Immortal time bias for life-long conditions in retrospective observational studies using electronic health records

Breaking Down Bias: A Methodological Primer on Identifying, Evaluating, and Mitigating Bias in Cardiovascular Research

Systematic error, often referred to as bias is an inherent challenge in observational cardiovascular research, and has the potential to profoundly influence the design, conduct, and interpretation of study results. If not carefully considered and managed, bias can lead to spurious results, which can misinform clinical practice or public health initiatives and compromise patient outcomes. This methodological primer offers a concise introduction to identifying, evaluating, and mitigating bias in observational cardiovascular research studies that examine the causal association between an exposure (or treatment) and an outcome. Using high-profile examples from the cardiovascular literature, this review provides a theoretical overview of 3 main types of bias-selection bias, information bias, and confounding-and discusses the implications of specialized types of biases commonly encountered in longitudinal cardiovascular research studies, namely, competing risks, immortal time bias, and confounding by indication. Furthermore, strategies and tools that can be used to minimize and assess the influence of bias are highlighted, with a specific focus on using the target trial framework, directed acyclic graphs, quantitative bias analysis, and formal risk of bias assessments. This review aims to assist researchers and health care professionals in designing observational studies and selecting appropriate methodologies to reduce bias, ultimately enhancing the estimation of causal associations in cardiovascular research.

Old drugs, new challenges: reassigning drugs for cancer therapies

The "War on Cancer" began with the National Cancer Act of 1971 and despite more than 50 years of effort and numerous successes, there still remains much more work to be done. The major challenge remains the complexity and intrinsic polygenicity of neoplastic diseases. Furthermore, the safety of the antitumor therapies still remains a concern given their often off-target effects. Although the amount of money invested in research and development required to introduce a novel FDA-approved drug has continuously increased, the likelihood for a new cancer drug's approval remains limited. One interesting alternative approach, however, is the idea of repurposing of old drugs, which is both faster and less costly than developing new drugs. Repurposed drugs have the potential to address the shortage of new drugs with the added benefit that the safety concerns are already established. That being said, their interactions with other new drugs in combination therapies, however, should be tested. In this review, we discuss the history of repurposed drugs, some successes and failures, as well as the multiple challenges and obstacles that need to be addressed in order to enhance repurposed drugs' potential for new cancer therapies.

Addressing immortal time bias in precision medicine: Practical guidance and methods development

OBJECTIVE

To compare theoretical strengths and limitations of common immortal time adjustment methods, propose a new approach using multiple imputation (MI), and provide practical guidance for using MI in precision medicine evaluations centered on a real-world case study.

STUDY SETTING AND DESIGN

Methods comparison, guidance, and real-world case study based on previous literature. We compared landmark analysis, time-distribution matching, time-dependent analysis, and our proposed MI application. Guidance for MI spanned (1) selecting the imputation method; (2) specifying and applying the imputation model; and (3) conducting comparative analysis and pooling estimates. Our case study used a matched cohort design to evaluate overall survival benefits of whole-genome and transcriptome analysis, a precision medicine technology, compared to usual care for advanced cancers, and applied both time-distribution matching and MI. Bootstrap simulation characterized imputation sensitivity to varying data missingness and sample sizes.

DATA SOURCES AND ANALYTIC SAMPLE

Case study used population-based administrative data and single-arm precision medicine program data from British Columbia, Canada for the study period 2012 to 2015.

PRINCIPAL FINDINGS

While each method described can reduce immortal time bias, MI offers theoretical advantages. Compared to alternative approaches, MI minimizes information loss and better characterizes statistical uncertainty about the true length of the immortal time period, avoiding false precision. Additionally, MI explicitly considers the impacts of patient characteristics on immortal time distributions, with inclusion criteria and follow-up period definitions that do not inadvertently risk biasing evaluations. In the real-world case study, survival analysis results did not substantively differ across MI and time distribution matching, but standard errors based on MI were higher for all point estimates. Mean imputed immortal time was stable across simulations.

CONCLUSIONS

Precision medicine evaluations must employ immortal time adjustment methods for unbiased, decision-grade real-world evidence generation. MI is a promising solution to the challenge of immortal time bias.

The relationship between pneumonia and dental visits in patients with cerebral palsy: A nationwide registry-based cohort study in Taiwan

Background/purpose

In general, improving oral health can reduce the risk of pneumonia. The purpose of this retrospective cohort study was to investigate the risk of pneumonia between cerebral palsy (CP) cohort and non-CP cohort as well as the association of pneumonia with the number of dental visits in CP patients in Taiwan.

Materials and methods

We identified 10,544 patients who were diagnosed with CP between 2010 and 2019 from the Taiwan National Health Insurance Research Database. 63,264 individuals who had never been diagnosed with CP were captured and matched in a 6:1 ratio. Cox proportional hazard regression analysis was adopted to assess the hazard ratio (HR) of pneumonia between CP cohort and non-CP cohort.

Results

Our findings demonstrated that CP cohort had 2.619-fold risk for pneumonia as compared with non-CP cohort after adjustment (95 % CI = 2.574-2.813, P < 0.0001). Cox regression analysis indicated that the CP group had significantly higher incidence risk of pneumonia (log rank P < 0.0001). Higher risks of pneumonia in CP patients were associated with younger age, and lower urbanization level. Some comorbidities as well as history of pneumonia had the higher aHR for pneumonia within 180 days prior to index date. In addition, there was a positive association with the more than two dental visits for the decrease 0.887-fold incidence of pneumonia in CP-cohort as compared with no dental visit (95 % CI: 0.799-0.984).

Conclusion

Taken together, CP patients had a higher risk of pneumonia. Dental visits are beneficial for preventing the risk of pneumonia in CP patients. The patients with CP should receive regular dental checkup.

Impact of complementary health approaches on opioid prescriptions among veterans with musculoskeletal disorders - A retrospective cohort study

To examine whether complementary and integrative health approaches mitigate opioid prescriptions for pain and whether the relationship differs by post-dramatic stress disorder (PTSD) diagnosis, we followed 1,993,455 Veterans with musculoskeletal disorders during 2005-2017 using Veterans Healthcare Administration electronic health records. Complementary and integrative health (CIH) approaches were defined as ≥ 1 primary care visits for meditation, Yoga, and acupuncture etc using natural language processing. Opioid prescriptions were ascertained from pharmacy dispensing records. A propensity score was estimated and used to match one control Veteran to each CIH recipient. Over the 2-year follow-up period after the index diagnosis, 140,902 (7.1 %) Veterans received ≥ 1 modalities. Among the matched analytic sample (272,296 Veterans), the likelihood of dispensing opioid prescriptions was significantly lower for Veterans in the CIH group than their controls [adjusted hazard ratio (aHR), 0.45 (95 % Confidence Intervals (CI): 0.44-0.46)]. The association did not differ between Veterans with [aHR: 0.46 (95 % CI: 0.45-0.47)] and without [aHR: 0.44 (95 % CI: 0.43-0.45)] PTSD. In sensitivity analyses, the exposure group had 3.82 (95 % CI: 3.76-3.87) months longer restricted mean survival time to opioid initiation, 2 % (95 % CI: 4 %-1 %) lower morphine equivalent and 17 % lower total days' supply (95 % CI: 18 %-16 %). The relationship remains significant but was attenuated after eliminating waiting time for the exposure group (aHR, 0.63 (95 % CI: 0.62-0.64)). These observations suggest that CIH approaches may help reduce opioid prescriptions for Veterans with musculoskeletal disorders and related pain. The impact of the timing of receiving such approaches warrants further investigation. PERSPECTIVE: This article presents a quasi-experimental investigation into potential benefit of complementary and integrative health approaches (CIH) on de-prescribing opioids. The findings may potentially help clinicians who are seeking non-pharmacological alternative options to manage patient pain and opioid dependence".

Reducing Information and Selection Bias in EHR-Linked Biobanks via Genetics-Informed Multiple Imputation and Sample Weighting

Electronic health records (EHRs) are valuable for public health and clinical research but are prone to many sources of bias, including missing data and non-probability selection. Missing data in EHRs is complex due to potential non-recording, fragmentation, or clinically informative absences. This study explores whether polygenic risk score (PRS)-informed multiple imputation for missing traits, combined with sample weighting, can mitigate missing data and selection biases in estimating disease-exposure associations. Simulations were conducted for missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR) conditions under different sampling mechanisms. PRS-informed multiple imputation showed generally lower bias, particularly when combined with sample weighting. For example, in biased samples of 10,000 with exposure and outcome MAR data, PRS-informed imputation had lower percent bias (3.8%) and better coverage rate (0.883) compared to PRS-uninformed (4.5%; 0.877) and complete case analyses (10.3%; 0.784) in covariate-adjusted, weighted, multiple imputation scenarios. In a case study using Michigan Genomics Initiative (n=50,026) data, PRS-informed imputation aligned more closely with a sample-weighted All of Us-derived benchmark than analyses ignoring missing data and selection bias. Researchers should consider leveraging genetic data and sample weighting to address biases from missing data and non-probability sampling in biobanks.

Methodological biases in observational hospital studies of COVID-19 treatment effectiveness: pitfalls and potential

Introduction

This study aims to discuss and assess the impact of three prevalent methodological biases: competing risks, immortal-time bias, and confounding bias in real-world observational studies evaluating treatment effectiveness. We use a demonstrative observational data example of COVID-19 patients to assess the impact of these biases and propose potential solutions.

Methods

We describe competing risks, immortal-time bias, and time-fixed confounding bias by evaluating treatment effectiveness in hospitalized patients with COVID-19. For our demonstrative analysis, we use observational data from the registry of patients with COVID-19 who were admitted to the Bellvitge University Hospital in Spain from March 2020 to February 2021 and met our predefined inclusion criteria. We compare estimates of a single-dose, time-dependent treatment with the standard of care. We analyze the treatment effectiveness using common statistical approaches, either by ignoring or only partially accounting for the methodological biases. To address these challenges, we emulate a target trial through the clone-censor-weight approach.

Results

Overlooking competing risk bias and employing the naïve Kaplan-Meier estimator led to increased in-hospital death probabilities in patients with COVID-19. Specifically, in the treatment effectiveness analysis, the Kaplan-Meier estimator resulted in an in-hospital mortality of 45.6% for treated patients and 59.0% for untreated patients. In contrast, employing an emulated trial framework with the weighted Aalen-Johansen estimator, we observed that in-hospital death probabilities were reduced to 27.9% in the "X"-treated arm and 40.1% in the non-"X"-treated arm. Immortal-time bias led to an underestimated hazard ratio of treatment.

Conclusion

Overlooking competing risks, immortal-time bias, and confounding bias leads to shifted estimates of treatment effects. Applying the naïve Kaplan-Meier method resulted in the most biased results and overestimated probabilities for the primary outcome in analyses of hospital data from COVID-19 patients. This overestimation could mislead clinical decision-making. Both immortal-time bias and confounding bias must be addressed in assessments of treatment effectiveness. The trial emulation framework offers a potential solution to address all three methodological biases.

Comparison of outcomes between preoperative and postoperative systemic treatment in patients with hepatocellular carcinoma: a SEER database-based study

Background

Significant advancements in systemic treatment for hepatocellular carcinoma have been made in recent years. However, the optimal timing of systemic treatment before or after surgery remains unknown. This study aims to evaluate the impact of sequencing systemic treatment and surgical intervention on the long-term prognosis of hepatocellular carcinoma patients.

Methods

In our study, we analyzed data from patients diagnosed with primary liver cancer (2004-2015) extracted from the SEER database. Patients who underwent both systemic treatment and surgical intervention were selected, divided into preoperative and postoperative systemic therapy groups. The primary endpoint of the study is overall survival(OS), and the secondary endpoint is cancer-specific survival (CSS). Propensity score matching (PSM) reduced the influence of confounding factors, while Kaplan-Meier curves and a multivariable Cox proportional hazards model accounted for variables during survival analysis.

Results

A total of 1918 eligible HCC patients were included, with 1406 cases in the preoperative systemic treatment group and 512 cases in the postoperative systemic treatment group. Survival analysis showed that both the preoperative group demonstrated longer median overall survival (OS) and median cancer-specific survival (CSS) before and after PSM. After conducting multivariate COX regression analysis with stepwise adjustment of input variables, the postoperative systemic treatment group continued to exhibit a higher risk of all-cause mortality (HR: 1.84, 95% CI: 1.55-2.1) and cancer-specific mortality (HR: 2.10, 95% CI: 1.73-2.54). Subgroup analysis indicated consistent results for overall survival (OS) across different subgroups.

Conclusions

Hepatocellular carcinoma patients from the SEER database who received preoperative systemic therapy had superior OS and CSS compared to those who received postoperative systemic therapy.

Unlocking hidden potential: advancements, approaches, and obstacles in repurposing drugs for cancer therapy

High rates of failure, exorbitant costs, and the sluggish pace of new drug discovery and development have led to a growing interest in repurposing "old" drugs to treat both common and rare diseases, particularly cancer. Cancer, a complex and heterogeneous disease, often necessitates a combination of different treatment modalities to achieve optimal outcomes. The intrinsic polygenicity of cancer, intricate biological signalling networks, and feedback loops make the inhibition of a single target frequently insufficient for achieving the desired therapeutic impact. As a result, addressing these complex or "smart" malignancies demands equally sophisticated treatment strategies. Combinatory treatments that target the multifaceted oncogenic signalling network hold immense promise. Repurposed drugs offer a potential solution to this challenge, harnessing known compounds for new indications. By avoiding the prohibitive costs and long development timelines associated with novel cancer drugs, this approach holds the potential to usher in more effective, efficient, and cost-effective cancer treatments. The pursuit of combinatory therapies through drug repurposing may hold the key to achieving superior outcomes for cancer patients. However, drug repurposing faces significant commercial, technological and regulatory challenges that need to be addressed. This review explores the diverse approaches employed in drug repurposing, delves into the challenges faced by the drug repurposing community, and presents innovative solutions to overcome these obstacles. By emphasising the significance of combinatory treatments within the context of drug repurposing, we aim to unlock the full potential of this approach for enhancing cancer therapy. The positive aspects of drug repurposing in oncology are underscored here; encompassing personalized treatment, accelerated development, market opportunities for shelved drugs, cancer prevention, expanded patient reach, improved patient access, multi-partner collaborations, increased likelihood of approval, reduced costs, and enhanced combination therapy.

Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview

Electronic Health Records (EHR) are increasingly being perceived as a unique source of data for clinical research as they provide unprecedentedly large volumes of real-time data from real-world settings. In this review of the secondary uses of EHR, we identify the anticipated breadth of opportunities, pointing out the data deficiencies and potential biases that are likely to limit the search for true causal relationships. This paper provides a comprehensive overview of the types of biases that arise along the pathways that generate real-world evidence and the sources of these biases. We distinguish between two levels in the production of EHR data where biases are likely to arise: (i) at the healthcare system level, where the principal source of bias resides in access to, and provision of, medical care, and in the acquisition and documentation of medical and administrative data; and (ii) at the research level, where biases arise from the processes of extracting, analyzing, and interpreting these data. Due to the plethora of biases, mainly in the form of selection and information bias, we conclude with advising extreme caution about making causal inferences based on secondary uses of EHRs.

Is male gynaecomastia associated with an increased risk of death? A nationwide register-based cohort study

OBJECTIVE

Recent evidence supports that gynaecomastia may predict long-term morbidity, but evidence on the association with death and causes of death in males with gynaecomastia is lacking. The objective of this work is to estimate the risk of death in men diagnosed with gynaecomastia and evaluate whether this was conditional on underlying aetiologies of gynaecomastia.

DESIGN

A nationwide register-based cohort study.

SETTING

Nationwide Danish national health registries.

PARTICIPANTS

Males were diagnosed with incident gynaecomastia (n=23 429) from 1 January 1995 to 30 June 2021, and each was age and calendar matched to five randomly population-based males without gynaecomastia (n=117 145).

INTERVENTIONS

Not applicable.

PRIMARY AND SECONDARY OUTCOMES

Gynaecomastia was distinguished between males without (idiopathic) and males with a known pre-existing risk factor. Cox regression models and Kaplan-Meier analyses estimated associations between gynaecomastia and death (all cause/cause specific).

RESULTS

We identified a total of 16 253 males with idiopathic gynaecomastia and 7176 with gynaecomastia and a known pre-existing risk factor. Of these, 1093 (6.7%) and 1501 (20.9%) died during follow-up, respectively. We detected a 37% increased risk of all-cause death in males with gynaecomastia in the entire cohort (HR 1.37; 95% CI 1.31 to 1.43). Death risk was highest in males diagnosed with gynaecomastia and a known pre-existing risk factor (HR 1.75; 95% CI 1.64 to 1.86) compared with males with idiopathic gynaecomastia (HR 1.05; 95% CI 0.98 to 1.13). Specific causes of increased death were malignant neoplasms and circulatory, pulmonary and gastrointestinal diseases. Of the latter, an over fivefold risk of death from liver disease was detected (HR 5.05; 95% CI 3.97 to 6.42).

CONCLUSIONS

Males diagnosed with gynaecomastia are at higher risk of death, observed mainly in males with a known pre-existing risk factor of gynaecomastia. These findings will hopefully stimulate more awareness among healthcare providers to potentially apply interventions that aid in alleviating underlying risk factors in males with this condition.

Current Research Status and Implication for Further Study of Real-World Data on East Asian Traditional Medicine for Heart Failure: A Scoping Review

This study used real-world data (RWD) to explore the long-term effects of East Asian traditional medicine (EATM) on heart failure (HF). A comprehensive search was conducted across five databases to identify relevant studies, which were then reviewed using the Arksey and O'Malley scoping review framework. The analysis focused on a descriptive examination of the long-term outcomes associated with EATM intervention. Methodologically, the study explored various aspects, including study subjects, interventions, applied clinical outcomes, and statistical methods. Out of 258 studies, 12 were selected. Eight studies involved patients with HF, while the others used HF as an outcome. Datasets from the National Health Insurance Research Database were used in Taiwan, while electronic medical record data were used in China and Japan. EATM interventions have been found to be associated with lower mortality and readmission rates. One study indicated that an increased dose of Fuzi, a botanical drug, or prompt use of Fuzi after diagnosis led to a decreased mortality hazard ratio. In two studies examining readmission rates, a significant increase was observed in the non-exposed group, with odds ratios of 1.28 and 1.18. Additionally, in patients with breast cancer, the subdistribution hazard ratio for the occurrence of doxorubicin-induced HF was reduced to 0.69. Although cohort studies with survival analysis were common, methodological flaws, such as issues with statistical methods and HF diagnosis, were identified. Despite these challenges, the study observed an association between EATM and improved clinical outcomes in patients with HF, emphasizing the potential of RWD studies to complement randomized controlled trials, especially for longer-term follow-ups. These results provide foundational data for future RWD research.

Natural history of Duchenne muscular dystrophy in the United Kingdom: A descriptive study using the Clinical Practice Research Datalink

BACKGROUND

Duchenne muscular dystrophy (DMD) is a rare, muscle-degenerative disease predominantly affecting males. Natural history models capture the full disease pathway under current care and combine with estimates of new interventions' effects to assess cost-effectiveness by health technology decision-makers. These models require mortality estimates throughout a patient's lifetime, but rare disease datasets typically contain relatively few patients with short follow-ups. Alternative (published) sources of mortality data may therefore be required.

METHODS

The Clinical Practice Research Datalink (CPRD) was evaluated as a source of mortality and natural history data for future economic evaluations of health technologies for DMD and rare diseases in general in the UK population. This retrospective longitudinal cohort study provides flexible parametric estimates of mortality rates and survival probabilities in the current UK DMD population through primary/secondary records in the CPRD since 1990. It also investigates clinically significant milestones such as corticosteroid use, spinal surgery, and cardiomyopathy in these patients.

RESULTS

A total of 1121 male patients were included in the study, observed from 0.7 to 48.9 years. Median life expectancy was 25.64 years (95% confidence interval 24.73, 26.47), consistent with previous global estimates. This has improved to 26.47 (25.16, 27.89) years in patients born after 1990. The median ages at corticosteroid initiation, spinal surgery, ventilation, and cardiomyopathy diagnosis were 6.06 years (5.77, 6.29), 14.79 years (14.29, 15.09), 16.97 years (16.50, 18.31), and 15.26 years (14.22, 16.70), respectively.

CONCLUSIONS

Estimates of mortality in UK-based DMD patients are age-specific in a uniquely large and nationally representative sample from the CPRD.

Proton pump inhibitor use and the risk of metachronous gastric cancer after H. pylori eradication in patients who underwent endoscopic resection for gastric neoplasms: A population-based cohort study

BACKGROUND

The association between proton pump inhibitors (PPI) use and gastric cancer remains controversial.

AIMS

To investigate the impact of long-term PPI use on metachronous gastric cancer after Helicobacter pylori eradication in high-risk patients who underwent endoscopic resection of gastric neoplasms.

METHODS

Using the Korean National Health Insurance Services database, we identified 1836 PPI users and 12,218 non-users among patients who received H. pylori eradication therapy after endoscopic resection for gastric neoplasms between 2009 and 2014. We then compared the incidence of metachronous gastric cancer between the PPI user and non-user groups. We conducted sensitivity analysis using various time lags and propensity score-matched analysis to ensure the robustness of the results.

RESULTS

After a median follow-up of 7.3 years, the incidence of metachronous gastric cancer was significantly higher in the PPI user group than in the non-user group, with a crude hazard ratio of 6.20 (95% confidence interval, 5.78-6.65). After adjustment, PPI use was associated with the development of metachronous gastric cancer, with an adjusted hazard ratio of 5.51 (95% confidence interval, 5.12-5.92). The PPI user group was categorised into three subgroups according to the cumulative PPI dose; the increased risk of metachronous gastric cancer remained significant regardless of the PPI dose. Moreover, these results remained robust after applying various time lags and propensity score-matched analyses.

CONCLUSIONS

Long-term PPI use is associated with an increased risk of metachronous gastric cancer in patients who undergo H. pylori eradication therapy after endoscopic resection of gastric neoplasms.

Flexible parametric methods for calculating life expectancy in small populations

BACKGROUND

Life expectancy is a simple measure of assessing health differences between two or more populations but current life expectancy calculations are not reliable for small populations. A potential solution to this is to borrow strength from larger populations from the same source, but this has not formally been investigated.

METHODS

Using data on 451,222 individuals from the Clinical Practice Research Datalink on the presence/absence of intellectual disability and type 2 diabetes mellitus, we compared stratified and combined flexible parametric models, and Chiang's methods, for calculating life expectancy. Confidence intervals were calculated using the Delta method, Chiang's adjusted life table approach and bootstrapping.

RESULTS

The flexible parametric models allowed calculation of life expectancy by exact age and beyond traditional life expectancy age thresholds. The combined model that fit age interaction effects as a spline term provided less bias and greater statistical precision for small covariate subgroups by borrowing strength from the larger subgroups. However, careful consideration of the distribution of events in the smallest group was needed.

CONCLUSIONS

Life expectancy is a simple measure to compare health differences between populations. The use of combined flexible parametric methods to calculate life expectancy in small samples has shown promising results by allowing life expectancy to be modelled by exact age, greater statistical precision, less bias and prediction of different covariate patterns without stratification. We recommend further investigation of their application for both policymakers and researchers.

Mortality among older adults with osteoporotic vertebral fracture

We evaluated whether older adults who received kyphoplasty had reduced risk of mortality compared to those who did not. In unmatched analyses, those receiving kyphoplasty were at reduced risk of death but after matching on age and medical complications, patients who received kyphoplasty were at increased risk of death.

PURPOSE

In previous observational studies, kyphoplasty for treatment of osteoporotic vertebral fractures has been associated with decreased mortality compared to conservative management. The purpose of this research was to determine whether older adults who received kyphoplasty had reduced risk of mortality compared to matched patients who did not.

METHODS

Retrospective cohort study of US Medicare enrollees with osteoporotic vertebral fractures between 2017-2019 comparing patients who underwent kyphoplasty to those who did not. We identified 2 control groups a priori: 1) non-augmented patients who met inclusion criteria (group 1); 2) propensity-matched patients on demographic and clinical variables (group 2). We then identified additional control groups using matching for medical complications (group 3) and age + comorbidities (group 4). We calculated hazard ratios (HRs) and 95% confidence intervals (95% CIs) associated with mortality.

RESULTS

A total of 235,317 patients (mean (± standard deviation) age 81.1 ± 8.3 years; 85.8% female) were analyzed. In the primary analyses, those who received kyphoplasty were at reduced risk of death compared to those who did not: adjusted HR (95% CI) in group 1 = 0.84 (0.82, 0.87); and in group 2 = 0.88 (0.85, 0.91). However, in post hoc analyses, patients who received kyphoplasty were at increased risk of death: adjusted HR (95% CI) in group 3 = 1.32 (1.25, 1.41) and 1.81 (1.58, 2.09) in group 4.

CONCLUSION

An apparent benefit of kyphoplasty on mortality among patients with vertebral fractures was not present after rigorous propensity matching, illustrating the importance of comparing similar individuals when evaluating observational data.

Implementation of the trial emulation approach in medical research: a scoping review

BACKGROUND

When conducting randomised controlled trials is impractical, an alternative is to carry out an observational study. However, making valid causal inferences from observational data is challenging because of the risk of several statistical biases. In 2016 Hernán and Robins put forward the 'target trial framework' as a guide to best design and analyse observational studies whilst preventing the most common biases. This framework consists of (1) clearly defining a causal question about an intervention, (2) specifying the protocol of the hypothetical trial, and (3) explaining how the observational data will be used to emulate it.

METHODS

The aim of this scoping review was to identify and review all explicit attempts of trial emulation studies across all medical fields. Embase, Medline and Web of Science were searched for trial emulation studies published in English from database inception to February 25, 2021. The following information was extracted from studies that were deemed eligible for review: the subject area, the type of observational data that they leveraged, and the statistical methods they used to address the following biases: (A) confounding bias, (B) immortal time bias, and (C) selection bias.

RESULTS

The search resulted in 617 studies, 38 of which we deemed eligible for review. Of those 38 studies, most focused on cardiology, infectious diseases or oncology and the majority used electronic health records/electronic medical records data and cohort studies data. Different statistical methods were used to address confounding at baseline and selection bias, predominantly conditioning on the confounders (N = 18/49, 37%) and inverse probability of censoring weighting (N = 7/20, 35%) respectively. Different approaches were used to address immortal time bias, assigning individuals to treatment strategies at start of follow-up based on their data available at that specific time (N = 21, 55%), using the sequential trial emulations approach (N = 11, 29%) or the cloning approach (N = 6, 16%).

CONCLUSION

Different methods can be leveraged to address (A) confounding bias, (B) immortal time bias, and (C) selection bias. When working with observational data, and if possible, the 'target trial' framework should be used as it provides a structured conceptual approach to observational research.

Lung Function Decline in Cystic Fibrosis: Impact of Data Availability and Modeling Strategies on Clinical Interpretations

Rationale: Studies estimating the rate of lung function decline in cystic fibrosis have been inconsistent regarding the methods used. How the methodology used impacts the validity of the results and comparability between studies is unknown. Objectives: The Cystic Fibrosis Foundation established a work group whose tasks were to examine the impact of differing approaches to estimating the rate of decline in lung function and to provide analysis guidelines. Methods: We used a natural history cohort of 35,252 individuals with cystic fibrosis aged ⩾6 years in the Cystic Fibrosis Foundation Patient Registry (CFFPR), 2003-2016. Modeling strategies using linear and nonlinear forms of marginal and mixed-effects models, which have previously quantified the rate of forced expiratory volume in 1 second (FEV1) decline (percent predicted per year), were evaluated under clinically relevant scenarios of available lung function data. Scenarios varied by sample size (overall CFFPR, medium-sized cohort of 3,000 subjects, and small-sized cohort of 150), data collection/reporting frequency (encounter, quarterly, and annual), inclusion of FEV1 during pulmonary exacerbation, and follow-up length (<2 yr, 2-5 yr, entire duration). Results: Rate of FEV1 decline estimates (percent predicted per year) differed between linear marginal and mixed-effects models; overall cohort estimates (95% confidence interval) were 1.26 (1.24-1.29) and 1.40 (1.38-1.42), respectively. Marginal models consistently estimated less rapid lung function decline than mixed-effects models across scenarios, except for short-term follow-up (both were ∼1.4). Rate of decline estimates from nonlinear models diverged by age 30. Among mixed-effects models, nonlinear and stochastic terms fit best, except for short-term follow-up (<2 yr). Overall CFFPR analysis from a joint longitudinal-survival model implied that an increase in rate of decline of 1% predicted per year in FEV1 was associated with a 1.52-fold (52%) increase in the hazard of death/lung transplant, but the results exhibited immortal cohort bias. Conclusions: Differences were as high as 0.5% predicted per year between rate of decline estimates, but we found estimates were robust to lung function data availability scenarios, except short-term follow-up and older age ranges. Inconsistencies among previous study results may be attributable to inherent differences in study design, inclusion criteria, or covariate adjustment. Results-based decision points reported herein will support researchers in selecting a strategy to model lung function decline most reflective of nuanced, study-specific goals.

Data Challenges for Externally Controlled Trials: Viewpoint

The preferred evidence of a large randomized controlled trial is difficult to adopt in scenarios, such as rare conditions or clinical subgroups with high unmet needs, and evidence from external sources, including real-world data, is being increasingly considered by decision makers. Real-world data originate from many sources, and identifying suitable real-world data that can be used to contextualize a single-arm trial, as an external control arm, has several challenges. In this viewpoint article, we provide an overview of the technical challenges raised by regulatory and health reimbursement agencies when evaluating comparative efficacy, such as identification, outcome, and time selection challenges. By breaking down these challenges, we provide practical solutions for researchers to consider through the approaches of detailed planning, collection, and record linkage to analyze external data for comparative efficacy.

Association of blood lipids with onset and prognosis of amyotrophic lateral sclerosis: results from the ALS Swabia registry

BACKGROUND

To date, the role of blood lipid levels and their association with the onset and prognosis of ALS is controversial. We explored these associations in a large, population-based case-control study.

METHODS

Between October 2010 and June 2014, 336 ALS patients (mean age 65.7 ± 10.7; 57.7% male) and 487 sex- and age-matched controls from the same geographic region were recruited within the ALS registry in Southwest Germany. Triglycerides and cholesterol (high-density lipoprotein (HDL), low-density lipoprotein (LDL), total) were measured. The ALS cohort was followed up for vital status. Conditional logistic regression models were applied to calculate odds ratio (OR) for risk of ALS associated with serum lipid concentrations. In ALS patients only, survival models were used to appraise the prognostic value.

RESULTS

High concentration of total cholesterol (OR 1.60, 95% confidence interval (CI) 1.03-2.49, top vs. bottom quartile), but not HDL, LDL, LDL-HDL ratio, or triglycerides, was positively associated with the risk of ALS. During the median follow-up time of 88.9 months, 291 deaths occurred among 336 ALS patients. In the adjusted survival analysis, higher HDL (HR 1.72, 95% CI 1.19-2.50) and LDL cholesterol levels (HR 1.58, 95% CI 1.11-2.26) were associated with higher mortality in ALS patients. In contrast, higher triglyceride levels were associated with lower mortality (HR 0.68, 95% CI 0.48-0.96).

CONCLUSION

The results highlight the importance to distinguish cholesterol from triglycerides when considering the prognostic role of lipid metabolism in ALS. It further strengthens the rationale for a triglyceride-rich diet, while the negative impact of cholesterol must be further explored.

Health Needs and Their Relationship with Life Expectancy in People with and without Intellectual Disabilities in England

Health needs are common in people living with intellectual disabilities, but we do not know how they contribute to life expectancy. We used the Clinical Practice Research Datalink (CPRD) linked with hospital/mortality data in England (2017-2019) to explore life expectancy among people with or without intellectual disabilities, indicated by the presence or absence, respectively, of: epilepsy; incontinence; severe visual loss; severe visual impairment; severe mobility difficulties; cerebral palsy and PEG feeding. Life expectancy and 95% confidence intervals were compared using flexible parametric methods. At baseline, 46.4% (total n = 7794) of individuals with intellectual disabilities compared with 9.7% (total n = 176,807) in the comparison group had ≥1 health need. Epilepsy was the most common health need (18.7% vs. 1.1%). All health needs except hearing impairment were associated with shorter life expectancy: PEG feeding and mobility difficulties were associated with the greatest loss in life years (65-68% and 41-44%, respectively). Differential life expectancy attenuated but remained (≈12% life years lost) even after restricting the population to those without health needs (additional years expected to live at 10 years: 65.5 [60.3, 71.1] vs. 74.3 [73.8, 74.7]). We conclude that health needs play a significant role but do not explain all of the differential life expectancy experienced by people with intellectual disabilities.