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Utility of inverse probability weighting in molecular pathological epidemiology.

ABSTRACT: As one of causal inference methodologies, the inverse probability weighting (IPW) method has been utilized to address confounding and account for missing data when subjects with missing data cannot be included in a primary analysis. The transdisciplinary field of molecular pathological epidemiology (MPE) integrates molecular pathological and epidemiological methods, and takes advantages of improved understanding of pathogenesis to generate stronger biological evidence of causality and optimize strategies for precision medicine and prevention. Disease subtyping based on biomarker analysis of biospecimens is essential in MPE research. However, there are nearly always cases that lack subtype information due to the unavailability or insufficiency of biospecimens. To address this missing subtype data issue, we incorporated inverse probability weights into Cox proportional cause-specific hazards regression. The weight was inverse of the probability of biomarker data availability estimated based on a model for biomarker data availability status. The strategy was illustrated in two example studies; each assessed alcohol intake or family history of colorectal cancer in relation to the risk of developing colorectal carcinoma subtypes classified by tumor microsatellite instability (MSI) status, using a prospective cohort study, the Nurses' Health Study. Logistic regression was used to estimate the probability of MSI data availability for each cancer case with covariates of clinical features and family history of colorectal cancer. This application of IPW can reduce selection bias caused by nonrandom variation in biospecimen data availability. The integration of causal inference methods into the MPE approach will likely have substantial potentials to advance the field of epidemiology.

SUBMITTER: Liu L

PROVIDER: S-EPMC5948129 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Utility of inverse probability weighting in molecular pathological epidemiology.

Liu Li L Nevo Daniel D Nishihara Reiko R Cao Yin Y Song Mingyang M Twombly Tyler S TS Chan Andrew T AT Giovannucci Edward L EL VanderWeele Tyler J TJ Wang Molin M Ogino Shuji S

European journal of epidemiology 20171220 4

As one of causal inference methodologies, the inverse probability weighting (IPW) method has been utilized to address confounding and account for missing data when subjects with missing data cannot be included in a primary analysis. The transdisciplinary field of molecular pathological epidemiology (MPE) integrates molecular pathological and epidemiological methods, and takes advantages of improved understanding of pathogenesis to generate stronger biological evidence of causality and optimize s ...[more]

PMID: 29264788

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Project description:Study questionDoes inverse probability weighting (IPW) provide a more valid estimate of the cumulative incidence of live birth after multiple cycles of IVF?Summary answerIPW can provide a more accurate estimate of treatment success for counseling and decision-making regarding IVF.What is known alreadyDifferent approaches have been used to define and calculate IVF success; however, many of these approaches have limitations and potentially violate statistical assumptions. IPW can address potential selection bias that arises when people do not continue IVF treatment after a failed cycle.Study design, size, durationData were derived from a cohort study of women undergoing their first fresh embryo transfer IVF cycle at our institution between 1 January 1995 and 31 December 2014. All autologous cycles (fresh and frozen) were included, up to six total cycles.Participants/materials, setting, methodsWe identified 20 015 women who underwent 47 079 IVF cycles and had 10 031 live births during the study period. The cumulative incidence of live birth was calculated using three approaches. First, we used a standard Kaplan-Meier approach, 'the optimistic approach', censoring women when they dropped out of treatment. Second, we used a 'conservative' Kaplan-Meier approach that assumed women who dropped out of treatment did not achieve a live birth. Finally, we used IPW to calculate the probability of remaining in treatment, while accounting for differences in treatment drop out. IPW up-weights the data of those remaining under observation who resembled the women who dropped out of treatment, thereby decreasing the potential selection bias resulting from loss to follow-up. The IPW was incorporated into a Kaplan-Meier approach.Main results and the role of chanceThe cumulative incidence of live birth was 72.1% (95% CI: 71.0-73.1%) for the optimistic approach, 50.1% (49.4-50.8%) for the conservative approach and 66.8% (65.5-68.1%) for the IPW approach. Among women < 38 years of age, the cumulative incidence of live birth calculated by the IPW was slightly higher than that calculated by the optimistic approach. For women 41-42 years of age, the IPW cumulative incidence of live birth was slightly lower. The IPW was similar to the optimistic approach for the other age groups. The conservative estimate was lowest for all age groups.Limitations, reasons for cautionOnly clinical data recorded by the providers during an IVF cycle were used to generate weights for IPW. Covariates included: age, gravidity and year at the start of the cycle; primary infertility diagnosis; procedure type (i.e. whether a fresh or frozen embryo was transferred); number of mature oocytes retrieved; number of embryos transferred; cycle cancellation; pregnancy loss in the cycle; and insurance status. We were unable to determine exact reasons for treatment drop out (e.g. cessation of IVF treatment, transfer to another institution or spontaneous pregnancy). Our IPW model was moderately predictive based on the c-statistic from the calculation of the denominator of the weight; however, residual selection bias may remain due to the limited range of covariate data.Wider implications of the findingsIPW can be used in a variety of settings to address selection bias introduced by differential loss to follow up or treatment drop out.Study funding/competing interest(s)AMM was supported by National Institutes of Health (NIH) T32 HD052458-Boston University Reproductive, Perinatal and Pediatric Epidemiology Training Program. The authors report no conflicts of interest.Trial registration numberN/A.

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Utility of inverse probability weighting in molecular pathological epidemiology.

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Utility of inverse probability weighting in molecular pathological epidemiology.

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