Methods for Population Adjustment with Limited Access to Individual Patient Data: A Review and Simulation Study

Population-adjusted indirect comparisons estimate treatment effects when access to individual patient data is limited and there are cross-trial differences in effect modifiers. Popular methods include matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC). There is limited formal evaluation of these methods and whether they can be used to accurately compare treatments. Thus, we undertake a comprehensive simulation study to compare standard unadjusted indirect comparisons, MAIC and STC across 162 scenarios. This simulation study assumes that the trials are investigating survival outcomes and measure continuous covariates, with the log hazard ratio as the measure of effect. MAIC yields unbiased treatment effect estimates under no failures of assumptions. The typical usage of STC produces bias because it targets a conditional treatment effect where the target estimand should be a marginal treatment effect. The incompatibility of estimates in the indirect comparison leads to bias as the measure of effect is non-collapsible. Standard indirect comparisons are systematically biased, particularly under stronger covariate imbalance and interaction effects. Standard errors and coverage rates are often valid in MAIC but the robust sandwich variance estimator underestimates variability where effective sample sizes are small. Interval estimates for the standard indirect comparison are too narrow and STC suffers from bias-induced undercoverage. MAIC provides the most accurate estimates and, with lower degrees of covariate overlap, its bias reduction outweighs the loss in effective sample size and precision under no failures of assumptions. An important future objective is the development of an alternative formulation to STC that targets a marginal treatment effect.Comment: 73 pages (34 are supplementary appendices and references), 8 figures, 2 tables. Full article (following Round 4 of minor revisions). arXiv admin note: text overlap with arXiv:2008.0595

Caeiro, Oscar. Córdoba. Imágenes y visiones literarias. Córdoba, Alción Editora, 2004, 253 p.

Fil: Remiro, Susana

Un nuevo orden contra el derecho internacional: el caso de Kosovo

El profesor Remiro analiza la relación de confrontación del llamado "Nuevo Orden" con principios fundamentales del Derecho Internacional y, en particular, de la Carta de las NU, tomando como referencia la "campaña militar" de la OTAN contra la República Federativa de Yugoslavia, desatada por la cuestión del Kosovo

Transportability of model-based estimands in evidence synthesis

In evidence synthesis, effect modifiers are typically described as variables that induce treatment effect heterogeneity at the individual level, through treatment-covariate interactions in an outcome model parametrized at such level. As such, effect modification is defined with respect to a conditional measure, but marginal effect estimates are required for population-level decisions in health technology assessment. For non-collapsible measures, purely prognostic variables that are not determinants of treatment response at the individual level may modify marginal effects, even where there is individual-level treatment effect homogeneity. With heterogeneity, marginal effects for measures that are not directly collapsible cannot be expressed in terms of marginal covariate moments, and generally depend on the joint distribution of conditional effect measure modifiers and purely prognostic variables. There are implications for recommended practices in evidence synthesis. Unadjusted anchored indirect comparisons can be biased in the absence of individual-level treatment effect heterogeneity, or when marginal covariate moments are balanced across studies. Covariate adjustment may be necessary to account for cross-study imbalances in joint covariate distributions involving purely prognostic variables. In the absence of individual patient data for the target, covariate adjustment approaches are inherently limited in their ability to remove bias for measures that are not directly collapsible. Directly collapsible measures would facilitate the transportability of marginal effects between studies by: (1) reducing dependence on model-based covariate adjustment where there is individual-level treatment effect homogeneity and marginal covariate moments are balanced; and (2) facilitating the selection of baseline covariates for adjustment where there is individual-level treatment effect heterogeneity.Comment: 33 pages, 3 figures. Re-submitted to Statistics in Medicine after revision

El TJUE ¿asume la competencia exclusiva en la interpretación del AASPIC?

According to the autor, the ECJ has made a mistake by accepting its jurisdiction with regard to patents, that is, in a area where there is no European legislation. The Court has also wrongly interpreted the TRIPS Agreement 1994.D’après l’auteur, la CJUE a comis l’erreur d’accepter sa jurisdiction en matière de brevets, autrement dit, dans un domaine où il n’y a pas de législation européenne. Elle a aussi eu tort d’interpréter l’Accord ADPIC de 1994

Contracted phthalocyanine macrocycles: Conjugation with nanoparticles and the first synthesis of meso-substituted Boron SubTriBenzoDiAzaPorphyrin hybrids (SubTBDAPs)

The first part of this thesis is concerned with the attachment of subphthalocyanines to quantum dots. The macromolecule was chosen as encapsulant due to its perfect curvature and interesting optical properties. CdSe quantum dots are well-known materials with size-dependent properties that makes them unique. Hence, some subphthalocyanines bearing functional groups with affinity for the CdSe surface were synthesised by means of cross-coupling reactions. Small nanoparticle size (2.6 nm) quantum dots have been synthesised, capped with a layer of oleic acid molecules that confers them stability, and characterised by means of UV-Vis, emission and NMR spectroscopies. The original ligands were replaced by ligand exchange processes monitored by 1HNMR spectroscopy. The experiments consist of titrations of CdSe-OA solutions with known concentrations of the new ligand. In this work, titrations using 3-ethynylpyridine, benzyl alcohol and phenylacetic acid have been performed. Parallel results in our group, involving the herein synthesised subphthalocyanine bearing pyridyl – groups, towards the attachment to the nanoparticle’s surface are also described. Subphthalocyanine bearing three carboxylic acids is demonstrated to be the most promising candidate for this project. However, attempts to synthesise such a molecule by direct approach strategies were unsuccessful. Experiments with sodium periodate-subphthalocyanine conjugates gave promising and encouraging results, opening the possibility of accessing subphthalocyanines with carboxylic acid groups by means of deprotecting silyl groups. The second part of this thesis is based on the results obtained towards the first syntheses of hybrid structures whose structure lies between subphthalocyanines and subporphyrins, SubTriBenzoDiAzaPorphyrins (SubTBDAPs). The key intermediates are aminoisoindolene precursors that provide the methine bridge, and substitution with the meso-phenyl ring. Boron trichloride has been used in a two-step, one-pot reaction in a preliminary strategy to give access to the hybrids. However, trialkoxyborates are the preferred boron source; they provide the apical substituent, as well as act as Lewis acid and template. The latter synthesis is versatile and gives modest yields and highly pure materials, but most importantly allows control over the apical and the meso-substituents in one single step. The new hybrids exhibit interesting optical and rotational characteristics that have been fully investigated by means of UV-Vis, emission, NMR spectroscopies, variable temperature experiments and mass spectrometry. In this work the first crystal structures elucidated for a wide range of new hybrids are also presented

Population-Adjusted Indirect Treatment Comparisons with Limited Access to Patient-Level Data

Health technology assessment systems base their decision-making on health-economic evaluations. These require accurate relative treatment effect estimates for specific patient populations. In an ideal scenario, a head-to-head randomized controlled trial, directly comparing the interventions of interest, would be available. Indirect treatment comparisons are necessary to contrast treatments which have not been analyzed in the same trial. Population-adjusted indirect comparisons estimate treatment effects where there are: no head-to-head trials between the interventions of interest, limited access to patient-level data, and cross-trial differences in effect measure modifiers. Health technology assessment agencies are increasingly accepting evaluations that use these methods across a diverse range of therapeutic areas. Popular approaches include matching-adjusted indirect comparison (MAIC), based on propensity score weighting, and simulated treatment comparison (STC), based on outcome regression. There is limited formal evaluation of these methods and whether they can be used to accurately compare treatments. Thus, I undertake a review and a simulation study that compares the standard unadjusted indirect comparisons, MAIC and STC across 162 scenarios. This simulation study assumes that the trials are investigating survival outcomes and measure continuous covariates, with the log hazard ratio as the measure of effect — one of the most widely used setups in health technology assessment applications. MAIC yields unbiased treatment effect estimates under no failures of assumptions. The typical usage of STC produces bias because it targets a conditional treatment effect where the target estimand should be a marginal treatment effect. The incompatibility of estimates in the indirect comparison leads to bias as the measure of effect is non-collapsible. When adjusting for covariates, one must integrate or average the conditional model over the population of interest to recover a compatible marginal treatment effect. I propose a marginalization method based on parametric G-computation that can be easily applied where the outcome regression is a generalized linear model or a Cox model. In addition, I introduce a novel general-purpose method based on the ideas underlying multiple imputation, which is termed multiple imputation marginalization (MIM) and is applicable to a wide range of models, including parametric survival models. The approaches view the covariate adjustment regression as a nuisance model and separate its estimation from the evaluation of the marginal treatment effect of interest. Both methods can accommodate a Bayesian statistical framework, which naturally integrates the analysis into a probabilistic framework, typically required for health technology assessment. Another simulation study provides proof-of-principle for the methods and benchmarks their performance against MAIC and the conventional STC. The simulations are based on scenarios with binary outcomes and continuous covariates, with the log-odds ratio as the measure of effect. The marginalized outcome regression approaches achieve more precise and more accurate estimates than MAIC, particularly when covariate overlap is poor, and yield unbiased marginal treatment effect estimates under no failures of assumptions. Furthermore, regressionadjusted estimates of the marginal effect provide greater precision and accuracy than the conditional estimates produced by the conventional STC, which are systematically biased because the log-odds ratio is a non-collapsible measure of effect. The marginalization methods outlined in this thesis are necessary and important for health technology assessment more generally, because marginal treatment effects should be the preferred inferential target for reimbursement decisions at the population level. Treatment effectiveness inputs in health economic models are often informed by the treatment coefficient of a multivariable regression. An often overlooked issue is that this has a conditional interpretation, and that the coefficients of the regression must be marginalized over the target population of interest to produce a relevant estimate for reimbursement decisions at the population level