Optimal Adaptive Designs for Binary Response Trials With Three Treatments

A fundamental question in response–adaptive randomization is: What allocation proportion should we target to achieve required power while resulting in fewer treatment failures? For comparing two treatments, such optimal allocations are well studied in the literature. However, few authors address the question for multiple treatments and the generalization of optimal allocations is necessary in practice. We are interested in finding the optimal allocation proportion, which achieves a required power of a multivariate test of homogeneity in binary response experiments while minimizing expected treatment failures at the same time. We propose such an optimal allocation for three treatments by giving an analytical solution for the optimization problem. Numerical studies show that a response–adaptive randomization procedure that targets proposed optimal allocation is superior to complete randomization. We also discuss some future research topics and additional issues on optimal adaptive designs.     

Investigating Effects of Plasma Apolipoprotein E on Ischemic Heart Disease Using Mendelian Randomization Study

Background: Observationally plasma apolipoprotein E (apoE) is positively associated with ischemic heart disease (IHD). A Mendelian randomization (MR) study suggesting apoE is unrelated to cardiovascular mortality did not consider specific isoforms. We used MR to obtain estimates of plasma apoE2, apoE3 and apoE4 on IHD, low-density lipoprotein (LDL) and high-density lipoprotein (HDL) cholesterol, triglycerides and apolipoprotein B (apoB). Methods: We obtained independent genetic instruments from proteome genome-wide association studies (GWAS) and applied them to large outcome GWAS. We used univariable MR to assess the role of each isoform and multivariable MR to assess direct effects. Results: In univariable MR, apoE4 was positively associated with IHD (odds ratio (OR) 1.05, 95% confidence interval (CI) 1.01 to 1.09), but apoE2 and apoE3 were less clearly associated. Using multivariable MR an association of apoE2 with IHD (OR 1.16, 95% CI 0.98 to 1.38) could not be excluded, and associations of apoE3 and apoE4 with IHD were not obvious. In univariable MR, apoE2 and apoE4 were positively associated with apoB, and a positive association of apoE2 with LDL cholesterol could not be excluded. Using multivariable MR apoE2 was positively associated with LDL cholesterol, and associations with apoB could not be excluded. After adjusting for apoB, no direct effects of apoE isoforms on IHD were evident. Conclusions: Plasma apoE2 and apoE4 may play a role in lipid modulation and IHD. Whether apoE could be a potential therapeutic target requires further clarification when larger genetic studies of apoE isoforms are available.     

Coffee Consumption and Prostate Cancer Risk: Results from National Health and Nutrition Examination Survey 1999–2010 and Mendelian Randomization Analyses

The aim of this study was to examine the association between coffee and prostate cancer. Firstly, we conducted an observational study using data from National Health and Nutrition Examination Survey (NHANES) 1999–2010. Coffee intake was derived from 24 h dietary recalls. Weighted multivariable-adjusted logistic regression was applied to evaluate the association. Then, we performed Mendelian randomization (MR) to explore the possible causal effect of coffee on prostate cancer risk. Primary and secondary genetic instruments were obtained from genome-wide association studies among 375,833 and 91,462 individuals separately. Prostate cancer summary statistics were extracted from Prostate Cancer Association Group to Investigate Cancer-Associated Alterations in the Genome (PRACTICAL) (79,194 cases and 61,112 controls) and FinnGen project (4754 cases and 63,465 controls). Inverse variance weighted (IVW) was the primary analytical method. Through selection, we enrolled 8336 individuals (weighted number = 58,796,070) for our observational study in NHANES. Results suggested that there was no association between coffee and prostate cancer. MR analyses with primary genetic instruments also did not support a causal association between coffee intake and prostate cancer risk, whether using summary data from PRACTICAL (IVW: OR 1.001, 95% CI 0.997–1.005) or FinnGen (IVW: OR 1.005, 95% CI 0.998–1.012). Similar results were observed when using secondary genetic instruments. Therefore, our study did not support a causal association between coffee intake and prostate cancer risk. Further studies with a larger sample size are needed to examine if an association exists by different coffee bean types, roasting procedures, and brewing methods.     

The Homocysteine and Metabolic Syndrome: A Mendelian Randomization Study

Homocysteine (Hcy) is well known to be increased in the metabolic syndrome (MetS) incidence. However, it remains unclear whether the relationship is causal or not. Recently, Mendelian Randomization (MR) has been popularly used to assess the causal influence. In this study, we adopted MR to investigate the causal influence of Hcy on MetS in adults using three independent cohorts. We considered one-sample MR and two-sample MR. We analyzed one-sample MR in 5902 individuals (2090 MetS cases and 3812 controls) from the KARE and two-sample MR from the HEXA (676 cases and 3017 controls) and CAVAS (1052 cases and 764 controls) datasets to evaluate whether genetically increased Hcy level influences the risk of MetS. In observation studies, the odds of MetS increased with higher Hcy concentrations (odds ratio (OR) 1.17, 95%CI 1.12–1.22, p < 0.01). One-sample MR was performed using two-stage least-squares regression, with an MTHFR C677T and weighted Hcy generic risk score as an instrument. Two-sample MR was performed with five genetic variants (rs12567136, rs1801133, rs2336377, rs1624230, and rs1836883) by GWAS data as the instrumental variables. For sensitivity analysis, weighted median and MR–Egger regression were used. Using one-sample MR, we found an increased risk of MetS (OR 2.07 per 1-SD Hcy increase). Two-sample MR supported that increased Hcy was significantly associated with increased MetS risk by using the inverse variance weighted (IVW) method (beta 0.723, SE 0.119, and p < 0.001), the weighted median regression method (beta 0.734, SE 0.097, and p < 0.001), and the MR–Egger method (beta 2.073, SE 0.843, and p = 0.014) in meta-analysis. The MR–Egger slope showed no evidence of pleiotropic effects (intercept −0.097, p = 0.107). In conclusion, this study represented the MR approach and elucidates the significant relationship between Hcy and the risk of MetS in the Korean population.     

Evaluating causal associations between chronotype and fatty acids and between fatty acids and type 2 diabetes: A Mendelian randomization study

Background and aimsPreference for activity in the morning or evening (chronotype) may impact type 2 diabetes (T2D) risk factors. Our objective was to use Mendelian randomization (MR) to evaluate whether there are causal links between chronotype and one potential T2D risk factor, total fatty acids (TOTFA), and between TOTFA and T2D.Methods and resultsWe estimated the causal effect of: 1) morning chronotype on TOTFA; and 2) higher TOTFA on T2D. We found that: a) morning compared to evening chronotype was associated with lower TOTFA levels (inverse-weighted variance (IVW) estimate −0.21; 95% CI −0.38, −0.03; raw P = 0.02; FDR-corrected P 0.04) and b) elevated TOTFA levels were protective against T2D (IVW estimate −0.23; 95% CI −0.41, −0.05; raw P = 0.01; FDR-corrected P = 0.03). Based on this finding, we further hypothesized that healthy fats would show a similar pattern and performed MR of a) morning chronotype on omega-3 (Omega-3), monounsaturated (MUFA), and polyunsaturated (PUFA) fatty acids; and b) MR of each of these fat types on T2D. We observed the same mediating-type pattern for chronotype, MUFA, and T2D as we had for chronotype, TOTFA, and T2D, and morning chronotype was associated with lower Omega-3.ConclusionOur findings provide suggestive, new information about relationships among chronotype, TOTFA, and T2D and about chronotype as a factor influencing Omega-3, MUFA, and TOTFA levels. In addition, we validated previous knowledge about MUFA and T2D. Morning chronotypes may predispose towards lower levels of TOTFA and some healthy fats, whereas higher levels of TOTFA and MUFA may protect against T2D.     

Lipoprotein(a): An underrecognized genetic risk factor for malignant coronary artery disease in young Indians

Malignant coronary artery disease (CAD) refers to a severe and extensive atherosclerotic process involving multiple coronary arteries in young individuals (aged <45 years in men and <50 years in women) with a low or no burden of established risk factors. Indians, in general, develop acute myocardial infarction (AMI) about 10 years earlier; AMI rates are threefold to fivefold higher in young Indians than in other populations. Although established CAD risk factors have a predictive value, they do not fully account for the excessive burden of CAD in young Indians. Lipoprotein(a) (Lp(a)) is increasingly recognized as the strongest known genetic risk factor for premature CAD, with high levels observed in Indians with malignant CAD. High Lp(a) levels confer a twofold to threefold risk of CAD—a risk similar to that of established risk factors, including diabetes. South Asians have the second highest Lp(a) levels and the highest risk of AMI from the elevated levels, more than double the risk observed in people of European descent. Approximately 25% of Indians and other South Asians have elevated Lp(a) levels (≥50 mg/dl), rendering Lp(a) a risk factor of great importance, similar to or surpassing diabetes. Lp(a) measurement is ready for clinical use and should be an essential part of all CAD research in Indians.     

Brick tunnel randomization for unequal allocation to two or more treatment groups

Studies with unequal allocation to two or more treatment groups often require a large block size for permuted block allocation. This could present a problem in small studies, multi-center studies, or adaptive design dose-finding studies. In this paper, an allocation procedure, which generalizes the maximal procedure by Berger, Ivanova, and Knoll to the case of K≥2 treatment groups and any allocation ratio, is offered. Brick tunnel (BT) randomization requires the allocation path drawn in the k-dimensional space to stay close to the allocation ray that corresponds to the targeted allocation ratio. Specifically, it requires the allocation path to be confined to the set of the k-dimensional unitary cubes that are pierced by the allocation ray (the 'brick tunnel'). The important property of the BT randomization is that the transition probabilities at each node within the tunnel are defined in such a way that the unconditional allocation ratio is the same for every allocation step. This property is not necessarily met by other allocation procedures that implement unequal allocation.     

A Bayesian response-adaptive covariate-balanced randomization design with application to a leukemia clinical trial

We propose a Bayesian response-adaptive covariate-balanced (RC) randomization design for multiple-arm comparative clinical trials. The goal of the design is to skew the allocation probability to more efficacious treatment arms, while also balancing the distribution of the covariates across the arms. In particular, we first propose a new covariate-adaptive randomization (CA) method based on a prognostic score that naturally accommodates continuous and categorical prognostic factors and automatically assigns imbalance weights to covariates according to their importance in response prediction. We then incorporate this CA design into a group sequential response-adaptive randomization (RA) scheme. The resulting RC randomization design combines the advantages of both CA and RA randomizations and meets the design goal. We illustrate the proposed design through its application to a phase II leukemia clinical trial, and evaluate its operating characteristics through simulation studies.     

Mendelian randomization analysis of case-control data using structural mean models

'Instrumental Variable' (IV) methods provide a basis for estimating an exposure's causal effect on the risk of disease. In Mendelian randomization studies, where genetic information plays the role of the IV, IV analyses are routinely performed on case-control data, rather than prospectively collected observational data. Although it is a well-appreciated fact that ascertainment bias may invalidate such analyses, ad hoc assumptions and approximations are made to justify their use. In this paper we attempt to explain and clarify why they may fail and show how they can be adjusted for improved performance. In particular, we propose consistent estimators of the causal relative risk and odds ratio if a priori knowledge is available regarding either the population disease prevalence or the population distribution of the IV (e.g. population allele frequencies). We further show that if no such information is available, approximate estimators can be obtained under a rare disease assumption. We illustrate this with matched case-control data from the recently completed EPIC study, from which we attempt to assess the evidence for a causal relationship between C-reactive protein levels and the risk of Coronary Artery Disease.     

尿调节素及UMOD基因与慢性肾病的相关性研究进展

尿调节素,又称Tamm-Horsfal蛋白,是由肾小管髓袢升支粗段的小管上皮细胞分泌、也是正常人尿液中含量最多的蛋白。研究发现尿调节素具有多种生物学功能,如调节水盐代谢、调节免疫、防止肾结石产生和防止尿路感染等。近期全基因组关联分析发现编码尿调节素的基因UMOD启动子区单核苷酸多态性与慢性肾病的进展及预后具有很高关联度,并进一步通过孟德尔随机化方法证实尿调节素与慢性肾脏病的因果关系。