Andrew Vickers

@vickersbiostats.bsky.social

Biostatistician at Memorial Sloan Kettering Cancer Center. Special interest in prostate cancer, risk prediction, patient-reported outcomes, decision-making.

Hiring right now and the surnames on our shortlist have the following origins: Jewish, Chinese, Irish, West African, East European, English, South Asian, Spanish. What a joy to live in a country where we get so many different histories and perspectives.

When I report that I am doing a randomized trial, this is in distinction to a study with non-randomized or historical controls. But when a researcher claims to be analyzing "real world data", this is in distinction to what exactly?

Looking for the best papers on the positive and negative likelihood ratio for prostate MRI. What is LR+ and LR- separately for PIRADS 3, 4 and 5? Problem is, this can only be derived from a study where everyone had a biopsy or long-term follow-up.

One of my true heroes. She managed to find a work around to a catch-22 that male doctors had created to prevent women becoming doctors.

Admirable Women@admirablewomen.bsky.social · 2mo ago

Physician & suffragist Elizabeth Anderson was born #OTD in 1836. She was the: * First woman to qualify in the UK as a physician & surgeon & first MD in France * Co-founder of the first hospital staffed by women * First dean of a British medical school * First female mayor in the #UK #WomenInSTEM

A gorgeous 19th-century black-and-white studio portrait of Elizabeth Garrett Anderson (1836–1917), the pioneering physician who shattered gender barriers as Britain's first openly qualified female doctor and the co-founder of the London School of Medicine for Women. Captured in profile facing left, Dr. Anderson sits in a dark wooden armchair, her body slightly inclined forward over a small ornate side table. Her pose reflects deep concentration and intellectual engagement; her left hand holds an open book, while her right elbow rests on the table, lifting her hand to cradle her temple. Her expression is calm yet intensely focused, with downcast eyes fixed on the text.She wears a tailored 19th-century dress featuring a structured bodice, striped lapels, tight sleeves with crisp white cuffs, and a full, heavy skirt flowing into a bustle. Her dark hair is swept back into a neat braided bun. A secondary closed book rests on the table, and a decorative tapestry hangs in the background. The studio lighting casts a soft focus across the scene, creating a serious, contemplative mood that emphasizes her lifelong dedication to medical science and education.

Lots of interesting arguments against removing "cancer" label from pattern 3 prostate disease. For instance, pattern 3 involves basal cells & often has PTEN loss. Question now is whether these are a hill worth other people dying on. jamanetwork.com/journals/jam...

Prostate Cancer Mortality After Relabeling Low-Grade Prostate Cancer as Precancerous

This decision analytical model study evaluates the effects of relabeling grade group 1 prostate cancer on prostate cancer mortality in the US.

jamanetwork.com

PSA screening reduces cancer-specific mortality (PCSM). Questions remain about effect on all-cause (OS). PSA could reduce PCSM not OS only with deaths from other causes, eg complications of surgery. Please reply with list of screening-induced causes of death.

Just submitted a paper to "Diabetes & Metabolic Syndrome: Clinical Research & Reviews". Was asked by editor *before* they would peer review: my h-index ; all publications in past 12 months, along with impact factor of each journal in which they were published". #RealScience

If only AI / ML had been around when I was training, I wouldn’t have had to learn about things like causal inference, how to evaluate prediction models or even, say, the importance of data quality. What a waste of time all that was!

Any suggestions as to great resources (lectures on YouTube, short didactic papers etc), to teach novice researchers about RCTs? Design, endpoints, consent, eligibility criteria, IRB etc etc any and all of it.

1) A covariate that is predictive of outcome should be in the model even if unpredictive of assignment (eg matched pairs design). 2) A covariate that is not predictive of outcome should not be in the model, even if predictive of assignment. 3) The propensity score is stupid.