Evaluating possible confounding by prescriber in comparative effectiveness research.

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An even clearer portrait of bias in observational studies?

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Evaluating possible confounding by prescriber in comparative effectiveness research.

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2015 nî lūn-bûn

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2015年論文

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Evaluating possible confounding by prescriber in comparative effectiveness research.

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Evaluating possible confounding by prescriber in comparative effectiveness research.

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Evaluating possible confounding by prescriber in comparative effectiveness research.

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P1476

Evaluating possible confounding by prescriber in comparative effectiveness research.

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Jessica M Franklin

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Krista F Huybrechts

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Robert J Glynn

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Sebastian Schneeweiss

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P2860

Propensity score weighting with multilevel data.

Within-center matching performed better when using propensity score matching to analyze multicenter survival data: empirical and Monte Carlo studies.

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

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

Instrumental variable methods in comparative safety and effectiveness research

Variable selection for propensity score models

Insights into different results from different causal contrasts in the presence of effect-measure modification.

The implications of propensity score variable selection strategies in pharmacoepidemiology: an empirical illustration.

Effects of adjusting for instrumental variables on bias and precision of effect estimates.

Instruments for causal inference: an epidemiologist's dream?

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238-241

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scholarly article

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10.1097/EDE.0000000000000241

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2015-03-01T00:00:00Z

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25643103

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4347927

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Evaluating possible confounding by prescriber in comparative effectiveness research.

about

P2860

P2860

Evaluating possible confounding by prescriber in comparative effectiveness research.

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P1476

P2093

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