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Sökning: L773:0277 6715 OR L773:1097 0258 > The median hazard r...

The median hazard ratio : a useful measure of variance and general contextual effects in multilevel survival analysis

Austin, Peter C. (författare)
Inst Clin Evaluat Sci, G106,2075 Bayview Ave, Toronto, ON M4N 3M5, Canada.;Univ Toronto, Inst Hlth Management Policy & Evaluat, Toronto, ON, Canada.;Sunnybrook Res Inst, Schulich Heart Res Program, Toronto, ON, Canada.,University of Toronto
Wagner, Philippe (författare)
Uppsala University,Lund University,Lunds universitet,Uppsala universitet,Centrum för klinisk forskning, Västerås,Lund Univ, Unit Social Epidemiol, Fac Med, Malmo, Sweden.,Ortopedi, Lund,Sektion III,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,Orthopaedics (Lund),Section III,Department of Clinical Sciences, Lund,Faculty of Medicine
Merlo, Juan (författare)
Lund University,Lunds universitet,Socialepidemiologi,Forskargrupper vid Lunds universitet,Social Epidemiology,Lund University Research Groups,Skåne University Hospital
Inst Clin Evaluat Sci, G106,2075 Bayview Ave, Toronto, ON M4N 3M5, Canada;Univ Toronto, Inst Hlth Management Policy & Evaluat, Toronto, ON, Canada.;Sunnybrook Res Inst, Schulich Heart Res Program, Toronto, ON, Canada. University of Toronto (creator_code:org_t)
2016-11-25
2017
Engelska.
Ingår i: Statistics in Medicine. - : WILEY. - 0277-6715 .- 1097-0258. ; 36:6, s. 928-938
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • Multilevel data occurs frequently in many research areas like health services research and epidemiology. A suitable way to analyze such data is through the use of multilevel regression models (MLRM). MLRM incorporate cluster-specific random effects which allow one to partition the total individual variance into between-cluster variation and between-individual variation. Statistically, MLRM account for the dependency of the data within clusters and provide correct estimates of uncertainty around regression coefficients. Substantively, the magnitude of the effect of clustering provides a measure of the General Contextual Effect (GCE). When outcomes are binary, the GCE can also be quantified by measures of heterogeneity like the Median Odds Ratio (MOR) calculated from a multilevel logistic regression model. Time-to-event outcomes within a multilevel structure occur commonly in epidemiological and medical research. However, the Median Hazard Ratio (MHR) that corresponds to the MOR in multilevel (i.e., 'frailty') Cox proportional hazards regression is rarely used. Analogously to the MOR, the MHR is the median relative change in the hazard of the occurrence of the outcome when comparing identical subjects from two randomly selected different clusters that are ordered by risk. We illustrate the application and interpretation of the MHR in a case study analyzing the hazard of mortality in patients hospitalized for acute myocardial infarction at hospitals in Ontario, Canada. We provide R code for computing the MHR. The MHR is a useful and intuitive measure for expressing cluster heterogeneity in the outcome and, thereby, estimating general contextual effects in multilevel survival analysis.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Hälsovetenskap -- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Health Sciences -- Public Health, Global Health, Social Medicine and Epidemiology (hsv//eng)
NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Nyckelord

Median Hazard Ratio
Median Odds Ratio
clustered data
multilevel analysis
frailty models
survival analysis

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