Systematic assessment of pharmaceutical prescriptions in association with cancer risk: a method to conduct a population-wide medication-wide longitudinal study is a research paper published in Scientific Reports (2016). On theSindex it has a DataRank of 1.3. It has been cited 46 times, with 21 citing works in its 1-hop citation network.
It is a public health priority to identify the adverse and non-adverse associations between pharmaceutical medications and cancer. We search for and evaluate associations between all prescribed medications and longitudinal cancer risk in participants of the Swedish Cancer Register (N = 9,014,975). We associated 552 different medications with incident cancer risk (any, breast, colon, and prostate) during 5.5 years of follow-up (7/1/2005-12/31/2010) in two types of statistical models, time-to-event and case-crossover. After multiple hypotheses correction and replication, 141 (26%) drugs were associated with any cancer in a time-to-event analysis constraining drug exposure to 1 year before first cancer diagnosis and adjusting for history of medication use. In a case-crossover analysis, 36 drugs (7%) were associated with decreased cancer risk. 12 drugs were found in common in both analyses with concordant direction of association. We found 14, 10, 7% of all drugs associated with colon, prostate, and breast cancers in time-to-event models. We only found 1, 2%, and 0% for these cancers, respectively, in case-crossover analyses. Pharmacoepidemiologic analyses of cancer risk are sensitive to modeling choices and false-positive findings are a threat. Medication-wide analyses using different analytical models may help suggest consistent signals of increased cancer risk.
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Base Score Contribution
0.578
From this paper's citation signal
Citation Network Contribution
0.708
From 14 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 45% comes from its base citations and 55% from the citation network (14 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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