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Mendelian randomization is the use of genetic variants to assess the effect of intervening on a risk factor using observational data. We consider the scenario in which there is a pharmacomimetic (i.e., treatment-mimicking) genetic variant that can be used as a proxy for a particular pharmacological treatment that changes the level of the risk factor. If the association of the pharmacomimetic genetic variant with the risk factor is stronger in one subgroup of the population, then we may expect the effect of the treatment to be stronger in that subgroup. We test for gene–gene interactions in the associations of variants with a modifiable risk factor, where one genetic variant is treated as pharmacomimetic and the other as an effect modifier, to find genetic subgroups of the population with different predicted response to treatment. If individual genetic variants that are strong effect modifiers cannot be found, moderating variants can be combined using a random forest of interaction trees method into a polygenic response score, analogous to a polygenic risk score for risk prediction. We illustrate the application of the method to investigate effect heterogeneity in the effect of statins on low-density lipoprotein cholesterol. 相似文献
94.
Two‐stage randomization designs are broadly accepted and becoming increasingly popular in clinical trials for cancer and other chronic diseases to assess and compare the effects of different treatment policies. In this paper, we propose an inferential method to estimate the treatment effects in two‐stage randomization designs, which can improve the efficiency and reduce bias in the presence of chance imbalance of a robust covariate‐adjustment without additional assumptions required by Lokhnygina and Helterbrand (Biometrics, 63:422‐428)'s inverse probability weighting (IPW) method. The proposed method is evaluated and compared with the IPW method using simulations and an application to data from an oncology clinical trial. Given the predictive power of baseline covariates collected in this real data, our proposed method obtains 17–38% gains in efficiency compared with the IPW method in terms of overall survival outcome. Copyright © 2017 John Wiley & Sons, Ltd. 相似文献
95.
Chunlei Ke Jianming Wang Charlie Zhang Qi Jiang Steven Snapinn 《Statistics In Biopharmaceutical Research》2017,9(2):225-233
Stratified randomization is widely used in clinical trials to achieve balance of the treatment assignment with regard to important prognostic factors. Sometimes the prognostic or stratification factor is concerned with patient medical and medication history, for which accurate information may not be available at the time of randomization. Therefore, the value of the stratification factor provided at randomization may later be identified to be incorrect. When such errors occur, patients may be allocated into strata they do not belong to. The intended treatment balance regarding the stratification factors may be disrupted. We evaluated the impact of such errors in stratification on the intended balance. We further investigated the analysis options in presence of stratification errors under both randomization and population models. Some simulations were performed to evaluate the treatment balance and properties of analysis approaches. We also provided experience with some real clinical trials. 相似文献
96.
Maximum type I error rate inflation from sample size reassessment when investigators are blind to treatment labels
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Consider a parallel group trial for the comparison of an experimental treatment to a control, where the second‐stage sample size may depend on the blinded primary endpoint data as well as on additional blinded data from a secondary endpoint. For the setting of normally distributed endpoints, we demonstrate that this may lead to an inflation of the type I error rate if the null hypothesis holds for the primary but not the secondary endpoint. We derive upper bounds for the inflation of the type I error rate, both for trials that employ random allocation and for those that use block randomization. We illustrate the worst‐case sample size reassessment rule in a case study. For both randomization strategies, the maximum type I error rate increases with the effect size in the secondary endpoint and the correlation between endpoints. The maximum inflation increases with smaller block sizes if information on the block size is used in the reassessment rule. Based on our findings, we do not question the well‐established use of blinded sample size reassessment methods with nuisance parameter estimates computed from the blinded interim data of the primary endpoint. However, we demonstrate that the type I error rate control of these methods relies on the application of specific, binding, pre‐planned and fully algorithmic sample size reassessment rules and does not extend to general or unplanned sample size adjustments based on blinded data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. 相似文献
97.
An evaluation of constrained randomization for the design and analysis of group‐randomized trials
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Fan Li Yuliya Lokhnygina David M. Murray Patrick J. Heagerty Elizabeth R. DeLong 《Statistics in medicine》2016,35(10):1565-1579
In group‐randomized trials, a frequent practical limitation to adopting rigorous research designs is that only a small number of groups may be available, and therefore, simple randomization cannot be relied upon to balance key group‐level prognostic factors across the comparison arms. Constrained randomization is an allocation technique proposed for ensuring balance and can be used together with a permutation test for randomization‐based inference. However, several statistical issues have not been thoroughly studied when constrained randomization is considered. Therefore, we used simulations to evaluate key issues including the following: the impact of the choice of the candidate set size and the balance metric used to guide randomization; the choice of adjusted versus unadjusted analysis; and the use of model‐based versus randomization‐based tests. We conducted a simulation study to compare the type I error and power of the F‐test and the permutation test in the presence of group‐level potential confounders. Our results indicate that the adjusted F‐test and the permutation test perform similarly and slightly better for constrained randomization relative to simple randomization in terms of power, and the candidate set size does not substantially affect their power. Under constrained randomization, however, the unadjusted F‐test is conservative, while the unadjusted permutation test carries the desired type I error rate as long as the candidate set size is not too small; the unadjusted permutation test is consistently more powerful than the unadjusted F‐test and gains power as candidate set size changes. Finally, we caution against the inappropriate specification of permutation distribution under constrained randomization. An ongoing group‐randomized trial is used as an illustrative example for the constrained randomization design. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
98.
Gabriel Cuellar‐Partida Yi Lu Pik Fang Kho Alex W. Hewitt H.‐Erich Wichmann Seyhan Yazar Dwight Stambolian Joan E. Bailey‐Wilson Robert Wojciechowski Jie Jin Wang Paul Mitchell David A. Mackey Stuart MacGregor 《Genetic epidemiology》2016,40(1):66-72
Myopia is the largest cause of uncorrected visual impairments globally and its recent dramatic increase in the population has made it a major public health problem. In observational studies, educational attainment has been consistently reported to be correlated to myopia. Nonetheless, correlation does not imply causation. Observational studies do not tell us if education causes myopia or if instead there are confounding factors underlying the association. In this work, we use a two‐step least squares instrumental‐variable (IV) approach to estimate the causal effect of education on refractive error, specifically myopia. We used the results from the educational attainment GWAS from the Social Science Genetic Association Consortium to define a polygenic risk score (PGRS) in three cohorts of late middle age and elderly Caucasian individuals (N = 5,649). In a meta‐analysis of the three cohorts, using the PGRS as an IV, we estimated that each z‐score increase in education (approximately 2 years of education) results in a reduction of 0.92 ± 0.29 diopters (P = 1.04 × 10?3). Our estimate of the effect of education on myopia was higher (P = 0.01) than the observed estimate (0.25 ± 0.03 diopters reduction per education z‐score [~2 years] increase). This suggests that observational studies may actually underestimate the true effect. Our Mendelian Randomization (MR) analysis provides new evidence for a causal role of educational attainment on refractive error. 相似文献
99.
Frank Dudbridge 《Genetic epidemiology》2016,40(4):268-272
Much of the genetic basis of complex traits is present on current genotyping products, but the individual variants that affect the traits have largely not been identified. Several traditional problems in genetic epidemiology have recently been addressed by assuming a polygenic basis for disease and treating it as a single entity. Here I briefly review some of these applications, which collectively may be termed polygenic epidemiology. Methodologies in this area include polygenic scoring, linear mixed models, and linkage disequilibrium scoring. They have been used to establish a polygenic effect, estimate genetic correlation between traits, estimate how many variants affect a trait, stratify cases into subphenotypes, predict individual disease risks, and infer causal effects using Mendelian randomization. Polygenic epidemiology will continue to yield useful applications even while much of the specific variation underlying complex traits remains undiscovered. 相似文献
100.
Kazutaka Ohi Kentaro Takai Ayumi Kuramitsu Shunsuke Sugiyama Midori Soda Kiyoyuki Kitaichi Toshiki Shioiri 《European psychiatry》2021,64(1)
BackgroundIntelligence is inversely associated with schizophrenia (SCZ) and bipolar disorder (BD); it remains unclear whether low intelligence is a cause or consequence. We investigated causal associations of intelligence with SCZ or BD risk and a shared risk between SCZ and BD and SCZ-specific risk.MethodsTo estimate putative causal associations, we performed multi-single nucleotide polymorphism (SNP) Mendelian randomization (MR) using generalized summary-data-based MR (GSMR). Summary-level datasets from five GWASs (intelligence, SCZ vs. control [CON], BD vs. CON, SCZ + BD vs. CON, and SCZ vs. BD; sample sizes of up to 269,867) were utilized.ResultsA strong bidirectional association between risks for SCZ and BD was observed (odds ratio; ORSCZ → BD = 1.47, p = 2.89 × 10−41, ORBD → SCZ = 1.44, p = 1.85 × 10−52). Low intelligence was bidirectionally associated with a high risk for SCZ, with a stronger effect of intelligence on SCZ risk (ORlower intelligence → SCZ = 1.62, p = 3.23 × 10−14) than the reverse (ORSCZ → lower intelligence = 1.06, p = 3.70 × 10−23). Furthermore, low intelligence affected a shared risk between SCZ and BD (OR lower intelligence → SCZ + BD = 1.23, p = 3.41 × 10−5) and SCZ-specific risk (ORlower intelligence → SCZvsBD = 1.64, p = 9.72 × 10−10); the shared risk (ORSCZ + BD → lower intelligence = 1.04, p = 3.09 × 10−14) but not SCZ-specific risk (ORSCZvsBD → lower intelligence = 1.00, p = 0.88) weakly affected low intelligence. Conversely, there was no significant causal association between intelligence and BD risk (p > 0.05).ConclusionsThese findings support observational studies showing that patients with SCZ display impairment in premorbid intelligence and intelligence decline. Moreover, a shared factor between SCZ and BD might contribute to impairment in premorbid intelligence and intelligence decline but SCZ-specific factors might be affected by impairment in premorbid intelligence. We suggest that patients with these genetic factors should be categorized as having a cognitive disorder SCZ or BD subtype. 相似文献