Binary Exposure Variables Research Articles

Increased Postoperative Opioid Consumption in the Presence of Coadministration of 5-Hydroxytryptamine Type 3 Antagonists with Acetaminophen: A Hospital Registry Study.

Acetaminophen and 5-hydroxytryptamine type 3 (5-HT3) receptor antagonists are administered as standard prophylaxes for postoperative pain, nausea, and vomiting. Preclinical studies, however, suggest that 5-HT3 antagonists may compromise acetaminophen's analgesic effect. This hospital registry study investigates whether 5-HT3 antagonists mitigate the analgesic effect of prophylactic acetaminophen in a perioperative setting. This study included 55,016 adult patients undergoing general anesthesia for ambulatory procedures at a tertiary healthcare center in Massachusetts from 2015 to 2022. Using binary exposure variables and a comprehensive selection of preplanned patient- and procedure-related covariates for confounder control, the authors investigated whether intraoperative 5-HT3 antagonists affected the association between pre- or intraoperative acetaminophen and postoperative opioid consumption, gauged by opioid dose in milligram oral morphine equivalents (OME) administered in the postanesthesia care unit. A multivariable, zero-inflated negative binomial regression model was applied. A total of 3,166 patients (5.8%) received only acetaminophen, 15,438 (28.1%) only 5-HT3 antagonists, 31,850 (57.9%) both drugs, and 4,562 (8.3%) neither drug. The median postanesthesia care unit opioid dose was 7.5 mg OME (interquartile range, 7.5 to 14.3 mg OME) among 16,640 of 55,016 (30.2%) patients who received opioids, and the mean opioid dose was 3.2 mg OME across all patients (maximum cumulative dose, 20.4 mg OME). Acetaminophen administration was associated with a -5.5% (95% CI, -9.6 to -1.4%; P = 0.009; adjusted absolute difference, -0.19 mg OME; 95% CI, -0.33 to -0.05; P = 0.009) reduction in opioid consumption among patients who did not receive a 5-HT3 antagonist, while there was no effect in patients who received a 5-HT3 antagonist (adjusted absolute difference, 0.00 mg OME; 95% CI, -0.06 to 0.05; P = 0.93; P for interaction = 0.013). A dose-dependent association of pre- or intraoperative acetaminophen with decreased postoperative opioid consumption was not observed when 5-HT3 antagonists were coadministered, suggesting that physicians might consider reserving 5-HT3 antagonists as rescue medication for postoperative nausea or vomiting when acetaminophen is administered for pain prophylaxis.

Tutorial on causal mediation analysis with binary variables: An application to health psychology research.

Mediation analysis has been widely applied to explain why and assess the extent to which an exposure or treatment has an impact on the outcome in health psychology studies. Identifying a mediator or assessing the impact of a mediator has been the focus of many scientific investigations. This tutorial aims to introduce causal mediation analysis with binary exposure, mediator, and outcome variables, with a focus on the resampling and weighting methods, under the potential outcomes framework for estimating natural direct and indirect effects. We emphasize the importance of the temporal order of the study variables and the elimination of confounding. We define the causal effects in a hypothesized causal mediation chain in the context of one exposure, one mediator, and one outcome variable, all of which are binary variables. Two commonly used and actively maintained R packages, mediation and medflex, were used to analyze a motivating example. R code examples for implementing these methods are provided. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The Co-Twin Control Design: Implementation and Methodological Considerations.

Establishing causal relationships in observational studies is an important step in research and policy decision making. The association between an exposure and an outcome can be confounded by multiple factors, often making it hard to draw causal conclusions. The co-twin control design (CTCD) is a powerful approach that allows for the investigation of causal effects while controlling for genetic and shared environmental confounding factors. This article introduces the CTCD and offers an overview of analysis methods for binary and continuous outcome and exposure variables. Tools for data simulation are provided, along with practical guidance and accompanying scripts for implementing the CTCD in R, SPSS, and Stata. While the CTCD offers valuable insights into causal inference, it depends on several assumptions that are important when interpreting CTCD results. By presenting a broad overview of the CTCD, this article aims to equip researchers with actionable recommendations and a comprehensive understanding of the design's strengths and limitations.

Adjusting for misclassification of an exposure in an individual participant data meta-analysis.

A common problem in the analysis of multiple data sources, including individual participant data meta-analysis (IPD-MA), is the misclassification of binary variables. Misclassification may lead to biased estimators of model parameters, even when the misclassification is entirely random. We aimed to develop statistical methods that facilitate unbiased estimation of adjusted and unadjusted exposure-outcome associations and between-study heterogeneity in IPD-MA, where the extent and nature of exposure misclassification may vary across studies. We present Bayesian methods that allow misclassification of binary exposure variables to depend on study- and participant-level characteristics. In an example of the differential diagnosis of dengue using two variables, where the gold standard measurement for the exposure variable was unavailable for some studies which only measured a surrogate prone to misclassification, our methods yielded more accurate estimates than analyses naive with regard to misclassification or based on gold standard measurements alone. In a simulation study, the evaluated misclassification model yielded valid estimates of the exposure-outcome association, and was more accurate than analyses restricted to gold standard measurements. Our proposed framework can appropriately account for the presence of binary exposure misclassification in IPD-MA. It requires that some studies supply IPD for the surrogate and gold standard exposure, and allows misclassification to follow a random effects distribution across studies conditional on observed covariates (and outcome). The proposed methods are most beneficial when few large studies that measured the gold standard are available, and when misclassification is frequent.

On the evolution of concepts of causal and preventive interdependence in epidemiology in the late 20th century.

The 1970s and 1980s saw the appearance of many papers on the topics of synergy, antagonism, and similar concepts of causal interactions and interdependence of effects, with a special emphasis on distinguishing these concepts from that of statistical interaction - the need for a product term in a model. As an example, Miettinen defined "synergism" as "the existence of instances in which both risk factors are needed for the effect", whereas "antagonism" is where "at least one [factor] can block the solo effect of the other". In response, Greenland and Poole constructed a systematic analysis of 16 possible individual response patterns in a deterministic causal model for two binary exposure variables, and showed how these patterns can be mapped onto nine types of sufficient causes, which in turn can be simplified into four intuitive categories. Although these and other papers recognized that epidemiology cannot directly study biological mechanisms underlying interaction, they showed how it can usefully study causal and preventive interdependence - which, despite its mechanistic agnosticism, has important implications for clinical decision making as well as for public health.

Extension of PERMANOVA to Testing the Mediation Effect of the Microbiome.

Recently, we have seen a growing volume of evidence linking the microbiome and human diseases or clinical outcomes, as well as evidence linking the microbiome and environmental exposures. Now comes the time to assess whether the microbiome mediates the effects of exposures on the outcomes, which will enable researchers to develop interventions to modulate outcomes by modifying microbiome compositions. Use of distance matrices is a popular approach to analyzing complex microbiome data that are high-dimensional, sparse, and compositional. However, the existing distance-based methods for mediation analysis of microbiome data, MedTest and MODIMA, only work well in limited scenarios. PERMANOVA is currently the most commonly used distance-based method for testing microbiome associations. Using the idea of inverse regression, here we extend PERMANOVA to test microbiome-mediation effects by including both the exposure and the outcome as covariates and basing the test on the product of their F statistics. This extension of PERMANOVA, which we call PERMANOVA-med, naturally inherits all the flexible features of PERMANOVA, e.g., allowing adjustment of confounders, accommodating continuous, binary, and multivariate exposure and outcome variables including survival outcomes, and providing an omnibus test that combines the results from analyzing multiple distance matrices. Our extensive simulations indicated that PERMANOVA-med always controlled the type I error and had compelling power over MedTest and MODIMA. Frequently, MedTest had diminished power and MODIMA had inflated type I error. Using real data on melanoma immunotherapy response, we demonstrated the wide applicability of PERMANOVA-med through 16 different mediation analyses, only 6 of which could be performed by MedTest and 4 by MODIMA.

Concordance of Occupational Exposure Assessment between the Canadian Job-Exposure Matrix (CANJEM) and Expert Assessment of Jobs Held by Women.

To compare the exposure data generated by using the Canadian job-exposure matrix (CANJEM) with data generated by expert assessment, for jobs held by women. We selected 69 occupational agents that had been assessed by experts for each of 3403 jobs held by 998 women in a population-based case-control study of lung cancer. We then assessed the same agents among the same jobs by linking their occupation codes to CANJEM and thereby derived probability of exposure to each of the agents in each job. To create binary exposure variables, we dichotomized probability of exposure using two cutpoints: 25 and 50% (referred to as CANJEM-25% and CANJEM-50%). Using jobs as units of observation, we estimated the prevalence of exposure to each selected agent using CANJEM-25% and CANJEM-50%, and using expert assessment. Further, using expert assessment as the gold standard, for each agent, we estimated CANJEM's sensitivity, specificity, and kappa. CANJEM-based prevalence estimates correlated well with the prevalences assessed by the experts. When comparing CANJEM-based exposure estimates with expert-based exposure estimates, sensitivity, specificity, and kappa varied greatly among agents, and between CANJEM-25% and CANJEM-50% probability of exposure. With CANJEM-25%, the median sensitivity, specificity, and kappa values were 0.49, 0.99, and 0.46, respectively. Analogously, with CANJEM-50%, the corresponding values were 0.26, 1.00, and 0.35, respectively. For the following agents, we observed high concordance between CANJEM- and expert-based assessments (sensitivity ≥0.70 and specificity ≥0.99): fabric dust, cotton dust, synthetic fibres, cooking fumes, soldering fumes, calcium carbonate, and tin compounds. We present concordance estimates for each of 69 agents. Concordance between CANJEM and expert assessment varied greatly by agents. Our results indicate which agents provide data that mimic best those obtained with expert assessment.

Bounding Causes of Effects With Mediators

Suppose X and Y are binary exposure and outcome variables, and we have full knowledge of the distribution of Y, given application of X. We are interested in assessing whether an outcome in some case is due to the exposure. This “probability of causation” is of interest in comparative historical analysis where scholars use process tracing approaches to learn about causes of outcomes for single units by observing events along a causal path. The probability of causation is typically not identified, but bounds can be placed on it. Here, we provide a full characterization of the bounds that can be achieved in the ideal case that X and Y are connected by a causal chain of complete mediators, and we know the probabilistic structure of the full chain. Our results are largely negative. We show that, even in these very favorable conditions, the gains from positive evidence on mediators is modest.

Propensity score analysis for a semi-continuous exposure variable: a study of gestational alcohol exposure and childhood cognition.

Propensity score methodology has become increasingly popular in recent years as a tool for estimating causal effects in observational studies. Much of the related research has been directed at settings with binary or discrete exposure variables with more recent work involving continuous exposure variables. In environmental epidemiology, a substantial proportion of individuals is often completely unexposed while others may experience heavy exposure leading to an exposure distribution with a point mass at zero and a heavy right tail. We suggest a new approach to handle this type of exposure data by constructing a propensity score based on a two-part model and show how this model can be used to more reliably adjust for covariates of a semi-continuous exposure variable. We also consider the case when a misspecified propensity score is used in a regression adjustment and derive an explicit form of the bias. We show that the potential bias gets smaller as the estimated propensity score gets closer to the true expectation of the exposure variable given a set of observed covariates. While this result pertains to a more general setting, we use it to evaluate the potential bias in settings in which the true exposure has a semi-continuous structure. We also evaluate and compare the performance of our proposed method through simulation studies relative to a simpler linear regression-based propensity score for a continuous exposure variable as well as through direct covariate adjustment. Overall, we find that using a propensity score constructed via a two-part model significantly improves the regression estimate when the exposure variable is semi-continuous in nature. Specifically when the proportion of non-exposed subjects is high and the effects of covariates on exposure and outcome are strong, the proposed two-part propensity score method outperforms the more standard competing methods. We illustrate our method using data from the Detroit Longitudinal Cohort Study in which the exposure variable reflects gestational alcohol exposure featuring zero values and a long tail.

A flexible hierarchical framework for improving inference in area-referenced environmental health studies.

Study designs where data have been aggregated by geographical areas are popular in environmental epidemiology. These studies are commonly based on administrative databases and, providing a complete spatial coverage, are particularly appealing to make inference on the entire population. However, the resulting estimates are often biased and difficult to interpret due to unmeasured confounders, which typically are not available from routinely collected data. We propose a framework to improve inference drawn from such studies exploiting information derived from individual-level survey data. The latter are summarized in an area-level scalar score by mimicking at ecological level the well-known propensity score methodology. The literature on propensity score for confounding adjustment is mainly based on individual-level studies and assumes a binary exposure variable. Here, we generalize its use to cope with area-referenced studies characterized by a continuous exposure. Our approach is based upon Bayesian hierarchical structures specified into a two-stage design: (i) geolocated individual-level data from survey samples are up-scaled at ecological level, then the latter are used to estimate a generalized ecological propensity score (EPS) in the in-sample areas; (ii) the generalized EPS is imputed in the out-of-sample areas under different assumptions about the missingness mechanisms, then it is included into the ecological regression, linking the exposure of interest to the health outcome. This delivers area-level risk estimates, which allow a fuller adjustment for confounding than traditional areal studies. The methodology is illustrated by using simulations and a case study investigating the risk of lung cancer mortality associated with nitrogen dioxide in England (UK).

Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates

Mendelian randomization uses genetic variants to make causal inferences about a modifiable exposure. Subject to a genetic variant satisfying the instrumental variable assumptions, an association between the variant and outcome implies a causal effect of the exposure on the outcome. Complications arise with a binary exposure that is a dichotomization of a continuous risk factor (for example, hypertension is a dichotomization of blood pressure). This can lead to violation of the exclusion restriction assumption: the genetic variant can influence the outcome via the continuous risk factor even if the binary exposure does not change. Provided the instrumental variable assumptions are satisfied for the underlying continuous risk factor, causal inferences for the binary exposure are valid for the continuous risk factor. Causal estimates for the binary exposure assume the causal effect is a stepwise function at the point of dichotomization. Even then, estimation requires further parametric assumptions. Under monotonicity, the causal estimate represents the average causal effect in ‘compliers’, individuals for whom the binary exposure would be present if they have the genetic variant and absent otherwise. Unlike in randomized trials, genetic compliers are unlikely to be a large or representative subgroup of the population. Under homogeneity, the causal effect of the exposure on the outcome is assumed constant in all individuals; rarely a plausible assumption. We here provide methods for causal estimation with a binary exposure (although subject to all the above caveats). Mendelian randomization investigations with a dichotomized binary exposure should be conceptualized in terms of an underlying continuous variable.

Negative control exposure studies in the presence of measurement error: implications for attempted effect estimate calibration.

BackgroundNegative control exposure studies are increasingly being used in epidemiological studies to strengthen causal inference regarding an exposure-outcome association when unobserved confounding is thought to be present. Negative control exposure studies contrast the magnitude of association of the negative control, which has no causal effect on the outcome but is associated with the unmeasured confounders in the same way as the exposure, with the magnitude of the association of the exposure with the outcome. A markedly larger effect of the exposure on the outcome than the negative control on the outcome strengthens inference that the exposure has a causal effect on the outcome.MethodsWe investigate the effect of measurement error in the exposure and negative control variables on the results obtained from a negative control exposure study. We do this in models with continuous and binary exposure and negative control variables using analysis of the bias of the estimated coefficients and Monte Carlo simulations.ResultsOur results show that measurement error in either the exposure or negative control variables can bias the estimated results from the negative control exposure study.ConclusionsMeasurement error is common in the variables used in epidemiological studies; these results show that negative control exposure studies cannot be used to precisely determine the size of the effect of the exposure variable, or adequately adjust for unobserved confounding; however, they can be used as part of a body of evidence to aid inference as to whether a causal effect of the exposure on the outcome is present.

Antepartum complications and perinatal mortality in rural Bangladesh

BackgroundDespite impressive improvements in maternal survival throughout the world, rates of antepartum complications remain high. These conditions also contribute to high rates of perinatal deaths, which include stillbirths and early neonatal deaths, but the extent is not well studied. This study examines patterns of antepartum complications and the risk of perinatal deaths associated with such complications in rural Bangladesh.MethodsWe used data on self-reported antepartum complications during the last pregnancy and corresponding pregnancy outcomes from a household survey (N = 6,285 women) conducted in Sylhet district, Bangladesh in 2006. We created three binary outcome variables (stillbirths, early neonatal deaths, and perinatal deaths) and three binary exposure variables indicating antepartum complications, which were antepartum hemorrhage (APH), probable infection (PI), and probable pregnancy-induced hypertension (PIH). We then examined patterns of antepartum complications and calculated incidence rate ratios (IRR) to estimate the associated risks of perinatal mortality using Poisson regression analyses. We calculated population attributable fraction (PAF) for the three antepartum complications to estimate potential risk reductions of perinatal mortality associated them.ResultsWe identified 356 perinatal deaths (195 stillbirths and 161 early neonatal deaths). The highest risk of perinatal death was associated with APH (IRR = 3.5, 95% CI: 2.4–4.9 for perinatal deaths; IRR = 3.7, 95% CI 2.3–5.9 for stillbirths; IRR = 3.5, 95% CI 2.0–6.1 for early neonatal deaths). Pregnancy-induced hypertension was a significant risk factor for stillbirths (IRR = 1.8, 95% CI 1.3–2.5), while PI was a significant risk factor for early neonatal deaths (IRR = 1.5, 95% CI 1.1–2.2). Population attributable fraction of APH and PIH were 6.8% and 10.4% for perinatal mortality and 7.5% and 14.7% for stillbirths respectively. Population attributable fraction of early neonatal mortality due to APH was 6.2% and for PI was 7.8%.ConclusionsIdentifying antepartum complications and ensuring access to adequate care for those complications are one of the key strategies in reducing perinatal mortality in settings where most deliveries occur at home.

Assessing natural direct and indirect effects for a continuous exposure and a dichotomous outcome.

Recent advances in the literature on mediation have extended from traditional linear structural equation modeling approach to causal mediation analysis using potential outcomes framework. Pearl proposed a mediation formula to calculate expected potential outcomes used in the natural direct and indirect effects definition under the key sequential ignorability assumptions. Current methods mainly focused on binary exposure variables, and in this article, this approach is further extended to settings in which continuous exposures may be of interest. Focusing on a dichotomous outcome, we give precise definitions of the natural direct and indirect effects on both the risk difference and odds ratio scales utilizing the empirical joint distribution of the exposure and baseline covariates from the whole sample analysis population. A mediation-formula based approach is proposed to estimate the corresponding causal quantities. Simulation study is conducted to assess the statistical properties of the proposed method and we illustrate our approach by applying it to the Jackson Heart Study to estimate the mediation effects of diabetes on the relation between obesity and chronic kidney disease. Sensitivity analysis is performed to assess the impact of violation of no unmeasured mediator-outcome confounder assumption.

Prevalence, correlates and pattern of Hepatitis B among antenatal clinic attenders in Yaounde-Cameroon: is perinatal transmission of HBV neglected in Cameroon?

BackgroundFew studies have evaluated the prevalence of HBV in the general Cameroonian population or among antenatal attendants. The aim of this study was to determine the prevalence, correlates and patterns of Hepatitis B surface antigen among pregnant women attending antenatal care in Yaounde-Cameroon.MethodsThis was a cross-sectional multicenter study carried out in a referral hospital and two secondary hospitals in Yaounde, the capital of Cameroon. The study lasted 15 months (March 2011 to June 2012), and recruited 959 pregnant women. Patient recruitment was consecutive. The HBsAg was tested using the Monalisa HBsAg Ultra ELISA kit. Other hepatitis B markers were equally tested.We used the statistical package for social sciences (SPSS) version 14.0 software to conduct a quantitative analysis of the derived data. Simple descriptive statistics such as means, standard deviations, and proportions were used to describe the data. We tested for association in categorical variables using the chi-squared (χ2) test. The odds ratio (OR) and the corresponding 95% confidence intervals (95% CI) were used to summarise the strength of association between specific binary exposure and outcome variables. The level of statistical significance for the study was set at p < 0.05.ResultsThe prevalence of hepatitis B infection (HBsAg) among antenatal clinic attenders in our setting was 7.7%. Amongst these women, just 5.4% were previously aware of their HBsAg status. The rate of HBV infectivity was high, with 28% of HBsAg positive women having evidence of HBeAg in their plasma, and up to 45.8% of these women lacking antibodies against hepatitis B e antigen (anti-HBe). About 41% of the pregnant women had had previous contact with HBV as evidenced by the positive status for anti-HBc.Just 2.7% of the pregnant women had previously been vaccinated against HBV. The mean age for HBsAg positivity in our setting was 26.9 ±4.7 years, and the most affected age group was the 25 – 29 years age group. There was no statistically significant association between age or other socio-demographic risk factors and HBsAg status. Numerous risk factors for HBV acquisition exists in our settings, but amongst these, only a history of a contact with hepatitis B infection was found to be significantly associated with HBsAg positivity (OR 1.63, 95% C.I 1.15-2.30). Finally, the coinfection rate of HBV/HIV was 0.74%.ConclusionThe prevalence of hepatitis B among pregnant women in Cameroon is high, and the pattern tends towards high infectivity and therefore increased risk of perinatal HBV transmission. These highlight the need to step up preventive efforts against hepatitis B infection and perinatal HBV transmission in our community.

Extended Matrix and Inverse Matrix Methods Utilizing Internal Validation Data When Both Disease and Exposure Status Are Misclassified.

The problem of misclassification is common in epidemiological and clinical research. In some cases, misclassification may be incurred when measuring both exposure and outcome variables. It is well known that validity of analytic results (e.g. point and confidence interval estimates for odds ratios of interest) can be forfeited when no correction effort is made. Therefore, valid and accessible methods with which to deal with these issues remain in high demand. Here, we elucidate extensions of well-studied methods in order to facilitate misclassification adjustment when a binary outcome and binary exposure variable are both subject to misclassification. By formulating generalizations of assumptions underlying well-studied "matrix" and "inverse matrix" methods into the framework of maximum likelihood, our approach allows the flexible modeling of a richer set of misclassification mechanisms when adequate internal validation data are available. The value of our extensions and a strong case for the internal validation design are demonstrated by means of simulations and analysis of bacterial vaginosis and trichomoniasis data from the HIV Epidemiology Research Study.

General Multiple Mediation Analysis With an Application to Explore Racial Disparities in Breast Cancer Survival

Mediation refers to the effect transmitted by mediators that intervenes in the relationship between an exposure and a response variable. Mediation analysis has been broadly studied in many fields. However, it remains a challenge for researchers to differentiate individual effect from multiple mediators. This paper proposes general definitions of mediation effects that are consistent for all different types (categorical or continuous) of response, exposure, or mediation variables. With these definitions, multiple mediators can be considered simultaneously, and the indirect effects carried by individual mediators can be separated from the total effect. Moreover, the derived mediation analysis can be performed with general predictive models. For linear predictive models with continuous mediators, we show that the proposed method is equivalent to the conventional coefficients product method. We also establish the relationship between the proposed definitions of direct or indirect effect and the natural direct or indirect effect for binary exposure variables. The proposed method is demonstrated by both simulations and a real example examining racial disparities in three-year survival rates for female breast cancer patients in Louisiana.

A structured approach to modelling the effects of binary exposure variables over the life course

Background There is growing interest in the relationship between time spent in adverse circumstances across life course and increased risk of chronic disease and early mortality. This accumulation hypothesis is usually tested by summing indicators of binary variables across the life span to form an overall score that is then used as the exposure in regression models for health outcomes. This article highlights potential issues in the interpretation of results obtained from such an approach.Methods We propose a model-building framework that can be used to formally compare alternative hypotheses on the effect of multiple binary exposure measurements collected across the life course. The saturated model where the order and value of the binary variable at each time point influence the outcome of interest is compared with nested alternative specifications corresponding to the critical period, cumulative risk or hypotheses about the effect of changes in environment. This framework is illustrated with data on adult body mass index and socioeconomic position measured once in childhood and twice in adulthood from the Medical Research Council National Survey of Health and Development, using a series of liner regression models.Results We demonstrate how analyses that only consider the association of a cumulative score with a later outcome may produce misleading results.Conclusion We recommend comparing a set of nested models—each corresponding to the accumulation, critical period and effect modification hypotheses—to an all-inclusive (saturated) model. This approach can provide a formal and clearer understanding of the relative merits of these alternative hypotheses.

Bayesian analysis of misclassified binary data from a matched case-control study with a validation sub-study

Bayesian methods are proposed for analysing matched case-control studies in which a binary exposure variable is sometimes measured with error, but whose correct values have been validated for a random sample of the matched case-control sets. Three models are considered. Model 1 makes few assumptions other than randomness and independence between matched sets, while Models 2 and 3 are logistic models, with Model 3 making additional distributional assumptions about the variation between matched sets. With Models 1 and 2 the data are examined in two stages. The first stage analyses data from the validation sample and is easy to perform; the second stage analyses the main body of data and requires MCMC methods. All relevant information is transferred between the stages by using the posterior distributions from the first stage as the prior distributions for the second stage. With Model 3, a hierarchical structure is used to model the relationship between the exposure probabilities of the matched sets, which gives the potential to extract more information from the data. All the methods that are proposed are generalized to studies in which there is more than one control for each case. The Bayesian methods and a maximum likelihood method are applied to a data set for which the exposure of every patient was measured using both an imperfect measure that is subject to misclassification, and a much better measure whose classifications may be treated as correct. To test methods, the latter information was suppressed for all but a random sample of matched sets.

Estimating the distribution of lag in the effect of short-term exposures and interventions: adaptation of a non-parametric regression spline model.

Information on the distribution of lag duration between exposure or intervention and the subsequent changes in risk can help in assessing the impact of exposure, predicting cost-effectiveness of intervention, and understanding the underlying biological mechanisms. Previous approaches focused more on optimizing the strength of the exposure-disease association than on directly estimating lag duration. We propose an alternative approach applicable to the analysis of the lagged effects of binary exposure variables. The density function of the distribution of lags is estimated based on flexible modelling of changes in hazard ratio of exposed versus unexposed subjects. The methodology is evaluated in a simulation study and is applied to the Framingham data to investigate the lagged effect of smoking cessation on coronary heart disease risk.