Applying Trial-Derived Treatment Effects to Real-World Populations:Generalizing Cost-Effectiveness Estimates When Modeling Complex Hazards

Objectives: Generalizability of trial-based cost-effectiveness estimates to real-world target populations is important for decision making. In the context of independent aggregate time-to-event baseline and relative effects data, complex hazards can make modeling of data for use in economic evaluation challenging. Our article provides an overview of methods that can be used to apply trial-derived relative treatment effects to external real-world baselines when faced with complex hazards and follows with a motivating example. Methods: Approaches for applying trial-derived relative effects to real-world baselines are presented in the context of complex hazards. Appropriate methods are applied in a cost-effectiveness analysis using data from a previously published study assessing the real-world cost-effectiveness of a treatment for carcinoma of the head and neck as a motivating example. Results: Lack of common hazards between the trial and target real-world population, a complex baseline hazard function, and nonproportional relative effects made the use of flexible models necessary to adequately estimate survival. Assuming common distributions between trial and real-world reference survival substantially affected survival and cost-effectiveness estimates. Modeling time-dependent vs proportional relative effects affected estimates to a lesser extent, dependent on assumptions used in cost-effectiveness modeling. Conclusions: Appropriately capturing reference treatment survival when attempting to generalize trial-derived relative treatment effects to real-world target populations can have important impacts on cost-effectiveness estimates. A balance between model complexity and adequacy for decision making should be considered where multiple data sources with complex hazards are being evaluated.</p

Applying Trial-Derived Treatment Effects to Real-World Populations:Generalizing Cost-Effectiveness Estimates When Modeling Complex Hazards

Objectives: Generalizability of trial-based cost-effectiveness estimates to real-world target populations is important for decision making. In the context of independent aggregate time-to-event baseline and relative effects data, complex hazards can make modeling of data for use in economic evaluation challenging. Our article provides an overview of methods that can be used to apply trial-derived relative treatment effects to external real-world baselines when faced with complex hazards and follows with a motivating example. Methods: Approaches for applying trial-derived relative effects to real-world baselines are presented in the context of complex hazards. Appropriate methods are applied in a cost-effectiveness analysis using data from a previously published study assessing the real-world cost-effectiveness of a treatment for carcinoma of the head and neck as a motivating example. Results: Lack of common hazards between the trial and target real-world population, a complex baseline hazard function, and nonproportional relative effects made the use of flexible models necessary to adequately estimate survival. Assuming common distributions between trial and real-world reference survival substantially affected survival and cost-effectiveness estimates. Modeling time-dependent vs proportional relative effects affected estimates to a lesser extent, dependent on assumptions used in cost-effectiveness modeling. Conclusions: Appropriately capturing reference treatment survival when attempting to generalize trial-derived relative treatment effects to real-world target populations can have important impacts on cost-effectiveness estimates. A balance between model complexity and adequacy for decision making should be considered where multiple data sources with complex hazards are being evaluated.</p

Risk of hip, subtrochanteric, and femoral shaft fractures among mid and long term users of alendronate: nationwide cohort and nested case-control study

Objectives To determine the skeletal safety and efficacy of long term (≥10 years) alendronate use in patients with osteoporosis. Design Open register based cohort study containing two nested case control studies. Setting Nationwide study of population of Denmark. Participants 61 990 men and women aged 50-94 at the start of treatment, who had not previously taken alendronate, 1996-2007. Interventions Treatment with alendronate. Main outcome measures Incident fracture of the subtrochanteric femur or femoral shaft (ST/FS) or the hip. Non-fracture controls from the cohort were matched to fracture cases by sex, year of birth, and year of initiation of alendronate treatment. Conditional logistic regression models were fitted to calculate odds ratios with and without adjustment for comorbidity and comedications. Sensitivity analyses investigated subsequent treatment with other drugs for osteoporosis. Results 1428 participants sustained a ST/FS (incidence rate 3.4/1000 person years, 95% confidence interval 3.2 to 3.6), and 6784 sustained a hip fracture (16.2/1000 person years, 15.8 to 16.6). The risk of ST/FS was lower with high adherence to treatment with alendronate (medication possession ratio (MPR, a proxy for compliance) >80%) compared with poor adherence (MPR 80% was associated with a decreased risk of hip fracture (0.73, 0.68 to 0.78; P<0.001) as was longer term cumulative use for 5-10 dose years (0.74, 0.67 to 0.83; P<0.001) or ≥10 dose years (0.74, 0.56 to 0.97; P=0.03). Conclusions These findings support an acceptable balance between benefit and risk with treatment with alendronate in terms of fracture outcomes, even for over 10 years of continuous use

Random effects modelling versus logistic regression for the inclusion of cluster-level covariates in propensity score estimation: a Monte Carlo simulation and registry cohort analysis

Purpose: Surgeon and hospital-related features, such as volume, can be associated with treatment choices and outcomes. Accounting for these covariates with propensity score (PS) analysis can be challenging due to the clustered nature of the data. We studied six different PS estimation strategies for clustered data using random effects modelling (REM) compared with logistic regression. Methods: Monte Carlo simulations were used to generate variable cluster-level confounding intensity [odds ratio (OR) = 1.01–2.5] and cluster size (20–1,000 patients per cluster). The following PS estimation strategies were compared: i) logistic regression omitting cluster-level confounders; ii) logistic regression including cluster-level confounders; iii) the same as ii) but including cross-level interactions; iv), v), and vi), similar to i), ii), and iii), respectively, but using REM instead of logistic regression. The same strategies were tested in a trial emulation of partial versus total knee replacement (TKR) surgery, where observational versus trial-based estimates were compared as a proxy for bias. Performance metrics included bias and mean square error (MSE). Results: In most simulated scenarios, logistic regression, including cluster-level confounders, led to the lowest bias and MSE, for example, with 50 clusters × 200 individuals and confounding intensity OR = 1.5, a relative bias of 10%, and MSE of 0.003 for (i) compared to 32% and 0.010 for (iv). The results from the trial emulation also gave similar trends. Conclusion: Logistic regression, including patient and surgeon-/hospital-level confounders, appears to be the preferred strategy for PS estimation

Risk of adverse events following the initiation of antihypertensives in older people with complex health needs:a self-controlled case series in the United Kingdom

BACKGROUND: We assessed the risk of adverse events-severe acute kidney injury (AKI), falls and fractures-associated with use of antihypertensives in older patients with complex health needs (CHN). SETTING: UK primary care linked to inpatient and mortality records. METHODS: The source population comprised patients aged &gt;65, with ≥1 year of registration and unexposed to antihypertensives in the year before study start. We identified three cohorts of patients with CHN, namely, unplanned hospitalisations, frailty (electronic frailty index deficit count ≥3) and polypharmacy (prescription of ≥10 medicines). Patients in any of these cohorts were included in the CHN cohort. We conducted self-controlled case series for each cohort and outcome (AKI, falls, fractures). Incidence rate ratios (IRRs) were estimated by dividing event rates (i) during overall antihypertensive exposed patient-time over unexposed patient-time; and (ii) in the first 30 days after treatment initiation over unexposed patient-time. RESULTS:Among 42,483 patients in the CHN cohort, 7,240, 5,164 and 450 individuals had falls, fractures or AKI, respectively. We observed an increased risk for AKI associated with exposure to antihypertensives across all cohorts (CHN: IRR 2.36 [95% CI: 1.68-3.31]). In the 30 days post-antihypertensive treatment initiation, a 35-50% increased risk for falls was found across all cohorts and increased fracture risk in the frailty cohort (IRR 1.38 [1.03-1.84]). No increased risk for falls/fractures was associated with continuation of antihypertensive treatment or overall use. CONCLUSION: Treatment with antihypertensives in older patients was associated with increased risk of AKI and transiently elevated risk of falls in the 30 days after starting antihypertensive therapy.</p

The Challenges and Opportunities of Pharmacoepidemiology in Bone Diseases

Altres ajuts: This work was supported by the National Health Medical Research Council Australia (NHMRC project ID; DA 1114676, DB 1073430, and JRC 1008219). This work was partially supported by the NIHR Biomedical Research Centre, Oxford. DPA is funded by a National Institute for Health Research Clinician Scientist award (CS-2013-13-012). This article presents independent research funded by the National Institute for Health Research (NIHR). Other funding bodies were the Bupa Health Foundation (formerly MBF Foundation) and the Mrs Gibson and Ernst Heine Family Foundation. The views expressed are those of the authors and not necessarily those of the NHMRC and the NIHR.Pharmacoepidemiology is used extensively in osteoporosis research and involves the study of the use and effects of drugs in large numbers of people. Randomized controlled trials are considered the gold standard in assessing treatment efficacy and safety. However, their results can have limited external validity when applied to day-to-day patients. Pharmacoepidemiological studies aim to assess the effect/s of treatments in actual practice conditions, but they are limited by the quality, completeness, and inherent bias due to confounding. Sources of information include prospectively collected (primary) as well as readily available routinely collected (secondary) (eg, electronic medical records, administrative/claims databases) data. Although the former enable the collection of ad hoc measurements, the latter provide a unique opportunity for the study of large representative populations and for the assessment of rare events at relatively low cost. Observational cohort and case-control studies, the most commonly implemented study designs in pharmacoepidemiology, each have their strengths and limitations. However, the choice of the study design depends on the research question that needs to be answered. Despite the many advantages of observational studies, they also have limitations. First, missing data is a common issue in routine data, frequently dealt with using multiple imputation. Second, confounding by indication arises because of the lack of randomization; multivariable regression and more specific techniques such as propensity scores (adjustment, matching, stratification, trimming, or weighting) are used to minimize such biases. In addition, immortal time bias (time period during which a subject is artefactually event-free by study design) and time-varying confounding (patient characteristics changing over time) are other types of biases usually accounted for using time-dependent modeling. Finally, residual "uncontrolled" confounding is difficult to assess, and hence to account for it, sensitivity analyses and specific methods (eg, instrumental variables) should be considered. © 2018 The Authors JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research

90-Day all-cause mortality can be predicted following a total knee replacement:an international, network study to develop and validate a prediction model

Purpose: The purpose of this study was to develop and validate a prediction model for 90-day mortality following a total knee replacement (TKR). TKR is a safe and cost-effective surgical procedure for treating severe knee osteoarthritis (OA). Although complications following surgery are rare, prediction tools could help identify high-risk patients who could be targeted with preventative interventions. The aim was to develop and validate a simple model to help inform treatment choices. Methods: A mortality prediction model for knee OA patients following TKR was developed and externally validated using a US claims database and a UK general practice database. The target population consisted of patients undergoing a primary TKR for knee OA, aged ≥ 40 years and registered for ≥ 1 year before surgery. LASSO logistic regression models were developed for post-operative (90-day) mortality. A second mortality model was developed with a reduced feature set to increase interpretability and usability. Results: A total of 193,615 patients were included, with 40,950 in The Health Improvement Network (THIN) database and 152,665 in Optum. The full model predicting 90-day mortality yielded AUROC of 0.78 when trained in OPTUM and 0.70 when externally validated on THIN. The 12 variable model achieved internal AUROC of 0.77 and external AUROC of 0.71 in THIN. Conclusions: A simple prediction model based on sex, age, and 10 comorbidities that can identify patients at high risk of short-term mortality following TKR was developed that demonstrated good, robust performance. The 12-feature mortality model is easily implemented and the performance suggests it could be used to inform evidence based shared decision-making prior to surgery and targeting prophylaxis for those at high risk. Level of evidence: III.</p

Longitudinal trajectories of frailty are associated with short-term mortality in older people: a joint latent class models analysis using two UK primary care databases

Objective: Frailty is a dynamic health state that changes over time. Our hypothesis was that there are identifiable subgroups of the older population that have specific patterns of deterioration. The objective of this study was to evaluate the application of joint latent class model (JLCM) in identifying trajectories of frailty progression over time and their group-specific risk of death in older people. Study design and setting: The primary care records of UK patients, aged over 65 as of January 1st 2010, included in the CPRD: GOLD and AURUM databases, were analysed and linked to mortality data. Electronic frailty index (eFI) scores were calculated at baseline and annually in subsequent years (2010-2013). JLCM was used to divide the population into clusters with different trajectories and associated mortality hazard ratios (HR). The model was built in GOLD and validated in AURUM. Results: Five trajectory clusters were identified and characterised based on baseline and speed of progression: low-slow, low-moderate, low-rapid, high-slow and high-rapid. The high-rapid cluster had the highest average starting eFI score; 7.9, while low-rapid cluster had the steepest rate of eFI progression; 1.7. Taking the low-slow cluster as reference, low-rapid and high-rapid had the highest HRs: 3.73 (95%CI 3.71 to 3.76) and 3.63 (3.57 to 3.69), respectively. Good validation was found in the AURUM population. Conclusion: Our research found that there are vulnerable subgroups of the older population who are currently frail or have rapid frailty progression. Such groups may be targeted for greater healthcare monitoring

Trends of use and characterisation of anti-dementia drugs users:a large multinational-network population-based study

Background: An updated time-trend analysis of anti-dementia drugs (ADDs) is lacking. The aim of this study is to assess the incident rate (IR) of ADD in individuals with dementia using real-world data. Setting: Primary care data (country/database) from the UK/CPRD-GOLD (2007–20), Spain/SIDIAP (2010–20) and the Netherlands/IPCI (2008–20), standardised to a common data model. Methods: Cohort study. Participants: dementia patients ≥40 years old with ≥1 year of previous data. Follow-up: until the end of the study period, transfer out of the catchment area, death or incident prescription of rivastigmine, galantamine, donepezil or memantine. Other variables: age/sex, type of dementia, comorbidities. Statistics: overall and yearly age/sex IR, with 95% confidence interval, per 100,000 person-years (IR per 105 PY (95%CI)). Results: We identified a total of (incident anti-dementia users/dementia patients) 41,024/110,642 in UK/CPRD-GOLD, 51,667/134,927 in Spain/SIDIAP and 2,088/17,559 in the Netherlands/IPCI. In the UK, IR (per 105 PY (95%CI)) of ADD decreased from 2007 (30,829 (28,891–32,862)) to 2010 (17,793 (17,083–18,524)), then increased up to 2019 (31,601 (30,483 to 32,749)) and decrease in 2020 (24,067 (23,021–25,148)). In Spain, IR (per 105 PY (95%CI)) of ADD decreased by 72% from 2010 (51,003 (49,199–52,855)) to 2020 (14,571 (14,109–15,043)). In the Netherlands, IR (per 105 PY (95%CI)) of ADD decreased by 77% from 2009 (21,151 (14,967–29,031)) to 2020 (4763 (4176–5409)). Subjects aged ≥65–79 years and men (in the UK and the Netherlands) initiated more frequently an ADD. Conclusions: Treatment of dementia remains highly heterogeneous. Further consensus in the pharmacological management of patients living with dementia is urgently needed.</p