Assessing Heterogeneity of Treatment Effects: Are Authors Misinterpreting Their Results? - Wikidata - wikimedia - TriplyDB

Assessing Heterogeneity of Treatment Effects: Are Authors Misinterpreting Their Results?

Assessing Heterogeneity of Treatment Effects: Are Authors Misinterpreting Their Results?

http://www.wikidata.org/entity/Q33618795

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A comprehensive review of nongenetic prognostic and predictive factors influencing the heterogeneity of outcomes in advanced non-small-cell lung cancer A systematic review of individual patient data meta-analyses on surgical interventions.Personalizing Behavioral Interventions Through Single-Patient (N-of-1) Trials.A framework for the analysis of heterogeneity of treatment effect in patient-centered outcomes research.Social Relationships and Salivary Telomere Length Among Middle-Aged and Older African American and White Adults.'Spin' in published biomedical literature: A methodological systematic review An approach to addressing subpopulation considerations in systematic reviews: the experience of reviewers supporting the U.S. Preventive Services Task Force.A Comparison of Subgroup Analyses in Grant Applications and Publications

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P2860

Assessing Heterogeneity of Treatment Effects: Are Authors Misinterpreting Their Results?

http://www.wikidata.org/entity/Q33618795

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2010 nî lūn-bûn

@nan

2010 թուականի Փետրուարին հրատարակուած գիտական յօդուած

@hyw

2010 թվականի փետրվարին հրատարակված գիտական հոդված

@hy

2010年の論文

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2010年論文

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2010年論文

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2010年論文

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2010年論文

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2010年論文

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2010年论文

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Assessing Heterogeneity of Treatment Effects: Are Authors Misinterpreting Their Results?

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Assessing Heterogeneity of Treatment Effects: Are Authors Misinterpreting Their Results?

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Assessing Heterogeneity of Treatment Effects: Are Authors Misinterpreting Their Results?

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J.1475-6773.2009.01064.X

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Health Services Research

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Assessing Heterogeneity of Treatment Effects: Are Authors Misinterpreting Their Results?

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Erik Fernandez Y Garcia

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Hien Nguyen

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Naihua Duan

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Nicole B Gabler

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P2860

Subgroup analysis in therapeutic trials

The challenge of subgroup analyses--reporting without distorting

The revised CONSORT statement for reporting randomized trials: explanation and elaboration

The number needed to treat: a clinically useful measure of treatment effect

Subgroup analysis and other (mis)uses of baseline data in clinical trials.

Testing for statistical discrimination by race/ethnicity in panel data for depression treatment in primary care.

The new NIH and FDA medical research policies: targeting gender, promoting justice.

Calculating the number needed to treat for trials where the outcome is time to an event

Subgroups, treatment effects, and baseline risks: some lessons from major cardiovascular trials.

Subgroup analyses in randomised controlled trials: quantifying the risks of false-positives and false-negatives.

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283-301

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scholarly article

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10.1111/J.1475-6773.2009.01064.X

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Richard L. Kravitz

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