text:
I am not Sorrow
April 18, 2016 April 19, 2016 rememberplantsLeave a comment
You may have heard this phrase before, it goes something like this…
However, are you really? All those feelings in there, all those bones and tissues and fluids and juices, and you are naming it all, sorrow?
This was pointed out to me in immersing myself in the Spanish speaking language. I started to see in translating the phrases that we use, that these words can be traps, or not. For example, in Spanish, we say, "Lo siento" when expressing "I am sorry" yet its literal translation is "I feel it." How profound! Instead of calling yourself or someone else an entire emotion, we only claim the temporal nature of feeling it, which is not forever. It passes. It is allowed to leave.
In the same way, there are two forms of saying "to be" in Spanish, Ser and Estar. Ser is the way of permanence, like, "Este arbol es un roble (This tree is an oak)" or "Yo soy Italiano ( I am Italian)." Estar is used with temporary conditions, like "Esta lloviendo (It is raining)" or "Estoy feliz/triste (I am happy/sad)." Do you see what happens once again? They don't claim to be an emotion, which like the weather, is in and of itself a fleeting and never permanent experience. Que wow, no?
Meditation:
Watch how the words you choose to empower can be trappings. You are not your emotions, yet you do feel them as they come and go, in time. Maybe we can start to use "I feel…" instead of "I am…". Gentle awareness, like usual, is key in even knowing that these things occur.
Feeling feely,

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As my faithful readers know, about a week ago I decided to throw myself into a 2k-a-day wordcount challenge.
Here's why. It's not that I can't write 2000 words a day. I can. I proved this to myself quickly. I put my nose to the grindstone and dutifully provided the number of words I needed to satisfy this goal. But I noticed, after a few days of this, that my writing was getting out of control.
The best way to explain this feeling would be to use a skiing metaphor. I love skiing, I'll even go down black diamonds if my husband coaxes me with promises of hot chocolate (and this is saying much since I had a rather serious skiing accident on one during my high-school years). I love going fast and hearing the shred of ice and snow under my blades. But there's nothing I hate more than going fast and feeling out of control. You know what I'm talking about, like any bump or hitch will send you flying into a cloud of powder and disjointed skis.
Well, writing 2k a day had a very similar feeling. I didn't feel in control of my story. Instead the demand for word-count was dragging me around by the hair, forcing me to write shoddy descriptions and words just for the sake of themselves. I didn't have time to mull over my characters of where the story was going, which meant that I took it down the wrong path more than once.
It was good for me to do this experiment, because it taught me a lot about my writing process. I'm a slow writer. I need time and space and freedom to delve into the story between the words and discover what I'm trying to say.
So today, I'm going to go back to the 6000 words I wrote and carve them down and figure out what this story needs in order to make it shine.


A Sidmouth shop providing mobility equipment and disability aids will shut at the end of the month.
The road to the vote for women was fraught with opposition across the country and even from women themselves.
Crews from Sidmouth Lifeboat and Beer Coastguard were called to help a man in his 80s who had broken his leg on a coast path at Littlecombe Shute.


text:
Meta-analytic estimation of measurement variability and assessment of its impact on decision-making: the case of perioperative haemoglobin concentration monitoring
Emmanuel Charpentier1,
Vincent Looten1,
Björn Fahlgren1,
Alexandre Barna1 &
Loïc Guillevin2
BMC Medical Research Methodology volume 16, Article number: 7 (2016) Cite this article
As a part of a larger Health Technology Assessment (HTA), the measurement error of a device used to monitor the hemoglobin concentration of a patient undergoing surgery, as well as its decision consequences, were to be estimated from published data.
A Bayesian hierarchical model of measurement error, allowing the meta-analytic estimation of both central and dispersion parameters (under the assumption of normality of measurement errors) is proposed and applied to published data; the resulting potential decision errors are deduced from this estimation. The same method is used to assess the impact of an initial calibration.
The posterior distributions are summarized as mean ± sd (credible interval). The fitted model exhibits a modest mean expected error (0.24 ± 0.73 (−1.23 1.59) g/dL) and a large variability (mean absolute expected error 1.18 ± 0.92 (0.05 3.36) g/dL). The initial calibration modifies the bias (−0.20 ± 0.87 (−1.99 1.49) g/dL), but the variability remains almost as large (mean absolute expected error 1.05 ± 0.87 (0.04 3.21) g/dL). This entails a potential decision error ("false positive" or "false negative") for about one patient out of seven.
The proposed hierarchical model allows the estimation of the variability from published aggregates, and allows the modeling of the consequences of this variability in terms of decision errors. For the device under assessment, these potential decision errors are clinically problematic.
The CEDIT1 is a Health Technology Assessment (HTA) agency within the University Hospitals in Paris (AP-HP2). It is in charge since 1982 of advising the senior management about the adoption and use of innovative medical technologies in AP-HP's hospitals.
We have had to assess, in a limited time frame, the possible impact of the introduction of a device3 monitoring the hemoglobin concentration of patients undergoing surgical intervention. This device is used to produce a measurement (SpHb) of the current hemoglobin concentration by means of a sensor which is a variation of the pulse oxymetry sensors; this measure is supposed to replace the measurement (tHb) produced by a laboratory analyzer, thus avoiding the wait for the laboratory results (an element that could be important in a surgical context) and the disruption in the laboratory work flow caused by unplanned requests.
Previous studies of this device in various clinical settings showed that its measurement errors were large but almost symmetric around 0. A recent meta-analysis [1] aggregated the results reported in 32 papers, 13 of which reported results of operating room use; the average mean error (bias) in this surgical subgroup was 0.4 g/dL, but the measurement error standard deviation was larger than 1 g/dL in 15 of the 16 measurement series reported by these 13 papers.
The authors report a bias whose confidence interval includes 0, but they state "We have not found any publications that provide statistical methods to quantify the uncertainty of SD in meta-analysis". Therefore, its clinical conclusions are based on hypotheses on the possible standard deviation of the measurement errors, without estimating it. The authors complete their conclusion on the bias by warning that "the wide LOA [limits of agreement] mean clinicians should be cautious when making clinical decisions based on these devices".
In order to assess the usability of this device, our HTA therefore required the assessment of decision error risks, hence the need to estimate not only the bias (which can be done by a variety of methods, see [2] for an example), but also the variability of the measurements used in this decision. In other words, the use of this device requires not only the assessment of a (possibly "significant") bias (i.e. an average error whose confidence/credible interval does not contain 0), but also of its variability (e.g. by estimating its standard deviation). This allows us to estimate the probability of a potential clinical decision error.
However, as pointed out by [1], such methods for meta-analytic assessment of variability are almost nonexistent in the field (see Discussion), hence our proposal.
We also wanted to assess the impact of an initial calibration of the device (proposed by some authors in order to remove patient-specific systematic errors) which consists in the subtraction from a given measure SpHb of an initial error SpHb0−tHb0 obtained from initial calibrating measurements of SpHb and tHb:
$${~}_{c}\text{SpHb}\,=\,\text{SpHb}-\left(\text{SpHb}_{0}-\text{tHb}_{0}\right) $$
Therefore, we propose a Bayesian model allowing to pool the information given in various papers about the distribution of measurement errors, and to use this estimation to assess its impact in the modeling of the clinical decision error risks of these two modes of use of the device.
We repeated the published search strategy of [1] on Pubmed and Embase databases, and augmented this search by manual search in the references marked as "Related to" by Pubmed; we then obtained full texts of a first selection of papers, whose "References" section was used to complete the search. Our selection was driven by the following criteria :
The device whose operating characteristics were reported in the paper had to use the same operating principle as our target device.
The paper had to report clinical use during a surgical intervention.
The paper had to report an estimation of both mean and standard deviation of the differences of paired reference (tHb) and device-derived (SpHb) measurements made at the same time, or at least to quote some indicator (such as Bland & Altman's LOA [3]) enabling to reconstruct these measures.
The selected papers were analyzed to extract and/or reconstruct sample sizes, observed point estimates of mean and standard deviation of each study population.
For the intended use case (monitoring of hemoglobin concentration in the operating room), the measurement given by reference methods is the only available reference, and the anesthesiologists' methods are built against this measure. Therefore, we ignored its possible errors and choose to consider tHb, as our standard.
In the selected papers, the same patient may have coupled tHb/SpHb measurements at one or more occasions; we shall see (see Table 1) that in most papers, these different occasions are merged in the same series, without information about intra- and inter-patient variabilities: other papers reported separately measurements made at different occasions, but without information on the possible correlation of measurement errors on the same patient.
Table 1 Data extracted from the literature
Therefore, when a paper reported more than one series of measurement errors (i.e. set of assessments of this error made in the same circumstances on independent patients), these series were kept separate, and analyzed as independent: these series were usually characterized by a factor (e.g. operating phase) strongly linked to hemoglobin concentration, overwhelming the (weak) patient-related factors.
In other words, we ignored a possible "paper" level in our model.
Raw SpHb
We postulated that in each series i in the literature, the individual measurement errors e i,j,k =SpHb i,j,k −tHb i,j,k in patient j of the series i at occasion k are normally distributed (Eq. (1) below). We also postulated that the series-specific means μ i of measurement errors (i.e. the series-specific biases) are normally distributed in the (hypothetical) population of all possible repetitions of such studies, with a population-level mean μ m (overall bias) and a population-level standard deviation σ m (2); similarly, the series-specific standard deviations σ i are supposed to have a lognormal (μ s ,σ s ) distribution in the population (3).
$$\begin{array}{*{20}l} \mathrm{e}_{\text{\textit{i,j,k}}} & \sim {\mathcal{N}}\left(\mu_{i}, {\sigma_{i}^{2}}\right) \end{array} $$
((1))
$$\begin{array}{*{20}l} \mu_{i} & \sim {\mathcal{N}}\left(\mu_{m}, {\sigma_{m}^{2}}\right) \end{array} $$
$$\begin{array}{*{20}l} \sigma_{i} & \sim {\mathcal{L}\mathcal{N}}\left(\mu_{ls}, \sigma_{ls}^{2}\right),~\text{which we shall use as:} \\ \log\sigma_{i}\ & \sim {\mathcal{N}}\left(\mu_{ls}, \sigma_{ls}^{2}\right) \end{array} $$
The postulate of normality of measurement errors (1) allows us to use two well-known results of the sampling theory from normal distributions to derive the likelihoods of the usual m and s estimators of μ and σ from a sample of size n:
$$\begin{array}{*{20}l} \sqrt{n_{i}-1}\,\frac{m_{i}-\mu_{i}}{s_{i}} & \sim t_{n_{i}-1}\qquad\text{and, independently,} \end{array} $$
$$\begin{array}{*{20}l}[-2pt] (n_{i}-1)\,\frac{{s_{i}^{2}}}{{\sigma_{i}^{2}}} & \sim \chi^{2}_{n_{i}-1} \end{array} $$
(4) and (5) allow us to compute the likelihoods of the published series-level estimators m i and s i instead of requiring patient-level data e i,j,k .
Calibrated SpHb
The error for occasion k in patient j in series i, e i,j,k , is defined by e i,j,k =SpHb i,j,k −tHb i,j,k . The error of c SpHb ("calibrated error") c e i,j,k will be:
$$\begin{array}{*{20}l} {~}_{c}e_{\text{\textit{i,j,k}}} & = {~}_{c}\text{SpHb}_{\text{\textit{i,j,k}}}-\text{tHb}{i,j,k} \\ {} & = \text{SpHb}_{\text{\textit{i,j,k}}}-\left(\text{SpHb}_{i,j,0}-\text{tHb}_{i,j,0}\right)-\text{tHb}{i,j,k}\\ {} & = \left(\text{SpHb}_{\text{\textit{i,j,k}}}-\text{tHb}{i,j,k}\right) - \left(\text{SpHb}_{i,j,0}-\text{tHb}_{i,j,0}\right)\\ & = e_{\text{\textit{i,j,k}}}-e_{i,j,0}\,. \end{array} $$
Now, in each series i, we can decompose e i,j,k as the sum of a series-specific bias μ i , a patient specific random effect f i,j distributed with mean 0 and variance \({\tau _{i}^{2}}\), and an occasion-specific random residual g i,j,k distributed with mean 0 and variance \(\upsilon _{i,j}^{2}\).
Suppose further that these terms are independent and, for simplicity, homoscedastic in each series4 (i.e. for all patients j of the series i, \(\upsilon _{i,j}^{2}={\upsilon _{i}^{2}}\)). Then, \(\forall i, \text {Var}\left (e_{\text {\textit {i,j,k}}}\right)={\sigma _{i}^{2}}=\text {Var}\left (\mu _{i}+f_{i,j}+g_{\text {\textit {i,j,k}}}\right)={\tau _{i}^{2}}+{\upsilon _{i}^{2}}\). However,
$$\begin{array}{*{20}l} {~}_{c}e_{\text{\textit{i,j,k}}} & = \mu_{i}+f_{i,j}+g_{\text{\textit{i,j,k}}}-\left(\mu_{i}+f_{i,j}+g_{i,j,0}\right)\\ & = g_{\text{\textit{i,j,k}}}-g_{i,j,0} \end{array} $$
Therefore, \(\text {Var}\left ({~}_{c}e_{\text {\textit {i,j,k}}}\right)=2{\upsilon _{i}^{2}}\). The ratio of corrected to raw measurement standard errors is:
$$\theta_{i}\,= \sqrt{\frac{2{\upsilon_{i}^{2}}}{{\tau_{i}^{2}}+{\upsilon_{i}^{2}}}}\,. $$
Under our assumptions, this ratio can take values between 0 (all error is patient-specific, with no residue, υ=0) and \(\sqrt {2}\) (all error is random, with no patient-specific component, τ=0). Both cases make sense in the current context.
The definition of the calibrated error implies (6) that it is (positively) correlated to the raw error; therefore, their difference should be (negatively) correlated to the raw error, and so should be their means.
It is equivalent to estimate τ and υ or σ and θ. The latter allows, as we shall see, to model series with and without calibrated errors in the same way.
We model the impact of calibration as variations of the measurement error's mean and standard deviation (modeled, as before, as being normally distributed):
$$\begin{array}{*{20}l} {~}_{c}e_{\text{\textit{i,j,k}}} & \sim {\mathcal{N}}\left(\mu\text{\scriptsize c}_{i}, \sigma\text{\scriptsize c}_{i}^{2}\right) \end{array} $$
$$\begin{array}{*{20}l} \mu\text{\scriptsize c}_{i} & = \mu_{i} + \delta_{i} \end{array} $$
$$\begin{array}{*{20}l} \sigma\text{\scriptsize c}_{i} & = \sigma_{i}\,\theta_{i} \end{array} $$
We model the position parameters μ i and δ i of individual series as having a bivariate normal distribution; similarly, we model their (suitably transformed) spread parameters σ i and θ i as bivariate normally distributed:
$${} {\fontsize{9pt}{9.3pt}\begin{aligned} {\mu_{i} \choose \delta_{i}} \sim\mathcal{MVN} \left({\mu_{m} \choose \mu_{\delta}}, \left({{\sigma_{m}^{2}} \atop \rho_{p}\sigma_{m}\sigma_{\delta}} \ \ \ {\rho_{p}\sigma_{m}\sigma_{\delta} \atop \sigma_{\delta}^{2}} \right)\right) \end{aligned}} $$
((10))
$${} {\fontsize{9pt}{9.3pt}\begin{aligned} {\log\sigma_{i} \choose \log\frac{\theta_{i}}{\sqrt{2}-\theta_{i}}} \sim\,\mathcal{MVN} \left({\mu_{ls} \choose \mu_{lt}}, \left({\sigma_{lt}^{2} \atop \rho_{s}\sigma_{ls}\sigma_{lt}}\ \ \ {\rho_{s}\sigma_{ls}\sigma_{lt} \atop \sigma_{lt}^{2}} \right)\right)\end{aligned}} $$
and, as before, (7) allows us to use (4) and (5), mutatis mutandis, to compute the likelihoods from the published data.
From (10)–(11) and the properties of the multivariate normal distribution, it follows that the marginal distribution of μ i is given by (2) and that the marginal distribution of logσ i is given by (3); therefore, despite the appearances, (2)–(3) describe the same model as (10)–(11) when the calibrated data are unknown.
Model implementation and fitting
A Bayesian implementation of this model was fitted by MCMC methods, using the Stan [4] modeling language through the rstan [5] interface to R [6]. The model uses Eqs. (4) and (5) to compute the likelihood of the data and directly implements Eqs. (2) and (3) for series without calibrated SpHb and (8) to (11) for series with calibrated SpHb.
Using (1) and (7), we also sampled the relevant parameters of a new study and of a new observation within this study at each iteration of the MCMC sampling, thus obtaining a sample representative of the (predictive) distribution of measurement errors without being constrained by the particulars of any study. This simulation of the characteristics of the device in a new setting is the basis of our inferences on its performance.
Since our data (means and log-standard deviations of errors in the published series) were already more or less centered around 0 and scaled about 1, we followed [7, 8] and choose a Cauchy(0,3) density as a weakly informative prior distribution for the location parameters μ m ,μ δ and the transformed spread parameters μ ls and μ lt , a half Cauchy(0,3) T[0,] for the standard deviations σ m ,σ δ ,σ ls and σ lt , and a Uniform(-1,1) distribution for the correlation coefficients ρ p and ρ s . This choice allows for a weakly informative prior distribution robust with respect to a few outlier values without expressing unreasonable a prori beliefs in very large values of the parameters they model.
The resulting program is available as the Additional file 1; it is also part of the the noweb source of the present paper (see the Additional file 2 for instructions).
The convergence of the MCMC chains was checked by visual assessment of the MCMC traces (see Additional file 3), the ratios of MCMC standard deviation to standard deviation for each parameter of the model (see Additional file 4) and the chain convergence indicator \(\widehat {\mathrm {R}}\) (see [9]). The quality of the model was assessed by placing each observed quantity in the a posteriori distribution of the parameter it estimates (see Additional file 5).
Diagnostic impact assessment
We used the bias and standard deviation values created during model parameter estimation to assess the impact of measurement errors in terms of decision errors. We postulated that the true values tHb of hemoglobin concentration were uniformly distributed on the [4 12] g/dL range.
Let f the density of the measurement error E (whose realizations are the e i,j,k observations whose mean and standard deviation estimates are reported), and g the density of tHb (F and G being their respective distributions). The probability of observing a measurement SpHb lower than some threshold t (a "positive" reading in our case) is:
$$\begin{array}{*{20}l} \Pr(\text{SpHb}<t) & = \Pr(\text{tHb}+E<t) \\ & = \int_{x}\Pr(x+E<t)\,g(x)\,dx \\ & = \int_{x}\Pr(E<t-x)\,g(x)\,dx \\ & = \int_{x}\left(\int_{e<t-x}\!f(e,x)\,de\right)\,g(x)\,d(x) \\ & = \int_{x}\int_{-\infty}^{t-x}\!f(e,x)\,de\,g(x)\,dx \end{array} $$
Similarly, the probability of a "true positive" is:
$$\begin{array}{*{20}l} {}\Pr(\text{SpHb}<t\wedge{}\text{tHb}<t) & = \int_{x<t}\int_{-\infty}^{t-x}\!f(e,x)\,de\,g(x)\,dx \end{array} $$
Since we modeled errors independent of "true" values tHb, these expressions simplify in:
$$\begin{array}{*{20}l} \Pr(\text{SpHb}<t) & = \int_{x} F(t-x)\,g(x)\,dx \end{array} $$
$$\begin{array}{*{20}l} \Pr(\text{SpHb}<t\wedge{}\text{tHb}<t) & = \int_{-\infty}^{t} F(t-x)\,g(x)\,dx \end{array} $$
The probability of a "positive" case being G(t) by definition, (14) and (15) are sufficient to compute the sensitivity, specificity and positive and negative predictive values.
The diagnostic impact of measurement errors depends on the distribution of the true values tHb. For reasons discussed below, we choose to assess this impact by postulating a uniform distribution of tHb on a range spanning the clinically useful range of threshold values. According to the literature, this range is about 6 to 10 g/dL [10–12]. Therefore, our impact assessment used an uniform distribution over the range from 4 to 12 g/dL.
The literature review led us to select 21 papers [13–33] reporting 34 distinct estimations of the mean and standard deviation of measurement error; among these, four papers [24, 27, 28, 32] report the characteristics of measurement error after initial calibration in five series. The data extracted from the literature are listed in Table 1.
Model fit
In the text, posterior distributions are summarized as mean ± sd (credible interval) unit; the bounds of the credible intervals are the.025 and.975 quantiles. The full set of summary statistics for the MCMC sample can be found in the Additional file 4.
Analysis of raw SpHb measurement errors
The population-level results of the model fitting for raw SpHb measurement errors are depicted in Fig. 1 and summarized in Table 2; Table 3 summarizes predictive error results, i.e. bias and standard deviation in a new study (new setting), and mean error, squared error and absolute error for an new observation.
Meta-analysis of raw SpHb errors. Forest plot depicting the data from the literature (top) and the inferences made on bias and measurement error by model fitting (bottom). Top: square: reported bias (average error) m i (size proportional to weight), whiskers: possible errors (m i ±1.96s d i ). Bottom: credible regions for bias of a new study (μ ∗) and error of a new observation (e ∗,∗,∗)
Table 2 Estimates of the population-level distribution of measurement errors of raw SpHb
Table 3 Replication simulation results for raw SpHb
The overall mean error (bias) of raw SpHb has mean 0.23 ± 0.12 (−0.02 0.46) g/dL; the measurement error of raw SpHb is distributed around this mean with log-standard deviation 0.23 ± 0.04 (0.15 0.30) g/dL.
The mean expected bias (systematic error expected in a new study) is 0.24 ± 0.73 (−1.23 1.59) g/dL. The mean expected error (new measurement error in a new study) is 0.27 ± 1.47 (−2.56 3.26) g/dL, whereas the mean expected absolute error is 1.18 ± 0.92 (0.05 3.36) g/dL, and the root of the mean quadratic expected error is 1.50 g/dL.
Impact of calibration
The population-level estimates of the impact of calibration are presented in Table 4 and Fig. 2 and the simulation-based estimates of the resulting measurement errors are presented in Table 5, which also reports the expected bias correction and expected ratio of raw and calibrated standard deviations (inflation/deflation factor).
Meta-analysis of calibrated SpHb errors. See Fig. 1 for legend
Table 4 Estimates of the population-level distribution of corrections to measurement error allowed by calibration
Table 5 Replication simulation results for calibrated SpHb
One notes that, whereas the bias correction is almost systematically negative (−0.42 ± 0.20 (−0.83 0.02) g/dL), the impact of calibration on standard error and expected errors is modest (the mean expected absolute error is 1.05 ± 0.87 (0.04 3.21) g/dL, which is not much less than in the non-calibrated case), and has a non-negligible probability of enlarging the standard error (actually, for a new study, Pr(θ>1)≈ 0.102).
Estimation of clinical impact
The decisional impact of measurement errors of raw SpHb is summarized in Table 6 in terms of sensitivity, specificity, positive and negative predictive values (conditional probabilities) as well as accuracy and probability of a decision error (absolute probabilities); these results are illustrated in Fig. 3. Similarly, the Table 7 and the Fig. 4 summarize the diagnostic impact of measurement errors of calibrated SpHb. The resultant risks of decision errors and their credible regions are graphically compared in Fig. 5.
Diagnostic values of raw SpHb. Mean values and 95 % credible intervals; left: sensitivity, specificity; right: predictive values
Diagnostic values of calibrated SpHb. Mean values and 95 % credible intervals; left: sensitivity, specificity; right: predictive values
Decision error risks. Mean values and 95 % credible intervals
Table 6 Clinical impact of measurement errors of raw SpHb
Table 7 Clinical impact of measurement errors of calibrated SpHb
Study-level modeling
A mixed-model meta-analysis requires the estimation of one study-level parameter per data point, plus any population-level parameters necessary to the model (in our case, population level mean and standard deviation or, in the case of c SpHb, differences with SpHb means and ratios to the SpHb standard deviation). This is true both for frequentist, ML-based, estimation and for Bayesian model fitting. Therefore, published meta-analyses usually do not allow for checking of their assumptions on which estimations and inferences are based on the sole basis of published data.
In our case, Eqs. (4) and (5) are crucial. The former is uncontroversial: this result is known to be asymptotically true for any sample of independent and identically distributed observation sampled from a distribution for which the central limit theorem holds; its rate of convergence is known to be good enough for almost any "large" sample (one finds often N>30 in common practice in applied statistics), and often considered sufficient for "reasonably" distributed small samples.
The latter is valid only for i.i.d. samples of normally distributed variables. We are not aware of any general asymptotic results concerning the estimation of variability parameters. This scarceness of general results, already noted by [1] has also motivated a recent paper by Nakagawa et al. [34], where the authors build tools for meta-analytic estimation of variability; the relevant tool for their question being the Coefficient of Variation, and ratios thereof, they derive the relevant estimators and their properties.
Their work is based on an unbiased estimator of the log of the standard deviation σ:
$$\widehat{\log{\sigma}}\,=\,\log{s}+\frac{1}{2(n-1)} $$
This equality can be derived from the left-hand side of (5). The authors add: It is assumed that with a large sample size and sufficiently large value of σ, logσ is normally distributed with variance \(s^{2}_{\log \sigma }\). They support this assumption by referencing a 1987 paper by Raudenbush & Bryk, which indeed derive a large-sample theory for this case ([35], pp 250–1). Unfortunately, this paper also states that "First, the underlying data must be assumed normally distributed, an assumption which can be checked by conventional methods". (ibid., p. 252).
In other words, the validity of (5) depends on the accuracy and rate of convergence of logs− logσ to a normal. We are not aware of any analytic or simulation convergence results for this quantity, but the convergence rate of a \({\chi ^{2}_{n}}\) distribution to a normal is known to be slow.
Since the individual data are unavailable by hypothesis in a meta-analytic context, the normality of the distribution of these data cannot be "checked". The rate of asymptotic convergence to normality being unknown, the assumption of normality of individual data is a strong necessity of validity of our modeling.
We are not aware of any other literature pertinent to the meta-analytic estimtion of variability.
Population-level modeling
The modeling of means (Eq. 2) is the de facto standard in meta-analysis. The modeling of standard deviations (Eq. 3) is less so:
By analogy with the sampling distribution of the variance of identically sized normal samples, a gamma distribution was a "natural" candidate for this modeling. However, the interpretation of its parameters was delicate, and the elicitation of priors to these parameters even more so. Therefore, we choose to use a lognormal model of the standard deviations population. The point of this choice was to get a parametrization allowing easy interpretation and easy prior elicitation.
We also modeled μ i independent of σ i ; this assumption simplifies programming, and appears reasonable: in the original data, the correlation of biases and standard deviations is 0.013 (similarly, the correlation of mean c SpHb and their standard deviations is −0.53, with only 3 d.f.).
Modeling of calibrated SpHb
The rationale for modeling c SpHb as we did has been exposed supra. We could have also used a single model, using only (10) and (11) and treating the (hypothetical) values of c SpHb in studies not reporting it as missing data (supplementary parameters to the model). The results should be equivalent, but the programming would have been more awkward.
Prior distributions
We needed to give our hyperparameters a proper prior distribution, in order to get proper posterior distributions and to be able to use the log-posterior samples to estimate a Bayes factor. However, we had very little information on the distribution of our subject of interest before reading the relevant papers; therefore, we choose to use weakly informative priors. Centering them on 0 was uncontroversial. The difficulty was in the choice of shape and scale.
It has been noted that the common \({\mathcal {N}}(0,V^{2})\) for some very large standard deviation V, often used as a "weakly informative" prior distribution, expresses a prior belief of absolute values larger than V of about 0.32. Choosing an unreasonably high value of V is hardly defensible in face of the subject matter.
Our choice of priors was remotely inspired by the work of Gelman et al. [7, 8] and we borrowed their proposed functional form, except for correlation coefficients where a Uniform(−1,1) was a natural choice.
Clinical impact assessment
We choose to report the clinical performance of the device by the (absolute) probability of a (potential) decision error; this index seemed more clinically intepretable and useable than specificities and sensitivities (which can be traded off against one another by the choice of threshold), which are conditional probabilities.
Postulating the independence of values of measured variable and measurement error allowed us to use (14) and (15), which can be simply computed, at least with our choice of population distribution of the variable, with a standard numerical integration routine.
They can even be explicitly solved in some cases: for example, a normal density of values and a normal density of errors convolve to a normal density, which can be trivially used to compute the probability of errors. However, this model would not have reasonable medical support in our case.
Without this postulate, the multiple integrals (12) and (13) are much more problematic to compute numerically, and a better solution would probably be to estimate them by one form or another of Monte Carlo integration.
We had also to choose a range of "useful" values to assess the potential clinical impact of the measurement errors.
It is obvious that real values quite far from the decision threshold do not contribute to false positive/negative (the probability of a large error is small).
On the other hand, a small region quite close to the decision threshold overstates the risk of false results. For example, with a symmetric error density, given a threshold T in a study i with average bias μ i , the rate of false negative a test region (T+μ i +ε T+μ i +2ε) would have a limit of \(\frac {1}{2}\) for ε→0+.
Similarly, a strong mode would overstate the importance the weight of the region surrounding that mode.
The anesthesiological literature shows that a "reasonable" region for transfusion decision threshold is [6 10] g/dL; the choice of a threshold in this range for a given patient depends on various domain- and patient-specific factors.
It was therefore necessary to cover this range (with extension to "likely" regions), without justification to choose a mode. This led us to choose the [4 12] g/dL.
A better choice would have been to model the distribution of tHb of measurements done for clinical reasons (i.e. excluding the systematic or calibration measurements). The source papers, however, did not document this information in any usable form.
The posterior distribution of measurement errors in a new study is slightly asymmetric around 0 and gives a non-negligible probability to large measurement errors; one also notes that the mean absolute expected error is a large fraction of the range of clinically useful range of measurement.
One notes that the convergence of the estimation of c SpHb-related parameters by MCMC is more difficult than for raw SpHb-related ones (smaller n_eff): this might be accounted for by the very low number of available data; only five series after calibration have been published, which might be the absolute minimal sample size for estimating variability.
One also notes that the mean calibrated measurement error is negative. This might have a natural explanation: the calibration is made at the beginning of an intervention, when SpHb is, in general, normal for the patient, whereas the clinically useful measurements are done during intervention, when SpHb might have been lowered by surgical hemorrhage and subsequent perfusion. Several authors [17, 20, 21, 23, 26] have reported, with various levels of precision, a relation between (true) hemoglobin concentration and measurement error; this might explain why the correction, computed on a high-hemoglobin concentration basis, is insufficient to cancel the actual bias observed in low-hemoglobin concentration conditions.
One notes, however, that none of these papers reporting this value-error relationship gave enough detail to allow modeling; our analysis, which is therefore compelled to ignore it, is therefore a simplification of the real situation (but probably not an oversimplification).
Use of trends
Several authors have reported to have used SpHb (or SpHb c) in terms of trends in time, allowing them to assess the need for a reference measurement tHb rather than the need for transfusion; other authors suggested using these trends, but without reporting actual use. However, these reports were not precise and consistent enough to allow a modeling of this use without resorting to correspondence with the original authors for crucial details. Our time limits precluded such a research.
The probability of a "false report" (false positive or false negative) varies slightly over the range of clinically useful thresholds; however, this probability (13–14 % for raw SpHb, around 13 % for calibrated SpHb) remains clinically problematic: it would affect about one patient out of seven. However, the risk of "effective clinical decision error" is probably lower: the hemoglobin concentration is but one input in a complex decision process, whose analysis on the basis of published information is impossible.
The asymmetry of the diagnostic value curves around the midpoint of the clinically useful range (Figs. 3 and 4) is a consequence of the slight biases of raw SpHb and calibrated SpHb.
One should note (see Fig. 5) that the risks of "false reports" are much more uncertain for calibrated SpHb than for raw SpHb, a consequence (again) of the very low number of published studies on calibrated SpHb.
The present study has a number of restrictions that limit its significance: Literature review The allocated time for our review precluded an extensive search for gray literature; it also precluded querying the original authors for precisions about their results. Limiting a meta-analysis to formally published data is known to reinforce imprecision.
Similarly, we did not conduct a formal bias risk assessment; this, however, was of lesser consequence for our goals. Study design We did not compare the proposed monitor and the reference method (laboratory measurement) to a common (hypothetical) "gold standard"; instead, we assessed the impact of substituting the monitoring to the reference method in terms of clinical decision differences. Since the reference method is the current "clinical gold standard", it is supposed perfect for clinical purposes, and its possible false positives and false negatives are ignored.
Such a comparison, which might have been be worthy if the proposed monitoring had a variability close to the variability of the reference method (various sources quote a mean absolute expected error in the 0.1–0.3 g/dL range), would need an assessment of the reference method, unavailable from published data. Modeling We didn't even consider fitting a so-called "fixed effects" model, considering that heterogeneity of published data was self-evident.
Lack of time precluded a sensitivity study of the impact of the shape of the study-level parameters distribution and most notably of the shape of the population-level parameters distribution. Our choices seem "reasonable", but their impact has not been assessed. Further work should assess these impacts.
Similarly, the impact of a departure from the assumption of normality of measurement errors should be systematically assessed, both analytically and with simulation approaches.
Our goals in modeling were limited to the assessment of measurement error and its consequences in terms of decisional errors. In contrast, the authors of [1] created a multiple regression model allowing them to assess the impact of various covariates. This modeling, probably very interesting to anesthesiologists and physiologists, was out of our scope of assessing the practical usability of the device under examination.
Finally, we did not try to assess the reality of the impact of calibration in terms of "hypothesis testing" or "model comparison": this question was not in our scope of interest. Clinical impact estimation Modeling the clinical consequences of "false reports" is a much more intricate problem, requiring the modelization of a large body of medical knowledge. This question was widely out of the limited scope of the present study.
It should be noted that the main result in terms of clinical impact is an absolute probability of error rather than a conditional probability (such as sensitivity, specificity or predictive values).
This study has shown that:
Under the assumption of normality, a hierarchical model of variability can be built and used to estimate the variability of a phenomenon from published aggregate data, without recourse to individual data.
This estimation can be used to assess the decisional (binary) consequences of the variability of the phenomenon of interest.
The device of interest has been shown to have a mean absolute expected error of 1.18 ± 0.92 (0.05 3.36) g/dL, which is large when compared to the clinically useful range of measurements.
The mean measurement error (bias) is 0.24 ± 0.73 (−1.23 1.59) g/dL, whose 95 % credible interval contains 0, and which is negligible compared to the mean absolute expected error.
This measurement error entails a risk of decision error potentially impacting one patient out of seven, which is clinically problematic.
This risk of "false report" is therefore much less a consequence of the mean expected error (bias) than a consequence of the mean absolute error (variability of the measurement).
A calibration of the device using an initial reference measurement does not change this situation to any clinically relevant extent.
The proposed model, whose range of validity remains to be assessed, allows estimation of the variability-bound decision errors risk of a measurement from published aggregates; in the motivating example of hemoglobin concentration monitoring, this estimation shows that its clinical use is problematic.
Ethical approval and consent
The present paper illustrates the proposed model with an example using already-published data. The authors did not check the conformance of the original papers to the Declaration of Helsinki and relied on the original papers publishers' checks.
Standards of reporting
The present paper illustrates the proposed model with an example using already-published data; however, it does not aim to be a full-fledged systematic review of the motivating case. In consequence, the authors did not use the PRISMA checklist; this is discussed as one of the study limitations.
The data set supporting the results of this article is included within the article as Table 1 and its Additional file 6, noweb source of the article.
1 Comité d'Évaluation et de Diffusion des Innovations Technologiques de l'AP-HP.
2 Assistance Publique — Hôpitaux de Paris.
3 Masimo Radical-7, Masimo Corp., USA. This device uses an extension of plethysmography by evaluating skin reflectance at 12 different wavelengths.
4 The assumption of homoscedasticity of the residual errors allows for a simpler expression of the decomposition of the total error, but is not a necessary condition of validity; the assumption of independence is more crucial.
HTA:
Health Technology Assessment: "the systematic evaluation of the properties and effects of a health technology, addressing the direct and intended effects of this technology, as well as its indirect and unintended consequences, and aimed mainly at informing decision making regarding health technologies" [36]
tHb:
Hemoglobin concentration as measured by the reference method
SpHb:
Hemoglobin concentration as measured by the device of interest
cSpHb:
Hemoglobin concentration as measured by the device of interest and corrected of initial bias
Limits of agreement, a term widely used in methods comparison studies. See [3]
\({\mathcal {N}}(\mu, \sigma ^{2})\) :
Denotes a normal density of mean μ and variance σ 2
MCMC:
Monte-Carlo Markov Chains
\({\mathcal {L}N}(\mu, \sigma ^{2})\) :
Denotes a lognormal density of location parameter μ and spread parameter σ 2
\({\mathcal {M}VN}(\boldsymbol {\mu },\boldsymbol {\Sigma })\) :
Denotes a multivariate normal density of mean vector μ and variance-covariance matrix Σ
t n :
Denotes a standard Student's t density with n degrees of freedom
\({\chi ^{2}_{n}}\) :
Denotes a chi-squared density with n degrees of freedom
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HtaGlossary.net | health technology assessment (HTA). http://htaglossary.net/health+technology+assessment+%28HTA%29.
The authors warmly thank Pr Bruno Falissard (Hôpital Paul-Brousse (AP-HP) and Unité INSERM U669), who declined authorship of this paper notwithstanding the importance of his contributions, criticisms and suggestions.
The authors also wish to thank Dr Shinichi Nakagawa, Associate Professor of Behavioural Ecology, University of Otago, who was kind enough to send us a copy of his paper unobtainable from french public universities library system.
The authors wish to thank Dr Ian R White, MRC Biostatistic Unit, and Pr Sylwia Bujkiewicz, Lecturer in Biostatistics, University of Leicester, reviewers of earlier versions of the present paper, for their insightful and helpful comments and suggestions which, among other enhancements, corrected a serious deficiency of a previous version of the presented model.
In memoriam Jean Charpentier, Aug 9, 1930 — Sep 7, 2015.
Secrétariat Scientifique du CEDIT — Assistance Publique - Hôpitaux de Paris, 3, Avenue Victoria, Paris, F-75186, France
Emmanuel Charpentier
, Vincent Looten
, Björn Fahlgren
& Alexandre Barna
CEDIT — Assistance Publique - Hôpitaux de Paris, 3, Avenue Victoria, Paris, F-75186, France
Loïc Guillevin
Search for Emmanuel Charpentier in:
Search for Vincent Looten in:
Search for Björn Fahlgren in:
Search for Alexandre Barna in:
Search for Loïc Guillevin in:
Correspondence to Emmanuel Charpentier.
Starting from a larger assessment study led by EC and AB, EC designed the study, extracted the analyzed data, conceived, refined and ran the probabilistic model, proposed its medical implications and drafted successive versions of the paper; EC, VL and BF searched and collected the primary data, validated data extraction, criticized the model, the results and their interpretation; AB and LG contributed to reframing the methods and results in the larger HTA context, and provided much needed medical criticism and, therefore, a large part of the discussion. However, the authors wish to underscore that this paper, as any scientific paper, results from a team work, where questions and criticisms of some authors were as important in the development of the paper as answers from other authors; therefore, the authors do not wish to separate their respective contributions. All authors read and approved the final manuscript.
Additional file 1
Stan implementation of the meta-analytic model. (STAN 7.66 kb)
(Unix) text file: how to reproduce this paper (incl.software requirements). (TXT 1.70 kb)
MCMC traces (post-warmup). (PDF 2170.88 kb)
Summary of the posterior distribution of all parameters in the model. (PDF 41.3 kb)
Boxplots of study-level parameters distributions against the observed data they model. (PDF 27.2 kb)
noweb (knitr) source of this paper. Includes data, R and Stan code, bibliographic database. (RNW 182 kb)
Charpentier, E., Looten, V., Fahlgren, B. et al. Meta-analytic estimation of measurement variability and assessment of its impact on decision-making: the case of perioperative haemoglobin concentration monitoring. BMC Med Res Methodol 16, 7 (2016) doi:10.1186/s12874-016-0107-5
meta-analysis as topic
Observer variation
Predictive value of tests
intraoperative
physiologic/methods
Biological markers/blood
Hemoglobinometry
AMS Subject Classification
Primary 62F15
secondary 92B15


Since the US elections in November global stock markets are up $14trn and global bond markets are up $2trn, see chart below. This is a significant wealth effect for consumers and corporates, which should continue to support consumer spending and capex decisions in the US and Europe.
Sharon Lee We all know staying active is important, but sometimes we need a little push. Here are 10 dream homes that will give you the motivation you need. 1. Basketball 2571 Del Lago Drive, Fort Lauderdale, FL listed by Gilles Rais with Coldwell Banker Residential Real Estate for $10,900,000….
The Euro Properties' project 118 East 59th Street received "The Best High-Rise Residential Development" from iProperty Best Development Awards 2015.
by Bill McBride on 12/29/2015 09:21:00 AM S&P/Case-Shiller released the monthly Home Price Indices for October ("September" is a 3 month average of August, September and October prices). This release includes prices for 20 individual cities, two composite indices (for 10 cities and 20 cities) and the monthly National index….
Fancy buying a Singapore-style ultra-luxury apartment in New York?
euroadm :: in November 30, 2015 :: in Blog :: Comments Off on Fancy buying a Singapore-style ultra-luxury apartment in New York?


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Here's a brief update of my trip up north (to Colorado)… I drove the first five hundred miles the first day, leaving the easier (four hundred miles) to the next. It's not a particularly scenic drive until you get on I-25 from Raton (New Mexico) to Pueblo, Colorado. But, all of that sagebrush and dry, flat land gets a great pay-off when you go on the Raton Pass – through some of the most beautiful mountains. Truly gorgeous.
I don't mind the drive (though it is a little long) because I always have my audible.com books with me. This time, I had both Patricia Wentworth's Miss Silver and Ellis Peters' Brother Cadfael in the car with me as "passengers". Perhaps this is why the trip doesn't seem so unappealingly long. I am "kept company" with the words of two of my favorite mystery book authors. Following along as two of my favorite sleuths put together the pieces of their mysteries is a great road-trip companion.
Of course, as usual, some of the scenes I saw along the way would make me stop my audible book for a few minutes so that I could enjoy the sights and think. I revert to one particular car game from my past, when my children were young. I find myself seeing old brick farm houses and wondering about the lives of the people inside, I see them all sitting around a dinner table – passing the mashed potatoes and thick, creamy gravy. I wonder if they enjoy reading books on cold winter nights, if they preserve their own jams and jellies, in short, I think about all of the generations of families who lived in some of these old farm houses.
Danna, I am glad that you had 2 such great companions for your trip. When I was very young, I thought I was very daring to read a mystery story. That was one of Patricia Wentworth's Miss Silver's. In those days, mysteries were blood and guts. Nice school girls did not read them. Now I can enjoy Cozies withouy feeling guilty. I hope all is well with your folks.
I felt like I was riding along with you and your passengers Miss Silver and Brother Cadfael. Thanks for the nice visual.
Smiled at the thought of Miss Silver in the front passenger seat and Brother Cadfael in the back seat.
It sent me on a search of Patricia Wentworth and Ellis Peters. I know I haven't read any Ellis Peters' books and I don't think I have read any of Patricia Wentworths unless it was many years ago when I read Agatha Christie's books. Now I have some more new (to me) authors to try. Looks like one can still purchase most of Ellis Peters but a lot of Patricia Wentworths are out of print. I wish out of print books would be made available in Kindle form. Thanks for taking us cozy mystery readers along on your trip.
I would liken Patricia Wentworth to Agatha Christie. As for Cadfael, the PBS Brother Cadfael mystery series was based on these books by Ellis Peters. I happen to have seen the PBS shows, which led me to the mystery novels.
(Now that I have my Kindle, I know what you mean about the out-of-print books not being available. I hope they hurry up and get them. I wanted to get all of Cyril Hare's (Inspector Mallet and Francis Pettigrew) and Stuart Palmer's (Hildegarde Withers) mystery series for my Kindle, but found that they are not available… (hopefully)YET!
I do the same thing with the houses and barns! Only sometimes I wonder what ghosts linger there, if there was ever a murder or what secrets hide out in that barn…I wonder if the family kept a horse or a donkey and where the kids waited for the school bus.
Sometimes on the flats, I wonder about the people who first tried to cross it in a covered wagon. Lord, that would have been a long and impossible trip waiting to find water and waiting to find a place that looked habitable.
Glad you are safely at your destination. Enjoy yourself!
Oh my gosh, Maria! Does anyone remember the Gordon Lightfoot song "If you could read my mind…"?
You and I must have the same brain waves while driving the Texas flatlands!
I just checked BN and most of Patricia Wentworth's books are available for the NOOK. Amazon has some of them for the Kindle. Amazon is cheaper by $.20 than BN.
I took a second look after your response about Patricia's Wentworth's out of print books on e-books. I received a new Kindle Fire for Christmas. When I looked yesterday only saw 3 Miss Silver books on Amazon Kindle. Today went to B&N and saw they had most of Miss Silver's books on the Nook. Sputtering I went back to Amazon and looked again. Instead of three e-book titles, there were now quite a few. Don't know what I did yesterday.
I hadn't used my Kindle Fire to download ebooks yet.
So I downloaded Grey Mask. Worked slick. So thanks to you I used my Kindle Fire for the first time. Thanks!
I too just down loaded "Grey Mask". I began to read it, but I obtained a number of library books, so I will put it aside for a time. Such memories reading it evoked.
I just wish they would make more of her non-Miss Silver books available in e-book format. I started out reading her stand alones, which are more romantic suspense, and I loved them – they are why I was willing to try the Miss Silvers in the first place. So far I've only seen one non-Miss Silver Patricia Wentworth in e-books and it was obviously a very early effort of the author.
Lynn, you have a real treat coming if you haven't read Ellis Peters. The Brother Cadfaels are wonderful. She also wrote some historical novels under her real name, Edith Pargeter.
If you can't find the Miss Silver books to purchase, try interlibrary loan; a lot of large libraries do keep older books.
I also like the Sister Fidelma mysteries by Peter Tremayne. She is a nun and dalagh (lawyer) in ancient Ireland.
I am going to read a Brother Cadfael after Danna and you have written about the series. When I saw the first name Ellis, I thought it was a male author.
Will look up more information on Peter Tremayne. Sound like another good series. It sounds intriguing about a series in ancient Ireland.
I read a lot like everyone on here. Am surprised how many new authors I am learning about.
Danna your description of your trip takes me back to the days when I was showing dogs and would make the long drive to CO. for the Rocky Mountain circuit. Piloting a 40-ft motorhome over Wolf Creek pass (only in summer) could be interesting but was a much shorter drive than the long way 'round to Durango and then to Farmington NM. There were taped audio books at the time but the pickings were slim, particularly in the mystery category.
I've also been a big fan of Brother Cadfael since the first book appeared just as I was finishing the last of the Inspector Felse mysteries (although I didn't know it was the final one at the time).
I also enjoyed her historical novels – the first brought from the UK by my dad after he had spent several months tracing ancestors in Wales, as well as England, Scotland and Orkney.
Her writing style truly draws one into the story and the word pictures she paints of the locales are far better than most writers. In some of the descriptions I can actually feel myself in the places.
Yep, not much scenery through Texas. Hope your trip is wonderful.
you do make the trip sound so wonderful! I love Miss Silver and have found a couple of authors that are new to me here today!
Just wanted to add how much I love the audible.com releases of the Patricia Wentworth mysteries. Every month when I get my new credits I eagerly check for any new releases; not only are the books perfect for audiobooks (so descriptive), but the performer they have doing the readings is just wonderful.
As for the Brother Cadfael's, I only wish they had Derek Jacobi doing the performance – he has such a beautiful voice and after seeing the BBC/PBS versions of several of the books he IS Brother Cadfael to me.


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WHITE WIDDOW
www.whitewiddow.com.au
'Serenade', the second album by Australian melodic rock band White Widdow, possesses a high quality production, tight musicianship and, on the surface, has much to offer. The opening tracks 'Cry Wolf' and 'Strangers In The Night' kick off with an intro very much like the Survivor classic 'American Heartbeat' but this early promise soon fades. Unfortunately the songs are mostly competent but, apart from the stunning 'Mistake', tend to lack any really memorable choruses or killer hooks. It doesn't take long before you lose interest in White Widdow's repetitious, keyboard dominated sound. 'Serenade' is largely AOR by numbers and generally void of the depth of songwiritng that makes the likes of Boston/Foreigner such great bands. It is also a long way short of classics recorded by bands listed as their major influences such as Survivor's 'Vital Signs' or White Sister's self-titled debut. Devoted fans of this genre will still probably find enough on this album to enjoy but I feel everyone else will find 'Serenade' both underwhelming and uninspiring.
AOR Heaven
Review by Dave Crewe
1) Cry Wolf
2) Strangers in the Night
3) Do You Remember
4) Reckless Nights
5) How Far I Run
6) Serenade
7) Show Your Cards
8) Mistake
9) Patiently
10) Love Won't Wait
"Devoted fans of this genre will still probably find enough on this album to enjoy but I feel everyone else will find 'Serenade' both underwhelming and uninspiring."


I'm a huge December lover & lover of Christmas. There were a good five years where this was ruined a little bit for me as my work was so all consuming over this time. So today, on this first day of Blogmas I'm really excited about finally being able to take part!
For those of you who don't know what Blogmas is, it's where bloggers post every single day over the December Christmas season. Everyone does it really differently but the way I'll be doing it is at least one post every day right up until New Years Eve.
I'm really looking forward to the festive season this year! Even though decorations have now been up in the city and other public places for a few weeks. Today I'm putting out my Christmas decorations and buying Christmas Pyjamas today.
For the first time in a long time I'm going to be able to travel back to New Zealand and spend Christmas with my family. I'm so excited about this and I've been thinking a lot about Christmas' in the past with all the nostalgic feels.
I'm a Sydney based blogger so that means Christmas for me isn't about being surrounded by the beautiful snow and warm cosy Christmas traditions. It's about Summer, BBQs, going to the beach, long summer days and all of the great food that comes along with that.
For health reasons I've recently adopted a plant based diet. For me that means a slow transition into a completely vegan lifestyle. I'll be looking at all the ways I can eat vegan and make more sustainable choices over the holiday period.
Related: 9 fail-safe ways to stick to your eating plan over the holidays.
I've got some great content planned over the next few weeks and leading in to 2019. From party outfit planning, gift guides, holiday travel tips to what's on my Christmas list there's something for everyone over Blogmas!


GQ's editors select the best brand spanking-new menswear, accessories, sneakers, and more from the last seven days worth blowing your disposable income on.
The Canadian-born brunette Lexi Wood loves fashion, has no time for haters, and knows her way around a good selfie.
While foraging for GQ's Best New American Restaurants of 2017, Brett Martin stumbled across the steak that won't destroy your night, an absolutely stunning room, and one delicious sandwich.
Kid Rock wears a backwards fedora to meet President Trump with Sarah Palin and, apparently, every other time he's stepped out of the house.
Time to face facts: You're not gonna finish all those shows.


St. Lawrence University's Alumni Network held onto its No.
Money magazine recently listed St.
The Princeton Review commended St.
The Office of Admissions is hosting a Senior Visit Day on Friday, January 18.
What do you want to know about St. Lawrence?
High school students from around the region, country and world who are visiting St.
Summer is upon us! As you and your family begin to make plans, be sure to add St.


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Thrills on Blueberry Hill: St. Louis neighborhood formed around iconic song, cafe
CR Rae
More Content Now
It might be a topic for conversation around the dinner table one night — is there really a Blueberry Hill?
There is — one in Texas and one in Alaska — but the one that most relates to the song and is named after it is the Blueberry Hill Café in St. Louis, home to rock 'n roll legend Chuck Berry.
A legendary venue
Blueberry Hill Café is in the Delmar Loop in St. Louis and was one of Berry's favorite places to perform. This area is a place where visitors can still find a thrill or two.
Across the street from the famous café is a statue of Berry with guitar in hand. He is known as the father of rock 'n roll, and St. Louis refers to the small red brick home where he lived from 1950 to 1958 as "the birthplace of modern rock 'n roll" because of what Berry accomplished while living at 3137 Whittier St. Berry wrote some of his most famous hits there, including "Johnny B. Goode," "Sweet Little Sixteen," "Roll Over Beethoven" and "Maybelline."
The Blueberry Hill Café is a landmark in northern St. Louis. It opened in 1972 when there was basically nothing in the area. The intent behind the café was to have a place that was all about music, with pop culture memorabilia and great food. The café began with a hot dog machine, beer and beer nuts. Eventually, they expanded the kitchen to allow for a grill and they became famous for their burgers.
The idea of having a music theme was evident from the beginning; an important item in the café was a jukebox. People came from far away to hear songs from its 30,000 records rotated in and out.
The café continued to expand when the Elvis Room was opened in 1985, which allowed for live music to be performed. Today, the Blueberry also has the Duck Room that hosts live music. Berry inaugurated the room and played more than 200 monthly concerts there.
The Blueberry Hill Café is also known for its large collection of celebrity photos and keepsakes. Basically, if you can name a musician or actor, they are most likely on the wall, from Beyonce to Taylor Swift, Bob Dylan and Frosty the Snowman. Everyone will find something in the cafe's more than 100 showcases: "Howdy Doody" dolls and figures from "South Park," "Lord of the Rings" and "Hopalong Cassidy" to name a few.
Around the loop
The once desolate area of Delmar Loop is now a revitalized neighborhood of six blocks. Visitors can take a 2.2-mile trolley ride from one end of the district to the Missouri History Museum in Forest Park, with 10 stops in between. The loop area offers visitors 140 specialty shops, 60 multinational restaurants, 10 galleries, 40 boutique/gift shops and 12 entertainment venues. It is a street filled with live music options.
Just down the street is the Moonrise Hotel, where visitors will find a 3,000-pound moon in the rooftop bar. It is the world's largest man-made moon. A variety of moon treasures can also be found in the hotel lobby display cases.
A walk along the street takes visitors to Vintage Vinyl, which sells records from artists from all genres.
The stars come out at the St. Louis Walk of Fame, which honors more than 150 celebrities from the city. A brass star with information about each is placed in the sidewalk. The names include Miles Davis, Harry Caray, Stan Musial, Vincent Price, and Ike and Tina Turner.
After you have eaten, enjoyed the live music, shopped, seen the stars and had fun, don't forget to snap a photo with the Chuck Berry statue. Then, just as the day begins to turn to night, visitors will see the district turn to neon. A neon man and woman are lighted as though they are bowling on the roof of Pin-Up Bowl, where travelers will also find the world's oldest bowling balls.
For more information go to visittheloop.com and explorestlouis.com for hotel, food and entertainment information.


Become a crowd pleaser as you dress up as your favorite stage performer, even if you can't play an instrument! We can transmogrify you into one of the Greats in just a few minutes … Elvis, the Beatles, Marilyn Monroe, Michael Jackson, whomever you might imagine. Or run away and join the circus as a clown, ringmaster, carnival entertainer or strange member of the freak show. Our wide range of costumes can make you an instant celebrity, even someone who is just famous for being famous. It's all in the hair, in the outfits, in the shoes (if you're a clown) or in hundreds of accessories that you can piece together to be … anyone. Maybe even yourself!


Dissent is something officials do not take kindly to in the modern game, but it was former Arsenal youngster Sanchez Watt's name that got him into trouble in a bizarre mix-up on Tuesday.
When referee Dean Hulme asked the number 10 for his name to put it in the book he thought the 27-year-old was expressing dissent by saying "what".
After three requests for the striker's name he issued another booking and showed him a red card, much to the disbelief of Watt.
Watt took to Twitter on Wednesday, posting a facepalm emoji in a tweet quoting the previous night's incident, before adding a video of rapper Birdman asking the Breakfast Club to "stop playing with my name", as well as a laughing emoji.


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redpajama_set_name: RedPajamaStackExchange


Thanks for hosting :) Have a fantabulous week!
This is my first time linking up. Thanks so much for hosting.
What would we do without the parties! Thanks for hosting each week. Such inspiration to be found here!
Thanks so much for hosting the wonderful party!! Hope you have a great week!