Biased Estimates Research Articles

Psychosocial Health Among Young Adults With Kidney Failure: A Longitudinal Follow-up of the SPEAK (Surveying Patients Experiencing Young Adult Kidney Failure) Study

Rationale & ObjectiveThere have been no longitudinal studies examining the evolution of psychosocial health of young adults with kidney failure as they age. We aimed to address this in the SPEAK-2 [Surveying Patients Experiencing young Adult Kidney failure-2] study. Study Design5-year follow-up longitudinal survey of the original SPEAK cohort. Setting& Participants: 16- to 30-year-olds in the UK receiving KRT between 2015-2017 who participated in the SPEAK study. ExposuresKidney failure, and KRT modality. OutcomesPsychosocial health and lifestyle behaviours. Analytical ApproachWithin-cohort changes in psychosocial health were analysed using the paired t-test, Wilcoxon signed-rank test and McNemar’s test. We compared responses to the age-matched population, and examined the impact of changes in KRT modality on psychological health, using linear regression for continuous outcome variables and logistic, ordered logistic and multinomial logistic regression for binary, ordered categorical and unordered categorical variables respectively. ResultsWe obtained 158 survey responses; 129 had previously responded to SPEAK. Of these, 90% had a kidney transplant. Compared to the general population, respondents were less likely to be married or have children and were more likely to be living with their parents. They had nearly 15 times greater odds of being unable to work due to health (OR, 14.41; 95% CI, 8.0-26.01; P<0.001). Respondents had poorer quality of life and mental wellbeing, and were more likely to report psychological problems (OR 5.37; 95% CI 3.45-8.35; P<0.001). A negative association between remaining on or moving to dialysis and psychosocial health was observed, although this was attenuated when controlling for the psychosocial state in SPEAK. LimitationsLow response rate resulting in imprecise and potentially biased estimates, and impact of Covid-19 pandemic while survey was active on psychosocial health. ConclusionsYoung adults with kidney failure have persistent poorer psychosocial health compared to their healthy peers as they age. Our findings also suggest a potential causal relationship between KRT modality and psychosocial health.

Stereo Visual Inertial Pose Estimation Based on Feedforward and Feedbacks

In this article, we present a stereo visual inertial pose estimation method based on feedforward and feedbacks. Compared to the widely used filter-based or optimization-based approaches, the proposed method only stores the most recent pose and measurements and thus can achieve fast processing. A gradient decreased feedback, a roll-pitch feedforward, and a bias estimation feedback, are introduced to fuse the vision and the inertial measurements. This system, which is called feedforward and feedback based visual inertial system (FVIS), is evaluated on the popular European robotics challenge micro aerial vehicle (EuRoC MAV) dataset. FVIS achieves high accuracy and robustness with respect to existing visual inertial simultaneous localization and mapping (SLAM) approaches. FVIS has also been implemented and tested on a unmanned aerial vehicle (UAV) platform. The source code developed during this study is available publicly.

A REML method for the evidence-splitting model in network meta-analysis.

Checking for possible inconsistency between direct and indirect evidence is an important task in network meta-analysis. Recently, an evidence-splitting (ES) model has been proposed, that allows separating direct and indirect evidence in a network and hence assessing inconsistency. A salient feature of this model is that the variance for heterogeneity appears in both the mean and the variance structure. Thus, full maximum likelihood (ML) has been proposed for estimating the parameters of this model. Maximum likelihood is known to yield biased variance component estimates in linear mixed models, and this problem is expected to also affect the ES model. The purpose of the present paper, therefore, is to propose a method based on residual (or restricted) maximum likelihood (REML). Our simulation shows that this new method is quite competitive to methods based on full ML in terms of bias and mean squared error. In addition, some limitations of the ES model are discussed. While this model splits direct and indirect evidence, it is not a plausible model for the cause of inconsistency.

Robust Generalized Scalar-on-Tensor Regression

High-dimensional (HD) data, such as images and profiles, are commonly collected from complex systems and contain significant explanatory and predictive information for effective systems monitoring and control. Therefore, developing accurate and robust predictive models based on HD data is crucial. In literature, various methods, including linear scalar-on-tensor regression, are developed to model a complex system based on HD data. However, existing estimation techniques ignore the presence of outliers and are prone to biased estimations. This paper proposes a robust scalar-on-tensor regression framework that handles multi-dimensional HD input data when the data contain outliers. Our proposed estimation method is constructed using maximum Lq-likelihood estimation instead of the classical maximum likelihood estimation. The asymptotic analysis under the Gaussian distribution assumption and the guideline on choosing the tuning parameter of our proposed method is provided. Several simulations and case studies evaluate the proposed method’s efficacy compared to several benchmark methods in the literature.

Code observation-specific biases estimation for the modernized multi-GNSS and multi-frequency signals: An approach considering signal distortion biases

With the continuous development of global navigation satellite systems (GNSS) modernization, the code bias presented in the form of observation-specific biases (OSB) is more flexible and effective when processing modern multi-frequency and multi-GNSS signals, and therefore widely accepted by the GNSS community. However, the newly reported signal distortion biases (SDB) are ignored in the existing OSB estimation methods, which may affect the reliability of the final OSB estimation. In addition, the existing studies about SDB estimation are independent of OSB estimation, and thus a set of SDB products is generated independent of OSB products, which undoubtedly brings more complicated correction operations to GNSS users. In view of it, this study proposes an approach to code OSB estimation for multi-frequency and multi-GNSS arbitrary signals by reparameterization, which considers the SDB parameter. The aim of this study is to generate a set of code OSB products that take into account SDB parameters for the GNSS users to use. Due to considering the influence of SDB on raw code observations, the OSB estimation obtained by the proposed method is related to specific satellite-receiver pairs, which makes it different from the traditional OSB estimation methods. To achieve this, an extended model for multi-frequency and multi-GNSS arbitrary signal is first established based on the undifferenced and uncombined model, in which the SDB parameter is taken into account by re-parameterization. Based on the proposed model, three types of code bias that contain the OSB information can be obtained for the subsequent OSB estimation. Then, the three types of code bias in the first step are used as virtual observations for multi-frequency and multi-GNSS arbitrary signal OSB estimation through a two-step method. The proposed method is validated with a whole month of real measured data from 418 stations in MGEX and GA CORS to generate OSBs for GPS, GLONASS, and Galileo. The comparison between the estimated OSBs and the OSB products from CAS indicates the reliability of the proposed method. Besides, all types of OSB for four receiver groups are generated based on the proposed method to analyze the relationship between OSB and receiver groups. Results indicate that the estimated OSBs are related to receiver groups, and specific differences exist between different satellites and GNSS signals. It also proves that the OSB estimation is biased by the inconsistencies of the deviation in different receiver groups, which is more obvious for GLONASS.

Iteratively reweighted accurate sparse low-rank matrix estimation algorithm for bearing fault diagnosis

Bearing fault diagnosis is significant to ensure the safe functioning of mechanical systems. One of the key issues in bearing fault diagnosis is the accurate and effective extraction of fault characteristic features. In order to further explore the construction of penalty function, further alleviate the problem of bias estimation and enhance the capabilities of the sparse low-rank (SLR) method in effectively diagnosing bearing faults, in this paper, an iteratively reweighted accurate sparse low-rank (IRASLR) matrix estimation algorithm is proposed. Taking the sparse and low-rank properties of fault transient signals in time–frequency domain into account, an optimization problem is formulated. Two penalty functions, derived from a core minimax-concave penalty (MCP), induce sparse and low-rank properties, respectively. Specifically, to facilitate the design of weighting schemes, we construct an equivalent variant of MCP (EVMCP). By separately extending EVMCP to matrix elements and singular values, we obtain equivalent weighted MCP and equivalent weighted matrix γ-norm. Finally, based on the proximal operators of the two penalty functions, we deduce an IRASLR algorithm under the alternating direction method of the multipliers (ADMM) framework. The performance of IRASLR is verified by processing simulated and experimental signals, while the superiority of the algorithm is confirmed through comparative experiments.

Adoption of drought tolerant maize varieties and farmers’ access to credit in Nigeria: Implications on productivity

In this study, we examined the determinants of choice of variety including DTMV, local variety and combination of the two varieties using multivariate probit model. In addition, we examined factors influencing adoption of DTMV and access to credit among the maize farmers using endogenous switching probit model (ESPM). Consequently, we ascertained the probable effects of adoption of DTMV on maize productivity using endogenous switching regression model (ESRM). We used IPWRA to correct for biased estimates of ATT, ATE and POM of the endogenous treatment. Our results are in four folds: First, from the multivariate probit model, we found that age, gender, household size, farming experience, membership in farmers association, access to extension contacts, location and access to credit variables influenced the maize farmers’ decision to choose between DTMV, local varieties and combination of both. Second, the result of the ESPM shows that variables age, gender, farm size, years of formal education and membership in association affects maize farmers’ access to credit. Third, from ESRM results, we found that gender, household size, marital status, membership in association, access to extension contacts and location variables translates to increase maize productivity for the maize farmers who adopted DTMV. Lastly, the result of IPWRA indicates that adoption of DTMV increased the productivity of the maize farmers. To ensure access to DTMV seed varieties, stakeholders and government should take lead in dissemination of these varieties to reach the target farmers.

Inference on regression model with misclassified binary response

Misclassification of binary responses, if ignored, may severely bias the maximum likelihood estimators (MLEs) of regression parameters. For such data, a binary regression model incorporating non-differential classification errors is extensively used by researchers in different application contexts. We strongly caution against indiscriminate use of this model considering the fact that it suffers from a serious estimation problem due to confounding of the unknown misclassification probabilities with the regression parameters, and thus, may lead to a highly biased estimate. To overcome this problem, we propose here the use of an internal validation sample in addition to the main sample. Assuming differential classification errors, we consider MLEs of the regression parameters based on the joint likelihood of the main sample and the internal validation sample. We then develop a rigorous asymptotic theory for the joint MLEs under standard assumptions. To facilitate its easy implementation for inference, we propose a bootstrap approximation to the asymptotic distribution and prove its consistency. The results of the simulation studies suggest that even an extremely small validation sample may lead to a vastly improved inference. Finally, the methodology is illustrated with a real-life survey data.

Does strict invariance matter? Valid group mean comparisons with ordered-categorical items

Measurement invariance (MI) of a psychometric scale is a prerequisite for valid group comparisons of the measured construct. While the invariance of loadings and intercepts (i.e., scalar invariance) supports comparisons of factor means and observed means with continuous items, a general belief is that the same holds with ordered-categorical (i.e., ordered-polytomous and dichotomous) items. However, as this paper shows, this belief is only partially true—factor mean comparison is permissible in the correctly specified scalar invariance model with ordered-polytomous items but not with dichotomous items. Furthermore, rather than scalar invariance, full strict invariance—invariance of loadings, thresholds, intercepts, and unique factor variances in all items—is needed when comparing observed means with both ordered-polytomous and dichotomous items. In a Monte Carlo simulation study, we found that unique factor noninvariance led to biased estimations and inferences (e.g., with inflated type I error rates of 19.52%) of (a) the observed mean difference for both ordered-polytomous and dichotomous items and (b) the factor mean difference for dichotomous items in the scalar invariance model. We provide a tutorial on invariance testing with ordered-categorical items as well as suggestions on mean comparisons when strict invariance is violated. In general, we recommend testing strict invariance prior to comparing observed means with ordered-categorical items and adjusting for partial invariance to compare factor means if strict invariance fails.

Quantum-First (Q-F): Clinical Bridging Study for FMS-like Tyrosine Kinase 3-Internal Tandem Duplication ( FLT3-ITD) Companion Diagnostic Development

Background Q-F (NCT02668653) showed that the highly potent, selective, type 2 FLT3 inhibitor quizartinib (Q) + standard chemotherapy ± transplantation, followed by Q monotherapy for ≥36 cycles, reduced the relative risk of death by 22.4% vs placebo (P) in newly diagnosed (nd) FLT3-ITD+ AML, with HR of 0.776 and P value of 0.0324 (PMID: 37116523). In Q-F, FLT3-ITD mutation status was determined using a FLT3-ITD mutation detection clinical trial assay (CTA) validated under design control by Navigate BioPharma Services, Inc. We describe the results of the bridging study, aimed to show agreement between the CTA &amp; the LeukoStrat CDx FLT3 Mutation Assay (CDx, by Invivoscribe) in FLT3-ITD+ pt selection and to determine if Q efficacy (overall survival [OS]) was maintained in nd FLT3-ITD+ AML pts from Q-F, if pts had been selected using the CDx. Methods In both CTA &amp; CDx, DNA, extracted from bone marrow (n=884 each) or peripheral blood (n=139 each), was amplified via PCR and amplicons were detected via capillary electrophoresis. A sample was considered CTA+ if the variant allele frequency ( FLT3-ITD/total FLT3) was ≥3% and CDx+ if the signal ratio (SR; FLT3-ITD/ FLT3 WT) was ≥0.05. The agreement between CTA &amp; CDx was based on evaluating CTA+ &amp; CTA− samples with the CDx assay. A primary analysis included the CDx detected (CDx+ &amp; CDx−) and the CDx invalid results. A secondary analysis used CDx+ and CDx− results only. To establish agreement between CDx &amp; CTA, positive % agreement (PPA) and negative % agreement (NPA) were determined using CTA results as reference for the agreement analysis set (AAS). Concordance was established if the lower bounds of the 95% CIs for both PPA &amp; NPA exceeded 90% for the analysis that included the invalid CDx results. Median OS in the subgroups was calculated based on Kaplan-Meier estimates. Stratified Cox proportional hazards regression model was used to estimate HRs, 95% CI, and P value. Results Full analysis set (N=3468) included all Q-F screened CTA+ pts (n=863), all screened CTA− pts (n=2556), and pts with unknown CTA status not eligible for randomization due to other criteria (n=49). Of these, 1032 pts formed the primary analysis set (PAS), including all pts randomized in Q-F with samples available for CDx testing (N=513: Q, n=254; P, n=259) and a randomly selected subset of CTA− pts (n=519). The ascertainment rate was 95.2% (513/539), as 26 of the 539 pts randomized in Q-F were excluded from the bridging study. Within the PAS, 3 samples were not tested by CDx due to insufficient volume/DNA amount. The AAS (N=1029: CTA+, n=513; CTA−, n=516) included pts in the PAS with valid CTA results and tested with CDx. In the AAS, 6 samples (3 CTA+, 3 CTA−) did not yield valid CDx results, resulting in 1023 CDx-evaluable total pts (CTA+, n=510; CTA−, n=513). Among 510 CTA+ samples, 483 were CDx+. Among 513 CTA− samples, 513 were CDx−. Therefore, 996 samples yielded concordant results, 27 samples yielded discordant results, and 6 samples did not yield a valid CDx result for comparison. Point estimates of PPA &amp; NPA were 94.2% &amp; 99.4%, respectively, with invalid CDx results included in the calculation, and 94.7% &amp; 100%, respectively, without invalid CDx results. The lower bounds of the 95% CIs were all above the corresponding acceptance criterion of 90% for PPA &amp; NPA (Table 1). In Q-F, the prevalence of CTA+ was 24.9% among screened pts (863/3468), whereas in the bridging study, 49.9% (510/1023) of pts were CTA+: this enrichment in CTA+ pts could lead to a biased estimate of the agreement between CDx &amp; CTA when using CDx as reference. The positive predictive value (PPV) and negative predictive value (NPV) of the CDx adjusted for this enrichment ± invalid CDx results, showed that the lower bounds of the 95% CIs were all &amp;gt;95% (Table 1). The efficacy OS analysis in the intent-to-treat (ITT) CDx+ population (ITT CDx+=CTA+ &amp; CDx+; N=483: Q, n=242; P, n=241) demonstrated a clinically relevant OS improvement with Q (median OS of 29.4 months) vs P (median OS of 14.8 months), resulting in 14.6 months prolongation of median OS, with an HR of 0.794 (95% CI 0.621-1.014), corresponding to a 20.6% reduction in relative risk of death (Figure 1). Conclusions This study showed 1) agreement between CDx &amp; CTA in identifying nd FLT3-ITD+ AML pts and 2) that OS benefit provided by Q in the ITT CDx+ population is comparable with the OS benefit in the ITT population of Q-F. The LeukoStrat CDx FLT3 Mutation Assay aids in assessing AML pts for Q therapy.

Simultaneous estimation of SAR, thermal diffusivity, and damping using periodic power modulation for MRgFUS quality assurance

Purpose: A crucial aspect of quality assurance in thermal therapy is periodic demonstration of the heating performance of the device. Existing methods estimate the specific absorption rate (SAR) from the temperature rise after a short power pulse, which yields a biased estimate as thermal diffusion broadens the apparent SAR pattern. To obtain an unbiased estimate, we propose a robust frequency-domain method that simultaneously identifies the SAR as well as the thermal dynamics. Methods: We propose a method consisting of periodic modulation of the FUS power while recording the response with MR thermometry (MRT). This approach enables unbiased measurements of spatial Fourier coefficients that encode the thermal response. These coefficients are substituted in a generic thermal model to simultaneously estimate the SAR, diffusivity, and damping. The method was tested using a cylindrical phantom and a 3 T clinical MR-HIFU system. Three scenarios with varying modulation strategies are chosen to challenge the method. The results are compared to the well-known power pulse technique. Results: The thermal diffusivity is estimated at 0.151 mm2s–1 with a standard deviation of 0.01 mm2s–1 between six experiments. The SAR estimates are consistent between all experiments and show an excellent signal-to-noise ratio (SNR) compared to the well established power pulse method. The frequency-domain method proved to be insensitive to B 0-drift and non steady-state initial temperature distributions. Conclusion: The proposed frequency-domain estimation method shows a high SNR and provided reproducible estimates of the SAR and the corresponding thermal diffusivity. The findings suggest that frequency-domain tools can be highly effective at estimating the SAR from (biased) MRT data acquired during periodic power modulation.

Correcting Regressor-Endogeneity Bias via Instrument-Free Joint Estimation Using Semiparametric Odds Ratio Models

Endogenous regressors can lead to biased estimates for causal effects using methods assuming regressor–error independence. To correct for endogeneity bias, the authors propose a new method that accounts for the regressor–error dependence using flexible semiparametric odds ratio conditional models; the approach requires neither parametric distributional assumptions nor tuning parameters for modeling endogenous regressors’ distributions conditional on the error term and exogenous regressors. Inference is achieved via optimizing the profile likelihood concentrating on the parameters of interest. The proposed approach requires no use of instrumental variables (IVs), observed or latent, that must satisfy the stringent condition of exclusion restriction. Nonnormally distributed endogenous regressors are required for model identification with a normal error distribution. The approach's flexibility in capturing regressor–error dependence increases the capability of IV-free endogeneity correction and provides opportunities to improve the accuracy of causal effect estimation. Unlike existing IV-free methods, the proposed approach can handle discrete endogenous regressors with few levels, such as binary regressors or count regressors with small means, and is thus applicable to a plethora of applications involving such regressors. The authors demonstrate the versatility of the approach for binary, count, and continuous endogenous regressors using comprehensive simulation studies and empirical data.

Accurate bias estimation with applications to focused model selection

AbstractWe derive approximations to the bias and squared bias with errors of order where is the sample size. Our results hold for a large class of estimators, including quantiles, transformations of unbiased estimators, maximum likelihood estimators in (possibly) incorrectly specified models, and functions thereof. Furthermore, we use the approximations to derive estimators of the mean squared error (MSE) which are correct to order . Since the variance of many estimators is of order , this level of precision is needed for the MSE estimator to properly take the variance into account. We also formulate a new focused information criterion (FIC) for model selection based on the estimators of the squared bias. Lastly, we illustrate the methods on data containing the number of battle deaths in all major inter‐state wars between 1823 and the present day. The application illustrates the potentially large impact of using a less‐accurate estimator of the squared bias.

Adaptive Feed-Forward Control for Gust Load Alleviation on a Flying-Wing Model Using Multiple Control Surfaces

Based on measured gust information, a multi-input multi-output (MIMO) adaptive feed-forward control scheme for gust load alleviation (GLA) on a semi-span flying-wing aircraft using multiple control surfaces is proposed. In order to remedy weight drift and biased estimation problems that are commonly encountered in adaptive control, the circular leaky LMS (CLLMS) algorithm is employed, which utilizes gust measurement information, filtered reference signals, and error signals to update controller parameters online. The results demonstrate that good load reductions are achieved in both continuous and discrete gust environments. For instance, the designed GLA control system leads to an 80.72% reduction in the root-mean-square (RMS) values of wing-root bending moment in the Dryden gust environment and a 77.59% reduction of its maximum value in the 1-cos discrete gust condition. Based on the limited power of the actuator and the limited authority for control surface deflections when integrating GLA into the flight control system, a weight-updating algorithm with deflection angle and rate constraints on control surfaces is proposed. The simulation results show that the strict constraints on control surface deflections will degrade the GLA performance. Finally, the influence of the partial jamming fault of actuators on GLA performance is studied. It is found that good GLA performance can be preserved despite the degraded performance during the initial stage of the actuator jamming fault. This is due to the robustness brought about by multiple control surfaces and the adaptability of the control algorithm.

Transcriptome-wide profiling of acute stress induced changes in ribosome occupancy level using external standards.

Acute cellular stress is known to induce a global reduction in mRNA translation through suppression of cap dependent translation. Selective translation in response to acute stress has been shown to play important roles in regulating the stress response. However, accurately profiling translational changes transcriptome-wide in response to acute cellular stress has been challenging. Commonly used data normalization methods operate on the assumption that any systematic shifts are experimental artifacts. Consequently, if applied to profiling acute cellular stress-induced mRNA translation changes, these methods are expected to produce biased estimates. To address this issue, we designed, produced, and evaluated a panel of 16 oligomers to serve as external standards for ribosome profiling studies. Using Sodium Arsenite treatment-induced oxidative stress in lymphoblastoid cell lines as a model system, we applied spike-in oligomers as external standards. We found our spike-in oligomers to display a strong linear correlation between the observed and the expected quantification, with small ratio compression at the lower concentration range. Using the expected fold changes constructed from spike-in controls, we found in our dataset that TMM normalization, a popular global scaling normalization approach, produced 87.5% false positives at a significant cutoff that is expected to produce only 10% false positive discoveries. In addition, TMM normalization produced a systematic shift of fold change by 3.25 fold. These results highlight the consequences of applying global scaling approaches to conditions that clearly violate their key assumptions. In contrast, we found RUVg normalization using spike-in oligomers as control genes recapitulated the expected stress induced global reduction of translation and resulted in little, if any, systematic shifts in the expected fold change. Our results clearly demonstrated the utility of our spike-in oligomers, both for constructing expected results as controls and for data normalization.

Estimating ground surface visibility on thermal images from drone wildlife surveys in forests

A thermal drone – an unmanned aerial vehicle equipped with a thermal camera – is a new tool in wildlife research. It enables effective surveys of animal species difficult to monitor with conventional methods. Over open terrain the detection of warm-blooded large animals with a thermal drone is nearly certain. However, in forest surveys, individuals hidden under the tree canopy may remain undetected which can lead to biased estimates of population size. Thus, it is important to estimate the percentage of surveyed area not obscured by tree canopy where animals can be easily detected. This figure may be used as a simple correction factor for animal counts from thermal images (thermograms) or as one of the covariates in more advanced statistical models of population density. We tested four methods of automatic image segmentation: 1) Otsu global thresholding, 2) local thresholding with Gaussian kernel, 3) Chan-Vese active contour model without edges, and 4) logistic regression and assessed each of their performances in differentiating between canopy and forest floor on a sample of 100 thermograms acquired with a drone during wildlife surveys in a range of forest habitats. We used segmentation results to calculate the estimated percentage of visible ground surface on images and compared it to the percentages assessed by manual classification. The Otsu global thresholding gave results that were in best agreement with the manual classification, had the fastest computing time and was superior to the other three segmentation methods. Visual inspection of the Otsu segmentation results showed that this method accurately separates tree canopy and ground surface on thermograms obtained across all forest types as well as in partially open areas. However, landscape features such as roads or water bodies that have a temperature similar to that of trees may cause segmentation errors and lead to underestimation of the ground visibility. While the use of Otsu segmentation to automatically assess percentage of visible ground surface on thermal images from drone surveys has certain limitations, the method is fast and can be readily applied by wildlife researchers. This approach can aid in correcting bias in estimates of animal population density and contribute to a better understanding of the use of thermal drones for wildlife surveys in forests.

Cox regression with linked data.

Record linkage is increasingly used, especially in medical studies, to combine data from different databases that refer to the same entities. The linked data can bring analysts novel and valuable knowledge that is impossible to obtain from a single database. However, linkage errors are usually unavoidable, regardless of record linkage methods, and ignoring these errors may lead to biased estimates. While different methods have been developed to deal with the linkage errors in the generalized linear model, there is not much interest on Cox regression model, although this is one of the most important statistical models in clinical and epidemiological research. In this work, we propose an adjusted estimating equation for secondary Cox regression analysis, where linked data have been prepared by a third-party operator, and no information on matching variables is available to the analyst. Through a Monte Carlo simulation study, the proposed method is shown to lead to substantial bias reductions in the estimation of the parameters of the Cox model caused by false links. An asymptotically unbiased variance estimator for the adjusted estimators of Cox regression coefficients is also proposed. Finally, the proposed method is applied to a linked database from the Brest stroke registry in France.

On the methodological framework of composite index under complex surveys and its application in development of food consumption index for India

Indices are created by consolidating multidimensional data into a single representative measure known as an index, using a fundamental mathematical model. Most present indices are essentially the averages or weighted averages of the variables under study, ignoring multicollinearity among the variables, with the exception of the existing Ordinary Least Squares (OLS) estimator based OLS-PCA index methodology. Many existing surveys adopt survey designs that incorporate survey weights, aiming to obtain a representative sample of the population while minimizing costs. Survey weights play a crucial role in addressing the unequal probabilities of selection inherent in complex survey designs, ensuring accurate and representative estimates of population parameters. However, the existing OLS-PCA based index methodology is designed for simple random sampling and is incapable of incorporating survey weights, leading to biased estimates and erroneous rankings that can result in flawed inferences and conclusions for survey data. To address this limitation, we propose a novel Survey Weighted PCA (SW-PCA) based Index methodology, tailored for survey-weighted data. SW-PCA incorporates survey weights, facilitating the development of unbiased and efficient composite indices, improving the quality and validity of survey-based research. Simulation studies demonstrate that the SW-PCA based index outperforms the OLS-PCA based index that neglects survey weights, indicating its higher efficiency. To validate the methodology, we applied it to a Household Consumer Expenditure Survey (HCES), NSS 68th Round survey data to construct a Food Consumption Index for different states of India. The result was significant improvements in state rankings when survey weights were considered. In conclusion, this study highlights the crucial importance of incorporating survey weights in index construction from complex survey data. The SW-PCA based Index provides a valuable solution, enhancing the accuracy and reliability of survey-based research, ultimately contributing to more informed decision-making.

Bias and mean squared error in Mendelian randomization with invalid instrumental variables.

Mendelian randomization (MR) is a statistical method that utilizes genetic variants as instrumental variables (IVs) to investigate causal relationships between risk factors and outcomes. Although MR has gained popularity in recent years due to its ability to analyze summary statistics from genome-wide association studies (GWAS), it requires a substantial number of single nucleotide polymorphisms (SNPs) as IVs to ensure sufficient power for detecting causal effects. Unfortunately, the complex genetic heritability of many traits can lead to the use of invalid IVs that affect both the risk factor and the outcome directly or through an unobserved confounder. This can result in biased and imprecise estimates, as reflected by a larger mean squared error (MSE). In this study, we focus on the widely used two-stage least squares (2SLS) method and derive formulas for its bias and MSE when estimating causal effects using invalid IVs. Using those formulas, we identify conditions under which the 2SLS estimate is unbiased and reveal how the independent or correlated pleiotropic effects influence the accuracy and precision of the 2SLS estimate. We validate these formulas through extensive simulation studies and demonstrate the application of those formulas in an MR study to evaluate the causal effect of the waist-to-hip ratio on various sleeping patterns. Our results can aid in designing future MR studies and serve as benchmarks for assessing more sophisticated MR methods.

Can financial subsidy increase electric vehicle (EV) penetration---evidence from a quasi-natural experiment

Electric vehicles (EVs) are considered a promising solution for reducing emissions in urban transportation and addressing energy crises. Several countries, including China, have implemented direct financial subsidies to encourage the adoption of EVs. However, there is a lack of comprehensive evaluation regarding the effectiveness of these subsidies and their interaction with non-financial incentive policies. Additionally, the design of subsidy policies can be endogenous, potentially leading to biased estimations of their effects on EV penetration. To address these issues, this study utilizes a “quasi-natural experiment" approach, taking advantage of the gradual reduction of nationwide EV subsidies by the Chinese government due to financial constraints, rather than market conditions, thus mitigating the endogeneity problem. The study collects panel data from 224 prefecture-level cities in China and employs a modified panel data fixed effects model to quantify the impact of financial subsidies on EV sales and market shares. The analysis focuses on subsidy variations during six periods. The main empirical findings are as follows: (1) 1 % increase in purchase subsidies leads to a 1.36 % increase in EV sales and a 2.31 % increase in EV market share. (2) Non-financial policies, such as parking benefits, promotional goals, and restrictions on the number of vehicles, also contribute to boosting EV sales. (3) The effects of purchase subsidies vary across cities with different socio-economic conditions. (4) Some non-financial policies are found to enhance the positive effects of subsidy policies.