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06.09.19 1 Direktion Pflege/MTT Universitäre Forschung Pflege/Hebammen www.inselgruppe.ch www.nursing.unibas.ch Using DAGs to unpick nurse staffing, workload and rationing of care Michael Simon, PhD, RN DAGs • DAG -> Directed Acyclic Graph • DAGs are graphical causal models • Sometimes called non-parametric structural equation models • DAGs are visual representations of qualitative causal assumptions • DAGs allow to explicitly describe the dependencies between variables and whether theses are testable given the data. Elwert, F. (2013). Graphical causal models. Handbook of causal analysis for social research , Springer: 245-273. Did he just say … CAUSATION?

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Page 1: DAGs & Rationing - RANCARE...Pearl, J. (2009). Causal inference in statistics: An overview. Statistics surveys, 3, 96-146. Causal inference tasks require expert knowledge not only

06.09.19

1

Direktion Pflege/M TT

Universitäre Forschung

Pflege/Hebammen

www.inselgruppe.chwww.nursing.unibas.ch

Using DAGs to unpick nurse staffing, workload

and rationing of care Michael Simon, PhD, RN

DAGs

• DAG -> Directed Acyclic Graph• DAGs are graphical causal models• Sometimes called non-parametric structural equation models• DAGs are visual representations of qualitative causal

assumptions• DAGs allow to explicitly describe the dependencies between

variables and whether theses are testable given the data.

Elwert, F. (2013). Graphical causal models. Handbook of causal analysis for social research, Springer: 245-273.

Did he just say…

CAUSATION?

Page 2: DAGs & Rationing - RANCARE...Pearl, J. (2009). Causal inference in statistics: An overview. Statistics surveys, 3, 96-146. Causal inference tasks require expert knowledge not only

06.09.19

2

Association versus Causation“An associational concept is any relationship that can be defined in terms of a joint distribution of observed variables, and a causal concept is any relationship that cannot be defined from the distribution alone.”

Universität Basel, Department Public Health | PFLEGEWISSENSCHAFT3

Pearl, J. (2009). Causal inference in statistics: An overview. Statistics surveys, 3, 96-146.

Causal inference tasks require expert knowledge not only to specify the question (the causal effect of what treatment on what outcome) and identify/generate relevant data sources, but also to describe the causal structure of the system under study. Causal knowledge, usually in the form of unverifiable assumptions, is necessary to guide the data analysis and to provide a justification for endowing the resulting numerical estimates with a causal interpretation.

Hernán, M. A., Hsu, J., & Healy, B. (2019). A Second Chance to Get Causal Inference Right A Classification of Data Science Tasks. CHANCE, 32.1(1), 42-49.

Assume you compare the outcome of two groups with…

Universität Basel, Department Public Health | PFLEGEWISSENSCHAFT4

…a

RCT

…a

Case-control study

… how is information about the design/structure/data generation process represented?

Page 3: DAGs & Rationing - RANCARE...Pearl, J. (2009). Causal inference in statistics: An overview. Statistics surveys, 3, 96-146. Causal inference tasks require expert knowledge not only

06.09.19

3

The ladder of causality

3. level: imaginingWas it X that caused Y

2. level: doingHow would the variable X change if I change Y

1. level: observingHow are the variables X and Y related

Universität Basel, Department Public Health | PFLEGEWISSENSCHAFT5

Pearl, J., & Mackenzie, D. (2018). The book of why: the new science of cause and effect: Basic Books.

Data science tasks

Universität Basel, Department Public Health | PFLEGEWISSENSCHAFT6

Hernán, M. A., Hsu, J., & Healy, B. (2019). A Second Chance to Get Causal Inference Right A Classification of Data Science Tasks. CHANCE, 32.1(1), 42-49.

Page 4: DAGs & Rationing - RANCARE...Pearl, J. (2009). Causal inference in statistics: An overview. Statistics surveys, 3, 96-146. Causal inference tasks require expert knowledge not only

06.09.19

4

«Inference Engine» of the structuralcausal model

Pearl, J., & Mackenzie, D. (2018). The book of why: the new science of cause and effect: Basic Books.

DAG terminology

X Z Y

Nodes/Variable

Edges

Universität Basel, Department Public Health | PFLEGEWISSENSCHAFT9

Parent/Ancestor

Child/Descendant

Page 5: DAGs & Rationing - RANCARE...Pearl, J. (2009). Causal inference in statistics: An overview. Statistics surveys, 3, 96-146. Causal inference tasks require expert knowledge not only

06.09.19

5

Junctions: Chains

X Z Y

Two variables, X and Y, are conditionally independent given Z, if there is only one unidirectional path between X

and Y and Z.

Pearl, Judea, Madelyn Glymour, and Nicholas P Jewell. 2016. Causal inference in statistics: A primer (John Wiley & Sons).

Example:

X= working hoursZ= trainingY= race time

Working hours, training hours and race time are dependent. When controlled for training hoursworking hours and race time are independent.

Junctions: Forks

X

ZY

If a variable X is a common cause of variables Y and Z, and there is only one path between Y and Z, the Y and Z

are independent conditional on X.

Pearl, Judea, Madelyn Glymour, and Nicholas P Jewell. 2016. Causal inference in statistics: A primer (John Wiley & Sons).

Example:

X= temperatureY= ice cream salesZ= violent crimes

Temperature, ice cream sales and violent crimesare dependent. When controlled for temperatureice cream sales and violent crimes are independent.

Page 6: DAGs & Rationing - RANCARE...Pearl, J. (2009). Causal inference in statistics: An overview. Statistics surveys, 3, 96-146. Causal inference tasks require expert knowledge not only

06.09.19

6

Junctions: CollidersX

Z

Y

If a variable Z is the collision node between two variables X and Y, and there is only one path between X and Y, then X and Y are unconditionally independent, but are conditional dependent on

Z and any descendent of Z (W).

w

Pearl, Judea, Madelyn Glymour, and Nicholas P Jewell. 2016. Causal inference in statistics: A primer (John Wiley & Sons).

Example:

X= musical talentY= gpa (grades)Z= scholarship

Musical talent and gpa are independent. However, whencontrolled for scholarship status (and any ancestor of z) they become dependent.

Z

d(irectional)-seperation

Z

W

X

If all path between two nodes are blocked, they are d-separated. An unblocked paths can be considered as pipes, with dependence running through it like water. A path can be unconditionally or conditionally blocked. An unconditional blockage can only be achieved through colliders, while chains can conditionally block paths.

U

Pearl, Judea, Madelyn Glymour, and Nicholas P Jewell. 2016. Causal inference in statistics: A primer (John Wiley & Sons).

Y

W

Page 7: DAGs & Rationing - RANCARE...Pearl, J. (2009). Causal inference in statistics: An overview. Statistics surveys, 3, 96-146. Causal inference tasks require expert knowledge not only

06.09.19

7

Williams, T. C., C. C. Bach, N. B. Matthiesen, T. B. Henriksen and L. Gagliardi (2018). "Directed acyclic graphs: a tool for causal studies in paediatrics." Pediatric Research84(4): 487-493.

Does increasing nurse staffingreduce rationing of care?

Universität Basel, Department Public Health | PFLEGEWISSENSCHAFT15

Page 8: DAGs & Rationing - RANCARE...Pearl, J. (2009). Causal inference in statistics: An overview. Statistics surveys, 3, 96-146. Causal inference tasks require expert knowledge not only

06.09.19

8

Applying DAGs to the phenomenom ofrationing of care

Schubert, M., T. R. Glass, S. P. Clarke, B. Schaffert-Witvliet and S. De Geest(2007). "Validation of the Basel Extent of Rationing of Nursing Care instrument." Nurs Res56(6): 416-424.

Universität Basel, Department Public Health | PFLEGEWISSENSCHAFT16

Simple model 1: Mediator

BERNCAMISSCARE

etc.

Patient-to-nurseratio S R

WWorkload

NASA tlx

Staffing Rationing

R ~ S (total effect)R ~ S + W (direct effect)

Page 9: DAGs & Rationing - RANCARE...Pearl, J. (2009). Causal inference in statistics: An overview. Statistics surveys, 3, 96-146. Causal inference tasks require expert knowledge not only

06.09.19

9

Simple model 2: ConfounderStaffing & ResourceAdequacy A

Adequacy

S R

WWorkload

Staffing Rationing

W

R ~ S + W (total effect is biased!)

A R ~ S + A (unbiased, total effect) R ~ S + A + W (unbiased, direct effect)

Simple model 3: Collider

AAdequacy

S R

WWorkload

Staffing Rationing

W

A

Biased!

Page 10: DAGs & Rationing - RANCARE...Pearl, J. (2009). Causal inference in statistics: An overview. Statistics surveys, 3, 96-146. Causal inference tasks require expert knowledge not only

06.09.19

10

Empirical model?

• Deciding based on model fit• Backward selection based on p• Machine learning

Biased!

Summary

• Application of DAGs in applied disciplines still underdeveloped• Examples shown very simplistic, more realistic models will be

key• DAGs (SCM) important tool(s) to relate our understanding of

how the world works to empirical data and analysis• Unfinished care would profit from DAGs in terms of research

designs, but also conceptual clarity

Page 11: DAGs & Rationing - RANCARE...Pearl, J. (2009). Causal inference in statistics: An overview. Statistics surveys, 3, 96-146. Causal inference tasks require expert knowledge not only

06.09.19

11

Direktion Pflege/M TT

Universitäre Forschung

Pflege/Hebammen

www.inselgruppe.chwww.nursing.unibas.ch

Michael Simon, PhD, RNAssociate Professor

Institute of Nursing ScienceUniversity of BaselBernoullistrasse 28CH-4056 Basel+41 (0) 61 267 09 [email protected]

@msimoninfo

Nursing Research UnitPersonalhaus 1, Raum 505Inselspital BernCH-3010 Bern+41 031 632 [email protected]

mailto:[email protected]

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