Missing Data Patterns Research Articles

Menstrual Cycle Tracking Applications and the Potential for Epidemiological Research: A Comprehensive Review of the Literature.

We reviewed published studies on menstrual cycle tracking applications (MCTAs) in order to describe the potential of MCTAs for epidemiologic research. A search of PubMed, Web of Science, and Scopus for MCTA literature yielded 150 articles. After exclusions, there were 49 articles that addressed the primary interest areas: 1) characteristics of MCTA users in research, 2) reasons women use or continue using MCTAs, 3) accuracy of identifying ovulation and utility at promoting and preventing pregnancy, and 4) quality assessments of MCTAs across several domains. MCTAs are an important tool for the advancement of epidemiologic research on menstruation. MCTA studies should describe the characteristics of their user-base and missing data patterns. Describing the motivation for using MCTAs throughout a user's life and validating the data collected should be prioritized in future research.

Investigating the impact of missing data imputation techniques on battery energy management system

Effective control of energy storage system (ESS), supplying an ancillary service to a grid, requires effective and critical calculation of state-of-charge (SoC). Charging and discharging values from battery operations are essential in calculating the efficiency and performance of a storage system. This information can also be a key to understand and forecast peak demand performance. Missing data is a real problem in any operations system, and it appears to be more common within powers systems due to sensor and/or network malfunctioning problems. Missing data imputation techniques have evolved in power systems research using smart meter data, but little research has gone into understanding how missing data can be best handled within storage management systems. This paper builds on a year's worth of charging and discharging data collected from a real 6MW/10MWh lithium-ion storage battery deployed on the distribution network at Leighton Buzzard, UK. Using R Studio version (1.3.959-1) open-source software, eight selected imputation techniques were applied in identifying the best suited technique in replacing various missing data amounts and patterns. Findings from the study open up avenues for discussion and debate in identifying an appropriate imputation technique within the storage management context. The study also provides a pioneering lead in understanding the importance of decomposition in evaluating the right imputation technique.

Co-active neuro-fuzzy inference system model as single imputation approach for non-monotone pattern of missing data

Data imputation aims to solve missing values problem which is common in nowadays applications. Many techniques have been proposed to solve this problem from statistical methods such as Mean/Mode to machine learning models. In this paper, an approach based on Co-active Neuro-Fuzzy Inference System named CANFIS-ART is proposed to automate data imputation procedure. This model is constructed from the Neural Network adaptative capabilities and fuzzy logic qualitative approach using the Fuzzy-ART algorithm. Performance of CANFIS-ART model is compared to other state-of-the-art imputation techniques such as Multilayer Perceptron or Hot-Deck, among others, using a total of eighteen databases exposed to a perturbation procedure based on the random generation of non-monotone missing values pattern. The data sets cover a wide range of fields, types of variables and sizes. A comparison of databases imputed by these models using a set of three classifiers has been conducted. A statistical analysis of these results employing Wilcoxon signed-ranked test has been included. Experiments show that CANFIS-ART approach not only outperforms these state-of-the-art techniques but also demonstrates a higher level of generalization capability, increasing the data quality contained in databases with missing values.

Unified approach for regression models with nonmonotone missing at random data

Unified approach (Chen and Chen in J R Stat Soc B 62(3):449–460, 2000) uses a working regression model to extract information from auxiliary variables in two-stage study for computing an efficient estimator of regression parameter. As far as we know, the method is limited to deal with missing complete at random data in a simple monotone missing data pattern. In this research, we extend the unified approach to estimate regression models with nonmonotone missing at random data. We describe an inverse probability weighting estimator condition on estimators from a set of working regression models which contains information from incomplete data and auxiliary variables. The proposed method is flexible and can easily accommodate incomplete data and auxiliary variables. We investigate the finite-sample performance of the proposed estimators using simulation studies and further illustrate the estimation method on a case–control study investigating the risk factors of hip fractures.

Safety Control Synthesis for Systems with Missing Measurements

Abstract We study the problem of synthesizing controllers that enforce hard state-constraints under occasionally missing state measurements. Although the problem can be solved as a partial information game via power set construction, it is undesired to do so because power set construction is computationally expensive. In this paper, we consider the instances where the missing data pattern can be specified by an automaton, and show that, for such cases, the synthesis problem admits a simpler solution. We introduce string automata and exploit the causality requirement to define the structure of the controller, and further use this structure to derive a product system. The synthesis problem then reduces to a full-information game on this product system. Two examples illustrate the efficacy of the proposed approach.

Imputing Satellite-Derived Aerosol Optical Depth Using a Multi-Resolution Spatial Model and Random Forest for PM2.5 Prediction

A task for environmental health research is to produce complete pollution exposure maps despite limited monitoring data. Satellite-derived aerosol optical depth (AOD) is frequently used as a predictor in various models to improve PM2.5 estimation, despite significant gaps in coverage. We analyze PM2.5 and AOD from July 2011 in the contiguous United States. We examine two methods to aid in gap-filling AOD: (1) lattice kriging, a spatial statistical method adapted to handle large amounts data, and (2) random forest, a tree-based machine learning method. First, we evaluate each model’s performance in the spatial prediction of AOD, and we additionally consider ensemble methods for combining the predictors. In order to accurately assess the predictive performance of these methods, we construct spatially clustered holdouts to mimic the observed patterns of missing data. Finally, we assess whether gap-filling AOD through one of the proposed ensemble methods can improve prediction of PM2.5 in a random forest model. Our results suggest that ensemble methods of combining lattice kriging and random forest can improve AOD gap-filling. Based on summary metrics of performance, PM2.5 predictions based on random forest models were largely similar regardless of the inclusion of gap-filled AOD, but there was some variability in daily model predictions.

Testing the Efficacy of a Multicomponent, Self-Guided, Smartphone-Based Meditation App: Three-Armed Randomized Controlled Trial.

BackgroundA growing number of randomized controlled trials (RCTs) suggest psychological benefits associated with meditation training delivered via mobile health. However, research in this area has primarily focused on mindfulness, only one of many meditative techniques.ObjectiveThis study aims to evaluate the efficacy of 2 versions of a self-guided, smartphone-based meditation app—the Healthy Minds Program (HMP)—which includes training in mindfulness (Awareness), along with practices designed to cultivate positive relationships (Connection) or insight into the nature of the self (Insight).MethodsA three-arm, fully remote RCT compared 8 weeks of one of 2 HMP conditions (Awareness+Connection and Awareness+Insight) with a waitlist control. Adults (≥18 years) without extensive previous meditation experience were eligible. The primary outcome was psychological distress (depression, anxiety, and stress). Secondary outcomes were social connection, empathy, compassion, self-reflection, insight, rumination, defusion, and mindfulness. Measures were completed at pretest, midtreatment, and posttest between October 2019 and April 2020. Longitudinal data were analyzed using intention-to-treat principles with maximum likelihood.ResultsA total of 343 participants were randomized and 186 (54.2%) completed at least one posttest assessment. The majority (166/228, 72.8%) of those assigned to HMP conditions downloaded the app. The 2 HMP conditions did not differ from one another in terms of changes in any outcome. Relative to the waitlist control, the HMP conditions showed larger improvements in distress, social connectedness, mindfulness, and measures theoretically linked to insight training (d=–0.28 to 0.41; Ps≤.02), despite modest exposure to connection- and insight-related practice. The results were robust to some assumptions about nonrandom patterns of missing data. Improvements in distress were associated with days of use. Candidate mediators (social connection, insight, rumination, defusion, and mindfulness) and moderators (baseline rumination, defusion, and empathy) of changes in distress were identified.ConclusionsThis study provides initial evidence of efficacy for the HMP app in reducing distress and improving outcomes related to well-being, including social connectedness. Future studies should attempt to increase study retention and user engagement.Trial RegistrationClinicalTrials.gov NCT04139005; https://clinicaltrials.gov/ct2/show/NCT04139005

Bias correction in self-reported high blood pressure prevalence based on objectively measured data

Abstract Background Reliable and precise estimates of high blood pressure (HBP) prevalence are essential to inform decision-making and policies evaluation. Self-reported HBP may be underestimated by surveys due to misclassification of health status by participants. Misclassification may lead to inaccurate inference. We aimed to assess a feasibility of correcting misclassification bias in self-reported HBP in the Portuguese component of the European Health Interview Survey (INS2014) using data on objective blood pressure measurements from a smaller health examination survey (INSEF). Methods We assumed that “true” measured HBP status was missing at random for INS2014 participants (n = 13937). Using a multiple imputation method of logistic regression for monotone missing data patterns, HBP was imputed for INS2014 sample. Auxiliary data on measured HBP from INSEF (n = 4910) was used. Crude and corrected HBP prevalence rates stratified by sex and age group were calculated. Results HBP prevalence based on INS2014 increased significantly after multiple imputation correction, from 22.0% [CI95%:20.6-23.5%] to 38.8% [35.4-41.9%] for men and from 26.8% [25.3-28.3%] to 33.0% [30.5-35.4%] for women. Corrected estimates were similar to the objectively measured from INSEF, 39.6% for men and 32.7% for women. Corrected HBP prevalence in INS2014 increased from 4.1% [2.9-5.9%] to 7.9% [4.0-11.8%] in 25-34 years old (yo); from 9.3% [7.8-11.0%] to 17.6% [14.0-21.3%] in 35-44 yo; from 22.5% [20.3-24.8%] to 38.7% [34.2-43.2%] in 45-54 yo; from 39.8% [37.2-42.4%] to 53.8% [49.1-58.5%] in 55-64 yo and from 54.3% [51.5-57.1%] to 67.7% [63.1-72.3%] in 65-74 yo. Corrected rates were similar to the obtained by INSEF (5.7%, 17.0%, 35.8%, 54.8% and 71.3%, respectively). Conclusions Our results highlight the importance of bias analysis when using self-reported data on HBP. Multiple imputation is a feasible approach to adjust misclassification bias in health outcomes collected by population surveys. Key messages The magnitude and direction of misclassification bias in self-reported health outcomes should be investigated. Multiple imputation is a feasible approach to misclassification bias correction in self-reported survey data.

Menstrual Cycle Tracking Applications and the Potential for Epidemiological Research: A Comprehensive Review of the Literature

We reviewed published studies on menstrual cycle tracking applications (MCTAs) in order to describe the potential of MCTAs for epidemiologic research. A search of PubMed, Web of Science, and Scopus for MCTA literature yielded 150 articles. After exclusions, there were 49 articles that addressed the primary interest areas: 1) characteristics of MCTA users in research, 2) reasons women use or continue using MCTAs, 3) accuracy of identifying ovulation and utility at promoting and preventing pregnancy, and 4) quality assessments of MCTAs across several domains. MCTAs are an important tool for the advancement of epidemiologic research on menstruation. MCTA studies should describe the characteristics of their user base and missing data patterns. Describing the motivation for using MCTAs throughout a user’s life and validating the data collected should be prioritized in future research.

The development and initial evaluation of the Diarrhoea Management Diary (DMD) in patients with metastatic breast cancer

PurposeChemotherapy-induced diarrhoea (CID) is a common, but often underreported problem in patients with breast cancer that has a profound effect on quality of life. It is best measured from a patient’s perspective, but tools are limited. The aim of this study was to develop and evaluate the Diarrhoea Management Diary (DMD), a self-report measure to assess CID, use of self-management strategies and treatment adherence.MethodsThe DMD was constructed using an iterative process of instrument development: concept elicitation (literature review), item generation and reduction (cognitive debriefing), and pilot testing in the target population. After translation into eight languages, the DMD was used in an international randomised trial for women receiving lapatinib and capecitabine for metastatic breast cancer with or without prophylactic octreotide. Patterns of missing data and sensitivity to change were examined.ResultsThe understandability and completeness of the 8-item DMD was confirmed in cognitive interviews and pilot testing. Practicability of the DMD was evaluated in 62 women with metastatic breast cancer (median age 57). Up to 68% reported CID at any given time-point, and 19% had diarrhoea at each time-point. Patients also described efficacy of different strategies for diarrhoea management. Missing data were associated with study discontinuation. DMD missing item response was 0.9%. Sensitivity to change was good at most assessment points.ConclusionsAlthough further psychometric testing is recommended, initial evaluation of the DMD showed good content validity and practicability in international research with cancer patients.

A Method for Sensor-Based Activity Recognition in Missing Data Scenario.

Sensor-based human activity recognition has various applications in the arena of healthcare, elderly smart-home, sports, etc. There are numerous works in this field—to recognize various human activities from sensor data. However, those works are based on data patterns that are clean data and have almost no missing data, which is a genuine concern for real-life healthcare centers. Therefore, to address this problem, we explored the sensor-based activity recognition when some partial data were lost in a random pattern. In this paper, we propose a novel method to improve activity recognition while having missing data without any data recovery. For the missing data pattern, we considered data to be missing in a random pattern, which is a realistic missing pattern for sensor data collection. Initially, we created different percentages of random missing data only in the test data, while the training was performed on good quality data. In our proposed approach, we explicitly induce different percentages of missing data randomly in the raw sensor data to train the model with missing data. Learning with missing data reinforces the model to regulate missing data during the classification of various activities that have missing data in the test module. This approach demonstrates the plausibility of the machine learning model, as it can learn and predict from an identical domain. We exploited several time-series statistical features to extricate better features in order to comprehend various human activities. We explored both support vector machine and random forest as machine learning models for activity classification. We developed a synthetic dataset to empirically evaluate the performance and show that the method can effectively improve the recognition accuracy from 80.8% to 97.5%. Afterward, we tested our approach with activities from two challenging benchmark datasets: the human activity sensing consortium (HASC) dataset and single chest-mounted accelerometer dataset. We examined the method for different missing percentages, varied window sizes, and diverse window sliding widths. Our explorations demonstrated improved recognition performances even in the presence of missing data. The achieved results provide persuasive findings on sensor-based activity recognition in the presence of missing data.

Connecting the dots in databases of endangered species: a Bayesian hierarchical imputation strategy for missing Peary caribou (Rangifer tarandus pearyi) population data

Peary caribou (Rangifer tarandus pearyi) is listed as Endangered under Canada's Species at Risk Act. Studying rare and endangered species can be particularly challenging due to the constraints posed by incomplete datasets owing to poor weather conditions, lack of technology, organizational deficiencies, and high survey costs in remote areas. A defensible way to remedy data gaps and thus improve the robustness of any given modelling exercise is through imputation schemes, which are typically selected on the basis of missing data patterns. In this study, we develop a regression-based imputation method aiming to identify the linkages among available population records in time and space. One of the fundamental assumptions of our imputation model is that there is a subset of primary islands that act as the core areas from where the Peary caribou populations migrate to secondary or satellite islands. Specifically, we delineated six distinct geographic clusters (Banks, Axel Heiberg, Bathurst, Boothia, Melville and Mackenzie King) across the Canadian Arctic Archipelago, and within each cluster Peary caribou seasonal inter-island movements take place in any given year. Parameterized with Bayesian inference techniques, our imputation modelling strategy has a flexible structure that can accommodate non-monotonic spatiotemporal patterns, and was thus able to capture more than 65% of the variability in our dataset. The two islands (Banks and Northwest Victoria) of the Banks island complex exhibited an average decline rate of 6% per year over the past four decades, which collectively reflects the dramatic population decrease from the early 1970s until the late 1990s, as well as the distinct recovery after the early 2000s. Similar “wax-and-wane” cycles characterize the Peary caribou population patterns on Melville and Bathurst island complexes. Our analysis provides evidence of positive rate of change of the population trends on Axel Heiberg and Ellesmere Islands, which likely stems from pockets on those islands, where favorable climatic/geomorphological conditions, and rich vegetation prevail. In stark contrast, overharvesting, higher predation, adverse climatic conditions, and human-induced forage shortage have led to a dramatic decline, nearing their extirpation, on the Boothia island complex.

Semiparametric model for regression analysis with nonmonotone missing data

Semiparametric likelihoods for regression models with missing at random data (Chen in J Am Stat Assoc 99:1176–1189, 2004, Zhang and Rockette in J Stat Comput Simul 77(2):163–173, 2007, Zhao et al. in Biom J 51: 123–136, 2009, Zhao in Commun Stat Theory Methods 38:3736–3744, 2009) are robust as they use nonparametric models for covariate distributions and do not require modeling the missing data probabilities. Furthermore, the EM algorithms based on the semiparametric likelihoods have closed form expressions for both E-step and M-step. As far as we know the semiparametric likelihoods can only deal with the simple monotone missing data pattern. In this research we extend the semiparemetric likelihood approach to deal with regression models with arbitrary nonmonotone missing at random data. We propose a pseudo-likelihood model, which uses an empirical distribution to model the conditional distribution of missing covariates given observed covariates for each missing data pattern separately. We show that an EM algorithm with closed form updating formulas can be used for computing maximum pseudo-likelihood estimates for regression models with nonmonotone missing data. We then propose estimating the asymptotic variance of the maximum pseudo-likelihood estimator through a profile log likelihood and the EM algorithm. We examine the finite sample performance of the new methods in simulation studies and further illustrate the methods in a real data example investigating high risk gambling behavior and the associated factors.

A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation.

Identifying disease‐associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high‐throughput methylation profiles at single‐base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long‐range correlations, data errors, and confounding from cell type mixtures. We propose a regression‐based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.

S12. A MACHINE LEARNING FRAMEWORK FOR ROBUST AND RELIABLE PREDICTION OF SHORT- AND LONG-TERM CLINICAL RESPONSE IN INITIALLY ANTIPSYCHOTIC-NAïVE SCHIZOPHRENIA PATIENTS BASED ON MULTIMODAL NEUROPSYCHIATRIC DATA

BackgroundThe treatment response of patients with schizophrenia is heterogeneous, and markers of clinical response are missing. Studies using machine learning approaches have provided encouraging results regarding prediction of outcomes, but replicability has been challenging. In the present study, we present a novel methodological framework for applying machine learning to clinical data. Herein, algorithm selection and other methodological choices were based on model performance on a simulated dataset, to minimize bias and avoid overfitting. We subsequently applied the best performing machine learning algorithm to a rich, multimodal neuropsychiatric dataset. We aimed to 1) classify patients from controls, 2) predict short- and long-term clinical response in a sample of initially antipsychotic-naïve first-episode schizophrenia patients, and 3) validate our methodological framework.MethodsWe included data from 138 antipsychotic-naïve, first-episode schizophrenia patients, who had undergone assessments of psychopathology, cognition, electrophysiology, structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Baseline diagnostic classification algorithms also included data from 151 matched healthy controls.Short-term treatment response was defined as change in psychopathology after the initial antipsychotic treatment period. Long-term treatment response (4–16 years) was based on data from Danish registers. The simulated dataset was generated to resemble the real data with respect to dimensionality, multimodality, and pattern of missing data. Noise levels were tunable to enable approximation to the signal-to-noise ratio in the real data. Robustness of the results was ensured by running two parallel, fundamentally different machine learning pipelines, a ‘single algorithm approach’ and an ‘ensemble approach’. Both pipelines included nested cross-validation, missing data imputation, and late integration.ResultsWe significantly classified patients from controls with a balanced accuracy of 64.2% (95% CI = [51.7, 76.7]) for the single algorithm approach and 63.1% (95% CI = [50.4, 75.8]) for the ensemble approach. Post hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- and long-term clinical response. To validate our methodological framework based on simulated data, we selected the best, a medium, and the most poorly performing algorithm on the simulated data and applied them to the real data. We found that the ranking of the algorithms was kept in the real data.DiscussionOur rigorous modelling framework incorporating simulated data and parallel pipelines discriminated patients from controls, but our extensive, multimodal neuropsychiatric data from antipsychotic-naïve schizophrenia patients were not predictive of the clinical outcome. Nevertheless, our novel approach holds promise as an important step to obtain reliable, unbiased results with modest sample sizes when independent replication samples are not available.

A tractable method to account for high-dimensional nonignorable missing data in intensive longitudinal data.

Despite the need for sensitivity analysis to nonignorable missingness in intensive longitudinal data (ILD), such analysis is greatly hindered by novel ILD features, such as large data volume and complex nonmonotonic missing-data patterns. Likelihood of alternative models permitting nonignorable missingness often involves very high-dimensional integrals, causing curse of dimensionality and rendering solutions computationally prohibitive to obtain. We aim to overcome this challenge by developing a computationally feasible method, nonlinear indexes of local sensitivity to nonignorability (NISNI). We use linear mixed effects models for the incomplete outcome and covariates. We use Markov multinomial models to describe complex missing-data patterns and mechanisms in ILD, thereby permitting missingness probabilities to depend directly on missing data. Using a second-order Taylor series to approximate likelihood under nonignorability, we develop formulas and closed-form expressions for NISNI. Our approach permits the outcome and covariates to be missing simultaneously, as is often the case in ILD, and can capture U-shaped impact of nonignorability in the neighborhood of the missing at random model without fitting alternative models or evaluating integrals. We evaluate performance of this method using simulated data and real ILD collected by the ecological momentary assessment method.

Transcriptome Ortholog Alignment Sequence Tools (TOAST) for phylogenomic dataset assembly

BackgroundAdvances in next-generation sequencing technologies have reduced the cost of whole transcriptome analyses, allowing characterization of non-model species at unprecedented levels. The rapid pace of transcriptomic sequencing has driven the public accumulation of a wealth of data for phylogenomic analyses, however lack of tools aimed towards phylogeneticists to efficiently identify orthologous sequences currently hinders effective harnessing of this resource.ResultsWe introduce TOAST, an open source R software package that can utilize the ortholog searches based on the software Benchmarking Universal Single-Copy Orthologs (BUSCO) to assemble multiple sequence alignments of orthologous loci from transcriptomes for any group of organisms. By streamlining search, query, and alignment, TOAST automates the generation of locus and concatenated alignments, and also presents a series of outputs from which users can not only explore missing data patterns across their alignments, but also reassemble alignments based on user-defined acceptable missing data levels for a given research question.ConclusionsTOAST provides a comprehensive set of tools for assembly of sequence alignments of orthologs for comparative transcriptomic and phylogenomic studies. This software empowers easy assembly of public and novel sequences for any target database of candidate orthologs, and fills a critically needed niche for tools that enable quantification and testing of the impact of missing data. As open-source software, TOAST is fully customizable for integration into existing or novel custom informatic pipelines for phylogenomic inference.Software, a detailed manual, and example data files are available through github carolinafishes.github.io

Missing Air Pollution Data Recovery Based on Long-Short Term Context Encoder

Air pollution has become a global challenge, and obtaining real-time air quality information is urgently needed. Although the governments have been trying their best in delivering accurate air quality reports, missing air pollution data remains a key challenge. Based on the temporal-spatial correlation of the data, we propose a novel long-short term context encoder (LSCE) structure for recovering missing air pollution data. The original context encoder approach based on image completion focuses on reconstructing rectangular missing regions. Differing from traditional methods, our fully convolutional neural network architecture enjoys the following novelties. First, LSCE can recover irregular missing data patterns. Second, we devise two data pre-processing strategies to produce two types of context encoders, namely, the long-short term cutting context encoder (LSCCE) and the long-short term sliding context encoder (LSSCE). Compared with LSCCE, LSSCE increases the number of training data matrixes. Finally, we investigate the significance of adaptive training in addressing different types of missing data. Our simulation results have demonstrated that our approach, especially, LSSCE, can outperform existing missing data recovery methods. Besides, our techniques can be widely applicable for recovering other temporally and spatially correlated missing data, such as vehicular traffic or meteorology data.

Statistical Guidelines for Handling Missing Data in Traumatic Brain Injury Clinical Research.

Missing data is a persistent and unavoidable problem in even the most carefully designed traumatic brain injury (TBI) clinical research. Missing data patterns may result from participant dropout, non-compliance, technical issues, or even death. This review describes the types of missing data that are common in TBI research, and assesses the strengths and weaknesses of the statistical approaches used to draw conclusions and make clinical decisions from these data. We review recent innovations in missing values analysis (MVA), a relatively new branch of statistics, as applied to clinical TBI data. Our discussion focuses on studies from the International Traumatic Brain Injury Research (InTBIR) initiative project: Transforming Research and Clinical Knowledge in TBI (TRACK-TBI), Collaborative Research on Acute TBI in Intensive Care Medicine in Europe (CREACTIVE), and Approaches and Decisions in Acute Pediatric TBI Trial (ADAPT). In addition, using data from the TRACK-TBI pilot study (n = 586) and the completed clinical trial assessing valproate (VPA) for the treatment of post-traumatic epilepsy (n = 379) we present real-world examples of typical missing data patterns and the application of statistical techniques to mitigate the impact of missing data in order to draw sound conclusions from ongoing clinical studies.

A portmanteau test for serial correlation in a linear panel model

We introduce the command xtserialpm to perform the portmanteau test developed in Jochmans (2019, Cambridge Working Papers in Economics No. 1993, University of Cambridge, Faculty of Economics). The procedure tests for serial correlation of arbitrary form in the errors of a linear panel model after estimation of the regression coefficients by the within-group estimator. The test is designed for short panels and can deal with general missing-data patterns. The test is different from the related portmanteau test of Inoue and Solon (2006, Econometric Theory 22: 835–851), which is performed by xtistest (Wursten, 2018, Stata Journal 18: 76–100), in that it allows for heteroskedasticity. In simulations documented below, xtserialpm is found to provide a more powerful test than xthrtest (Wursten 2018), which performs the test for first-order autocorrelation of Born and Breitung (2016, Econometric Reviews 35: 1290–1316). We also provide comparisons with xtistest and xtserial (Drukker, 2003, Stata Journal 3: 168–177). These tests perform well under stationarity but break down under even mild forms of heteroskedasticity.