tetano
Editor, Senior Moderator
J Clin Epidemiol. 2017 Feb 7. pii: S0895-4356(16)30266-9. doi: 10.1016/j.jclinepi.2017.01.008. [Epub ahead of print]
[h=1]Unmasking survival biases in observational treatment studies of influenza patients.[/h] Wolkewitz M[SUP]1[/SUP], Schumacher M[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] Several observational studies reported that Oseltamivir (Tamiflu) reduced mortality in infected and hospitalized patients. Due to the restriction of observation to hospital stay and time-dependent treatment assignment, such findings were prone to common types of survival bias (length, time-dependent and competing risk bias).
[h=4]METHODS:[/h] British hospital data from the FLU-CIN study group were used which included 1391 patients with confirmed pandemic influenza A/H1N1 2009 infection. We used a multi- state model approach with following states: hospital admission, Oseltamivir treatment, discharge and death. Time origin is influenza onset. We displayed individual data, risk sets, hazards and probabilities from multi-state models to study the impact of these three common survival biases.
[h=4]RESULTS:[/h] The correct hazard ratio of Oseltamivir for death was 1.03 (95%-CI: 0.64-1.66) and for discharge 1.89 (95%-CI: 1.65-2.16). Length bias increased both hazard ratios: HR(death)= 1.82 (95%-CI:1.12-2.98) and HR(discharge)= 4.44 (95%-CI: 3.90-5.05) whereas the time-dependent bias reduced them: HR(death)= 0.62 (95%-CI:0.39-1.00) and HR(discharge)= 0.85 (95%-CI:0.75-0.97). Length and time-dependent bias were less pronounced in terms of probabilities. Ignoring discharge as a competing event for hospital death led to a remarkable overestimation of hospital mortality and failed to detect the reducing effect of Oseltamivir on hospital stay.
[h=4]CONCLUSIONS:[/h] The impact of each of the three survival biases was remarkable and it can make NI ap- pear more effective or even harmful. Incorrect and misclassified risk sets were primary the source of biased hazard rates.
Copyright ? 2017. Published by Elsevier Inc.
[h=4]KEYWORDS:[/h] Tamiflu; competing risk bias; length bias; neuraminidase inhibitors; time-dependent bias
PMID: 28188897 DOI: 10.1016/j.jclinepi.2017.01.008
[PubMed - as supplied by publisher]
[h=1]Unmasking survival biases in observational treatment studies of influenza patients.[/h] Wolkewitz M[SUP]1[/SUP], Schumacher M[SUP]2[/SUP].
[h=3]Author information[/h]
[h=3]Abstract[/h] [h=4]BACKGROUND:[/h] Several observational studies reported that Oseltamivir (Tamiflu) reduced mortality in infected and hospitalized patients. Due to the restriction of observation to hospital stay and time-dependent treatment assignment, such findings were prone to common types of survival bias (length, time-dependent and competing risk bias).
[h=4]METHODS:[/h] British hospital data from the FLU-CIN study group were used which included 1391 patients with confirmed pandemic influenza A/H1N1 2009 infection. We used a multi- state model approach with following states: hospital admission, Oseltamivir treatment, discharge and death. Time origin is influenza onset. We displayed individual data, risk sets, hazards and probabilities from multi-state models to study the impact of these three common survival biases.
[h=4]RESULTS:[/h] The correct hazard ratio of Oseltamivir for death was 1.03 (95%-CI: 0.64-1.66) and for discharge 1.89 (95%-CI: 1.65-2.16). Length bias increased both hazard ratios: HR(death)= 1.82 (95%-CI:1.12-2.98) and HR(discharge)= 4.44 (95%-CI: 3.90-5.05) whereas the time-dependent bias reduced them: HR(death)= 0.62 (95%-CI:0.39-1.00) and HR(discharge)= 0.85 (95%-CI:0.75-0.97). Length and time-dependent bias were less pronounced in terms of probabilities. Ignoring discharge as a competing event for hospital death led to a remarkable overestimation of hospital mortality and failed to detect the reducing effect of Oseltamivir on hospital stay.
[h=4]CONCLUSIONS:[/h] The impact of each of the three survival biases was remarkable and it can make NI ap- pear more effective or even harmful. Incorrect and misclassified risk sets were primary the source of biased hazard rates.
Copyright ? 2017. Published by Elsevier Inc.
[h=4]KEYWORDS:[/h] Tamiflu; competing risk bias; length bias; neuraminidase inhibitors; time-dependent bias
PMID: 28188897 DOI: 10.1016/j.jclinepi.2017.01.008
[PubMed - as supplied by publisher]