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Comparison of Statistical Approaches for Dealing With Immortal Time Bias in Drug Effectiveness Studies.

ABSTRACT: 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 ?-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, 1995-2008).

SUBMITTER: Karim ME

PROVIDER: S-EPMC4983651 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Comparison of Statistical Approaches for Dealing With Immortal Time Bias in Drug Effectiveness Studies.

Karim Mohammad Ehsanul ME Gustafson Paul P Petkau John J Tremlett Helen H

American journal of epidemiology 20160725 4

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. U ...[more]

PMID: 27455963

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Project description:BackgroundRelatively little is known about the effect of traditional Chinese medicine (TCM) on prognosis of non-small cell lung cancer (NSCLC).MethodsIn this nationwide, multicenter, prospective, cohort study, eligible patients aged 18-75 years with radical resection, and histologically confirmed stage II-IIIA NSCLC were enrolled. All patients received 4 cycles of standard adjuvant chemotherapy. Patients who received Chinese herbal decoction and (or) oral Chinese patent medicine for a cumulative period of not less than 6 months were defined as TCM group, otherwise they were considered as control group. The primary endpoint was DFS calculated using the Kaplan-Meier method. A time-dependent Cox proportional hazards model was used to correct immortal time bias. The secondary endpoints included DFS in patients of different characteristics, and safety analyses. This study was registered with the Chinese Clinical Trial Registry (ChiCTR1800015776).ResultsA total of 507 patients were included (230 patients in the TCM group; 277 patients in the control group). The median follow-up was 32.1 months. 101 (44%) in the TCM group and 186 (67%) in the control group had disease relapse. The median DFS was not reached in the TCM group and was 19.4 months (95% CI, 14.2 to 24.6) in the control group. The adjusted time-dependent HR was 0.61 (95% CI, 0.47 to 0.78), equalling to a 39% reduction in the risk of disease recurrence with TCM. the number needed to treat to prevent one patient from relapsing was 4.29 (95% CI, 3.15 to 6.73) at 5 years. Similar results were observed in most of subgroups. Patients had a significant improvement in white blood cell decrease, nausea, decreased appetite, diarrhea, pain, and fatigue in the TCM group.ConclusionTCM may improves DFS and has a better tolerability profile in patients with stage II-IIIA NSCLC receiving standard chemotherapy after complete resection compared with those receiving standard chemotherapy alone. Further studies are warranted.

Project description:Background and objectiveObservational studies may provide valuable evidence on real-world causal effects of drug effectiveness in patients with coronavirus disease 2019 (COVID-19). As patients are usually observed from hospital admission to discharge and drug initiation starts during hospitalization, advanced statistical methods are needed to account for time-dependent drug exposure, confounding and competing events. Our objective is to evaluate the observational studies on the three common methodological pitfalls in time-to-event analyses: immortal time bias, confounding bias and competing risk bias.MethodsWe performed a systematic literature search on 23 October 2020, in the PubMed database to identify observational cohort studies that evaluated drug effectiveness in hospitalized patients with COVID-19. We included articles published in four journals: British Medical Journal, New England Journal of Medicine, Journal of the American Medical Association and The Lancet as well as their sub-journals.ResultsOverall, out of 255 articles screened, 11 observational cohort studies on treatment effectiveness with drug exposure-outcome associations were evaluated. All studies were susceptible to one or more types of bias in the primary study analysis. Eight studies had a time-dependent treatment. However, the hazard ratios were not adjusted for immortal time in the primary analysis. Even though confounders presented at baseline have been addressed in nine studies, time-varying confounding caused by time-varying treatment exposure and clinical variables was less recognized. Only one out of 11 studies addressed competing event bias by extending follow-up beyond patient discharge.ConclusionsIn the observational cohort studies on drug effectiveness for treatment of COVID-19 published in four high-impact journals, the methodological biases were concerningly common. Appropriate statistical tools are essential to avoid misleading conclusions and to obtain a better understanding of potential treatment effects.

Dataset Information

Comparison of Statistical Approaches for Dealing With Immortal Time Bias in Drug Effectiveness Studies.

Publications

Comparison of Statistical Approaches for Dealing With Immortal Time Bias in Drug Effectiveness Studies.

Similar Datasets

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