Analysis of Smoking Trends with Incomplete Longitudinal Binary Responses

Modeling post-holiday surge in COVID-19 cases in Pennsylvania counties

COVID-19 arrived in the United States in early 2020, with cases quickly being reported in many states including Pennsylvania. Many statistical models have been proposed to understand the trends of the COVID-19 pandemic and factors associated with increasing cases. While Poisson regression is a natural choice to model case counts, this approach fails to account for correlation due to spatial locations. Being a contagious disease and often spreading through community infections, the number of COVID-19 cases are inevitably spatially correlated as locations neighboring counties with a high COVID-19 case count are more likely to have a high case count. In this analysis, we combine generalized estimating equations (GEEs) for Poisson regression, a popular method for analyzing correlated data, with a semivariogram to model daily COVID-19 case counts in 67 Pennsylvania counties between March 20, 2020 to January 23, 2021 in order to study infection dynamics during the beginning of the pandemic. We use a semivariogram that describes the spatial correlation as a function of the distance between two counties as the working correlation. We further incorporate a zero-inflated model in our spatial GEE to accommodate excess zeros in reported cases due to logistical challenges associated with disease monitoring. By modeling time-varying holiday covariates, we estimated the effect of holiday timing on case count. Our analysis showed that the incidence rate ratio was significantly greater than one, 6-8 days after a holiday suggesting a surge in COVID-19 cases approximately one week after a holiday.

Passion for an activity: a new predictor of sleep quality

STUDY OBJECTIVES

The present research examines the relationship between people's frequent involvement in an activity they like and find important (i.e., a passion) and the quality of their sleep. Research on the dualistic model of passion has widely documented the relationship between individuals' type of passion-harmonious versus obsessive-and the quality of their mental and physical health. However, research has yet to examine the relationship between passion and sleep quality. Building on prior research has shown that obsessive (vs harmonious) passion is related to depressive mood symptoms-an important factor associated with sleep problems-we hypothesized that obsessive passion would be associated with overall worse sleep quality, whereas harmonious passion would predict better sleep quality.

METHODS

A sample of 1,506 Americans filled out an online questionnaire on sleep habits and passion. Sleep quality was measured using the Pittsburgh Sleep Quality Index. Hierarchical linear regressions and mediation analyses were carried out with results confirming our hypotheses.

RESULTS

Obsessive passion for an activity was associated with worse sleep quality, whereas harmonious passion was associated with better sleep quality, adjusting for demographics, the type of passionate activity and its self-reported importance, alcohol and tobacco consumption, BMI, self-reported health, and diagnosed health conditions. The relationship between both types of passion and sleep quality was mediated by depressive mood symptoms.

CONCLUSIONS

Our study presents evidence of a strong relationship between sleep quality and passion, opening the door for future research to create new interventions to improve people's sleep and, consequently, their well-being.

Linear Increments with Non-monotone Missing Data and Measurement Error

Linear increments (LI) are used to analyse repeated outcome data with missing values. Previously, two LI methods have been proposed, one allowing non‐monotone missingness but not independent measurement error and one allowing independent measurement error but only monotone missingness. In both, it was suggested that the expected increment could depend on current outcome. We show that LI can allow non‐monotone missingness and either independent measurement error of unknown variance or dependence of expected increment on current outcome but not both. A popular alternative to LI is a multivariate normal model ignoring the missingness pattern. This gives consistent estimation when data are normally distributed and missing at random (MAR). We clarify the relation between MAR and the assumptions of LI and show that for continuous outcomes multivariate normal estimators are also consistent under (non‐MAR and non‐normal) assumptions not much stronger than those of LI. Moreover, when missingness is non‐monotone, they are typically more efficient.

Robust inference for mixed censored and binary response models with missing covariates

In biomedical and epidemiological studies, often outcomes obtained are of mixed discrete and continuous in nature. Furthermore, due to some technical inconvenience or else, continuous responses are censored and also a few covariates cease to be observed completely. In this paper, we develop a model to tackle these complex situations. Our methodology is developed in a more general framework and provides a full-scale robust analysis of such complex models. The proposed robust maximum likelihood estimators of the model parameters are resistant to potential outliers in the data. We discuss the asymptotic properties of the robust estimators. To avoid computational difficulties involving irreducibly high-dimensional integrals, we propose a Monte Carlo method based on the Metropolis algorithm for approximating the robust maximum likelihood estimators. We study the empirical properties of these estimators in simulations. We also illustrate the proposed robust method using clustered data on blood sugar content from a clinical trial of individuals who were investigated for diabetes.

A novel application of the Intent to Attend assessment to reduce bias due to missing data in a randomized controlled clinical trial

BACKGROUND

Missing data are unavoidable in most randomized controlled clinical trials, especially when measurements are taken repeatedly. If strong assumptions about the missing data are not accurate, crude statistical analyses are biased and can lead to false inferences. Furthermore, if we fail to measure all predictors of missing data, we may not be able to model the missing data process sufficiently. In longitudinal randomized trials, measuring a patient's intent to attend future study visits may help to address both of these problems. Leon et al. developed and included the Intent to Attend assessment in the Lithium Treatment - Moderate dose Use Study (LiTMUS), aiming to remove bias due to missing data from the primary study hypothesis.

PURPOSE

The purpose of this study is to assess the performance of the Intent to Attend assessment with regard to its use in a sensitivity analysis of missing data.

METHODS

We fit marginal models to assess whether a patient's self-rated intent predicted actual study adherence. We applied inverse probability of attrition weighting (IPAW) coupled with patient intent to assess whether there existed treatment group differences in response over time. We compared the IPAW results to those obtained using other methods.

RESULTS

Patient-rated intent predicted missed study visits, even when adjusting for other predictors of missing data. On average, the hazard of retention increased by 19% for every one-point increase in intent. We also found that more severe mania, male gender, and a previously missed visit predicted subsequent absence. Although we found no difference in response between the randomized treatment groups, IPAW increased the estimated group difference over time.

LIMITATIONS

LiTMUS was designed to limit missed study visits, which may have attenuated the effects of adjusting for missing data. Additionally, IPAW can be less efficient and less powerful than maximum likelihood or Bayesian estimators, given that the parametric model is well specified.

CONCLUSIONS

In LiTMUS, the Intent to Attend assessment predicted missed study visits. This item was incorporated into our IPAW models and helped reduce bias due to informative missing data. This analysis should both encourage and facilitate future use of the Intent to Attend assessment along with IPAW to address missing data in a randomized trial.

Handling drop-out in longitudinal clinical trials: a comparison of the LOCF and MMRM approaches

This study compares two methods for handling missing data in longitudinal trials: one using the last-observation-carried-forward (LOCF) method and one based on a multivariate or mixed model for repeated measurements (MMRM). Using data sets simulated to match six actual trials, I imposed several drop-out mechanisms, and compared the methods in terms of bias in the treatment difference and power of the treatment comparison. With equal drop-out in Active and Placebo arms, LOCF generally underestimated the treatment effect; but with unequal drop-out, bias could be much larger and in either direction. In contrast, bias with the MMRM method was much smaller; and whereas MMRM rarely caused a difference in power of greater than 20%, LOCF caused a difference in power of greater than 20% in nearly half the simulations. Use of the LOCF method is therefore likely to misrepresent the results of a trial seriously, and so is not a good choice for primary analysis. In contrast, the MMRM method is unlikely to result in serious misinterpretation, unless the drop-out mechanism is missing not at random (MNAR) and there is substantially unequal drop-out. Moreover, MMRM is clearly more reliable and better grounded statistically. Neither method is capable of dealing on its own with trials involving MNAR drop-out mechanisms, for which sensitivity analysis is needed using more complex methods.

Genetic modifiers affecting severity of epilepsy caused by mutation of sodium channelScn2a

Mutations in the voltage-gated sodium channels SCN 1 A and SCN 2 A are responsible for several types of human epilepsy. Variable expressivity among family members is a common feature of these inherited epilepsies, suggesting that genetic modifiers may influence the clinical manifestation of epilepsy. The transgenic mouse model Scn 2 a(Q 54) has an epilepsy phenotype as a result of a mutation in Scn 2 a that slows channel inactivation. The mice display progressive epilepsy that begins with short-duration partial seizures that appear to originate in the hippocampus. The partial seizures become more frequent and of longer duration with age and often induce secondary generalized seizures. Clinical severity of the Scn 2 a(Q 54) phenotype is influenced by genetic background. Congenic C57BL/6J.Q 54 mice exhibit decreased incidence of spontaneous seizures, delayed seizure onset, and longer survival in comparison with [C57BL/6J x SJL/J]F(1).Q 54 mice. This observation indicates that strain SJL/J carries dominant modifier alleles at one or more loci that determine the severity of the epilepsy phenotype. Genome-wide interval mapping in an N(2) backcross revealed two modifier loci on Chromosomes 11 and 19 that influence the clinical severity of of this sodium channel-induced epilepsy. Modifier genes affecting clinical severity in the Scn 2 a(Q 54) mouse model may contribute to the variable expressivity seen in epilepsy patients with sodium channel mutations.

The use of weights to account for non-response and drop-out

BACKGROUND

Empirical studies in psychiatric research and other fields often show substantially high refusal and drop-out rates. Non-participation and drop-out may introduce a bias whose magnitude depends on how strongly its determinants are related to the respective parameter of interest.

METHODS

When most information is missing, the standard approach is to estimate each respondent's probability of participating and assign each respondent a weight that is inversely proportional to this probability. This paper contains a review of the major ideas and principles regarding the computation of statistical weights and the analysis of weighted data.

RESULTS

A short software review for weighted data is provided and the use of statistical weights is illustrated through data from the EDSP (Early Developmental Stages of Psychopathology) Study. The results show that disregarding different sampling and response probabilities can have a major impact on estimated odds ratios.

CONCLUSIONS

The benefit of using statistical weights in reducing sampling bias should be balanced against increased variances in the weighted parameter estimates.

Marginalized transition models for longitudinal binary data with ignorable and non-ignorable drop-out

We extend the marginalized transition model of Heagerty to accommodate non-ignorable monotone drop-out. Using a selection model, weakly identified drop-out parameters are held constant and their effects evaluated through sensitivity analysis. For data missing at random (MAR), efficiency of inverse probability of censoring weighted generalized estimating equations (IPCW-GEE) is as low as 40 per cent compared to a likelihood-based marginalized transition model (MTM) with comparable modelling burden. MTM and IPCW-GEE regression parameters both display misspecification bias for MAR and non-ignorable missing data, and both reduce bias noticeably by improving model fit.

Comparing longitudinal binary outcomes in an observational oral health study

Observational studies continue to be recognized as viable alternatives to randomized trials when making treatment group comparisons, in spite of drawbacks due mainly to selection bias. Sample selection models have been proposed in the economics literature, and more recently in the medical literature, as a method to adjust for selection bias due to observed and unobserved confounders in observational studies. Application of these models has been limited to cross-sectional observational data and to outcomes that are continuous in nature. In this paper we extend application of these models to include longitudinal studies and binary outcomes. We apply a two-stage probit model using GEE to account for correlated longitudinal binary chewing difficulty outcomes. Chewing difficulty was measured every six months during a 24-month period between two groups of subjects: those either receiving or not receiving dental care. Dental care use was measured at six-month intervals as well. Results from our proposed model are compared to results using a standard GEE model that ignores the potential selection bias introduced by unobserved confounders. In this application, accounting for selection bias made a major difference in the substantive conclusions about the outcomes of interest. This is due in part to an adverse selection phenomenon in which those most in need of treatment (and consequently most likely to benefit from it) are actually the ones least likely to seek treatment. Our application of sample selection models to binary longitudinal observational outcome data should serve as impetus for increased utilization of this promising set of models to other health outcomes studies.

Genetic loci modulating fitness and life span in Caenorhabditis elegans: categorical trait interval mapping in CL2a x Bergerac-BO recombinant-inbred worms

Quantitative trait loci (QTL) can implicate an unbiased sampling of genes underlying a complex, polygenic phenotype. QTL affecting longevity in Caenorhabditis elegans were mapped using a CL2a x Bergerac-BO recombinant-inbred population. Genotypes were compared at 30 transposon-specific markers for two paired sample sets totaling 171 young controls and 172 longevity-selected worms (the last-surviving 1%) from a synchronously aged population. A third sample set, totaling 161 worms from an independent culture, was analyzed for confirmation of loci. At least six highly significant QTL affecting life span were detected both by single-marker (chi(2)) analysis and by two interval-mapping procedures--one intended for nonparametric traits and another developed specifically for mapping of categorical traits. These life-span QTL were located on chromosomes I (near the hP4 locus), III (near stP127), IV (near stP44), V (a cluster of three peaks, near stP192, stP23, and stP6), and X (two distinct peaks, near stP129 and stP2). Epistatic effects on longevity were also analyzed by Fisher's exact test, which indicated a significant life-span interaction between markers on chromosomes V (stP128) and III (stP127). Several further interactions were significant in the initial unselected population; two of these, between distal loci on chromosome V, were completely eliminated in the long-lived subset. Allelic longevity effects for two QTL, on chromosomes IV and V, were confirmed in backcrossed congenic lines and were highly significant in two very different environments-growth on solid agar medium and in liquid suspension culture.

Performance of weighted estimating equations for longitudinal binary data with drop-outs missing at random

The generalized estimating equations (GEE) approach is commonly used to model incomplete longitudinal binary data. When drop-outs are missing at random through dependence on observed responses (MAR), GEE may give biased parameter estimates in the model for the marginal means. A weighted estimating equations approach gives consistent estimation under MAR when the drop-out mechanism is correctly specified. In this approach, observations or person-visits are weighted inversely proportional to their probability of being observed. Using a simulation study, we compare the performance of unweighted and weighted GEE in models for time-specific means of a repeated binary response with MAR drop-outs. Weighted GEE resulted in smaller finite sample bias than GEE. However, when the drop-out model was misspecified, weighted GEE sometimes performed worse than GEE. Weighted GEE with observation-level weights gave more efficient estimates than a weighted GEE procedure with cluster-level weights.