Polycystic Ovary Syndrome and Risk of Five Common Psychiatric Disorders Among European Women: A Two-Sample Mendelian Randomization Study

Background: Observational studies have implied an association between polycystic ovary syndrome (PCOS) and psychiatric disorders. Here we examined whether PCOS might contribute causally to such disorders, focusing on anxiety disorder (AD), bipolar disorder (BIP), major depression disorder (MDD), obsessive compulsive disorder (OCD), and schizophrenia (SCZ).Methods: Causality was explored using two-sample Mendelian randomization (MR) with genetic variants as instrumental variables. The genetic variants were from summary data of genome-wide association studies in European populations. First, potential causal effects of PCOS on each psychiatric disorder were evaluated, and then potential reverse causality was also assessed once PCOS was found to be causally associated with any psychiatric disorder. Causal effects were explored using inverse variance weighting, MR-Egger analysis, simulation extrapolation, and weighted median analysis.Results: Genetically predicted PCOS was positively associated with OCD based on inverse variance weighting (OR 1.339, 95% CI 1.083–1.657, p = 0.007), simulation extrapolation (OR 1.382, 95% CI 1.149–1.662, p = 0.009) and weighted median analysis (OR 1.493, 95% CI 1.145–1.946, p = 0.003). However, genetically predicted OCD was not associated with PCOS. Genetically predicted PCOS did not exert causal effects on AD, BIP, MDD, or SCZ.Conclusions: In European populations, PCOS may be a causal factor in OCD, but not AD, BIP, MDD, or SCZ.

Mismeasured Covariate in the Long-Term Survival of Colorectal Cancer

Background: Colorectal cancer (CRC) is one of the most important causes of morbidity and mortality worldwide. This study aimed to determine the effect of measurement error of risk factors on the cure fraction of CRC patients. Materials and Methods: This study was conducted using the medical records of 346 patients with CRC, who were followed up between 2006 and 2017 in Shiraz, Iran. In our data, lymph node ratio (LNR) was a characteristic measuring with error. This variable was used in the model with 0.04 and 0.8 of error variance. Nonmixture nonparametric cure rate model and its corrected forms, simulation-extrapolation (SIMEX) and corrected score (CS), were applied to the data. Results: In noncured cases, the mean survival time was 1115.45 (95% confidence interval, 1043.60-1187.30) days. The 1-, 3-, and 5-year survival rates were 0.93, 0.71, and 0.65, respectively. The proportion of cured patients was 65.2%. The SIMEX method did not change the effect of LNR substantially on cure fraction as compared with the naive method when the variance of measurement error was 0.04 and 0.80. The CS method changed the effect of LNR on cure fraction even when the variance of measurement error was 0.04. Conclusion: The best method to assess the effect of LNR on cure fraction was the naive method, and the CS method was not deemed to be a valid method to correct the measurement error in LNR. [GMJ.2019;8:e1413]

Simulation Extrapolation Method for Cox Regression Model with a Mixture of Berkson and Classical Errors in the Covariates using Calibration Data

Many biomedical or epidemiological studies often aim to assess the association between the time to an event of interest and some covariates under the Cox proportional hazards model. However, a problem is that the covariate data routinely involve measurement error, which may be of classical type, Berkson type or a combination of both types. The issue of Cox regression with error-prone covariates has been well-discussed in the statistical literature, which has focused mainly on classical error so far. This paper considers Cox regression analysis when some covariates are possibly contaminated with a mixture of Berkson and classical errors. We propose a simulation extrapolation-based method to address this problem when two replicates of the mismeasured covariates are available along with calibration data for some subjects in a subsample only. The proposed method places no assumption on the mixture percentage. Its finite-sample performance is assessed through a simulation study. It is applied to the analysis of data from an AIDS clinical trial study.

The simulation extrapolation technique meets ecology and evolution: A general and intuitive method to account for measurement error

Measurement error and other forms of uncertainty are commonplace in ecology and evolution and may bias estimates of parameters of interest. Although a variety of approaches to obtain unbiased estimators are available, these often require that errors are explicitly modeled and that a latent model for the unobserved error-free variable can be specified, which in practice is often difficult.Here we propose to generalize a heuristic approach to correct for measurement error, denoted as simulation extrapolation (SIMEX), to situations where explicit error modeling fails. We illustrate the application of SIMEX using the example of estimates of quantitative genetic parameters, e. g. inbreeding depression and heritability, in the presence of pedigree errors. Following the original SIMEX idea, the error in the pedigree is progressively increased to determine how the estimated quantities are affected by error. The observed trend is then extrapolated back to a hypothetical error-free pedigree, yielding unbiased estimates of inbreeding depression and heritability. We term this application of the SIMEX idea to pedigrees “PSIMEX”. We tested the method with simulated pedigrees with different pedigree structures and initial error proportions, and with real field data from a free-living population of song sparrows.The simulation study indicates that the accuracy and precision of the extrapolated error-free estimate for inbreeding depression and heritability are good. In the application to the song sparrow data, the error-corrected results could be validated against the actual values thanks to the availability of both an error-prone and an error-free pedigree, and the results indicate that the PSIMEX estimator is close to the actual value. For easy accessibility of the method, we provide the novel R-package PSIMEX.By transferring the SIMEX philosophy to error in pedigrees, we have illustrated how this heuristic approach can be generalized to situations where explicit latent models for the unobserved variables or for the error of the variables of interest are difficult to formulate. Thanks to the simplicity of the idea, many other error problems in ecology and evolution might be amenable to SIMEX-like error correction methods.