---
title: "Advanced topics for the qgcomp package: time-to-event, clustering, partial effects, weighting, missing data"
author: "Alexander Keil"
date: "`r Sys.Date()`"
#output: rmarkdown::pdf_document 
output: rmarkdown::html_vignette  
vignette: >
  %\VignetteIndexEntry{The qgcomp package: advanced usage}
  %\VignetteEngine{knitr::knitr}
  \usepackage[utf8]{inputenc}
---

# Table of Contents
1. [Example 7: time-to-event analysis and parallel processing](#ex-tte)
2. [Example 8: clustering](#ex-cluster)
3. [Example 9: partial effects](#ex-partial)
4. [Example 10: multinomial outcomes](#ex-multinom)
5. [Example 11: sample weighting from, e.g. NHANES](#ex-weighting)
6. [Missing data, limits of detection and multiple imputation](#missing)
10. [Acknowledgments](#ack)


```{r invisibles, echo=FALSE, results='markup', message=FALSE}
library("knitr")
#library("gWQS")
```

Load some packages and data from the basic vignette
```{r first step, echo=TRUE, results='markup', message=FALSE}
library("qgcomp")
library("ggplot2")
library("splines")
data("metals", package="qgcomp")
# using data from the intro vignette
Xnm <- c(
    'arsenic','barium','cadmium','calcium','chromium','copper',
    'iron','lead','magnesium','manganese','mercury','selenium','silver',
    'sodium','zinc'
)
covars = c('nitrate','nitrite','sulfate','ph', 'total_alkalinity','total_hardness')

cormat = cor(metals[,Xnm])

idx = which(cormat>0.6 & cormat <1.0, arr.ind = TRUE)
newXnm = unique(rownames(idx)) # iron, lead, and cadmium


```

### Example 7: time-to-event analysis and parallel processing<a name="ex-tte"></a>

- The `qgcomp` package utilizes the Cox proportional hazards models as the underlying model for 
  time-to-event analysis. The interpretation of a `qgcomp.glm.noboot` fit parameter is a conditional (on confounders)
  hazard ratio for increasing all exposures at once. The `qc.survfit1` object demonstrates a time-to-
  event analysis with `qgcompcox.noboot`. The default plot is similar to that of `qgcompcox.noboot`, 
  in that it yields weights and an overall mixture effect
  
```{r tm2evnt1, results='markup', fig.show='hold', fig.height=5, fig.width=7.5, cache=FALSE}
# non-bootstrapped version estimates a marginal structural model for the 
# confounder-conditional effect
survival::coxph(survival::Surv(disease_time, disease_state) ~ iron + lead + cadmium + 
                         arsenic + magnesium + manganese + mercury + 
                         selenium + silver + sodium + zinc +
                         mage35,
                         data=metals)
qc.survfit1 <- qgcomp.cox.noboot(survival::Surv(disease_time, disease_state) ~ .,expnms=Xnm,
                         data=metals[,c(Xnm, 'disease_time', 'disease_state')], q=4)
qc.survfit1
plot(qc.survfit1)

```
  
  
Marginal hazards ratios (and bootstrapped quantile g-computation in general) uses a slightly different approach to effect estimation that makes it more computationally demanding than other `qcomp` functions.
  
  To estimate a marginal hazards ratio, the underlying model is fit, and then new outcomes are simulated under the underlying model with a baseline hazard estimator (Efron's) - this simulation requires a large sample (controlled by MCsize) for accuracy. This approach is similar to other g-computation approaches to survival analysis, but this approach uses the exact survival times, rather than discretized survival times as are common in most g-computation analysis. Plotting a `qgcompcox.boot`object yields a set of survival curves (e.g.`qc.survfit2`) which comprise estimated survival curves (assuming censoring and late entry at random, conditional on covariates in the model) that characterize conditional survival functions (i.e. censoring competing risks) at various levels of joint-exposure (including the overall average - which may be slightly different from the observed survival curve, but should more or less agree).
  
```{r tm2evnt2, results='markup', fig.show='hold', fig.height=5, fig.width=7.5, cache=FALSE}
# bootstrapped version estimates a marginal structural model for the population average effect
#library(survival)
qc.survfit2 <- qgcomp.cox.boot(Surv(disease_time, disease_state) ~ .,expnms=Xnm,
                         data=metals[,c(Xnm, 'disease_time', 'disease_state')], q=4, 
                         B=5, MCsize=1000, parallel=TRUE, parplan=TRUE)
qc.survfit2

# testing proportional hazards (note that x=TRUE is not needed (and will cause an error if used))
survival::cox.zph(qc.survfit2$fit)
p2 = plot(qc.survfit2, suppressprint = TRUE)  
p2 + labs(title="Linear log(hazard ratio), overall and exposure specific")


```
  
  
All bootstrapped functions in `qgcomp` allow parellelization via the parallel=TRUE parameter (demonstrated with the non-liner fit in `qc.survfit3`). Only 5 bootstrap iterations are used here, which is not nearly enough for inference, and will actually be slower for parallel processing due to some overhead when setting up the parallel processes.

```{r tm2evnt3, results='markup', fig.show='hold', fig.height=5, fig.width=7.5, cache=FALSE}
qc.survfit3 <- qgcomp.cox.boot(Surv(disease_time, disease_state) ~ . + .^2,expnms=Xnm,
                         data=metals[,c(Xnm, 'disease_time', 'disease_state')], q=4, 
                         B=5, MCsize=1000, parallel=TRUE, parplan=TRUE)
qc.survfit3
p3 = plot(qc.survfit3, suppressprint = TRUE) 
p3 + labs(title="Non-linear log(hazard ratio) overall, linear exposure specific ln-HR")

```


*Technical Note:* this mode of usage is designed for simplicity. The implementation relies on the `future` and `future.apply` packages. Use guidelines of the `future` package dictates that the user should be able to control the future "plan", rather than embedding it in functions as has been done here. This slightly more advanced usage (which allows nesting within larger parallel schemes such as simulations) is demonstrated here by setting the "parplan" parameter to FALSE and explicitly specifying a "plan" outside of `qgcomp` functions. This will move much of the overhead due to parallel processing outside of the actual `qgcomp` functions. The final code block of this vignette shows how to exit the "plan" and return to standard evaluation via `plan(sequential)` - doing so at the end means that the next parallel call (with parplan=FALSE) we make will have lower overhead and run slightly faster.
```{r tm2evnt3b, results='markup', fig.show='hold', fig.height=5, fig.width=7.5, cache=FALSE}
future::plan(future::multisession)# parallel evaluation
qc.survfit3 <- qgcomp.cox.boot(Surv(disease_time, disease_state) ~ . + .^2,expnms=Xnm,
                         data=metals[,c(Xnm, 'disease_time', 'disease_state')], q=4, 
                         B=5, MCsize=1000, parallel=TRUE, parplan=FALSE)
qc.survfit3
p3 = plot(qc.survfit3, suppressprint = TRUE) 
p3 + labs(title="Non-linear log(hazard ratio) overall, linear exposure specific ln-HR")

```


Returning to substance: while `qgcompcox.boot` fits a smooth hazard ratio function, the hazard ratios contrasting specific quantiles with a referent quantile can be obtained, as demonstrated with `qc.survfit4`. As in `qgcomp.glm.boot` plots, the conditional model fit and the MSM fit are overlaid as a way to judge how well the MSM fits the conditional fit (and whether, for example non-linear terms should be added or removed from the overall fit via the degree parameter - we note here that we know of no statistical test for quantifying the difference between these lines, so this is up to user discretion and the plots are provided as visuals to aid in exploratory data analysis).
 
```{r tm2evnt4, results='markup', fig.show='hold', fig.height=5, fig.width=7.5, cache=FALSE}
qc.survfit4 <- qgcomp.cox.boot(Surv(disease_time, disease_state) ~ . + .^2,expnms=Xnm,
                         data=metals[,c(Xnm, 'disease_time', 'disease_state')], q=4, 
                         B=5, MCsize=1000, parallel=TRUE, parplan=FALSE, degree=2)
qc.survfit4
# examining the overall hazard ratio as a function of overall exposure
hrs_q = exp(matrix(c(0,0,1,1,2,4,3,9), ncol=2, byrow=TRUE)%*%qc.survfit4$msmfit$coefficients)
colnames(hrs_q) = "Hazard ratio"
print("Hazard ratios by quartiles (min-25%,25-50%, 50-75%, 75%-max)")
hrs_q

p4 = plot(qc.survfit4, suppressprint = TRUE) 
p4 + labs(title="Non-linear log(hazard ratio), overall and exposure specific") 

``` 

Testing proportional hazards is somewhat complicated with respect to interpretation. Consider first a linear fit from `qgcomp.cox.noboot`. Because the underlying model of a linear `qgcomp` fit is equivalent to the sum of multiple parameters, it is not clear how proportional hazards might be best tested for the mixture. One could examine test statistics for each exposure, but there may be some exposures for which the test indicates non-proportional hazards and some for which the test does not.

 The "GLOBAL" test in the cox.zph function from the `survival` package comes closest to what we might want, and gives an overall assessment of non-proportional hazards for all model terms simultaneously  (including non-mixture covariates). While this seems somewhat undesirable due to non-specificity, it is not necessarily important that only the mixture have proportional hazards, so it is useful and easily interpretable to have a global test of fit via GLOBAL. 
 
```{r tm2evnt5, results='markup', fig.show='hold', fig.height=5, fig.width=7.5, cache=FALSE}

# testing proportional hazards (must set x=TRUE in function call)
qc.survfit1ph <- qgcomp.cox.noboot(survival::Surv(disease_time, disease_state) ~ .,expnms=Xnm,
                         data=metals[,c(Xnm, 'disease_time', 'disease_state', "mage35")], q=4,
                         x=TRUE)
survival::cox.zph(qc.survfit1ph$fit)

```
 
 
 For a potentially non-linear/ non-additive fit from `qgcomp.cox.boot`, the issue is slightly more complicated by the fact that the algorithm will fit both the underlying model and a marginal structural model using the predictions from the underlying model. In order for the predictions to yield valid causal inference, the underlying model must be correct (which implies that proportional hazards hold). The marginal structural model proceeds assuming the underlying model is correct. Currently there is no simple way to allow for non-proportional hazards in the marginal structural model, but non-proportional hazards can be implemented in the conditional model via standard approaches to non-proportional hazards such as time-exposure-interaction terms. This is a rich field and discussion is beyond the scope of this document.
 
```{r tm2evnt6, results='markup', fig.show='hold', fig.height=5, fig.width=7.5, cache=FALSE}

# testing global proportional hazards for model (note that x=TRUE is not needed (and will cause an error if used))
phtest3 = survival::cox.zph(qc.survfit3$fit)
phtest3$table[dim(phtest3$table)[1],, drop=FALSE]

```
 
 
Late entry and counting-process style data will currently yield results in `qgcomp.cox.*` functions. There has been some testing of this in limited settings, but we note that this is still an experimental feature at this point that may not be valid in all cases and so it is not documented here. As much effort as possible to validate results through other means is needed  when using qgcomp in data subject to late-entry or when using counting-process style data.



### Example 8: clustering<a name="ex-cluster"></a>

Clustering on the individual or group level means that there are individual or group level characteristics which result in covariance between observations (e.g. within individual variance of an outcome may be much lower than the between individual variance). For linear models, the error term is assumed to be independent between observations, and clustering breaks this assumption. Ways to relax this assumption include empirical variance estimation and cluster-appropriate robust variance estimation (e.g. through the `sandwich` package in R). Another way is to use cluster-based bootstrapping, which samples clusters, rather than individual observations. `qgcomp.glm.boot` and `qgcomp.glm.ee` can be leveraged to produce clustering consistent estimates of standard errors for independent effects of exposure as well as the effect of the exposure as a whole. This is done using the `id` parameter of `qgcomp.glm.boot/ee` (which can only handle a single variable and so may not efficient for nested clustering, for example).

Below is a simple example with one simulated exposure. First the exposure data are 'pre-quantized' (so that one can verify that standard errors are appropriate using other means - this is not intended to show a suggested practice). Next the data are analyzed using a 1-component mixture in qgcomp - again, this is for verification purposes. The `qgcomp.glm.noboot` result yields a naive standard error of 0.0310 for the mixture effect:

```{r clustering, results='markup', fig.show='hold', fig.height=5, fig.width=7.5, cache=FALSE}
set.seed(2123)
N = 250
t = 4
dat <- data.frame(row.names = 1:(N*t))
dat <- within(dat, {
  id = do.call("c", lapply(1:N, function(x) rep(x, t)))
  u =  do.call("c", lapply(1:N, function(x) rep(runif(1), t)))
  x1 = rnorm(N, u)
  y = rnorm(N) + u + x1
})

# pre-quantize
expnms = c("x1")
datl = quantize(dat, expnms = expnms)

qgcomp.glm.noboot(y~ x1, data=datl$dat, family=gaussian(), q = NULL)

# neither of these ways yields appropriate clustering
#qgcomp.glm.noboot(y~ x1, data=datl$dat, id="id", family=gaussian(), q = NULL)
#qgcomp.glm.boot(y~ x1, data=datl$dat, family=gaussian(), q = NULL, MCsize=1000)
```

while the `qgcomp.glm.boot` result (not run, but shown in a comment below MCsize=5000, B=500) yields a corrected standard error of 0.0398, which is close to the robust standard error estimate of 0.0409 of `qgcomp.glm.ee`. Both of these methods can provide clustering-appropriate inference, wheras uncorrected estimates from `qgcomp.glm.noboot` cannot. The standard errors from the uncorrected fit are too low, but this may not always be the case. Similarly, use of an external package to estimate the sandwich (robust) standard error estimate of 0.0409 is shown below, which works here only because the mixture only has a single exposure. It should be noted that the sandwich variance estimator is for a conditional model parameter, wheras the estimate from `qgcomp.glm.ee` is from an MSM, so results will differ from standard sandwich estimators when there are covariates or multiple exposures. 

```{r clustering-2, results='markup', fig.show='hold', fig.height=5, fig.width=7.5, cache=FALSE}

# clustering by specifying id parameter on
#qgcomp.glm.boot(y~ x1, data=datl$dat, id="id", family=gaussian(), q = NULL, MCsize=1000, B = 5)
eefit <- qgcomp.glm.ee(y~ x1, data=datl$dat, id="id", family=gaussian(), q = NULL)
eefit
#qgcomp.glm.boot(y~ x1, data=datl$dat, id="id", family=gaussian(), q = NULL, MCsize=1000, B = 500)
#   Mixture slope parameters (bootstrap CI):
#   
#               Estimate Std. Error Lower CI Upper CI t value
#   (Intercept)  -0.4632     0.0730   -0.606    -0.32 3.3e-10
#   psi1          0.9550     0.0398    0.877     1.03       0

# This can be verified using the `sandwich` package 
#fitglm = glm(y~x1, data=datl$dat)
#sw.cov = sandwich::vcovCL(fitglm, cluster=~id, type = "HC0")[2,2]
#sqrt(sw.cov)
# [1] 0.0409
```

### Example 9: partial effects<a name="ex-partial"></a>

Returning to our original example (and adjusting for covariates), note that the default output for a `qgcomp.*.noboot` object includes "sum of positive/negative coefficients." These can be interpreted as "partial mixture effects" or effects of exposures with coefficients in a particular direction. This is displayed graphically via a plot of the qgcomp "weights," where all exposures that contribute to a given partial effect are on the same side of the plot.  Unfortunately, this does not yield valid inference for a true partial effect because it is a parameter conditional on the fitted results and thus does not represent the type of *a priori* hypothesis that is amenable to hypothesis testing and confidence intervals. Another way to think about this is that it is a data adaptive parameter and thus is subject to issues of overfit that are similar to issues with making inference from step-wise variable selection procedures.

```{r pe1, fig.height=5, fig.width=7.5}
(qc.fit.adj <- qgcomp.glm.noboot(y~.,dat=metals[,c(Xnm, covars, 'y')], expnms=Xnm, family=gaussian()))
plot(qc.fit.adj)
```

Fortunately, there is a way towards estimation of "partial effects." One way to do this is sample splitting, where the data are first randomly partitioned into a "training" and a "validation" data set. The assessment of whether a coefficient is positive or not occurs in the "training" set and then effect estimation for positive/negative partial effects occurs in the validation data. Basic simulations can show that such a procedure can yield valid (often called "honest") hypothesis tests and confidence intervals for partial effects, provided that there is no separate data exploration in the combined dataset to select the models. In the `qgcomp` package, the partitioning of the datasets into "training" and "validation" sets is done by the user, which prevents issues that may arise if this is done naively on a dataset that contains clusters (where we should partition based on clusters, rather than observations) or multiple observations per individual (all observations from an individual should be partitioned together). Here is an example of simple partitioning on a dataset that contains one observation per individual with no clustering. The downside of sample splitting is the loss of precision, because the final "validation" dataset comprises only a fraction of the original sample size. Thus, the estimation of partial effects is most appropriate with large sample sizes. We also note that these partial effect are only well defined when all effects are linear and additive, since whether a variable contributes to a positive or negative partial effect would depend on the value of that variable, so the valid estimation of "partial effects" is limited to settings in which the `qgcomp.*.noboot` objects are used for inference.
```{r pe2}
# 40/60% training/validation split
set.seed(123211)
trainidx <- sample(1:nrow(metals), round(nrow(metals)*0.4))
valididx <- setdiff(1:nrow(metals),trainidx)
traindata <- metals[trainidx,]
validdata <- metals[valididx,]
dim(traindata) # 40% of total
dim(validdata) # 60% of total
```

The qgcomp package then facilitates the analysis of these partitioned data to allow valid estimation and hypothesis testing of partial effects.  The `qgcomp.partials` function is used to estimate partial effects. Note that the variables with "negative effect sizes" differs slightly from the overall analysis given in the `qc.fit` object that represents our first pass analysis on these data. This is to be expected, and is a feature of this approach: different random subsets of the data will be expected to yield different estimated effects. If the true effect is null, then the estimated effects will vary from positive to negative around the null, and sample splitting is an important way to distinguish between estimates that reflect underlying patterns in the entire dataset from estimates that are simply due to natural sampling variation inherent to small and moderate samples. Note that fitting on these smaller datasets can sometimes result in perfect collinearity of exposures, in which case setting `bayes=TRUE` may be necessary to apply a ridge penalty to estimates.
```{r pe3a, fig.height=5, fig.width=7.5}
    

splitres <- qgcomp.partials(
  fun="qgcomp.glm.noboot", f=y~., q=4, 
  traindata=traindata[,c(Xnm, covars, "y")],validdata=validdata[,c(Xnm, covars, "y")], expnms=Xnm,
  bayes=FALSE, 
  .fixbreaks = TRUE, .globalbreaks=FALSE
  )
splitres
```

```{r pe3b, fig.height=5, fig.width=7.5}

plot(splitres$pos.fit)

```

Consistent with our overall results, the overall effect of metals on the outcome `y` is predominantly positive, which is driven mainly by calcium. The partial positive effect of psi=0.42 is slightly attenuated from the partial positive effect given in the original fit (0.44), but is slightly larger than the overall effect from the original fit (psi1=0.34). We note that the effect direction of cadmium is negative, even though it was selected based on positive associations in the training data. This suggests this variable has effects that are close to the null and their direction will depend on which subset of the data are used. This feature allows valid testing of hypotheses - a `global null` in which no exposures have effects will be characterized by variables that randomly switch effect directions between training and validation datasets, which will yield partial effect estimates close to the null with hypothesis tests that have appropriate type-1 error rates in large datasets. 

By default (subject to change) quantile cut points ("breaks") are defined within the training data and applied to the validation data. You may also change this behavior to allow the breaks to be defined using quantiles from the entire dataset, which treats the quantiles as fixed. This will be expected to improve stability in small samples and may eventually replace the default behavior as the quantiles themselves are not generally treated as random variables within quantile g-computation. For this particular dataset (and seed value), there is little impact of this setting on the results.
```{r pe3c, fig.height=5, fig.width=7.5}
    

splitres_alt <- qgcomp.partials(
  fun="qgcomp.glm.noboot", f=y~., q=4, 
  traindata=traindata[,c(Xnm, covars, "y")],validdata=validdata[,c(Xnm, covars, "y")], expnms=Xnm,
  bayes=FALSE, 
  .fixbreaks = TRUE, .globalbreaks=TRUE
  )
splitres_alt
```

One careful note: when there are multiple exposures with small positive or negative effects, the partial effects may be biased towards the null in studies with moderate or small sample sizes. This occurs because, in the training set, some exposures with small effects are likely to be mis-classified with regard to their effect direction. In some instances, both the positive and negative partial effects can be in the same direction. This occurs if individual effects are predominantly in one direction, but some are small and subject to having mis-classified directions. As one example: if there is a null overall effect, but there is a positive partial effect driven strongly by one exposure and a balancing negative partial effect driven by numerous weaker associations, partial effect estimates will not sum to the overall effect because the negative partial effect will experience more downward bias in typical sample sizes. Thus, when the overall effect does not equal the sum of the partial effects, there is likely some bias in at least one of the partial effect estimates. This is not a unique feature of quantile-based g-computation, but is also be a concern for methods that focus on estimation of partial effects, such as weighted quantile sum regression.

The larger question about interpretation (and its worth) of partial effects is left to the analyst. For large datasets with well characterized exposures that have plausible subsets of exposures that would be positively/negatively linearly associated with the outcome, the variables that partition into negative/positive partial effects may make some substantive sense. In more realistic settings that typify exposure mixtures, the partitioning will result in groups that don't entirely make sense. The "partial effect" yields the effect of increasing all exposures in the subset defined by positive coefficients in the training data, while holding all other exposures and confounders constant. In the setting where this corresponds to real world patterns (e.g. all exposures in the positive partial effect share a source), then this may be interpretable roughly as the effect of an action to intervene on the source of these exposures. In most settings, however, interpretation will not be this clear and should not be expected to map onto potential real-world interventions. We note that this is not a function of the quantile g-computation method, but just part of the general messiness of working with exposures mixture data. 

A more justifiable approach in terms of mapping effect estimates onto potential real-world actions would be choosing subsets of exposures based on prior subject matter knowledge, as we demonstrated above in example 5. This does not require sample splitting, but it does come at the expense of having to know more about the exposures and outcome under analysis. For example, our simulated outcome `y` may represent some outcome that we would expect to increase with so-called "essential" metals, or those that are necessary (at some small amount) for normal human functioning, but it may decrease with "potentially toxic" (non-essential) metals, or those that have no known biologic function and are more likely to cause harm rather than improve physiologic processes that lead to improved (larger) values of `y`. Qgcomp can be used to assess effects of these "sub-mixtures."

Here are results for the essential metals:
```{r pe4a}


nonessentialXnm <- c(
    'arsenic','barium','cadmium','chromium','lead','mercury','silver'
)
essentialXnm <- c(
  'sodium','magnesium','calcium','manganese','iron','copper','zinc','selenium'
)
covars = c('nitrate','nitrite','sulfate','ph', 'total_alkalinity','total_hardness')


(qc.fit.essential <- qgcomp.glm.noboot(y~.,dat=metals[,c(Xnm, covars, 'y')], expnms=essentialXnm, family=gaussian()))

```


Here are results for the non-essential metals:
```{r pe4b}
(qc.fit.nonessential <- qgcomp.glm.noboot(y~.,dat=metals[,c(Xnm, covars, 'y')], expnms=nonessentialXnm, family=gaussian()))
```
As shown from these results, the essential metals and minerals demonstrate an overall positive joint association with the outcome (controlling for non-essentials), whereas the partial effect of non-essential metals and minerals is close to null. This is close to the interpretation of the data adaptive selection of partial effects demonstrated above, but is interpretable in terms of how we might intervene (e.g. increase consumption of foods that are higher in essential metals and minerals).

### Example 10: multinomial outcomes<a name="ex-multinom"></a>
For outcomes modeled as 3+ discrete categories, qgcomp joint effect estimates are interpreted as a ratio of the probability of being in the referent category of the outcome and the probability of being in the index category.

First, we'll bring in data and create a multinomial outcome.

```{r multinomial, results='markup', fig.show='hold', fig.height=10, fig.width=7.5, cache=FALSE}
data("metals") # from qgcomp package
# create categorical outcome from the existing continuous outcome (usually, one will already exist)
metals$ycat = factor(quantize(metals, "y",q=4)$data$y, levels=c("0", "1", "2", "3"), labels=c("cct", "ccg", "aat", "aag")) 
# restrict to smaller dataset for simplicity
smallmetals = metals[,c("ycat", "arsenic", "lead", "cadmium", "mage35")]
```

Next, fit the model.
```{r multinomial-2, results='markup', fig.show='hold', fig.height=10, fig.width=7.5, cache=FALSE}

### 1: Define mixture and underlying model ####
mixture = c("arsenic", "lead", "cadmium")
f2 = ycat ~ arsenic + lead + cadmium + mage35

rr = qgcomp.multinomial.noboot(
 f2, 
 expnms = mixture,
 q=4, 
 data = smallmetals, 
 )


rr2 = qgcomp.multinomial.boot(
 f2, 
 expnms = mixture,
 q=4, 
 data = smallmetals, 
 B =2,  # set to higher values >200 in general usage
 MCSize=10000 # set to higher values in small samples
 )

summary(rr)
summary(rr2) # differs from `rr` primarily due to low `MCSize` value

 plot(rr) 
#plot(rr2) # not yet functional
```
Some of the convenience functions, like `plot` and `summary` are available for multinomial fits, and more functionality will be available in the future.

### Example 11: sample weighting from, e.g. NHANES<a name="ex-weighting"></a>

Often, we will want to apply sampling weights to data to make inference to a population that is either fully external to the analytic data or represents a larger population of which the analytic data are a subset. Weighting (e.g. sampling weights or inverse-odds weights) may be used for inference. This is common in analyses of National Health and Nutrition Examination Survey (NHANES) data, which provides a rich source of data for analysis of mixtures. Weighting is done using the "weights" parameter in calls to qgcomp methods. Notably, the default in `qgcomp.*.noboot` interprets these weights as frequency weights in which one individual represents multiple known individuals with identical data. Sampling weights, however, are not frequency weights in the strict sense, and should be handled differently. Survey-weighting methods can be used in general, but within the qgcomp package the appropriate functions are the `qgcomp.*.ee` functions, which use robust standard errors that address uncertainty due to sampling weights. Bootstrapping is another alternative that is not demonstrated here (though commented code is given below). Note that the unweighted estimate differs from the weighted estimates. The weighted estiamtes are equivalent (thought the bootstrap estimate may differ if non-exposure covariates are included in the model), though the standard errors and confidence intervals differ. If using inverse probability weighting (for example, to address loss-to-follow-up), then the `qgcomp.*.ee` or bootstrap methods should also be used.

```{r weighting, results='markup', fig.show='hold', fig.height=10, fig.width=7.5, cache=FALSE}

### 1: Define mixture and underlying model ####
set.seed(12321)
metals$samplingweights = exp(rnorm(nrow(smallmetals), -0.5, 1))

uw = qgcomp.glm.noboot(y~., expnms = Xnm,q=4, data = metals[,c(Xnm, covars, "y")])
wtd = qgcomp.glm.noboot(y~., expnms = Xnm,q=4, data = metals[,c(Xnm, covars, "y")], weights=metals$samplingweights)
wtd2 = qgcomp.glm.ee(y~., expnms = Xnm,q=4, data = metals[,c(Xnm, covars, "y")], weights=metals$samplingweights)
#wtd3 = qgcomp.glm.boot(y~., expnms = Xnm,q=4, data = metals[,c(Xnm, covars, "y")], weights=metals$samplingweights)

# unweighted
uw
# weighted with invalid standard error, confidence interval
wtd
# weighted with valid (robust) standard error, confidence interval
wtd2
# weighted with valid (bootstrap) standard error, confidence interval
#wtd3

```

## Missing data, limits of detection and multiple imputation<a name="missing"></a>
When carrying out data analysis using quantile g-computation, on can address missing data in much the same way as in standard regression analyses. A common approach is complete case analysis. While regression functions in R will automatically carry out complete case analyses when variables take on the value `NA` (denoting missingness in R), when using quantile g-computation it is encouraged that one explicitly create the complete case dataset explicitly and use that complete case dataset. Using the pre-installed R packages, this can be accomplished with the `complete.cases` function. 

The reason for this recommendation is that, while the regression analysis will be performed on complete cases (i.e. observations with non-missing values for all variables in the model), the calculation of quantiles for each exposures is done on an exposure-by-exposure basis, which can lead to numerical differences when explicitly using a dataset restricted to complete cases versus relying on automatically removing observations with `NA` values during analysis. 

Here is an artificial example that demonstrates the differences. Three analyses are run: one with the full data, one with complete case data (complete case analysis #1), and one with data in which `arsenic` has been randomly set to `NA` (complete case analysis #2).

There are numeric differences between the two complete case analyses, which can be traced to differences in the "quantized" exposures (other than arsenic) in the two approaches, which can be found by the `qx` data frame that is part of a `qgcompfit` object.

```{r md1a}
Xnm <- c(
    'arsenic','barium','cadmium','calcium','chromium','copper',
    'iron','lead','magnesium','manganese','mercury','selenium','silver',
    'sodium','zinc'
)
covars = c('nitrate','nitrite','sulfate','ph', 'total_alkalinity','total_hardness')
asmiss = metals
set.seed(1232)
asmiss$arsenic = ifelse(runif(nrow(metals))>0.7, NA, asmiss$arsenic)
cc = asmiss[complete.cases(asmiss[,c(Xnm, covars, "y")]),] # complete.cases gives a logical index to subset rows
dim(metals) # [1] 452  26
dim(cc) # [1] 320  26
```

Here we have results from the full data (for comparison purposes)
```{r md1b}
qc.base <- qgcomp.glm.noboot(y~.,expnms=Xnm, dat=metals[,c(Xnm, covars, 'y')], family=gaussian())
cat("Full data\n")
qc.base
```

Here we have results from a complete case analysis in which we have set some exposures to be missing and have explicitly excluded data that will be dropped from analysis.
```{r md1c}

qc.cc  <- qgcomp.glm.noboot(y~.,expnms=Xnm, dat=cc[,c(Xnm, covars, 'y')], family=gaussian())
cat("Complete case analyses\n")
cat("  #1 explicitly remove observations with missing values\n")
qc.cc

```

Finally we have results from a complete case analysis in which we have set some exposures to be missing, but we rely on R's automated dropping of observations with missing values.
```{r md1d}
qc.cc2 <- qgcomp.glm.noboot(y~.,expnms=Xnm, dat=asmiss[,c(Xnm, covars, 'y')], family=gaussian())



cat("  #1 rely on R handling of NA values\n")
qc.cc2

```

Now we can see a reason for the discrepancy between the methods above: when relying on R to drop missing values by allowing missing values for exposures in the analytic data, the quantiles of exposures will be done on all valid values for each exposure. In the complete case data, the quantiles will only be calculated among those with complete observations. The latter will generally be preferred because the quantiles for each exposure will be calculated on the same sample of individuals.

```{r md1e}
# calculation of arsenic quantiles is identical
all.equal(qc.cc$qx$arsenic_q, qc.cc2$qx$arsenic_q[complete.cases(qc.cc2$qx$arsenic_q)])
# all are equal

all.equal(qc.cc$qx$cadmium_q, qc.cc2$qx$cadmium_q[complete.cases(qc.cc2$qx$arsenic_q)])
# not equal
```


### Limits of detection
A common form of missing data that occurs in mixtures are exposure values that are missing due to being below the limit of detection. A common approach to such missing data is imputation, either through filling in small numeric values in place of the missing values, or in a more formal multiple imputation from a parametric model. Notably, with quantile g-computation, if the proportion of values below the limit of detection is below 1/q (the number of quantiles), all appropriate missing data approaches will yield the same answer. Thus, if one has 3 exposures each with 10% of the values below the limit of detection, one can impute small values below those limits (e.g. limit of detection divided by the square root of 2) and proceed with quantile g-computation on the imputed data. This analysis leverages the fact that, even if a value below the limit of detection cannot be known with certainty, the category score used in quantile g-computation is known with certainty. In cases with more than 1/q% measurements below the LOD, the packages `qgcomp` comes with a convenience function `mice.impute.leftcenslognorm` that can be used to impute values below the limit of detection from a left censored log-normal regression model.

### Multiple imputation
Multiple imputation uses multiple datasets with different imputed values for each missing value across datasets. Separate analyses are performed on each of these datasets, and the results are combined using standard rules. The function `mice.impute.leftcenslognorm` can be interfaced with the `mice` package for efficient programming of multiple imputation based analysis with quantile g-computation. Examples cannot be included here without explicitly installing the `mice` package, but an example can be seen in the help file for `mice.impute.leftcenslognorm`.

```{r parend, echo=TRUE}
# return to standard processing
future::plan(future::sequential) # return to standard evaluation
```


## References
Alexander P. Keil, Jessie P. Buckley, Katie M. O'Brien, Kelly K. Ferguson, Shanshan Zhao,Alexandra J. White. A quantile-based g-computation approach to addressing the effects of exposure mixtures. <https://doi.org/10.1289/EHP5838> 

## Acknowledgments<a name="ack"></a>

The development of this package was supported by NIH Grant RO1ES02953101. Invaluable code testing has been performed by Nicole Niehoff, Michiel van den Dries, Emily Werder, Jessie Buckley, Barrett Welch, Che-Jung (Rong) Chang, various github users, and Katie O'Brien.


