Positive Impact of [18F]FDG-PET/CT on Mortality in Patients With Staphylococcus aureus Bacteremia Explained by Immortal Time Bias.

BACKGROUND: Several studies have suggested that in patients with Staphylococcus aureus bacteremia (SAB) [18F] fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG-PET/CT) improves outcome. However, these studies often ignored possible immortal time bias. METHODS: Prospective multicenter cohort study in 2 university and 5 non-university hospitals, including all patients with SAB. [18F]FDG-PET/CT was performed on clinical indication as part of usual care. Primary outcome was 90-day all-cause mortality. Effect of [18F]FDG-PET/CT was modeled with a Cox proportional hazards model using [18F]FDG-PET/CT as a time-varying variable and corrected for confounders for mortality (age, Charlson score, positive follow-up cultures, septic shock, and endocarditis). Secondary outcome was 90-day infection-related mortality (assessed by adjudication committee) using the same analysis. In a subgroup-analysis, we determined the effect of [18F]FDG-PET/CT in patients with high risk of metastatic infection. RESULTS: Of 476 patients, 178 (37%) underwent [18F]FDG-PET/CT. Day-90 all-cause mortality was 31% (147 patients), and infection-related mortality was 17% (83 patients). The confounder adjusted hazard ratio (aHR) for all-cause mortality was 0.50 (95% confidence interval [CI]: .34-.74) in patients that underwent [18F]FDG-PET/CT. Adjustment for immortal time bias changed the aHR to 1.00 (95% CI .68-1.48). Likewise, after correction for immortal time bias, [18F]FDG-PET/CT had no effect on infection-related mortality (cause specific aHR 1.30 [95% CI .77-2.21]), on all-cause mortality in patients with high-risk SAB (aHR 1.07 (95% CI .63-1.83) or on infection-related mortality in high-risk SAB (aHR for 1.24 [95% CI .67-2.28]). CONCLUSIONS: After adjustment for immortal time bias [18F]FDG-PET/CT was not associated with day-90 all-cause or infection-related mortality in patients with SAB.

Immortal Time Bias With Time-Varying Exposures in Environmental Epidemiology: A Case Study in Lung Cancer Survival.

Immortal time bias is a well-recognized bias in clinical epidemiology but is rarely discussed in environmental epidemiology. Under the target trial framework, this bias is formally conceptualized as a misalignment between the start of study follow-up (time 0) and treatment assignment. This misalignment can occur when attained duration of follow-up is encoded into treatment assignment using minimums, maximums, or averages. The bias can be exacerbated in the presence of time trends commonly found in environmental exposures. Using lung cancer cases from the California Cancer Registry (2000-2010) linked with estimated concentrations of particulate matter less than or equal to 2.5 µm in aerodynamic diameter (PM2.5), we replicated previous studies that averaged PM2.5 exposure over follow-up in a time-to-event model. We compared this approach with one that ensures alignment between time 0 and treatment assignment, a discrete-time approach. In the former approach, the estimated overall hazard ratio for a 5-µg/m3 increase in PM2.5 was 1.38 (95% confidence interval: 1.36, 1.40). Under the discrete-time approach, the estimated pooled odds ratio was 0.99 (95% confidence interval: 0.98, 1.00). We conclude that the strong estimated effect in the former approach was likely driven by immortal time bias, due to misalignment at time 0. Our findings highlight the importance of appropriately conceptualizing a time-varying environmental exposure under the target trial framework to avoid introducing preventable systematic errors.

Prognostic impact of severe neutropenia in colorectal cancer patients treated with TAS-102 and bevacizumab, addressing immortal-time bias.

BACKGROUND: Several studies have reported an association between severe neutropenia and long-term survival in patients treated with trifluridine-tipiracil (TAS-102). Because some of these studies failed to address immortality time bias, however, their findings should be interpreted with caution. Additionally, the association between severe neutropenia and survival in patients receiving TAS-102 in combination with bevacizumab (Bmab) remains unclear. PATIENTS AND METHODS: We conducted a single-center retrospective cohort study in patients with colorectal cancer who received Bmab + TAS-102. We compared overall survival (OS) between patients who developed grade ≥ 3 neutropenia during the treatment period and those who did not. To account for immortal time bias, we used two approaches, time-varying Cox regression and landmark analysis. RESULTS: Median OS was 15.3 months [95% CI: 14.1-NA] in patients with grade ≥ 3 neutropenia and 10.0 months [95% CI: 8.1-NA] in those without. In time-varying Cox regression, onset grade ≥ 3 neutropenia was significantly related to longer survival after adjustment for age and modified Glasgow Prognostic Score. Additionally, 30-, 60-, 90-, and 120-day landmark analysis showed that grade ≥ 3 neutropenia was associated with longer survival after adjustment for age and modified Glasgow Prognostic Score, with respective HRs of 0.30 [0.10-0.90], 0.65 [0.30-1.42], 0.39 [0.17-0.90], and 0.41 [0.18-0.95]. CONCLUSION: We identified an association between long-term survival and the development of severe neutropenia during the early cycle of Bmab + TAS-102 using an approach that addressed immortality time bias.

Metanalyses on metformin's role in pancreatic cancer suffer from severe bias and low data quality - An umbrella review.

AIMS: Discrepancies in the preclinical evidence, retrospective studies, and randomized trials evaluating metformin's role in pancreatic cancer are difficult to disentangle. We aimed to critically and systematically examine the quality and sources of heterogeneity between meta-analyses investigating the association between metformin intake and the prognosis of patients with pancreatic cancer. MATERIALS AND METHODS: We performed a literature search on PubMed, MEDLINE, Embase, and Scopus on October 31, 2021 to identify meta-analyses investigating the impact of metformin treatment on the prognosis of patients with pancreatic cancer. Meta-analyses quality was assessed using according to the AMSTAR 2 criteria. We assessed bias in individual studies included in the meta-analyses, with particular attention to immortal time bias and quality of reporting. RESULTS: Eleven meta-analyses describing 24 individual studies were included. All meta-analyses were rated low (n = 5) or critically low (n = 6) quality. Only 4 followed PRISMA reporting guidelines and only in 5 presented data were sufficient to replicate the analyses. Most meta-analyses combined results from clinical trials and retrospective studies (n = 6); patients with different cancer stages (resectable vs advanced) and from studies with different control group definitions. Immortal time bias was present and not accounted for in most (65.2%) of the individual retrospective studies; almost all (n = 9) meta-analyses failed to identify and correct for this source of bias. CONCLUSIONS: Meta-analyses describing the association of metformin use in patients with pancreatic cancer are plagued by various types of bias inherent in retrospective studies. The quality of evidence linking metformin to decreased pancreatic cancer mortality is generally low.

Core concepts in pharmacoepidemiology: Key biases arising in pharmacoepidemiologic studies.

Pharmacoepidemiology has an increasingly important role in informing and improving clinical practice, drug regulation, and health policy. Therefore, unrecognized biases in pharmacoepidemiologic studies can have major implications when study findings are translated to the real world. We propose a simple taxonomy for researchers to use as a starting point when thinking through some of the most pervasive biases in pharmacoepidemiology. We organize this discussion according to biases best assessed with respect to the study population (including confounding by indication, channeling bias, healthy user bias, and protopathic bias), the study design (including prevalent user bias and immortal time bias), and the data source (including misclassification bias and missing data/loss to follow up). This tutorial defines, provides a curated list of recommended references, and illustrates through relevant case examples these key biases to consider when planning, conducting, or evaluating pharmacoepidemiologic studies.

Statins and Mortality in COPD: A Methodological Review of Observational Studies.

Randomized controlled trials and observational studies have reported conflicting results on the potential beneficial effects of statins on mortality in patients with chronic obstructive pulmonary disease (COPD). We performed a systematic search of the literature to review all observational studies reporting relative risks of death with statin use in COPD, focusing on potential sources of bias. We identified 15 observational studies, out of 2835, of which 12 were affected by time-related and other biases and the remaining 3 by confounding bias. All 15 studies were also subject to confounding bias due to lack of adjustment for important COPD-related factors. The risk of death associated with statin use was reduced across all 15 studies (pooled relative risk (PRR) 0.66; 95% CI: 0.59-0.74). The reduction was observed in 7 studies with immortal time bias (PRR 0.62; 95%: 0.53-0.72), two with collider-stratification bias (PRR 0.60; 95% CI: 0.45-0.80), one with time-window bias (RR 0.61; 95% CI: 0.38-0.98), one with immeasurable time bias (RR 0.50; 95% CI: 0.40-0.62), and one with exposure misclassification (RR 0.86; 95% CI: 0.72-1.03). The three studies that avoided these biases were, however, affected by confounding bias resulting in a PRR of 0.77 (95% CI: 0.61-0.98). In conclusion, the observational studies investigating statin use and mortality in COPD are affected by major biases, many of which can result in spurious protective effects. Well-designed observational studies that carefully emulate randomized trials are needed to resolve this uncertainty regarding the potential beneficial benefits of statins on mortality in patients with COPD.

Left truncation in linked data: A practical guide to understanding left truncation and applying it using SAS and R.

Time-to-event data such as time to death are broadly used in medical research and drug development to understand the efficacy of a therapeutic. For time-to-event data, right censoring (data only observed up to a certain point of time) is common and easy to recognize. Methods that use right censored data, such as the Kaplan-Meier estimator and the Cox proportional hazard model, are well established. Time-to-event data can also be left truncated, which arises when patients are excluded from the sample because their events occur before a specific milestone, potentially resulting in an immortal time bias. For example, in a study evaluating the association between biomarker status and overall survival, patients who did not live long enough to receive a genomic test were not observed in the study. Left truncation causes selection bias and often leads to an overestimate of survival time. In this tutorial, we used a nationwide electronic health record-derived de-identified database to demonstrate how to analyze left truncated and right censored data without bias using example code from SAS and R.

Immune-related adverse events in urothelial cancer patients: Adjustment for immortal time bias.

To investigate the association between the onset, severity, and type of immune-related adverse events (irAEs) and the efficacy of pembrolizumab in patients with platinum-pretreated advanced urothelial carcinoma (UC), we retrospectively collected clinical datasets of 755 patients and conducted landmark analysis. Patients who survived for fewer than 3 months were excluded from the evaluation to reduce the immortal time bias. In total, 620 patients were evaluated, of whom 220 patients (35.5%) experienced grade ≥2 irAEs, including 134 patients with grade 2 irAEs and 86 with grade ≥3 irAEs. Propensity score matching extracted 198 patients with and without grade ≥2 irAEs. The onset of grade ≥2 irAEs was associated with longer median progression-free survival (PFS) (8.3 months vs. 4.5 months, p = 0.003) and overall survival (OS) (20.4 months vs. 14.3 months, p = 0.031) and a higher objective response rate (ORR) (44.8% vs. 30.2%, p = 0.004). Patients with grade 2 irAEs had significantly better oncological outcomes (PFS, OS, and ORR) than grade ≤1 and ≥3 irAEs. Patients with grade ≥3 irAEs had worse outcomes than grade 2 irAEs. Endocrine and skin irAEs were related with better survival outcomes, and the rate of severities was lower in these categories. In conclusion, the occurrence of irAEs, particularly low-grade irAEs, was predictive of pembrolizumab efficacy in patients with platinum-pretreated advanced UC.

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

BACKGROUND: Immortal time bias is common in observational studies but is typically described for pharmacoepidemiology studies where there is a delay between cohort entry and treatment initiation. METHODS: This study used the Clinical Practice Research Datalink (CPRD) and linked national mortality data in England from 2000 to 2019 to investigate immortal time bias for a specific life-long condition, intellectual disability. Life expectancy (Chiang's abridged life table approach) was compared for 33,867 exposed and 980,586 unexposed individuals aged 10+ years using five methods: (1) treating immortal time as observation time; (2) excluding time before date of first exposure diagnosis; (3) matching cohort entry to first exposure diagnosis; (4) excluding time before proxy date of inputting first exposure diagnosis (by the physician); and (5) treating exposure as a time-dependent measure. RESULTS: When not considered in the design or analysis (Method 1), immortal time bias led to disproportionately high life expectancy for the exposed population during the first calendar period (additional years expected to live: 2000-2004: 65.6 [95% CI: 63.6,67.6]) compared to the later calendar periods (2005-2009: 59.9 [58.8,60.9]; 2010-2014: 58.0 [57.1,58.9]; 2015-2019: 58.2 [56.8,59.7]). Date of entry of diagnosis (Method 4) was unreliable in this CPRD cohort. The final methods (Method 2, 3 and 5) appeared to solve the main theoretical problem but residual bias may have remained. CONCLUSIONS: We conclude that immortal time bias is a significant issue for studies of life-long conditions that use electronic health record data and requires careful consideration of how clinical diagnoses are entered onto electronic health record systems.

Time-related biases in perinatal pharmacoepidemiology: A systematic review of observational studies.

BACKGROUND: Time-related biases, such as immortal time and time-window bias, frequently occur in pharmacoepidemiologic research. However, the prevalence of these biases in perinatal pharmacoepidemiology is not well understood. OBJECTIVE: To describe the frequency of time-related biases in observational studies of medications commonly used during pregnancy (antibiotic, antifungal, and antiemetic drugs) via systematic review. METHOD: We searched Medline and EMBASE for observational studies published between January 2013 and September 2020 and examining the association between antibiotic, antifungal, or antiemetic drugs and adverse pregnancy outcomes, including spontaneous abortion, stillbirth, preterm delivery, small-for-gestational age, pre-eclampsia, and gestational diabetes. The proportion of studies with time-related biases was estimated overall and by type (immortal time bias, time-window bias). RESULTS: Our systematic review included 20 studies (16 cohort studies, 3 nested case-control studies, and 1 case-control study), of which 12 examined antibiotic, 6 antiemetic, and 2 anti-fungal drugs. Eleven studies (55%) had immortal time bias due to the misclassification of unexposed, event-free person-time between cohort entry and exposure initiation as exposed. No included study had time-window bias. The direction of effect varied for both studies with and without time-related bias, with many studies reporting very wide confidence intervals around the effect estimates, thus making the direction of effect less interpretable. However, studies with time-related bias were more likely to show protective or null associations compared with studies without time-related bias. CONCLUSION: Time-related biases occur frequently in observational studies of drug effects during pregnancy. The use of appropriate study design and analytical approaches is needed to prevent time-related biases and ensure study validity.

Timing and severity of COVID-19 during pregnancy and risk of preterm birth in the International Registry of Coronavirus Exposure in Pregnancy.

BACKGROUND: Studies of preterm delivery after COVID-19 are often subject to selection bias and do not distinguish between early vs. late infection in pregnancy, nor between spontaneous vs. medically indicated preterm delivery. This study aimed to estimate the risk of preterm birth (overall, spontaneous, and indicated) after COVID-19 during pregnancy, while considering different levels of disease severity and timing. METHODS: Pregnant and recently pregnant people who were tested for or clinically diagnosed with COVID-19 during pregnancy enrolled in an international internet-based cohort study between June 2020 and July 2021. We used several analytic approaches to minimize confounding and immortal time bias, including multivariable regression, time-to-delivery models, and a case-time-control design. RESULTS: Among 14,264 eligible participants from 70 countries who did not report a pregnancy loss before 20 gestational weeks, 5893 had completed their pregnancies and reported delivery information; others were censored at time of their last follow-up. Participants with symptomatic COVID-19 before 20 weeks' gestation had no increased risk of preterm delivery compared to those testing negative, with adjusted risks of 10.0% (95% CI 7.8, 12.0) vs. 9.8% (9.1, 10.5). Mild COVID-19 later in pregnancy was not clearly associated with preterm delivery. In contrast, severe COVID-19 after 20 weeks' gestation led to an increase in preterm delivery compared to milder disease. For example, the risk ratio for preterm delivery comparing severe to mild/moderate COVID-19 at 35 weeks was 2.8 (2.0, 4.0); corresponding risk ratios for indicated and spontaneous preterm delivery were 3.7 (2.0, 7.0) and 2.3 (1.2, 3.9), respectively. CONCLUSIONS: Severe COVID-19 late in pregnancy sharply increased the risk of preterm delivery compared to no COVID-19. This elevated risk was primarily due to an increase in medically indicated preterm deliveries, included preterm cesarean sections, although an increase in spontaneous preterm delivery was also observed. In contrast, mild or moderate COVID-19 conferred minimal risk, as did severe disease early in pregnancy.

Nonaspirin nonsteroidal anti-inflammatory drugs and gastric cancer risk after Helicobacter pylori eradication: A territory-wide study.

BACKGROUND: Despite Helicobacter pylori (HP) eradication, individuals can still develop gastric cancer (GC). Prior studies have demonstrated that nonaspirin nonsteroidal anti-inflammatory drugs (NA-NSAIDs) reduce the risk of GC, but this may be caused by immortal time bias and failure to adjust for HP status. The objective of this study was to investigate whether NA-NSAIDs reduced the risk of GC in patients who undergo H. pylori eradication. METHODS: Adult patients who had received clarithromycin-based triple therapy between 2003 and 2016 were identified from a territory-wide health care database. Exclusion criteria included prior GC or GC diagnosed <6 months after HP eradication, prior gastrectomy, gastric ulcer after HP eradication, and failure of triple therapy. Covariates included age, sex, prior peptic ulcer disease, other comorbidities, and concurrent medications (aspirin, proton pump inhibitors, statins, and metformin). To avoid immortal time bias, NA-NSAID use (≥90 days) was treated as a time-dependent variable in a multivariable Cox model (time-dependent analysis). Time-independent analysis was also performed. RESULTS: During a median follow-up of 8.9 years (interquartile range, 5.4-12.6 years), 364 of 92,017 patients (0.4%) who underwent HP eradication developed GC. NA-NSAID use was associated with a significant reduction in the risk of GC in time-fixed analysis (adjusted hazard ratio [aHR], 0.65; 95% CI, 0.47-0.90), but not in time-dependent multivariable analysis (aHR, 1.35; 95% CI, 0.97-1.87). Time-dependent subgroup analyses also did not indicate any significant association between NA-NSAID use and either cardia GC (aHR, 0.75; 95% CI, 0.27-2.06) or noncardia GC (aHR, 1.28; 95% CI, 0.83-1.98). CONCLUSIONS: NA-NSAID use was not associated with a reduced risk of GC among patients who underwent HP eradication. The chemopreventive effect of NA-NSAIDs observed in prior studies may have been confounded by immortal time bias.

Immortal time bias exaggerates the effect of metformin on the risk of gastric cancer: A meta-analysis.

High heterogeneity has been reported among epidemiological studies exploring the relationship between metformin and the risk of gastric cancer. Immortal time bias might be one of the vital factors causing heterogeneity because of its widespread existence in pharmacological observational studies and it could severely exaggerate the drug's effectiveness. Immortal time bias could occur in an observational study if exposure status is determined based on a measurement or event that occurs after baseline. In this study, we aimed to assess whether immortal time bias is responsible for the false assumption that metformin reduces the risk of gastric cancer. We searched PubMed, Embase, Web of Science and Cochrane Library databases for relevant studies from the inception to August 9, 2020. The strength of the relationship was assessed using pooled relative risks (RRs) with corresponding 95% confidence intervals (95% CIs). Statistical analyses were carried out using a random-effects model. Pooled RR from 6 cohort studies with immortal time bias found a clear 33% reduced risk associated with metformin use (RR = 0.67, 95% CI = 0.59, 0.77; P < 0.001; I2 = 48.5%). However, pooled RR from 8 cohort studies without immortal time bias indicated no association between the use of metformin and gastric cancer risk (RR = 0.95, 95% CI = 0.85, 1.05; P = 0.317; I2 = 64.5%). From a univariate meta-regression model, the presence of immortal time bias was associated with a significant reduction of 29% in the effect estimate of metformin on gastric cancer risk (ratio of RR = 0.71, 95% CI = 0.58, 0.86; P = 0.002). This meta-analysis indicates that metformin use has no protective effect on gastric cancer risk. The relationship between metformin use and gastric cancer risk has been exaggerated as a result of the presence of immortal time bias. Further studies are required to confirm the results by controlling for immortal time bias based on appropriate study designs and statistical methods.

Analysis of time-to-event for observational studies: Guidance to the use of intensity models.

This paper provides guidance for researchers with some mathematical background on the conduct of time-to-event analysis in observational studies based on intensity (hazard) models. Discussions of basic concepts like time axis, event definition and censoring are given. Hazard models are introduced, with special emphasis on the Cox proportional hazards regression model. We provide check lists that may be useful both when fitting the model and assessing its goodness of fit and when interpreting the results. Special attention is paid to how to avoid problems with immortal time bias by introducing time-dependent covariates. We discuss prediction based on hazard models and difficulties when attempting to draw proper causal conclusions from such models. Finally, we present a series of examples where the methods and check lists are exemplified. Computational details and implementation using the freely available R software are documented in Supplementary Material. The paper was prepared as part of the STRATOS initiative.

Analytical challenges in estimating the effect of exposures that are bounded by follow-up time: experiences from the Blood Stream Infection-Focus on Outcomes study.

OBJECTIVE: To illustrate the challenges of estimating the effect of an exposure that is bounded by duration of follow-up on all-cause 28-day mortality, whilst simultaneously addressing missing data and time-varying covariates. STUDY DESIGN AND METHODS: BSI-FOO is a multicentre cohort study with the primary aim of quantifying the effect of modifiable risk factors, including time to initiation of therapy, on all-cause 28-day mortality in patients with bloodstream infection. The primary analysis involved two Cox proportional hazard models, first one for non-modifiable risk factors and second one for modifiable risk factors, with a risk score calculated from the first model included as a covariate in the second model. Modifiable risk factors considered in this study were recorded daily for a maximum of 28 days after infection. Follow-up was split at daily intervals from day 0 to 28 with values of daily collected data updated at each interval (i.e., one row per patient per day). ANALYTICAL CHALLENGES: Estimating the effect of time to initiation of treatment on survival is analytically challenging since only those who survive to time t can wait until time t to start treatment, introducing immortal time bias. Time-varying covariates representing cumulative counts were used for variables bounded by survival time e.g. the cumulative count of days before first receipt of treatment. Multiple imputation using chained equations was used to impute missing data, using conditional imputation to avoid imputing non-applicable data e.g. ward data after discharge. CONCLUSION: Using time-varying covariates represented by cumulative counts within a one row per day per patient framework can reduce the risk of bias in effect estimates. The approach followed uses established methodology and is easily implemented in standard statistical packages.

Prognostic Effect of Incidental Pulmonary Embolism on Long-Term Mortality in Cancer Patients.

BACKGROUND: The effect of incidental pulmonary embolism (PE) on long-term prognosis in cancer patients is unclear. This study assessed the characteristics of cancer and venous thromboembolism (VTE) and the effect of incidental PE identified by oncologists on long-term survival of patients with cancer.Methods and Results:This single-center, retrospective, cohort study used hospital-based cancer registry data from the Osaka International Cancer Institute linked with electronic medical records and administrative data from Japan's Diagnosis Procedure Combination Per-diem Payment System. Overall, 15,689 cancer patients underwent contrast-enhanced thoracic computed tomography during 2010-2018. After excluding patients with missing data, symptomatic patients, or patients with suspected PE, 174 with incidental PE (PE+ group) and 13,197 with no PE (PE- group) were identified. The total incidence of incidental PE was 1.3%. No deaths from thrombotic events were identified in the PE+ group. Both groups were adjusted for cancer- and VTE-related characteristics using inverse probability weighting. After adjusting for immortal time bias in the PE+ group, Kaplan-Meier analysis revealed that all-cause mortality was higher in the PE+ group (hazard ratio, 2.26; 95% confidence interval, 1.53-3.33). A Cox proportional hazard model revealed that metastatic cancer and a history of curative treatment were significant prognostic factors, whereas central PE and residual proximal deep vein thrombosis were not. CONCLUSIONS: Incidental PE in cancer patients indicates poorer prognosis. Cancer-related but not thrombosis-related factors determine prognosis.

Immortal Time Bias in the Analysis of Time-to-Event Data in Orthopedics.

Many outcomes in arthroplasty research are analyzed as time-to-event outcomes using survival analysis methods. When comparison groups are defined after a time-delayed exposure or intervention, a period of immortal time arises and can lead to biased results. In orthopedics research, immortal time bias often arises when a minimum amount of follow-up is required for study inclusion or when comparing outcomes in staged bilateral vs unilateral arthroplasty patients. We present an explanation of immortal time and the associated bias, describe how to correctly account for it using proper data preparation and statistical techniques, and provide an illustrative example using real-world arthroplasty data. We offer practical guidelines for identifying and properly handling immortal time to avoid bias. Please visit the followinghttps://youtu.be/58p8w5o-ci4for a video that explains the highlights of the paper in practical terms.

Increased risk of death associated with the use of proton-pump inhibitors in patients with dementia and controls - a pharmacoepidemiological claims data analysis.

BACKGROUND AND PURPOSE: The use of proton-pump inhibitors (PPIs) was reported to be associated with increased mortality risk and has been proposed as a potential risk factor for neurodegenerative diseases. We aimed to assess the impact of PPI use on survival in patients with dementia as compared with controls. METHODS: This register-based control-matched cohort study included 28 428 patients with dementia ascertained by the prescription of antidementia drugs and two control individuals matched by sex, age and area of residence for each patient with dementia during the study period from 1 January 2005 to 30 June 2016. Cumulative defined daily doses (DDDs) of PPIs were extracted from the health insurance prescription registries. A multivariate Cox regression model for non-proportional hazards was used to analyse mortality risk in dependence of PPI exposure, which was limited to 1 year preceding the date of cohort entry (index date) in order to avoid immortal time bias. RESULTS: The PPI exposure of 100 DDDs in the year before the index date was associated with an increased mortality risk in patients with dementia (adjusted hazard ratio, 1.07; 95% confidence intervals, 1.03-1.12), but also in controls (adjusted hazard ratio, 1.47; 95% confidence intervals, 1.31-1.64). The mortality risk in relation to PPI use was significantly lower in patients with dementia as compared with controls (P < 0.0001) and highest in the first 2 years after the index date in both cohorts. CONCLUSIONS: Our findings promote more stringent pharmacovigilance strategies to avoid PPI use in cases lacking a clear indication for therapy or where potential risks outweigh the benefits.

Time-related biases in pharmacoepidemiology.

PURPOSE: Observational studies using computerized healthcare databases have become popular to investigate the potential effectiveness of old drugs for new indications. Many of these studies reporting remarkable effectiveness were shown to be affected by different time-related biases. We describe these biases and illustrate their effects using a cohort of patients treated for chronic obstructive pulmonary disease (COPD). METHODS: The Quebec healthcare databases were used to form a cohort of 124 030 patients with COPD, 50 years or older, treated between 2000 and 2015. Inhaled corticosteroids (ICS) and long-acting bronchodilators were used as exposures, with diverse outcomes, including lung cancer, acute myocardial infarction and death, to illustrate protopathic, latency time, immortal time, time-window, depletion of susceptibles, and immeasurable time biases. RESULTS: Protopathic bias affected bronchodilator-defined cohort entry with an incident rate of lung cancer of 23.9 per 1000 in the first year, compared with around 12.0 in the subsequent years. When latency and immortal times were misclassified, ICS were associated with decreased incidence of lung cancer (hazard ratio [HR] 0.32; 95% CI: 0.30-0.34), compared with 0.50 (95% CI: 0.48-0.53) after correcting for immortal time bias and 0.96 (95% CI: 0.91-1.02) after also correcting for latency time bias. Time-window, depletion of susceptibles and immeasurable time biases also affected the findings similarly. CONCLUSIONS: Many observational studies of new indications for older drugs reporting unrealistic effectiveness were affected by avoidable time-related biases. The apparent effectiveness often disappears with proper design and analysis. Future studies should consider these time-related issues to avoid severely biased results.

Metformin and pancreatic cancer survival: Real effect or immortal time bias?

High heterogeneity has been reported among cohort studies investigating the association between metformin and pancreatic cancer survival. Immortal time bias may be one importance source of heterogeneity, as it is widely present in previous cohort studies and may severely impair the validity. Our study aimed to examine whether metformin therapy improves pancreatic cancer survival, and to assess the impact of immortal time bias on the effect estimation of metformin in cohort studies. PubMed, EMbase and SciVerse Scopus were searched. Pooled relative risks (RRs) were derived using a random-effects model. Pooled RR from the six studies without immortal time bias showed no association between metformin and mortality in pancreatic cancer patients (RR 0.93, 95% CI 0.82, 1.05; p = 0.22 and I2 = 75%). In contrast, pooled RR from the nine studies with immortal time bias showed a reduction of 24% in mortality associated with metformin (RR 0.76, 95% CI 0.69, 0.84; p < 0.001 and I2 = 1%). From a meta-regression model, existence of immortal time bias was associated with a reduction of 18% in the effect estimate of metformin on pancreatic cancer survival (ratio of RR 0.82, 95% CI 0.70, 0.96; p = 0.02). In conclusions, cumulative evidence from cohort studies does not support a beneficial effect of metformin on pancreatic cancer survival. The association between metformin and pancreatic cancer survival has been greatly exaggerated in previous cohort studies due to the wide existence of immortal time bias. More rigorous designs and statistical methods are needed to account for immortal time bias.