Quantitative Evaluation of the Trade-Off of Strengthened Instruments and Sample Size in Observational Studies

Illness perception profile among cancer patients and its influencing factors: A cross-sectional study

PURPOSE

The purpose of this study was to explore latent profiles of illness perception among cancer patients and its influencing factors.

METHODS

This study was a cross-sectional study adopting convenience sampling to select cancer patients from two hospitals in China. A total of 286 patients completed Brief Illness Perception Questionnaire, Post-traumatic Growth Inventory, Fear of Disease Progression Questionnaire and Psychosocial Adjustment to Illness Scale. Latent profile analysis and multiple linear regression were performed to explore the subgroups and factors influencing classification.

RESULTS

Three subgroups were identified, which were labelled as "Moderate Illness Perception Group" (16.8%; C1), "High Illness Perception with Heightened Concerns Group" (68.5%; C2) and "High Resilience and Low Symptomatic Impact Group" (14.7%; C3). Specifically, "Normal", "Mild symptom" and "Bed time during the day <50%" of "Functional Status" were more associated with C3. "Worker", "Farmer" and "Self-employed" were more associated with C1 and C2. Patients who had more "knowledge of the disease" were more associated with C2 and C3, who had less "post-traumatic growth" were more associated with C1, and who had less "fear of disease progression" and more "psychosocial adjustment" were more associated with C3 (all P < 0.05).

CONCLUSIONS

There was significant variability of illness perception among three subgroups of cancer patients, which emphasized the complexity of psychological condition. The insights derived from these distinct profiles enables tailored interventions and patient-centered communication strategies. However, integrating objective measures or biomarkers is needed to complement self-reported data.

Pharmacoepidemiology: A time for a new multidisciplinary approach to precision medicine

The advent of the genomic age has created a rapid increase in complexity for the development and selection of drug treatments. A key component of precision medicine is the use of genetic information to improve therapeutic effectiveness of drugs and prevent potential adverse drug reactions. Pharmacoepidemiology, as a field, uses observational methods to evaluate the safety and effectiveness of drug treatments in populations. Pharmacoepidemiology by virtue of its focus, tradition, and research orientation can provide appropriate study designs and analysis methods for precision medicine. The objective of this manuscript is to demonstrate how pharmacoepidemiology can impact and shape precision medicine and serve as a reference for pharmacoepidemiologists interested in contributing to the science of precision medicine. This paper depicts the state of the science with respect to the need for pharmacoepidemiology and pharmacoepidemiological methods, tools and approaches for precision medicine; the need for and how pharmacoepidemiologists use their skills to engage with the precision medicine community; and recommendations for moving the science of precision medicine pharmacoepidemiology forward. We propose a new integrated multidisciplinary approach dedicated to the emerging science of precision medicine pharmacoepidemiology.

A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity

There is a fast-growing literature on estimating optimal treatment regimes based on randomized trials or observational studies under a key identifying condition of no unmeasured confounding. Because confounding by unmeasured factors cannot generally be ruled out with certainty in observational studies or randomized trials subject to noncompliance, we propose a general instrumental variable approach to learning optimal treatment regimes under endogeneity. Specifically, we establish identification of both value function E [ Y D ( L ) ] for a given regime D and optimal regimes arg max D E [ Y D ( L ) ] with the aid of a binary instrumental variable, when no unmeasured confounding fails to hold. We also construct novel multiply robust classification-based estimators. Furthermore, we propose to identify and estimate optimal treatment regimes among those who would comply to the assigned treatment under a monotonicity assumption. In this latter case, we establish the somewhat surprising result that complier optimal regimes can be consistently estimated without directly collecting compliance information and therefore without the complier average treatment effect itself being identified. Our approach is illustrated via extensive simulation studies and a data application on the effect of child rearing on labor participation.

Long-term use of hydrocodone vs. oxycodone in primary care

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.