Adjusting for Confounding in Early Postlaunch Settings: Going beyond Logistic Regression Models

Copyright © 2015 Wolters Kluwer Health, Inc. All rights reserved. Background: Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. Methods: We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. Results: At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than-100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of-8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. Conclusion: In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal

Human Genomics and Drug Development

Insights into the genetic basis of human disease are helping to address some of the key challenges in new drug development including the very high rates of failure. Here we review the recent history of an emerging, genomics-assisted approach to pharmaceutical research and development, and its relationship to Mendelian randomization (MR), a well-established analytical approach to causal inference. We demonstrate how human genomic data linked to pharmaceutically relevant phenotypes can be used for (1) drug target identification (mapping relevant drug targets to diseases), (2) drug target validation (inferring the likely effects of drug target perturbation), (3) evaluation of the effectiveness and specificity of compound-target engagement (inferring the extent to which the effects of a compound are exclusive to the target and distinguishing between on-target and off-target compound effects), and (4) the selection of end points in clinical trials (the diseases or conditions to be evaluated as trial outcomes). We show how genomics can help identify indication expansion opportunities for licensed drugs and repurposing of compounds developed to clinical phase that proved safe but ineffective for the original intended indication. We outline statistical and biological considerations in using MR for drug target validation (drug target MR) and discuss the obstacles and challenges for scaled applications of these genomics-based approaches

Cochrane corner: PCSK9 monoclonal antibodies for the primary and secondary prevention of cardiovascular disease

Introduction: Drug therapies targeted at the reduction of low-density lipoproteincholesterol (LDL-C) are mainstream in the treatment of cardiovascular disease (CVD) and particularly for the prevention of coronary heart disease. In patients who do not have a sufficient response to, or who do not tolerate traditional LDL-C-lowering therapies such as statins or ezetimibe, monoclonal antibodies (mAbs) against PCSK9 (PCSK9 inhibitors) may provide an alternative treatment. Non-mAb-based PCSK9 inhibitors such as inclisiran are also emerging but currently lack robust outcome data1 and their effects are not considered in the current review. In this synopsis, we summarise findings from a recent update of a Cochrane systematic review on the efficacy and safety of PCSK9 inhibitors.2 This article focuses on the effects on outcomes (CVD and total mortality), safety, and the quality of the evidence in studies of mAb PCSK9 inhibitors alirocumab and evolocumab. Most of the available studies compared PCSK9 mAb treatment against placebo (against a background of usual care including statin and or ezetimibe), with a smaller group of studies evaluating the effects of PCSK9 mAb directly against statins and/or ezetimibe (none of the trials compared PCSK9 exclusively against statin treatment). Methods: The following databases were systematically searched for suitable randomised controlled trials (RCTs): Cochrane Central Register of Controlled Trials, MEDLINE, Embase, Web of Science, ClinicalTrials.gov and the International Clinical Trials Registry Platform. Parallel-group and factorial RCTs with at least 24 weeks of follow-up were eligible; due to discontinuation of bococizumab and RG7652, studies examining these mAbs were excluded in this update. Summary of findings The 24 selected randomised trials (60 997 participants, box 1) predominantly included high-risk patients, for example, by enrolling patients with non-optimal LDL-C concentration despite treatment with statins or ezetimibe, or with a history of CVD. The study sample included 1879 who had familial hypercholesterolaemia (FH) (22% of the alirocumab participants and 38% of the evolocumab participants who provided information on FH status), and 18 908 (31%) with a diagnosis of type 2 diabetes mellitus (T2DM) at baseline (32% in alirocumab and 34% evolocumab trials; out of participants with reported T2DM status). Of the included patients, 4590 had no history of CVD (10% of the alirocumab patients and 7% of the evolocumab participants). Alirocumab was evaluated in 18 trials and evolocumab in 6 trials. Comparisons were made against placebo in 18 trials, ezetimibe and/or statins in 6 trials. Tables 1 and 2 display the key results of the meta-analysis for both PCSK9 inhibitors compared with placebo and with statins and/or ezetimibe, respectively

Cardiovascular risk prediction in type 2 diabetes: a comparison of 22 risk scores in primary care settings

AIMS/HYPOTHESIS: We aimed to compare the performance of risk prediction scores for CVD (i.e., coronary heart disease and stroke), and a broader definition of CVD including atrial fibrillation and heart failure (CVD+), in individuals with type 2 diabetes. METHODS: Scores were identified through a literature review and were included irrespective of the type of predicted cardiovascular outcome or the inclusion of individuals with type 2 diabetes. Performance was assessed in a contemporary, representative sample of 168,871 UK-based individuals with type 2 diabetes (age ≥18 years without pre-existing CVD+). Missing observations were addressed using multiple imputation. RESULTS: We evaluated 22 scores: 13 derived in the general population and nine in individuals with type 2 diabetes. The Systemic Coronary Risk Evaluation (SCORE) CVD rule derived in the general population performed best for both CVD (C statistic 0.67 [95% CI 0.67, 0.67]) and CVD+ (C statistic 0.69 [95% CI 0.69, 0.70]). The C statistic of the remaining scores ranged from 0.62 to 0.67 for CVD, and from 0.64 to 0.69 for CVD+. Calibration slopes (1 indicates perfect calibration) ranged from 0.38 (95% CI 0.37, 0.39) to 0.74 (95% CI 0.72, 0.76) for CVD, and from 0.41 (95% CI 0.40, 0.42) to 0.88 (95% CI 0.86, 0.90) for CVD+. A simple recalibration process considerably improved the performance of the scores, with calibration slopes now ranging between 0.96 and 1.04 for CVD. Scores with more predictors did not outperform scores with fewer predictors: for CVD+, QRISK3 (19 variables) had a C statistic of 0.68 (95% CI 0.68, 0.69), compared with SCORE CVD (six variables) which had a C statistic of 0.69 (95% CI 0.69, 0.70). Scores specific to individuals with diabetes did not discriminate better than scores derived in the general population: the UK Prospective Diabetes Study (UKPDS) scores performed significantly worse than SCORE CVD (p value <0.001). CONCLUSIONS/INTERPRETATION: CVD risk prediction scores could not accurately identify individuals with type 2 diabetes who experienced a CVD event in the 10 years of follow-up. All 22 evaluated models had a comparable and modest discriminative ability

Lipid lowering and Alzheimer's disease risk: a Mendelian randomization study

Objective: To examine whether genetic variation affecting the expression or function of lipid-lowering drug targets isassociated with Alzheimer disease (AD) risk, to evaluate the potential impact of long-term exposure to correspondingtherapeutics.Methods: We conducted Mendelian randomization analyses using variants in genes that encode the protein targets ofseveral approved lipid-lowering drug classes: HMGCR (encoding the target for statins), PCSK9 (encoding the target forPCSK9 inhibitors, eg, evolocumab and alirocumab), NPC1L1 (encoding the target for ezetimibe), and APOB (encodingthe target of mipomersen). Variants were weighted by associations with low-density lipoprotein cholesterol (LDL-C)using data from lipid genetics consortia (n up to 295,826). We meta-analyzed Mendelian randomization estimates forregional variants weighted by LDL-C on AD risk from 2 large samples (total n = 24,718 cases, 56,685 controls).Results: Models for HMGCR, APOB, and NPC1L1 did not suggest that the use of related lipid-lowering drug classeswould affect AD risk. In contrast, genetically instrumented exposure to PCSK9 inhibitors was predicted to increase ADrisk in both of the AD samples (combined odds ratio per standard deviation lower LDL-C inducible by the drug tar-get = 1.45, 95% confidence interval = 1.23–1.69). This risk increase was opposite to, although more modest than, thedegree of protection from coronary artery disease predicted by these same methods for PCSK9 inhibition.Interpretation: We did not identify genetic support for the repurposing of statins, ezetimibe, or mipomersen for ADprevention. Notwithstanding caveats to this genetic evidence, pharmacovigilance for AD risk among users of PCSK9inhibitors may be warranted

Linear regression and the normality assumption

Objectives: Researchers often perform arbitrary outcome transformations to fulfill the normality assumption of a linear regression model. This commentary explains and illustrates that in large data settings, such transformations are often unnecessary, and worse may bias model estimates. / Study Design and Setting: Linear regression assumptions are illustrated using simulated data and an empirical example on the relation between time since type 2 diabetes diagnosis and glycated hemoglobin levels. Simulation results were evaluated on coverage; i.e., the number of times the 95% confidence interval included the true slope coefficient. / Results: Although outcome transformations bias point estimates, violations of the normality assumption in linear regression analyses do not. The normality assumption is necessary to unbiasedly estimate standard errors, and hence confidence intervals and P-values. However, in large sample sizes (e.g., where the number of observations per variable is >10) violations of this normality assumption often do not noticeably impact results. Contrary to this, assumptions on, the parametric model, absence of extreme observations, homoscedasticity, and independency of the errors, remain influential even in large sample size settings. / Conclusion: Given that modern healthcare research typically includes thousands of subjects focusing on the normality assumption is often unnecessary, does not guarantee valid results, and worse may bias estimates due to the practice of outcome transformations

Polygenic risk scores for coronary artery disease and subsequent event risk amongst established cases

BACKGROUND: There is growing evidence that polygenic risk scores (PRS) can identify individuals with elevated lifetime risk of coronary artery disease (CAD). Whether they can also be used to stratify risk of subsequent events among those surviving a first CAD event remains uncertain, with possible biological differences between CAD onset and progression, and the potential for index event bias. METHODS: Using two baseline subsamples of UK Biobank; prevalent CAD cases (N = 10 287) and individuals without CAD (N = 393 108), we evaluated associations between a CAD PRS and incident cardiovascular and fatal outcomes. RESULTS: A 1 S.D. higher PRS was associated with increased risk of incident MI in participants without CAD (OR 1.33; 95% C.I. 1.29, 1.38), but the effect estimate was markedly attenuated in those with prevalent CAD (OR 1.15; 95% C.I. 1.06, 1.25); heterogeneity P = 0.0012. Additionally, among prevalent CAD cases, we found evidence of an inverse association between the CAD PRS and risk of all-cause death (OR 0.91; 95% C.I. 0.85, 0.98) compared to those without CAD (OR 1.01; 95% C.I. 0.99, 1.03); heterogeneity P = 0.0041. A similar inverse association was found for ischaemic stroke (Prevalent CAD (OR 0.78; 95% C.I. 0.67, 0.90); without CAD (OR 1.09; 95% C.I. 1.04, 1.15), heterogeneity P < 0.001). CONCLUSIONS: Bias induced by case stratification and survival into UK Biobank may distort associations of polygenic risk scores derived from case-control studies or populations initially free of disease. Differentiating between effects of possible biases and genuine biological heterogeneity is a major challenge in disease progression research

Assessment of practical applicability and clinical relevance of a commonly used LDL-C polygenic score in patients with severe hypercholesterolemia

Background and aims: Low-density lipoprotein cholesterol (LDL-C) levels vary in patients with familial hypercholesterolemia (FH) and can be explained by a single deleterious genetic variant or by the aggregate effect of multiple, common small-effect variants that can be captured in a polygenic score (PS). We set out to investigate the contribution of a previously published PS to the inter-individual LDL-C variation and coronary artery disease (CAD) risk in patients with a clinical FH phenotype. Methods: First, in a cohort of 628 patients referred for genetic FH testing, we evaluated the distribution of a PS for LDL-C comprising 12 genetic variants. Next, we determined its association with coronary artery disease (CAD) risk using UK Biobank data. Results: The mean PS was higher in 533 FH-variant-negative patients (FH/M-) compared with 95 FH-variant carriers (1.02 vs 0.94, p < 0.001). 39% of all patients had a PS equal to the top 20% from a population-based reference cohort and these patients were less likely to carry an FH variant (OR 0.22, 95% CI 0.10–0.48) compared with patients in the lowest 20%. In UK Biobank data, the PS explained 7.4% of variance in LDL-C levels and was associated with incident CAD. Addition of PS to a prediction model using age and sex and LDL-C did not increase the c-statistic for predicting CAD risk. Conclusions: This 12-variant PS was higher in FH/M- patients and associated with incident CAD in UK Biobank data. However, the PS did not improve predictive accuracy when added to the readily available characteristics age, sex and LDL-C, suggesting limited discriminative value for CAD