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Highly correlated exposures are ubiquitous in epidemiologic research and may arise as the result of an association between the measured exposures and one or more latent factors. For example, pesticide exposures for farm workers may be highly correlated because individuals apply multiple pesticides in a year, with choice of pesticide influenced by type of crop and pest.1,2 To depict this correlated exposure problem, let x1, …, xk denote the levels of k different exposure variables that are highly correlated due to an unmeasured variable or variables, and let y denote the outcome. Researchers will generally be interested in estimating effect measures β1,…,βk for exposures x1, …, xk. Hence, a common strategy is to fit the logistic regression model:
Unfortunately, maximum likelihood estimation of the model in expression (1) can fail to converge when predictors are highly correlated, and estimated coefficients may be unstable even when convergence is achieved.3
This problem has led many epidemiologists to fit logistic regression models incorporating one exposure variable at a time. However, the other exposure variables may be confounders and, if so, must be included to assess the causal effect of any specific exposure.4 Another commonly used strategy is to collapse the specific exposure information into summaries, such as a sum across chemicals in a class or an ever/never indicator. Unfortunately, this strategy results in a loss of information, does not allow inferences on effects of specific exposures, and can be sensitive to the chosen summary measure.
The problems associated with performing maximum likelihood estimation on correlated data have resulted in an increased use of hierarchical models.5 Ordinary regression models treat the outcome as a random variable, dependent on parameters. For example, in expression (1), yi is a random variable that depends on the parameters α0 and β1…βk. Hierarchical regression extends ordinary regression by also treating parameters as random variables depending on further coefficients through a prior distribution. Estimates obtained through hierarchical regression are shrinkage estimates in the sense that they are moved away from the asymptotically unbiased maximum likelihood estimate (MLE) and toward the center of the prior distribution. Shrinkage estimators are advantageous in 2 ways: they often have smaller frequentist mean squared error (MSE) and they represent incorporation of prior knowledge in the Bayesian sense.6 Such hierarchical models help circumvent problems associated with MLE. Namely, hierarchical models can estimate effects with lower MSE, even in the presence of high correlation.3,7
We discuss 4 Bayesian hierarchical models: 2 parametric models (P1 and P2) and 2 semiparametric models (SP1 and SP2). These 4 models differ in how their prior distribution is specified. The most common Bayesian hierarchical model found in epidemiologic research is the semi-Bayes model,5,8–13 which we refer to as model P1 (ie, the first parametric model). A typical prior distribution for βj (where j indexes the k coefficients in expression 1) is N(μ, φ2), where μ characterizes the investigator’s prior knowledge about the true value of βj and φ2 is the uncertainty regarding that value. Values for μ and φ2 are chosen based on substantive knowledge. The amount that the estimated effects are shrunk away from the MLEs and toward the prior mean is determined by the prior variance, φ2. A large prior variance indicates greater uncertainty about the effect size and causes less shrinkage.
Consider a model such as expression (1) in which 20 coefficients are estimated and each has a N(0, φ2) prior. Prior knowledge may exist about the variability of the estimates, but the data also contain information about that variability, with a simplistic estimate being the variance of the 20 MLEs about the prior mean. Model P1 incorporates prior knowledge by treating φ2 as known, but it ignores information regarding the variability of the coefficients that is contained in the observed data. Thus, model P1 has fixed shrinkage regardless of the support for the prior distribution provided by the data.
Consider, instead, a model that treats these prior parameters as random variables in turn having their own prior distributions (model P2). Unlike model P1, which has fixed shrinkage (because φ2 is constant), model P2 estimates φ2 by combining the observed data with prior knowledge about φ2. This allows the amount of shrinkage to vary depending on how well the data support the prior distribution. If the data lend some support to the prior distribution, model P2 can provide greater shrinkage than model P1. If the data lend little support to the prior distribution, model P2 will result in less shrinkage. In the discussion so far, all coefficients have been shrunk toward a common mean; however, it is straightforward to allow coefficients to be grouped into classes with each set of coefficients shrunk toward separate class-specific means.
Models P1 and P2 have potential disadvantages. A normally distributed prior is commonly assumed for historical reasons and computational convenience; however, results may be sensitive to this assumption. Second, for these methods to shrink estimates towards multiple prior means, the coefficients must be specified into classes (eg, if the coefficients are the effects of different pesticides, they could be classified as fungicides or fumigants to allow coefficients in those classes to be shrunk toward different means). However, it may be impossible to specify which effects should be grouped into which classes, or even how many classes there should be. A method that allows the data to guide the clustering of coefficients into classes would be preferable. To accomplish this, we place a Dirichlet process prior (DPP) on the distribution of the coefficients.14–16 A DPP allows researchers to specify their prior knowledge as being “similar” to a known parametric distribution (such as the normal) while remaining flexible enough to allow for substantial deviations from that distribution. Additionally, the DPP attempts to cluster coefficients into groups based on effect size. Coefficients are clustered together probabilistically (soft clustering) rather than with certainty (hard clustering) and this feature of DPPs can offer dramatic improvements in effect estimation. We will refer to this semiparametric model with DPP priors as model SP1.
In epidemiologic studies, some exposures will typically have virtually no effect, in which case they cannot confound the effect of any other exposure and we might prefer to exclude them from the model. Variable-selection techniques in the epidemiologic literature are limited, generally relying on backward or forward selection strategies that increase the type I error rate.17–19 However, there has been an increasing focus on variable-selection methods (implemented through variable-selection priors) in the statistics literature based on the advent of microarray technology.20,21
To account for the opportunity that some βj = 0, we propose a mixture prior that allows an unknown subset of the predictors to have zero coefficients.22,23 A coefficient is implicitly removed from the model when βj = 0, a probability we estimate by combining our prior knowledge of a null effect with the observed data. When using a DPP for the coefficients, the exposures are clustered into groups. By using this mixture prior, we also allow a cluster of exposures that has coefficients equal to zero. Adopting this prior distribution in the DPP to perform simultaneous variable selection and clustering has been shown to have excellent properties.24 We refer to the semi-parametric model with clustering of coefficients at zero as model SP2.
Both parametric models (P1 and P2) have been discussed in much greater detail elsewhere.5,6,11,12,25,26 Here, we illustrate some of their properties in the simple setting of an ordinary linear regression model in which centered covariates xi1…xik are regressed on an outcome yi. For ease of presentation, we assume the linear model has a known error term, σ2, no intercept, and that the covariates are orthogonal (ie, they are not correlated); however, the results are generalizable to nonorthogonal situations.
As mentioned previously, model P1 incorporates information on βj through a prior distribution. A typical specification for this model is:
where the prior mean, ηj, incorporates prior evidence regarding the size of the effect for the jth coefficient and the xij may be standardized so they are all on the same scale. Prior scientific knowledge may indicate that all coefficients have the same prior distribution, that some coefficients have one prior distribution while others have a different prior distribution, or that each coefficient has its own prior distribution. For example, if β1…βk are the effects of pesticides on retinal degeneration, one could assume that the effects of all pesticides are the same (eg, they all belong to the same class and have a common prior distribution), that the effect varies over different functional groups of pesticides (eg, they could be grouped into classes such as fungicide or fumigant, with each class having a different prior distribution), or that each pesticide has a different prior distribution.2 Indicator variables, zlj, denoting a pesticide class can be introduced into the prior distribution by allowing ηj = ∑l = 1p θlzlj. However, these classes need not be mutually exclusive and more complicated prior specifications can be included where biologically relevant. The prior variance φj2 represents the uncertainty that βj = ηj. The prior variance could be specified from a meta-analysis or could be calculated by choosing a range within which the researcher believes 95% of effect estimates on this topic would lie. Solving the standard confidence interval formula for the variance term allows the researcher to specify the prior variance. The lack of a prior distribution on ηj or φj2 is the distinguishing feature of model P1.
The posterior distribution (ie, the distribution that results when the prior distribution for βj is updated with the observed data) for βj is given by:
The posterior mean is an average of the prior mean (ηj) and the maximum likelihood estimate (∑xijyi/∑xij2), inversely weighted by their respective variances, φj2 and σ2/∑xij2. This is the essence of a shrinkage estimator: the posterior distribution of βj is shrunk towards its prior distribution. As the number of observations increase, the posterior distribution is weighted more heavily toward the observed data. With orthogonal data of moderate size, the observed data will quickly overwhelm anything but the strongest priors (ie, those with very small φ2), and estimates obtained from these parametric Bayesian models will be similar to the MLE. For concreteness, we generated a small (n = 50) dataset with 5 orthogonal covariates, none of which have an effect. We estimate β1…β5 using MLE and using model P1 in expression 2 with k = 5 and ηj = 0 for all j. Figure 1 shows the resulting distribution of the MLE of β1, as well as the Bayesian estimate with φj2 = 0.5, 1.0, and 2.0. Note that, on average, the MLE will be unbiased but in this single sample the results are far from the truth. The amount of shrinkage in the hierarchical models is a function of the prior variance: as the prior variance decreases (representing increasing certainty about the effect of β), the posterior distribution shrinks toward the prior mean.
Because φ2 is so vital to model P1, it is wise to vary it in sensitivity analyses and see how estimates change with different plausible values. In Figure 1, for example, φ2 = 1.0 may have been the best guess of the variance of β with sensitivity analyses conducted for φ2 = 2.0 and φ2 = 0.5. However, although there may be uncertainty regarding the prior variance (leading to sensitivity analyses), estimates from model P1 cannot account for this uncertainty.
Model P2 explicitly accounts for uncertainty in the prior variance by placing a prior distribution on φ2, resulting in estimates that are averaged over plausible values of φ2. Unlike the fixed shrinkage of model P1, model P2 adapts the shrinkage of βj based on the observed variability of β1…βk from their prior mean. Additionally, when the prior mean is a function of covariates (eg, ηj = ∑θlzlj), substantive information may exist for the effect of those variables and a prior distribution can be placed on those parameters. A typical specification for model P2 is:
Here, θl is the effect of a zlj covariate and its prior mean, μl, is the prior knowledge regarding the size of θl’s effect; the prior variance ωl2 represents uncertainty in that effect. The prior distribution for the φj2 is chosen as an inverse gamma (IG) distribution with parameters α1j and α2j. The inverse gamma distribution is a common choice for the prior distribution of a variance term because of its flexibility and computational convenience. The prior mean of φj2 is α2j/((α1j − 1) and its variance is α2j2/((α1j − 1)2(α1j − 2)). Model P1 is a special case of model P2 in which the variance of θ and φ2 goes to zero. In choosing values of α1 and α2 for an analysis, we suggest specifying a most likely value of φ2 (call this Eφ2) and a value for the variance of φ (call this Vφ2) such that 95% of the reasonable φ2 values would fall within the 95% confidence interval (CI) for IG(Eφ2, Vφ2). Solving the mean and variance equations for α1 and α2 gives: α1 = Eφ22/Vφ2 + 2 and α2 = Eφ23/Vφ2 + Eφ2. It is useful to plot a large number (n ≈ 10,000) of samples from the prior to ensure that the shape of distribution conforms to prior knowledge.
The full conditional posterior distributions for the parameters in model P2, assuming φ2 is the same for all βj (for simplicity) are:
The conditional distribution of φ2 in expression (7) is of particular interest. The adaptive shrinkage properties of model P2 are apparent from the ∑j(βj − ∑lzljθj)2 term, that represents the variation of the βj from their prior mean. As the variance of the parameters increases, the mean of φ2 also increases and when the variance decreases, the mean of φ2 decreases. Thus, if the data indicate that our prior specification of φ2 is too small, the posterior mean of φ2 is increased to reflect this. Because φ2 determines the amount of shrinkage, βj will be shrunk to a lesser extent. The converse is also true; when the data show little variability of the estimates from the prior mean, the posterior estimate of φ2 will decrease and cause greater shrinkage of βj to their prior distribution. This adaptive shrinkage is a potential improvement over model P1 that has a constant amount of shrinkage, regardless of the variability of the βj from the prior mean that is observed in the data. Model P2 also allows inferences to be more data-driven and less sensitive to the prior specification of μ and φ2.
The distribution of βj in expression (5) is similar to the distribution in expression (3). However, the distribution from model P1 is conditional on known values, while the distribution from model P2 is conditional on random variables (φ2 and θj). To average the distribution of βj over these random variables, we use Gibbs sampling (a type of Markov Chain Monte Carlo) that proceeds by iteratively drawing parameter values from the full conditional distributions in expressions (5, 6) and (7), given the value of the other random variables from the previous steps of the Gibbs sampler.27,28 After running the Gibbs sampler for a large number of iterations and discarding some initial number of iterations to allow for a burn-in period, the mean and variance of βj in the remaining samples are the mean and variance of the marginal posterior distribution of interest. For more information regarding burn-in period and convergence, consult Gelman et al.29 We also note that these algorithms (which can be implemented in programs such as WinBUGS [MRC Biostatistics Unit, Cambridge, UK]) generate the exact posterior distribution of the coefficients that is useful in small datasets. This result is an improvement over previous methods proposed for fitting model P1 that rely on asymptotic approximations.30
We analyze, under model P2, the dataset we previously examined for the model P1. The prior mean for all βj is zero and the parameters for the prior variance, φ2, are α1 = 1 and α2 = 1. We ran a Gibbs sampling algorithm for 50000 iterations and excluded the first 5000 iterations as a burn-in period. The marginal posterior distributions of β1 and φ2 are presented in Figure 2. The mean of β1 = −0.51, which is between the mean of the estimates from model P1 under the assumption of a fixed φ2 = 1 (β1 = −0.56) and φ2 = 0.5 (β1 = −0.43). Although the mean of the prior variance was 1 in the model P2, β1…β5 exhibited less variability than the prior indicated, and the posterior mean of φ2 (0.87) decreased to reflect this additional information. Thus, by incorporating information on φ2 that is contained in the data, we adaptively allow greater shrinkage of β1 towards its prior mean.
Although we have focused on linear regression with orthogonal data, the results can be generalized to correlated predictors and logistic regression. It is only for computational convenience that we have focused on linear models here. We implement logistic hierarchical models in simulations and the applied example presented later in this paper.
As we demonstrate (Appendix I, available with the online version of this article), models P1 and P2 can offer a distinct improvement over MLE. However, results of these models may be sensitive to the assumed prior distribution of βj and a nonparametric prior may be preferable. Further, when sufficient prior information exists, coefficients may be grouped into classes by incorporating second level coefficients; however, in many epidemiologic applications such prior knowledge may not exist. Instead, we explore a procedure that allows coefficients to be grouped into clusters based on similarity of effect sizes before shrinking them toward a prior distribution.
In Bayesian nonparametric inference, a common method to limit the dependence of a parameter on a prior distribution is to let the prior distribution be random. In the previous section we assumed βj ∼ N(μ, φ2). Instead, we could specify βj ∼ D, where D is a random distribution. Because D is random, we place a prior distribution on it; in this case we choose a Dirichlet process prior, D ∼ DPP(λD0), where D0 is the base distribution (such as a normal distribution) and λ is a precision parameter determining how closely D follows D0. As λ increases, D converges to D0, and the nonparametric approach reduces to the parametric models of the previous section. Smaller values of λ indicate less certainty that βj ∼ D0. Figure 3 presents 2 realizations of DPP(λD0) with D0 ≡ N(0, 1) and λ equal to either 1 or 100. The larger value of λ yields a distribution that resembles the base distribution, while the sample with λ = 1 shows no similarity to the D0.
A feature of the 2 distributions shown in Figure 3 is their discrete nature. Rather than being continuous, like the base distribution, a draw from a DPP is discrete, implying that any 2 (or more) coefficients have a nonzero probability of being clustered together and having the same effect size. The scale of the predictor may be important, and the predictors could potentially be rescaled to allow greater similarity among coefficients. The clustering feature can be seen more clearly through the (identical) representation of the DPP as a mixture distribution: βj ∼ ω0D0 + ω1∑i ≠ jδβi, where ω0 and ω1 are weights determined by λ. The term δβi indicates that, with probability ω1, βj is clustered with coefficient βi. The posterior probability of clustering coefficients depends on λ (smaller values of λ favor clustering) and the similarity of the magnitude of those coefficients (increased similarity favors clustering).
Consider 2 predictors, xim and xin, with effects βm and βn which follow some unknown distribution D that is assigned a DPP. This model estimates, based on prior knowledge and information in the data, a probability pmn that βm = βn. In the extreme (and unlikely) case where pmn = 1, coefficients xim and xin are estimating parameters with the same value (βm = βn). That is, the data contain twice as much information regarding the common effect, resulting in more precise effect estimates as well as less shrinkage toward the prior distribution. At the other extreme, if pmn = 0, the 2 coefficients do not aid in each other’s estimation. More commonly, pmn will be between 0 and 1, allowing βm and βn to add some information to one another’s estimation. This will result in different posterior distributions for the 2 coefficients that can have lower MSE than model P1 or P2 (see Appendix I). In the sense that model P1 allowed for constant shrinkage of all coefficients toward the prior mean, and model P2 allowed for adaptive shrinkage of all coefficients toward the prior mean, the semiparametric models (SP1 and SP2) allow individual coefficients to be adaptively shrunk toward the prior mean to different extents. The more likely coefficients are to be clustered together, the more information there is in the data regarding their common effect, and the less impact the prior specification will have.
This model is semiparametric because the distribution of the outcome, yi, is parametric, whereas the distribution of βj is nonparametric. The first semi-parametric model (SP1) is an extension of model P2 and can be specified as:
where G is a gamma distribution with mean ab and variance ab2. Placing a prior distribution on the precision parameter, λ, serves the same function as placing a parameter on φ2 in model P1; it allows the data to help guide inference rather than relying solely on prior knowledge. Generally, relatively noninformative values are chosen for a and b, such as a = 1, b = 1 or a = 0.1, b = 0.1. However, empirical Bayes methods are available to estimate this parameter as well.31 As with the model P2, estimating these parameters requires a Gibbs sampling algorithm.32,33
In many instances it may be useful to exclude variables that have no effect on the outcome or there may be prior substantive knowledge that the exposure has no effect. In either case, modification of the base distribution D0 in expression 8 allows a variable-selection prior distribution to be incorporated in a DPP model. Following the approach of Dunson et al,34 we specify a second semi-parametric model (SP2):
where δ0 indicates a point mass at the value 0. The base distribution has a value of 0 with probability π, and distribution N(μ, φ2) with probability 1 − π. This simple modification to the base distribution allows βj to be equal to 0, in which case it is effectively removed from the model. This exclusion can help increase the precision of estimates, particularly in the presence of highly correlated variables or in small datasets. An important feature of allowing coefficients to be equal to 0 is that it allows for easy testing of a point hypothesis, such as βj = 0. For instance, if a Gibbs sampling algorithm is run for R iterations and βj = 0 for r of those iterations, the posterior probability that βj = 0 is r/R.
When π = 0, model SP2 reduces to the model SP1. The coefficient π is given a beta(c, d) distribution to allow the data to inform the probability that a coefficient is zero. Elicitation of c and d can proceed by specifying the expected probability, Eπ, that a randomly selected coefficient is 0 and the variance surrounding that estimate, Vπ. Solving the equations for the mean and variance of the beta distribution:
Example: Application to Study of Pesticides and Retinal Degeneration
The Agricultural Health Study, which enrolled farmers who applied for pesticide licenses in Iowa or North Carolina between 1993 and 1997, has been described in more detail elsewhere.1 Kirrane et al2 recently examined the association between pesticide exposure and retinal degeneration among the farmers wives. Wives filled out a questionnaire with information on their medical and pesticide history. We analyzed the same data used by Kirrane et al in their analysis (31,173 women, 281 of whom experienced retinal degeneration), but we limit our analysis to herbicides, of which there are 18 unique chemicals. These 18 chemicals exhibited a wide range of correlation, from 0.06 to 0.58. Table 1 shows the 4 hierarchical models used to analyze the data. Prior parameter values are based on prior knowledge and are similar to those used in Kirrane et al. There is little evidence of an effect of herbicides on retinal degeneration, so we center our prior distributions at OR = 1.0. Gibbs sampling algorithms were programmed in Matlab (The Mathworks, Natick, MA) and run for 60,000 iterations, with the initial 5,000 excluded as a burn-in period.
To help illustrate the 4 hierarchical models, we present representations of the prior distributions for the effect of the herbicide imazethapyr (β1) in Figure 4. Since the prior distributions for models P2, SP1 and SP2 depend on random variables, we evaluate their prior distributions at the posterior mean of all other random variables (φ2 for modelP2; φ2, λ, and β2…β18 for model SP1; φ2, λ, β2…β18,and π for model SP2). The prior distribution for model P1 is determined by our belief that herbicides most likely have no effect on retinal degeneration (exp (μ) = 1.0) but that we are 95% certain the effect lies between approximately OR = 0.3 and OR = 3.1 (φ2 = 0.35). The prior distribution for β1 in model P2 is more complicated since φ2 is random. We observe little variability of the herbicide’s effects about the prior mean, leading to a smaller posterior estimate of φ2 = 0.11. Thus the prior distribution for β1 evaluated at φ2 = 0.11 is more concentrated around the null, leading to greater shrinkage of effects toward the prior mean. As indicated earlier, the prior distribution for model SP1 is a mixture of a normal distribution, with a mean OR = 1.0 and posterior estimate of φ2 = 0.19, and a set of point masses at the posterior estimates of β2…β18. The mean posterior value of λ = 4.3 indicates that the data provide somewhat more evidence in favor of normally distributed effects than indicated by the prior; this value implies that with probability 0.2, β1 is distributed according to N(0, 0.17), and with probability 0.8, β1 is assigned the value of one of the other coefficients, β2…β18. The prior distribution for model SP2 is similar to model SP1, except for a large point mass at 0. The posterior mean of π = 0.68 and λ = 1.5 imply that β1 is distributed according to N(0, 0.18) with probability 0.03 or set equal to one of β2…β18 with probability 0.29 or set equal to 0 with probability 0.68.
The results of the models are presented in Table 2. Figure 5 shows the posterior distribution of the effect of imazethapyr from the 4 hierarchical models. Model P1 estimated an effect of imazethapyr that was no longer statistically significant but still markedly elevated (OR = 1.7; 95% CI = 0.8–3.6). Models P2, SP1, and SP2 were all largely in agreement, indicating little evidence of effect of imazethapyr on retinal degeneration. The distribution of β1 estimated through model SP2 is of particular interest. The large spike observed at 0 is the posterior probability that β1 = 0 (P = 0.63). Also of interest in the posterior distribution from model SP2 is the fact that the most likely non-null effect is virtually null, leading us to suspect this variable may have no association with retinal degeneration.
Although highly correlated data are common in epidemiology, standard analyses can produce extremely imprecise confidence intervals or fail to converge altogether. We examined 4 Bayesian hierarchical models that perform well in this context. These models may have broad use in other areas, as well, such as in estimating models with a large number of predictors.
When deciding which of the 4 models to use in an analysis, consideration should be given to the properties of each model as well as the computational skill required to implement them. The 2 parametric models (P1 and P2) are the easiest computationally. Either model can be implemented in WinBUGS, using the code we provide in Appendix II. The advantages of model P2 may justify its use in preference to model P1. Model P1 assumes a fixed prior variance, whereas model P2 updates the prior variance based on the observed data. This “Bayesian learning” allows for adaptive shrinkage and makes estimates more data-driven and less sensitive to prior specification. However, as the sample size increases, the difference between model; P1 and P2 (and ML) tends to decrease.
Although more computationally intensive than the parametric models, the 2 semiparametric models presented here have very desirable properties in many situations. These models may be particularly useful when the researcher is unaware how to group coefficients. When some coefficients have similar true values, the semiparametric models can decrease MSE by aggregating data within clusters. Indeed, even if the true values of the coefficients are not exactly identical, “soft clustering” can still reduce MSE (see Appendix I). However, as the probability of clustering coefficients increases, models SP1 and SP2 can perform remarkably well. The decision whether to implement model SP1 or SP2 should be made on substantive grounds. When researchers have a high prior probability that many of the effects in question may be zero, the selection prior in model SP2 can help estimation. Model SP2 may be particularly useful in situations where hypothesis testing is required. However, when the true value of most coefficients is zero and only a few coefficients are nonzero (but still close to zero), model SP2 performs slightly worse than model SP1.
In summary, the challenges of analyzing highly correlated data can be greatly diminished using the Bayesian framework. The 2 parametric and 2 semiparametric models we examine in this paper provide useful alternatives to current maximum likelihood techniques. The choice of model should be guided by careful thought regarding the likely magnitude of effects, as well as whether many effects of similar sizes may be seen.
1. Alavanja M, Sandler DP, McMaster SB, et al. The Agricultural Health Study. Environ Health Perspect
2. Kirrane EF, Hoppin JA, Kamel F, et al. Retinal degeneration and other eye disorders in wives of farmer pesticide applicators enrolled in the Agricultural Health Study. Am J Epidemiol
3. Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics
4. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology
5. Greenland S. A semi-Bayes approach to the analysis of correlated multiple associations, with an application to an occupational cancer-mortality study. Stat Med
6. Greenland S. Principles of multilevel modelling. Int. J. Epid
7. Hoerl AE, Kennard RW. Ridge regression: applications to nonorthogonal problems. Technometrics
8. De Roos AJ, Poole C, Teschke K, et al. An application of hierarchical regression in the investigation of multiple paternal occupational exposures and neuroblastoma in offspring. Am J Ind Med
9. Engel SA, Erichsen HC, Savitz DA, et al. Risk of spontaneous preterm birth is associated with common proinflammatory cytokine polymorphisms. Epidemiology
10. Engel SA, Olshan AF, Savitz DA, et al. Risk of small-for-gestational age is associated with common anti-inflammatory cytokine polymorphisms. Epidemiology
11. Greenland S. Methods for epidemiologic analyses of multiple exposures: a review and comparative study of maximum-likelihood, preliminary-testing, and empirical-Bayes regression. Stat Med
12. Greenland S. Hierarchical regression for epidemiologic analyses of multiple exposures. Environ Health Perspect
. 1994;102(Suppl 8):33–39.
13. Greenland S, Poole C. Empirical-Bayes and semi-Bayes approaches to occupational and environmental hazard surveillance. Arch Environ Health
14. Ferguson TS. A Bayesian analysis of some nonparametric problems. Ann Stat
15. Ferguson TS. Prior distributions on spaces of probability measures. Ann Stat
16. Gopalan R, Berry DA. Bayesian multiple comparisons using Dirichlet process priors. J Am Stat Assoc
17. Leamer EE. Specification Searches: Ad Hoc Inference with Nonexperimental Data
. New York: Wiley 1978.
18. Raftery AE. Approximate Bayes factors and accounting for model uncertainty in generalised linear models. Biometrika
19. Draper D. Assessment and propagation of model uncertainty. J Roy Statist Soc B
20. Efron B, Tibshirani R. Empirical Bayes methods and false discovery rates for microarrays. Genet Epidemiol
21. Newton MA, Kendziorski CM, Blattner FR, et al. On differential variability of expression ratios: improving statistical inference about gene expression changes from microarray data. J Comput Biol
22. Geweke J. Variable selection and model comparison in regression. In: Bernardo JM, Berger JO, Dawid AP, et al, eds. Bayesian Statistics
5. Oxford: Oxford Press 1996:609–620.
23. Thomas DC, Siemiatycki J, Dewar R, et al. The problem of multiple inference in studies designed to generate hypotheses. Am J Epidemiol
24. Ishwaran H, Rao JS. Spike and slab variable selection: frequentist and Bayesian strategies. Ann Stat
25. Lindley DV, Smith AFM. Bayes estimates for the linear model. J Roy Statist Soc (Ser B)
26. Browne WJ, Draper D. A comparison of Bayesian and likelihood-based methods for fitting multilevel models. Biostatistics
27. Geman S, Geman D. Stochastic relaxation, Giggs distributions and the Bayesian restoration of images. IEEE Trans Pattern Analysis Machine Intelligence
28. Casella G, George E. Explaining the Gibbs Sampler. Am Stat
29. Gelman A, Carlin JB, Stern HS, et al. Bayesian Data Analysis
. New York: Chapman and Hall/CRC; 2000.
30. Witte JS, Greenland S, Kim L. Software for hierarchical modeling of epidemiologic data. Epidemiology
31. McAuliffe JD, Blei DM, Jordan MI. Nonparametric empirical Bayes for the Dirichlet process mixture model. Stat Comput
32. Escobar MD, West M. Bayesian density estimation and inference using mixtures. J Am Stat Assoc
33. Escobar MD, West M. Computing nonparametric hierarchical models. In: Dey D, Muller P, Sinha D, eds. Practical Nonparametric and Semiparametric Bayesian Statistics
. New York: Springer-Verlag; 1998:1–22.
34. Dunson DB, Herring AH, Mulherin-Engel SM. Bayesian selection and clustering of polymorphisms in functionally related genes. ISDS Tech Report, Duke University