The performance of different propensity score methods for estimating absolute effects of treatments on survival outcomes: A simulation study

Statistical Methods in Medical Research

Volumn 25, Issue 5, 2016, Pages 2214-2237

  Austin, Peter C ^a,b,c   Schuster, Tibor ^d,e  

^a INSTITUTE FOR CLINICAL EVALUATIVE SCIENCES   (Canada)

^b UNIVERSITY OF TORONTO   (Canada)

^c Sunnybrook Health Sciences Centre   (Canada)

^d MCGILL UNIVERSITY   (Canada)

^e MCGILL UNIVERSITY   (Canada)

Author keywords

inverse probability of treatment weighting; Monte Carlo simulations; observational study; propensity score; survival analysis; time to event outcomes

Indexed keywords

ABSOLUTE EFFECT; ALGORITHM; ARTICLE; COMPARATIVE STUDY; COMPUTER SIMULATION; CONTROLLED STUDY; DATA GENERATING PROCESS; DATA PROCESSING; EMPIRICAL TYPE I ERROR; ERROR; FOLLOW UP; INVERSE PROBABILITY OF TREATMENT WEIGHT; INVERSE PROBABILITY OF TREATMENT WEIGHTING USING THE PROPENSITY SCORE; KAPLAN MEIER METHOD; MARGINAL SURVIVAL FUNCTION; MONTE CARLO METHOD; NEAREST NEIGHBOUR CALIPER MATCHING; NEAREST NEIGHBOUR MATCHING; OUTCOME ASSESSMENT; PREVALENCE; PROBABILITY; PROPENSITY SCORE MATCHING; PROPENSITY SCORE METHOD; PROPENSITY SCORE STRATIFICATION; SENSITIVITY ANALYSIS; STANDARDIZATION; STATISTICAL ANALYSIS; STATISTICAL CONCEPTS; STATISTICAL DISTRIBUTION; STATISTICAL PARAMETERS; STATISTICAL SIGNIFICANCE; STRATIFICATION; SURVIVAL OUTCOME; SURVIVAL RATE; SURVIVAL TIME; TIME TO EVENT OUTCOME; TREATMENT RESPONSE; WEIGHTED LOG RANK TEST; HUMAN; OBSERVATIONAL STUDY; PROCEDURES; PROPENSITY SCORE; PROPORTIONAL HAZARDS MODEL; STATISTICAL BIAS; SURVIVAL ANALYSIS;

BIAS; HUMANS; MONTE CARLO METHOD; OBSERVATIONAL STUDIES AS TOPIC; PROPENSITY SCORE; PROPORTIONAL HAZARDS MODELS; SURVIVAL ANALYSIS;

EID: 84989847164     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280213519716     Document Type: Article

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