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The Met Office in the United Kingdom has developed a completely new probabilistic postprocessing system called IMPROVER to operate on outputs from its operational numerical weather prediction (NWP) forecasts and precipitation nowcasts. The aim is to improve weather forecast information to the public and other stakeholders while better exploiting the current and future generations of underpinning kilometer-scale NWP ensembles. We wish to provide seamless forecasts from nowcasting to medium range, provide consistency between gridded and site-specific forecasts, and be able to verify every stage of the processing. The software is written in a modern modular framework that is easy to maintain, develop, and share. IMPROVER allows forecast information to be provided with greater spatial and temporal detail and a faster update frequency than previous postprocessing. Independent probabilistic processing chains are constructed for each meteorological variable consisting of a series of processing stages that operate on predefined grids and blend outputs from several NWP inputs to give a frequently updated, probabilistic forecast solution. Probabilistic information is produced as standard, with the option of extracting a most likely or yes–no outcome if required. Verification can be performed at all stages, although it is only currently switched on for the most significant stages when run in real time. IMPROVER has been producing real-time output since March 2021 and became operational in spring 2022.

In this talk we describe how to fit structural mean models (SMMs), as proposed by Robins, using instrumental variables in the generalized method of moments (GMM) framework using Stata's gmm command. The GMM approach is flexible because it can fit overidentified models in which there are more instruments than endogenous variables. It also allows assessment of the joint validity of the instruments using Hansen's J test through Stata's estat overid gmm postestimation command. In the case of the logistic SMM, the approach also allows different first-stage association models. We show the relationship between the multiplicative SMM and the multiplicative GMM estimator implemented in the ivpois command of Nichols (2007). For the multiplicative SMM, we show—analogously to Imbens and Angrist (1994) for the linear case--that the estimate is a weighted average of local estimates using the instruments separately. To demonstrate the models, we use a Mendelian randomization example, in which genotypes found to be robustly associated with risk factors from genome-wide association studies are used as instrumental variables, thereby investigating the effect of being overweight on the risk of hypertension in the Copenhagen General Population Study.

A comprehensive range of user-written commands for meta-analysis is available in Stata and documented in detail in the recent book Meta-Analysis in Stata (Sterne, ed., 2009, [Stata Press]).The purpose of this session is to describe these commands, with a focus on recent developments and areas in which further work is needed. We will define systematic reviews and meta-analyses and will introduce the metan command, which is the main Stata meta-analysis command. We will distinguish between meta-analyses of randomized controlled trials and observational studies, and we will discuss the additional complexities inherent in systematic reviews of the latter. Meta-analyses are often complicated by heterogeneity, variation between the results of different studies beyond that expected due to sampling variation alone. Meta-regression, implemented in the metareg command, can be used to explore reasons for heterogeneity, although its utility in medical research is limited by the modest numbers of studies typically included in meta-analyses and the many possible reasons for heterogeneity. Heterogeneity is a striking feature of meta-analyses of diagnostic-test accuracy studies. We will describe how to use the midas and metandi commands to display and meta-analyse the results of such studies. Many meta-analysis problems involve combining estimates of more than one quantity: for example, treatment effects on different outcomes or contrasts among more than two groups. Such problems can be tackled using multivariate meta-analysis, implemented in the mvmeta command. We will describe how the model is fit, and when it may be superior to a set of univariate meta-analyses. Will will also illustrate its application in a variety of settings.

Observational epidemiological studies suffer from many potential biases, from confounding and from reverse causation, and this limits their ability to robustly identify causal associations. Several high-profile situations exist in which randomized controlled trials of precisely the same intervention that has been examined in observational studies have produced markedly different findings. In other observational sciences, the use of instrumental variable (IV) approaches has been one approach to strengthening causal inferences in non-experimental situations. The use of germline genetic variants that proxy for environmentally modifiable exposures as instruments for these exposures is one form of IV analysis that can be implemented within observational epidemiological studies. The method has been referred to as 'Mendelian randomization', and can be considered as analogous to randomized controlled trials. This paper outlines Mendelian randomization, draws parallels with IV methods, provides examples of implementation of the approach and discusses limitations of the approach and some methods for dealing with these.