ijsmr

International Journal of Statistics in Medical Research

Use of Self-Matching to Control for Stable Patient Characteristics While Addressing Time-Varying Confounding on Treatment Effect: A Case Study of Older Intensive Care Patients
Pages 8-16
Ling Han, M.A. Pisani, K.L.B. Araujo and Heather G. Allore
DOI:
http://dx.doi.org/10.6000/1929-6029.2016.05.01.2
Published: 08 January 2016


Abstract: Exposure-crossover design offers a non-experimental option to control for stable baseline confounding through self-matching while examining causal effect of an exposure on an acute outcome. This study extends this approach to longitudinal data with repeated measures of exposure and outcome using data from a cohort of 340 older medical patients in an intensive care unit (ICU). The analytic sample included 92 patients who received ≥1 dose of haloperidol, an antipsychotic medication often used for patients with delirium. Exposure-crossover design was implemented by sampling the 3-day time segments prior (Induction) and posterior (Subsequent) to each treatment episode of receiving haloperidol. In the full cohort, there was a trend of increasing delirium severity scores (Mean±SD: 4.4±1.7) over the course of the ICU stay. After exposure-crossover sampling, the delirium severity score decreased from the Induction (4.9) to the Subsequent (4.1) intervals, with the treatment episode falling in-between (4.5). Based on a GEE Poisson model accounting for self-matching and within-subject correlation, the unadjusted mean delirium severity scores was -0.55 (95% CI: -1.10, -0.01) points lower for the Subsequent than the Induction intervals. The association diminished by 32% (-0.38, 95%CI: -0.99, 0.24) after adjusting only for ICU confounding, while being slightly increased by 7% (-0.60, 95%CI: -1.15, -0.04) when adjusting only for baseline characteristics. These results suggest that longitudinal exposure-crossover design is feasible and capable of partially removing stable baseline confounding through self-matching. Loss of power due to eliminating treatment-irrelevant person-time and uncertainty around allocating person-time to comparison intervals remain methodological challenges.

Keywords: Exposure-crossover design, self-matching, confounding, causal effects, generalized estimating equation.
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International Journal of Statistics in Medical Research

The Validity of Disease-Specific Quality of Life Attributions Among Adults with Multiple Chronic Conditions
Pages 17-40
John E. Ware Jr., Barbara Gandek and Jeroan Allison
DOI:
http://dx.doi.org/10.6000/1929-6029.2016.05.01.3
Published: 08 January 2016


Abstract: Background: A crucial assumption underlying all disease-specific quality of life (QOL) measures, that patients can validly differentiate a specific disease in the presence of multiple chronic conditions, has not been tested using multiple methods. Our objective was to evaluate the convergent and discriminant validity of QOL attributions to specific diseases among adults with multiple chronic conditions (MCC).

Methods: Adults age 18 and older (N=4,480) sampled from eight pre-identified condition groups (asthma, COPD, angina/MI with angina, congestive heart failure, diabetes, chronic kidney disease, osteoarthritis, rheumatoid arthritis) completed an Internet survey. Comorbid conditions were determined using a 35-condition checklist. Product-moment correlations were analyzed separately by pre-identified condition group using the multitrait-multimethod of construct validation, where traits were defined by 9-26 conditions and each condition was measured by two methods: disease severity rating and Disease-specific Quality of Life Impact Scale (QDIS) global rating. A third method (symptom or clinical marker) was available for the eight pre-identified conditions. Convergent validity was supported when correlations among different methods of measuring the same condition (trait) were substantial (r≥ 0.40). Discriminant validity was supported when correlations between the same and different methods of measuring different conditions were significantly lower than corresponding convergent correlations.

Results: In support of convergent validity, 22 of 24 convergent correlations were substantial (r=0.38-0.84, median=0.53). In support of discriminant validity, 833 of 924 tests (90.2%) yielded significantly higher convergent than discriminant correlations across the eight pre-identified conditions. Exceptions to this pattern of results were most often observed for comorbid conditions within the same clinical area.

Conclusions: Collectively, convergent and discriminant test results support the construct validity of disease-specific QOL impact attributions across MCC within the eight pre-identified conditions. Noteworthy exceptions should be considered when interpreting some specific QOL impact attributions and warrant further study. Pursuit of a summary disease-specific QOL impact score standardized across MCC is recommended.

Keywords: Patient-reported outcomes, Health-related quality of life, Disease-specific measures, Multiple chronic conditions, Validity, Multitrait-multimethod analysis.
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International Journal of Statistics in Medical Research

An Empirical Method of Detecting Time-Dependent Confounding: An Observational Study of Next Day Delirium in a Medical ICU
Pages 41-47
T.E. Murphy, P.H. Van Ness, K.L.B. Araujo and M.A. Pisani
DOI:
http://dx.doi.org/10.6000/1929-6029.2016.05.01.4
Published: 08 January 2016


Abstract: Longitudinal research on older persons in the medical intensive care unit (MICU) is often complicated by the time-dependent confounding of concurrently administered interventions such as medications and intubation. Such temporal confounding can bias the respective longitudinal associations between concurrently administered treatments and a longitudinal outcome such as delirium. Although marginal structural models address time-dependent confounding, their application is non-trivial and preferably justified by empirical evidence. Using data from a longitudinal study of older persons in the MICU, we constructed a plausibility score from 0 – 10 where higher values indicate higher plausibility of time-dependent confounding of the association between a time-varying explanatory variable and an outcome. Based on longitudinal plots, measures of correlation, and longitudinal regression, the plausibility scores were compared to the differences in estimates obtained with non-weighted and marginal structural models of next day delirium. The plausibility scores of the three possible pairings of daily doses of fentanyl, haloperidol, and intubation indicated the following: low plausibility for haloperidol and intubation, moderate plausibility for fentanyl and haloperidol, and high plausibility for fentanyl and intubation. Comparing multivariable models of next day delirium with and without adjustment for time-dependent confounding, only intubation’s association changed substantively. In our observational study of older persons in the MICU, the plausibility scores were generally reflective of the observed differences between coefficients estimated from non-weighted and marginal structural models.

Keywords: Time dependent confounding, cross-correlation, longitudinal, marginal structural model, ICU.
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International Journal of Statistics in Medical Research

Individualized Absolute Risk Calculations for Persons with Multiple Chronic Conditions: Embracing Heterogeneity, Causality, and Competing Events
Pages 48-55
Heather Allore, Gail McAvay, Carlos A. Vaz Fragoso and Terrence E. Murphy
DOI:
http://dx.doi.org/10.6000/1929-6029.2016.05.01.5
Published: 08 January 2016


Abstract: Approximately 75% of adults over the age of 65 years are affected by two or more chronic medical conditions. We provide a conceptual justification for individualized absolute risk calculators for competing patient-centered outcomes (PCO) (i.e. outcomes deemed important by patients) and patient reported outcomes (PRO) (i.e. outcomes patients report instead of physiologic test results). The absolute risk of an outcome is the probability that a person receiving a given treatment will experience that outcome within a pre-defined interval of time, during which they are simultaneously at risk for other competing outcomes. This allows for determination of the likelihood of a given outcome with and without a treatment. We posit that there are heterogeneity of treatment effects among patients with multiple chronic conditions (MCC) largely depends on those coexisting conditions.

We outline the development of an individualized absolute risk calculator for competing outcomes using propensity score methods that strengthen causal inference for specific treatments. Innovations include the key concept that any given outcome may or may not concur with any other outcome and that these competing outcomes do not necessarily preclude other outcomes. Patient characteristics and MCC will be the primary explanatory factors used in estimating the heterogeneity of treatment effects on PCO and PRO. This innovative method may have wide-spread application for determining individualized absolute risk calculations for competing outcomes. Knowing the probabilities of outcomes in absolute terms may help the burgeoning population of patients with MCC who face complex treatment decisions.

Keywords: Multiple chronic disease, heterogeneity, propensity scores, longitudinal study, absolute risk, competing outcomes, decision tools.
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