Dataset Information

Correcting the Standard Errors of 2-Stage Residual Inclusion Estimators for Mendelian Randomization Studies.

ABSTRACT: Mendelian randomization studies use genotypes as instrumental variables to test for and estimate the causal effects of modifiable risk factors on outcomes. Two-stage residual inclusion (TSRI) estimators have been used when researchers are willing to make parametric assumptions. However, researchers are currently reporting uncorrected or heteroscedasticity-robust standard errors for these estimates. We compared several different forms of the standard error for linear and logistic TSRI estimates in simulations and in real-data examples. Among others, we consider standard errors modified from the approach of Newey (1987), Terza (2016), and bootstrapping. In our simulations Newey, Terza, bootstrap, and corrected 2-stage least squares (in the linear case) standard errors gave the best results in terms of coverage and type I error. In the real-data examples, the Newey standard errors were 0.5% and 2% larger than the unadjusted standard errors for the linear and logistic TSRI estimators, respectively. We show that TSRI estimators with modified standard errors have correct type I error under the null. Researchers should report TSRI estimates with modified standard errors instead of reporting unadjusted or heteroscedasticity-robust standard errors.

SUBMITTER: Palmer TM

PROVIDER: S-EPMC5860380 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Correcting the Standard Errors of 2-Stage Residual Inclusion Estimators for Mendelian Randomization Studies.

Palmer Tom M TM Holmes Michael V MV Keating Brendan J BJ Sheehan Nuala A NA

American journal of epidemiology 20171101 9

Mendelian randomization studies use genotypes as instrumental variables to test for and estimate the causal effects of modifiable risk factors on outcomes. Two-stage residual inclusion (TSRI) estimators have been used when researchers are willing to make parametric assumptions. However, researchers are currently reporting uncorrected or heteroscedasticity-robust standard errors for these estimates. We compared several different forms of the standard error for linear and logistic TSRI estimates i ...[more]

PMID: 29106476

Similar Datasets

Project description:BackgroundSpeech sound disorder in childhood poses a barrier to academic and social participation, with potentially lifelong consequences for educational and occupational outcomes. While most speech errors resolve by the late school-age years, between 2 and 5% of speakers exhibit residual speech errors (RSE) that persist through adolescence or even adulthood. Previous findings from small-scale studies suggest that interventions incorporating visual biofeedback can outperform traditional motor-based treatment approaches for children with RSE, but this question has not been investigated in a well-powered randomized controlled trial.Methods/designThis project, Correcting Residual Errors with Spectral, ULtrasound, Traditional Speech therapy Randomized Controlled Trial (C-RESULTS RCT), aims to treat 110 children in a parallel randomized controlled clinical trial comparing biofeedback and non-biofeedback interventions for RSE affecting the North American English rhotic sound /ɹ/. Eligible children will be American English speakers, aged 9-15 years, who exhibit RSE affecting /ɹ/ but otherwise show typical cognitive-linguistic and hearing abilities. Participants will be randomized, with stratification by site (Syracuse University or Montclair State University) and pre-treatment speech production ability, to receive either a motor-based treatment consistent with current best practices in speech therapy (40% of participants) or treatment incorporating visual biofeedback (60% of participants). Within the biofeedback condition, participants will be assigned in equal numbers to receive biofeedback in the form of a real-time visual display of the acoustic signal of speech or ultrasound imaging of the tongue during speech. The primary outcome measure will assess changes in the acoustics of children's production of /ɹ/ during treatment, while a secondary outcome measure will use blinded listeners to evaluate changes in the perceived accuracy of /ɹ/ production after the completion of all treatment. These measures will allow the treatment conditions to be compared with respect to both efficacy and efficiency.DiscussionBy conducting the first well-powered randomized controlled trial comparing treatment with and without biofeedback, this study aims to provide high-quality evidence to guide treatment decisions for children with RSE.Trial registrationClinicalTrials.gov identifier NCT03737318, November 9, 2018.

Project description:Purpose of review:In this paper, we summarize prior studies that have used Mendelian Randomization (MR) methods to study the effects of exposures, lifestyle factors, physical traits, and/or biomarkers on cancer risk in humans. Many such risk factors have been associated with cancer risk in observational studies, and the MR approach can be used to provide evidence as to whether these associations represent causal relationships. MR methods require a risk factor of interest to have known genetic determinants that can be used as proxies for the risk factor (i.e., "instrumental variables" or IVs), and these can be used to obtain an effect estimate that, under certain assumptions, is not prone to bias caused by unobserved confounding or reverse causality. This review seeks to describe how MR studies have contributed to our understanding of cancer causation. Recent findings:We searched the published literature and identified 76 MR studies of cancer risk published prior to October 31, 2017. Risk factors commonly studied included alcohol consumption, Vitamin D, anthropometric traits, telomere length, lipid traits, glycemic traits, and markers of inflammation. Risk factors showing compelling evidence of a causal association with risk for at least one cancer type include alcohol consumption (for head/neck and colorectal), adult body mass index (increases risk for multiple cancers, but decreases risk for breast), height (increases risk for breast, colorectal, and lung; decreases risk for esophageal), telomere length (increases risk for lung adenocarcinoma, melanoma, renal cell carcinoma, glioma, B-cell lymphoma subtypes, chronic lymphocytic leukemia, and neuroblastoma), and hormonal factors (affects risk for sex-steroid sensitive cancers). Summary:This review highlights alcohol consumption, body mass index, height, telomere length, and the hormonal exposures as factors likely to contribute to cancer causation. This review also highlights the need to study specific cancer types, ideally subtypes, as the effects of risk factors can be heterogeneous across cancer types. As consortia-based genome-wide association studies increase in sample size and analytical methods for MR continue to become more sophisticated, MR will become an increasingly powerful tool for understanding cancer causation.

Dataset Information

Correcting the Standard Errors of 2-Stage Residual Inclusion Estimators for Mendelian Randomization Studies.

Publications

Correcting the Standard Errors of 2-Stage Residual Inclusion Estimators for Mendelian Randomization Studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets