I used the NORDCAN database, which includes patients diagnosed with cancer from 1990 to 2016 from Denmark, Finland, Iceland, Norway, and Sweden. I adopted a model-based approach using flexible parametric survival models and compared them with nonparametric estimates. stpm2 and standsurv were used for obtaining parametric estimates, and strs for nonparametric estimates. I will discuss issues such as age standardization, model stability, winsorizing, and conditional survival.

I developed stpm2 in 2008, which added various extensions. However, the command is old and does not take advantage of some of the features Stata has added over the years. I will introduce stpm3, which has been completely rewritten and adds a number of useful features, including

• full support for factor variables (including for time-dependent effects).
• use of extended functions within a varlist. Incorporate various functions (splines, fractional polynomial functions, etc.) directly within a varlist. These also work when including interactions and time-dependent effects.
• easier and more intuitive predictions. These fully synchronize with the extended functions, making predictions for complex models with multiple interactions/nonlinear effects incredibly simple. Make predictions for specific covariate patterns, and perform various types of contrasts.
• directly saving predictions to one or more frames. This separates the data used to analyze the data for predictions.
• obtaining various marginal estimates using standsurv. This synchronizes with stpm3 factor variables and extended functions, making marginal estimates much easier and less prone to user mistakes for complex models.
• modeling on the log(hazard) scale. Do all the above for standard survival models, competing-risks models, multistate models, and relative survival models all within the same framework.

While it has considerable clinical relevance, there are also inherent limitations in relation to the fixed time point of 24 months and potential variation by treatment type and choice of comparison group. In this presentation, we will highlight some of the methodical limitations and present the first results from a large population-based cohort of FL patients. National register-based information has been combined with detailed medical record data to create a unique cohort with detailed treatment and follow-up information. We allow progression to be time varying and estimate relative rates, as well as OS by first-line treatment and timing of progression using an illness-death modeling approach. Stata packages merlin and multistate were applied, and example code will be presented. Our findings show that progression is associated with worse survival beyond the 24-month time point, illustrating the need for individualized management by timing of progression for optimal care of patients with FL.

In Stata, for example, the multistate packages command msset is a data-preparation tool to transform data in wide format into long format. However, msset is not optimal for multistate model settings with reversible transitions, that is, transitions that allow recovery from one state to another. In this case, correctly defining each transition’s risk time and event (status) without using a program may be preferred. This presentation aims to guide users to prepare data for a reversible multistate model from wide format to long format without using a data-preparation command and will provide tutorials with example data.

Although solutions to both characteristics have been described separately, no one to our knowledge has been handling the case when both characteristics are present. Left-censored data (A) are often wrongfully handled simply by using limit of detection (LOD), which leads to high mean estimates and too-low standard deviation estimates and hence incorrect cutoffs. Ignoring the characteristic (B), researchers often use transformations to handle the observed non-Gaussianity, which leads to too-high cutoffs and an increased proportion of false negatives (type 2 error). We propose a method based on normal quantile plots and OLS regressions to find the upper limit of a reference interval for measurements in a case characterized by A) and B). We also demonstrate how our method can be used to identify whether B) is present in a dataset. We demonstrate our proposed method using real data in two cases. Based on joint work by Niels Henrik Bruun, Stine Linding Andersen, Nanna Maria Uldall Torp, and Peter Astrup Christensen.

This presentation introduces the rqr command, which can be used to estimate residualized quantile regression (RQR) coefficients, and the rqrplot postestimation command, which can be used to effortlessly plot the coefficients. The main advantages of rqr compared with other Stata commands that estimate (unconditional) quantile treatment effects are that it can include high-dimensional fixed effects and that it is considerably faster than the other commands.

A challenge in such applications is presenting the magnitude of uncertainty emerging from the data in light of the assumed statistical model. The aim of this talk is to illustrate a visualization tool that follows the command drmeta to graph marginal and conditional quantiles of the predicted dose–response relationships based on weighted mixed-effects models estimated on tables of aggregated data. The developed postestimation command works with different study designs, dose transformations, and outcome measures; it allows the investigator to derive any quantile (0.01 to 0.99) of the pointwise dose–response relationship; it allows the investigator to define a fine grid of dose values and to choose a referent; it shades quantiles to help distinguishing common versus extreme quantiles; it allows the user to overlay the study-specific BLUPs; it returns both static images for research articles and interactive html visualizations for web dissemination; it is based on Plotly Graphing Library taking advantage of the Stata/Python integration. Real and simulated data will be used to illustrate the use of the postestimation command.

I illustrate how to obtain these treatment effects using GMM. Additionally, I show how some other proposed estimators for heterogeneous treatment effects can be fit using GMM.

The new command provides various copulas, allowing the user to choose a copula that best captures the dependence features of the data caused by the presence of common unobserved heterogeneity. Although the estimation of model parameters does not differ from the bivariate case, the existing community-contributed command bicop does not consider the structural model's recursive nature for predictions and doesn't enable margins as a postestimation command. rbicopula estimates the model parameters, computes treatment effects of the first dependent variable, and gives the marginal effects of independent variables. In addition, marginal effects can be decomposed into direct and indirect effects if covariates appear in both equations. Moreover, the postestimation commands incorporate two goodness-of-fit tests. Dependent variables of the recursive bivariate model may be binary, ordinal, or a mixture of both. I present and explain the rbicopula command and the available postestimation commands using simulated data and data from the Stata website.

This presentation will give an overview about how to estimate the number of common factors and how to test for cross-sectional dependence. It does so by presenting two community-contributed commands: xtnumfac and xtcd2. xtnumfac implements 10 different methods to estimate the number of factors, among them the popular methods by Bai and Ng (2002) and Ahn and Horenstein (2013). The degree of cross-section dependence is investigated using xtcd2. xtcd2 allows implements three different tests for cross-section dependence, based on Pesaran (2015), Juodis and Reese (2021), and Pesaran and Xie (2021). The presentation includes a review of the theory, a discussion of the commands, and empirical examples.

A key feature of the new command is that it tracks changes in the Stata code and executes the code only when needed, allowing for an efficient workflow. The command is useful for creating automated statistical reports, writing articles with data analysis, preparing slides for a methods course or a conference talk, or even writing a complete textbook with examples of applications.

In a cross-sectional study using data from the Swedish Medical Birth Register, we used logit to estimate odds ratios of adverse obstetric outcomes for different exposures. Adjusted absolute risks and absolute risks differences were estimated using the postestimation command margins. We will also discuss possibilities to extend these methods to matched cross-sectional data.