J. Carlos Sáez

Farmacia Hospitalaria. Hospital de León. “It's only rock and roll, but I like It”

[BIT Navarra @BITNavarra]

Actualización del documento de consenso sobre prevención de la fragilidad y caídas en la persona mayor (2026)

https://t.co/EOr5a4PKAC

[Institut Català d'Oncologia @ICO_oncologia]

L’@ICO_oncologia ha acollit la 6a edició del #Preceptorship de Farmàcia Oncològica!
12 professionals de tot l’Estat han participat en aquesta #formació per conèixer de prop com treballem l’atenció farmacèutica en #oncologia.
#FarmàciaHospitalària #ICONICpeople

[Lugar Cinema🎬 @LugarCinema]

Hoy hace 65 años que falleció Michael Curtiz y jamás podremos agradecerle lo suficiente que nos regalara una OBRA MAESTRA como Casablanca (1942)

[Sano y salvo @sanoysalvoblog]

Prescripción pragmática, adaptada al nivel de fragilidad de las personas mayores: nueva guía británica
. Flexibilizar objetivos y simplificar tratamientos
. Abordaje de 6 situaciones clínicas frecuentes
. Traducción al español guía y notas
+ info: https://t.co/DkhyUvRjhR
#SegPac

[Emilio Alegre (farm) @EmilioAlegre1]

DVRd en mieloma: ¿con o sin mantenimiento?
1⃣ Está implícitamente autorizado y financiado sin saber si funciona.
2⃣ Inhibirse -tras un buen IPT- como ha hecho la CIMP solo añade confusión.
3⃣ ¿Ahora qué? Conflictos, pacientes perjudicados y despilfarro: https://t.co/8FQ62RD4j6

[cinesthetic. @TheCinesthetic]

iconic.

[GENESIS-SEFH @GENESIS_SEFH]

📢‼️ Publicado el informe #GENESIS definitivo de #ELAFIBRANOR en el tratamiento de la colangitis biliar primaria. Puedes consultarlo en: https://t.co/LC0C6YRa6z

[Rosana Ferrero 📈📊🙌 @RosanaFerrero]

🚨Cinco años de un gráfico emblemático: el vacío de z-scores entre −2 y 2 en más de un millón de estudios de PubMed sigue mostrando un sesgo de publicación masivo. Los resultados no significativos desaparecen del registro.

#stats #datascience #research

https://t.co/jNroDvXeWR

[Yakup Ergün @dr_yakupergun]

Key Statistical Terminology

1. Hypothesis and P-value

In any clinical trial, the null hypothesis assumes there is no difference between treatment groups.
The P-value represents the probability of observing a difference at least as extreme as the one seen, if the null hypothesis were true.
A smaller P-value means the observed effect is unlikely due to chance, but it says nothing about how large or meaningful that effect is.

Example:
In DESTINY-Breast05, the invasive disease-free survival difference between arms had P < 0.001, suggesting the probability that this result occurred by chance is less than 0.1%.

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2. Statistical Significance

When the P-value falls below a predefined threshold (usually 0.05), the result is said to be statistically significant.
However, “significant” here does not mean “important” — it simply indicates that random variation is unlikely to explain the result.

Example:
A large colon cancer trial might report a median OS improvement of 0.8 months (P = 0.04). The result is statistically significant, but the clinical benefit is trivial.

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3. Type I and Type II Errors

Type I error (false positive): Concluding there is an effect when there is none.

Type II error (false negative): Failing to detect a true effect.

Example:
A small early study suggested T-DM1 improved OS, but later phase III data did not confirm it → likely Type I error.
Conversely, a small subgroup of MSI-H gastric cancer patients might truly benefit from immunotherapy, yet a low-powered trial could miss it → Type II error.

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4. Statistical Power

The probability of correctly detecting a true difference when one exists. Adequate power (usually ≥ 80%) depends on sample size, effect magnitude, and variability.

Example:
A 60-patient single-center trial may lack power to detect a modest immunotherapy effect, whereas a multi-thousand-patient phase III study can.

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5. Effect Size

Indicates how large the observed difference is.
While the P-value asks “does an effect exist?”, the effect size asks “how meaningful is it?”.

Example:
In DESTINY-Breast03, HR = 0.28 reflects not only a statistically significant result but also a clinically substantial effect size.

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6. Confidence Interval (CI)

Represents the precision of an estimate.
A 95% CI means that if the study were repeated many times, the true value would lie within this range 95% of the time.
Narrower intervals imply greater certainty.

Example:
In KEYNOTE-522, the pCR rate difference had a 95% CI = 6.3–21.4.
Because the interval did not cross zero, the effect was statistically significant.

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7. Correlation vs. Causation

Correlation indicates that two variables move together; causation means one directly influences the other.
Correlation alone never proves causality — a biological or mechanistic explanation is required.

Example:
In breast cancer, age correlates with ER positivity (older patients, higher ER expression) — a correlation, not causation.
By contrast, smoking causes lung cancer through mutagenic DNA damage — a causal link.

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8. Clinical Significance

Refers to whether a statistically significant result translates into a meaningful benefit for patients — improved survival, function, or quality of life.
Ideally, both statistical and clinical significance align.

Example:
A new agent extending median OS by two months (P < 0.05) may lack clinical relevance if toxicity is high.
Conversely, DESTINY-Breast trials show both statistical and clinical significance, with large, durable benefits.

[Emre Yekedüz @yekeduz_emre]

A must-read review on non-clear cell RCC! @JCOOP_ASCO

👉https://t.co/Ai1clNBHdT

@ASCO #Cancer #Oncology @OncoAlert @PBarataMD

[Oscar Arias @OACerebro]

Sample Size Calculation in Scientific Studies

🏎️ https://t.co/vwVhc2cdjY

[Yakup Ergün @dr_yakupergun]

#ESMO25
ADCs in chemo-pretreated HR+/HER2− mBC: Phase 3 studies

From Dr. Javier Cortés' great discussion on ADCs👇

[Jayen Thakker @JayenThakker]

How to Choose the Right Statistical Test

[Oncology Brothers @OncBrothers]

We now have more data on adj CDK4/6i at #ESMO25 + we’ll continue to use these Rx in metastatic settings as well. This is the table we’ve used (AEs + doses) for #Ribociclib #Abemaciclib, + #Palbociclib w/ @DrSGraff for our #ToxCheck 🗣️

#bcsm #OncTwitter @OncoAlert @OncUpdates

[Jorge Pedreira @pedreiramicina]

¿Por qué tardan en financiarse los medicamentos en España?

¿Está justificada esta espera?

Por fin podemos compartir este estudio que tanto trabajo nos ha costado: @Hilario_FH @Fonsecaea_ y Ana Coples.

👉https://t.co/DoyNgp7fdC

[GENESIS-SEFH @GENESIS_SEFH]

📢📢 “VENCERÉIS, PERO NO CONVENCERÉIS”. A propósito de una sentencia polémica.
https://t.co/7LGpT6EWuY

[Akshatha R @IDdocAkshatha]

#ECCMID2023
Tissue penetration of various antifungal drugs
https://t.co/WCGrEJ7qVj

[Mauricio Ambriz @AmbrizMau]

Elección del anticoagulante más adecuado en función del perfil del paciente:

Adv Ther 2023; DOI: https://t.co/f0m5GBlU4H

[Lost In Film @LostInFilm]

⚡️

[The Virus Monologues✊🏾🇵🇸 @virusmonologues]

We have new results on T cell responses to #Omicron, and its good news!
Paper submitted for peer review &amp; preprint:
https://t.co/40Y8obKzkK

📢TL;DR: Most of your T cell responses from vaccination or previous infection still recognise Omicron.
#Getvaccinated #GetthoseTcellsnow