the general population. We observed lower hazards of incident TB among PLWH on ART and/or isoniazid preventive therapy and receiving care in facilities offering TB treatment onsite.
Conclusions: Annual TB incidence rates among PLWH significantly declined during ART scale-up but remained higher than the general population. Increasing access to ART and isoniazid preventive therapy and co-location of HIV and TB treatment may further reduce TB incidence among PLWH
Statistical Methods for Dealing with Outcome Misclassification in Studies with Competing Risks Survival Outcomes
Indiana University-Purdue University Indianapolis (IUPUI)In studies with competing risks outcomes, misidentifying the event-type responsible
for the observed failure is, by definition, an act of misclassification. Several authors have
established that such misclassification can bias competing risks statistical analyses, and have
proposed statistical remedies to aid correct modeling. Generally, these rely on adjusting
the estimation process using information about outcome misclassification, but invariably
assume that outcome misclassification is non-differential among study subjects regardless
of their individual characteristics. In addition, current methods tend to adjust for the
misclassification within a semi-parametric framework of modeling competing risks data.
Building on the existing literature, in this dissertation, we explore the parametric modeling
of competing risks data in the presence of outcome misclassification, be it differential or
non-differential. Specifically, we develop parametric pseudo-likelihood-based approaches
for modeling cause-specific hazards while adjusting for misclassification information that is
obtained either through data internal or external to the current study (respectively, internal
or external-validation sampling). Data from either type of validation sampling are used
to model predictive values or misclassification probabilities, which, in turn, are used to
adjust the cause-specific hazard models. We show that the resulting pseudo-likelihood
estimates are consistent and asymptotically normal, and verify these theoretical properties
using simulation studies. Lastly, we illustrate the proposed methods using data from a
study involving people living with HIV/AIDS (PLWH)in the East-African consortium of the International Epidemiologic Databases for the Evaluation of HIV/AIDS (IeDEA EA). In
this example, death is frequently misclassified as disengagement from care as many deaths
go unreported to health facilities caring for these patients. In this application, we model
the cause-specific hazards of death and disengagement from care among PLWH after they
initiate anti-retroviral treatment, while adjusting for death misclassification.2021-03-1
A pseudo‐likelihood method for estimating misclassification probabilities in competing‐risks settings when true‐event data are partially observed
Outcome misclassification occurs frequently in binary-outcome studies and can result in biased estimation of quantities such as the incidence, prevalence, cause-specific hazards, cumulative incidence functions etc. A number of remedies have been proposed to address the potential misclassification of the outcomes in such data. The majority of these remedies lie in the estimation of misclassification probabilities, which are in turn used to adjust analyses for outcome misclassification. A number of authors advocate using a gold-standard procedure on a sample internal to the study to learn about the extent of the misclassification. With this type of internal validation, the problem of quantifying the misclassification also becomes a missing data problem as, by design, the true outcomes are only ascertained on a subset of the entire study sample. Although, the process of estimating misclassification probabilities appears simple conceptually, the estimation methods proposed so far have several methodological and practical shortcomings. Most methods rely on missing outcome data to be missing completely at random (MCAR), a rather stringent assumption which is unlikely to hold in practice. Some of the existing methods also tend to be computationally-intensive. To address these issues, we propose a computationally-efficient, easy-to-implement, pseudo-likelihood estimator of the misclassification probabilities under a missing at random (MAR) assumption, in studies with an available internal validation sample. We present the estimator through the lens of studies with competing-risks outcomes, though the estimator extends beyond this setting. We describe the consistency and asymptotic distributional properties of the resulting estimator, and derive a closed-form estimator of its variance. The finite-sample performance of this estimator is evaluated via simulations. Using data from a real-world study with competing risks outcomes, we illustrate how the proposed method can be used to estimate misclassification probabilities. We also show how the estimated misclassification probabilities can be used in an external study to adjust for possible misclassification bias when modeling cumulative incidence functions
Response and Overall Survival for Yttrium-90 Radioembolization of Hepatic Sarcoma: A Multicenter Retrospective Study
Purpose
To evaluate the effectiveness and safety of yttrium-90 transarterial radioembolization (TARE) for the treatment of primary and metastatic soft tissue sarcoma (STS) of the liver.
Materials and Methods
A retrospective review of 39 patients with primary (n = 2) and metastatic (n = 37) hepatic STS treated with TARE at 4 institutions was performed. Fourteen STS subtypes were included, with leiomyosarcoma being the most common (51%). TARE with glass (22 patients) or resin (17 patients) microspheres was performed, with single lobe (17 patients) or bilobar treatment (22 patients) based on disease burden. Adverse events of treatment, overall survival (OS), and tumor response at 3, 6, and 12 months after TARE were assessed per the Response Evaluation Criteria in Solid Tumors.
Results
Fourteen patients demonstrated either partial or complete response to therapy, with an objective response rate of 36%. Thirty patients (77%) demonstrated disease control (DC)—either stable disease or response to treatment. Median OS was 30 months (95% confidence interval 12–43 months) for all patients. DC at 3 months was associated with an increased median OS (44 months) compared with progressive disease (PD) (7.5 months; P < .0001). Patients with DC at 6 months also demonstrated an increased median OS (38 months) compared to patients with PD (17 months; P = .0443). Substantial adverse events included 1 liver abscess, 1 gastric ulceration, and 1 pneumonitis.
Conclusions
Patients with hepatic STS treated with TARE demonstrated a high rate of DC and a median OS of 30 months, which suggests a role for TARE in the palliation of hepatic STS
Neurodevelopment in Young Children Born to HIV-Infected Mothers: A Meta-analysis
CONTEXT:
HIV-infected (HIV+) children have worse neurodevelopmental outcomes compared with HIV-uninfected children. However, little is known regarding the differences in neurodevelopment between young HIV+ children, HIV-exposed but uninfected (HEU) children, and HIV-unexposed and uninfected (HUU) children.
OBJECTIVE:
To systematically review and meta-analyze data on neurodevelopmental performance between young HIV+, HEU, and HUU children.
DATA SOURCES:
We systematically searched the following electronic bibliographic databases: Ovid Medline, Embase, PsycINFO, Education Resources Information Center, and the Cochrane Database of Systematic Reviews.
STUDY SELECTION:
Studies were selected on the basis of defined inclusion criteria. Titles, abstracts, and full texts were assessed by 2 independent reviewers.
DATA EXTRACTION:
Data were extracted by 2 independent reviewers and cross-checked by 2 additional reviewers.
RESULTS:
Forty-five studies were identified for inclusion in the systematic review, and of these, 11 were included in the meta-analysis on the basis of availability of Bayley Scales of Infant and Toddler Development scores. Within the meta-analysis, when compared with their HUU peers, HIV+ and HEU children had lower cognitive and motor scores. HIV+ and HEU children with antiretroviral (ARV) exposure had lower cognitive and motor scores compared with those without ARV exposure.
LIMITATIONS:
We were unable to control adequately for intravenous drug use, geographic location, or quality of the assessment independently.
CONCLUSIONS:
Both HIV+ and HEU children had worse developmental outcomes compared with HUU children. HIV+ and HEU children with ARV exposure also had worse developmental outcomes compared with those without exposure; however, these results should be interpreted with caution. More research is needed to identify the impact of ARV exposure on young children