Separated at birth: statisticians, social scientists, and causality in health services research. - Wikidata - awgldk - TriplyDB

Separated at birth: statisticians, social scientists, and causality in health services research.

Separated at birth: statisticians, social scientists, and causality in health services research.

http://www.wikidata.org/entity/Q34724722

about

When is enough, enough? Understanding and solving your sample size problems in health services research.Falsification Testing of Instrumental Variables Methods for Comparative Effectiveness Research Different analyses estimate different parameters of the effect of erythropoietin stimulating agents on survival in end stage renal disease: a comparison of payment policy analysis, instrumental variables, and multiple imputation of potential outcome Thinking through health capacity development for Fragile States.Effect identification in comparative effectiveness research.Instrumental variable specifications and assumptions for longitudinal analysis of mental health cost offsets.Statistics and causality: separated to reunite-commentary on Bryan Dowd's "separated at birth".Commentary on Bryan Dowd's paper "separated at birth: statisticians, social scientists, and causality in health services research".

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P2860

Separated at birth: statisticians, social scientists, and causality in health services research.

http://www.wikidata.org/entity/Q34724722

description

2010 nî lūn-bûn

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2010 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած

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2010 թվականի նոյեմբերին հրատարակված գիտական հոդված

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2010年の論文

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2010年学术文章

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2010年学术文章

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2010年学术文章

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2010年学术文章

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2010年学术文章

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2010年學術文章

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Separated at birth: statistici ...... y in health services research.

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Separated at birth: statistici ...... y in health services research.

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J.1475-6773.2010.01203.X

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Health Services Research

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Separated at birth: statistici ...... y in health services research.

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Bryan E Dowd

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P2860

Estimating causal effects of treatments in randomized and nonrandomized studies.

Instrumental variables I: instrumental variables exploit natural variation in nonexperimental data to estimate causal relationships

Who is the marginal patient? Understanding instrumental variables estimates of treatment effects

Instrumental variables II: instrumental variable application-in 25 variations, the physician prescribing preference generally was strong and reduced covariate imbalance.

Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables.

The science of improvement.

Building Bridges Between Structural and Program Evaluation Approaches to Evaluating Policy

Analysis of observational studies in the presence of treatment selection bias: effects of invasive cardiac management on AMI survival using propensity score and instrumental variable methods

Instruments for causal inference: an epidemiologist's dream?

Logical, epistemological and statistical aspects of nature-nurture data interpretation.

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397-420

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scholarly article

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10.1111/J.1475-6773.2010.01203.X

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2

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46

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2010-11-24T00:00:00Z

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49634121

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21105867

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3064910

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