PMID- 35837735
OWN - NLM
STAT- MEDLINE
DCOM- 20220920
LR  - 20221207
IS  - 1477-0334 (Electronic)
IS  - 0962-2802 (Print)
IS  - 0962-2802 (Linking)
VI  - 31
IP  - 9
DP  - 2022 Sep
TI  - Hunting for protective drugs at the break of a pandemic: Causal inference from 
      hospital data.
PG  - 1803-1816
LID - 10.1177/09622802221098428 [doi]
AB  - At the break of a pandemic, the protective efficacy of therapeutic interventions 
      needs rapid evaluation. An experimental approach to the problem will not always 
      be appropriate. An alternative route are observational studies, whether based on 
      regional health service data or hospital records. In this paper, we discuss the 
      use of methods of causal inference for the analysis of such data, with special 
      reference to causal questions that may arise in a pandemic. We apply the methods 
      by using the aid of a directed acyclic graph (DAG) representation of the problem, 
      to encode our causal assumptions and to logically connect the scientific 
      questions. We illustrate the usefulness of DAGs in the context of a controversy 
      over the effects of renin aldosterone system inhibitors (RASIs) in hypertensive 
      individuals at risk of (or affected by) severe acute respiratory syndrome 
      coronavirus 2 disease. We consider questions concerning the existence and the 
      directions of those effects, their underlying mechanisms, and the possible 
      dependence of the effects on context variables. This paper describes the 
      cognitive steps that led to a DAG representation of the problem, based on 
      background knowledge and evidence from past studies, and the use of the DAG to 
      analyze our hospital data and assess the interpretive limits of the results. Our 
      study contributed to subverting early opinions about RASIs, by suggesting that 
      these drugs may indeed protect the older hypertensive Covid-19 patients from the 
      consequences of the disease. Mechanistic interaction methods revealed that the 
      benefit may be greater (in a sense to be made clear) in the older stratum of the 
      population.
FAU - Berzuini, Carlo
AU  - Berzuini C
AUID- ORCID: 0000-0001-6056-0489
AD  - Centre for Biostatistics, 171083School of Health Sciences, The University of 
      Manchester, Manchester, UK.
FAU - Bernardinelli, Luisa
AU  - Bernardinelli L
AD  - Department of Brain and Behavioural Sciences, 19001University of Pavia, Pavia, 
      Italy.
LA  - eng
PT  - Journal Article
DEP - 20220715
PL  - England
TA  - Stat Methods Med Res
JT  - Statistical methods in medical research
JID - 9212457
RN  - 0 (Protective Agents)
RN  - 4964P6T9RB (Aldosterone)
RN  - EC 3.4.23.15 (Renin)
SB  - IM
MH  - Aldosterone
MH  - Hospitals
MH  - Humans
MH  - Hypertension/complications
MH  - Pandemics
MH  - Protective Agents
MH  - Renin
MH  - *COVID-19 Drug Treatment
PMC - PMC9289643
OTO - NOTNLM
OT  - Covid-19
OT  - Observational studies
OT  - angiotensin receptor blocker
OT  - angiotensin-converting enzyme inhibitor
OT  - causal graphical models
OT  - causal inference
OT  - conditional independence
OT  - effect modifier
OT  - hospital data
OT  - hypertension
OT  - matching
OT  - mechanistic interaction
OT  - propensity score
OT  - renin Aldosterone System inhibitors
OT  - severe acute respiratory syndrome coronavirus 2
EDAT- 2022/07/16 06:00
MHDA- 2022/09/21 06:00
PMCR- 2022/09/01
CRDT- 2022/07/15 03:32
PHST- 2022/07/16 06:00 [pubmed]
PHST- 2022/09/21 06:00 [medline]
PHST- 2022/07/15 03:32 [entrez]
PHST- 2022/09/01 00:00 [pmc-release]
AID - 10.1177_09622802221098428 [pii]
AID - 10.1177/09622802221098428 [doi]
PST - ppublish
SO  - Stat Methods Med Res. 2022 Sep;31(9):1803-1816. doi: 10.1177/09622802221098428. 
      Epub 2022 Jul 15.