Evidence map›Paper›PMID 33936409›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020

Finding Causal Mechanistic Drug-Drug Interactions from Observational Data.

Sanjoy Dey, Ping Zhang, Mohamed Ghalwash, Chandramouli Maduri, Daby Sow, Zach Shahn

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sanjoy DeyIBM T. J. Watson Research Center, Yorktown Heights, NY, USA.
Ping ZhangThe Ohio State University, Columbus, OH, USA.
Mohamed GhalwashIBM T. J. Watson Research Center, Yorktown Heights, NY, USA.
Chandramouli MaduriIBM T. J. Watson Research Center, Yorktown Heights, NY, USA.
Daby SowIBM T. J. Watson Research Center, Yorktown Heights, NY, USA.
Zach ShahnIBM T. J. Watson Research Center, Yorktown Heights, NY, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many adverse drug reactions (ADRs) are caused by drug-drug interactions (DDIs), meaning they arise from concurrent use of multiple medications. Detecting DDIs using observational data has at least three major challenges: (1) The number of potential DDIs is astronomical; (2) Associations between drugs and ADRs may not be causal due to observed or unobserved confounding; and (3) Frequently co-prescribed drug pairs that each independently cause an ADR do not necessarily causally interact, where causal interaction means that at least some patients would only experience the ADR if they take both drugs. We address (1) through data mining algorithms pre-filtering potential interactions, and (2) and (3) by fitting causal interaction models adjusting for observed confounders and conducting sensitivity analyses for unobserved confounding. We rank candidate DDIs robust to unobserved confounding more likely to be real. Our rigorous approach produces far fewer false positives than past applications that ignored (2) and (3).

Indexed as

Drug InteractionsDrug-Related Side Effects and Adverse ReactionsData MiningHumansPharmaceutical PreparationsPharmaceutical Preparations

Identifiers

PMID33936409
PMCPMC8075465

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.