Does anyone know of a funder of clinical trials that has some form of open review? Or has openness about the applicant’s response, committee deliberations and funding decision to the peer reviewers?
I find it very important to do my part as a statistical reviewer of clinical trials, but my review for the Dutch funder ZonMW just disappeared in a black hole.
Please boost this question!
A detailed examination of the 1930 Lanarkshire Milk Experiment (LME) by the famous statistician William Sealy Gossett (“Student”), which appeared in Biometrika in 1931, is re-examined from a more modern perspective. The LME had a complicated design whereby 67 schools in Lanarkshire were allocated to receive either raw or pasteurised milk but pupils within the schools were allocated to either receive milk or to act as controls. Student’s criticisms are considered in detail and examined in terms of subsequent developments on the design and analysis of experiments, in particular as regards appropriate estimation of standard errors of treatment estimates when an incomplete blocks structure has been used. An analogy with a more modern trial in osteoarthritis is made. Suggestions are made as to how analysis might proceed if the original data were available. Some lessons for observational studies in epidemiology are drawn and it is speculated that hidden clustering structures might be an explanation as to why results may vary from observational study to observational study by more than conventionally calculated standard errors might suggest.
NEW PAPER
Overview of performance measures for time to event prediction models. Comes with elaborate R and SAS code
Journal version (paywall) 👉 https://doi.org/10.7326/M22-0844
Preprint 👉 https://doi.org/10.1101/2022.03.17.22272411
Risk prediction models need thorough validation to assess their performance. Validation of models for survival outcomes poses challenges due to the censoring of observations and the varying time horizon at which predictions can be made. This article describes measures to evaluate predictions and the potential improvement in decision making from survival models based on Cox proportional hazards regression. As a motivating case study, the authors consider the prediction of the composite outcome of recurrence or death (the “event”) in patients with breast cancer after surgery. They developed a simple Cox regression model with 3 predictors, as in the Nottingham Prognostic Index, in 2982 women (1275 events over 5 years of follow-up) and externally validated this model in 686 women (285 events over 5 years). Improvement in performance was assessed after the addition of progesterone receptor as a prognostic biomarker. The model predictions can be evaluated across the full range of observed follow-up times or for the event occurring by the end of a fixed time horizon of interest. The authors first discuss recommended statistical measures that evaluate model performance in terms of discrimination, calibration, or overall performance. Further, they evaluate the potential clinical utility of the model to support clinical decision making according to a net benefit measure. They provide SAS and R code to illustrate internal and external validation. The authors recommend the proposed set of performance measures for transparent reporting of the validity of predictions from survival models.
ICYMI:
Out of balance
An essay on covariate adjustment in randomized controlled trials in medicine.
Oh good! This study on preprints vs journal versions has a systematic group:19 preprint/journal pairs from a systematic review on Covid prediction models.
Reporting quality of preprints: lousy.
After peer review: trivially less lousy.
Hudda et al (incl @MaartenvSmeden)
https://www.jclinepi.com/article/S0895-4356(22)00323-7/fulltext
Judith ter Schure
Kane Carlock
Darren L Dahly PhD oMG FFs jFc
Hilda Bastian
Lord Blood 