@article {9307,
	title = {Comparison of statistical approaches for dealing with immortal time bias in drug effectiveness studies},
	journal = {American Journal of Epidemiology},
	volume = {184},
	year = {2016},
	pages = {325-335},
	abstract = {<p>In time-to-event analyses of observational studies of drug effectiveness, incorrect handling of the period between cohort entry and first treatment exposure during follow-up may result in immortal time bias. This bias can be eliminated by acknowledging a change in treatment exposure status with time-dependent analyses, such as fitting a time-dependent Cox model. The prescription time distribution matching (PTDM) method has been proposed as a simpler approach for controlling immortal time bias. Using simulation studies and theoretical quantification of bias, we compared the performance of the PTDM approach with that of the time-dependent Cox model in the presence of immortal time. Both assessments revealed that the PTDM approach did not adequately address immortal time bias. Based on our simulation results, another recently proposed observational data analysis technique, the sequential Cox approach, was found to be more useful than the PTDM approach (Cox: bias = -0.002, mean squared error = 0.025; PTDM: bias = -1.411, mean squared error = 2.011). We applied these approaches to investigate the association of β-interferon treatment with delaying disability progression in a multiple sclerosis cohort in British Columbia, Canada (Long-Term Benefits and Adverse Effects of Beta-Interferon for Multiple Sclerosis (BeAMS) Study, 1995{\textendash}2008).</p>
},
	keywords = {bias, confounding factors, epidemiologic methods, immortal time bias, Longitudinal Studies, Models, survival analysis},
	doi = {10.1093/aje/kwv445},
	author = {Karim, ME and Gustafson, P and Petkau, J and Tremlett, H and BeAMS Study Group}
}
@article {altman_mri-based_2012,
	title = {MRI-based clinical trials in relapsing-remitting multiple sclerosis: new sample size calculations based on a longitudinal model},
	journal = {Multiple Sclerosis Journal},
	volume = {18},
	number = {11},
	year = {2012},
	pages = {1600{\textendash}1608},
	abstract = {<p>BACKGROUND: Sample sizes for magnetic resonance imaging (MRI)-based clinical trials in multiple sclerosis (MS) generally assume that lesion counts are reasonably described by the negative binomial (NB) model. OBJECTIVE: This study aimed to assess the appropriateness of the NB model for lesion count data and to provide sample sizes for placebo-controlled, MRI-based clinical trials in relapsing-remitting MS using a more realistic model. METHODS: The fit of the NB model in each arm of five MS clinical trials was assessed using Pearson{\textquoteright}s chi-squared statistic. Required sample sizes associated with various tests of treatment effect were estimated by simulating data from a new, longitudinal model for repeated lesion count data on individual patients. RESULTS: Evidence (p \&lt; 0.05) against the NB model was found in at least one arm of four of the five trials. If a trial is designed using this model but the resulting clinical data do not follow its assumptions then this trial can be seriously under-powered for assessing differences in mean lesion counts. CONCLUSION: Sample sizes based on the longitudinal model are more realistic and often smaller than those previously reported using the NB model.</p>
},
	keywords = {Brain, Chi-Square Distribution, Computer Simulation, Controlled Clinical Trials as Topic, Endpoint Determination, Humans, Longitudinal Studies, magnetic resonance imaging, Models, multiple sclerosis, Predictive Value of Tests, Relapsing-Remitting, Sample Size, Statistical, Time Factors, Treatment Outcome},
	doi = {10.1177/1352458512444326},
	author = {Altman, RM and Petkau, A.John and Vrecko, D and Smith, A}
}
