Pretreatment Tables Predicting Pathologic Stage of Locally Advanced Prostate Cancer

BACKGROUND Pretreatment tables for the prediction of pathologic stage have been published and validated for localized prostate cancer (PCa). No such tables are available for locally advanced (cT3a) PCa. OBJECTIVE To construct tables predicting pathologic outcome after radical prostatectomy (RP) for patients with cT3a PCa with the aim to help guide treatment decisions in clinical practice. DESIGN, SETTING, AND PARTICIPANTS This was a multicenter retrospective cohort study including 759 consecutive patients with cT3a PCa treated with RP between 1987 and 2010. INTERVENTION Retropubic RP and pelvic lymphadenectomy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS Patients were divided into pretreatment prostate-specific antigen (PSA) and biopsy Gleason score (GS) subgroups. These parameters were used to construct tables predicting pathologic outcome and the presence of positive lymph nodes (LNs) after RP for cT3a PCa using ordinal logistic regression. RESULTS AND LIMITATIONS In the model predicting pathologic outcome, the main effects of biopsy GS and pretreatment PSA were significant. A higher GS and/or higher PSA level was associated with a more unfavorable pathologic outcome. The validation procedure, using a repeated split-sample method, showed good predictive ability. Regression analysis also showed an increasing probability of positive LNs with increasing PSA levels and/or higher GS. Limitations of the study are the retrospective design and the long study period. CONCLUSIONS These novel tables predict pathologic stage after RP for patients with cT3a PCa based on pretreatment PSA level and biopsy GS. They can be used to guide decision making in men with locally advanced PCa. PATIENT SUMMARY Our study might provide physicians with a useful tool to predict pathologic stage in locally advanced prostate cancer that might help select patients who may need multimodal treatment.

Making regression tables simplified

estout, introduced by Jann (Stata Journal 5: 288–308), is a useful tool for producing regression tables from stored estimates. However, its syntax is relatively complex and commands may turn out long even for simple tables. Furthermore, having to store the estimates beforehand can be cumbersome. To facilitate the production of regression tables, I therefore present here two new commands called eststo and esttab. eststo is a wrapper for offcial Stata’s estimates store and simplifies the storing of estimation results for tabulation. esttab, on the other hand, is a wrapper for estout and simplifies compiling nice-looking tables from the stored estimates without much typing. I also provide updates to estout and estadd.

Making regression tables from stored estimates

Organizing and archiving statistical results and processing a subset of those results for publication are important and often underestimated issues in conducting statistical analyses. Because automation of these tasks is often poor, processing results produced by statistical packages is quite laborious and vulnerable to error. I will therefore present a new package called estout that facilitates and automates some of these tasks. This new command can be used to produce regression tables for use with spreadsheets, LaTeX, HTML, or word processors. For example, the results for multiple models can be organized in spreadsheets and can thus be archived in an orderly manner. Alternatively, the results can be directly saved as a publication-ready table for inclusion in, for example, a LaTeX document. estout is implemented as a wrapper for estimates table but has many additional features, such as support for mfx. However, despite its flexibility, estout is—I believe—still very straightforward and easy to use. Furthermore, estout can be customized via so-called defaults files. A tool to make available supplementary statistics called estadd is also provided.

From regression estimates to document tables: output formatting using -estout-

Postestimation processing and formatting of regression estimates for input into document tables are tasks that many of us have to do. However, processing results by hand can be laborious, and is vulnerable to error. There are therefore many benefits to automation of these tasks while at the same time retaining user flexibility in terms of output format. The estout package meets these needs. estout assembles a table of coefficients, "significance stars", summary statistics, standard errors, t/z statistics, p-values, confidence intervals, and other statistics calculated for up to twenty models previously fitted and stored by estimates store. It then writes the table to the Stata log and/or to a text file. The estimates are formatted optionally in several styles: html, LaTeX, or tab-delimited (for input into MS Excel or Word). There are a large number of options regarding which output is formatted and how. This talk will take users through a range of examples, from relatively basic simple applications to complex ones.

MRTAB: Stata module to compute one- and two-way tables/graphs of multiple responses

mrtab tabulates multiple responses which are held as a set of indicator variables or as a set of polytomous response variables.