How to Apply Multiple Imputation in Propensity Score Matching with Partially Observed Confounders: A Simulation Study and Practical Recommendations

Ling, Albee Y.; Montez‐Rath, Maria E.; Mathur, Maya B.; Kapphahn, Kris; Desai, Manisha

doi:10.22237/jmasm/1608552120

Cited by 15 publications

(17 citation statements)

References 90 publications

(119 reference statements)

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“…We conducted an analysis based on propensity scores in accordance with the methodology of Saito et al 22 Second, we conducted logistic regression using the covariates described above to estimate the probability of developing cancer based on the baseline variables in each imputed dataset. 23 Then, we used a pooled propensity score to match, with replacement, subjects with and without incident cancer in a 1:4 ratio. We established a calliper width of 0.25 of the propensity score's standard deviation (SD), resulting in a better balance of subjects with incident cancer and those without as matched samples (C-statistic = 0.67).…”

Section: Discussionmentioning

confidence: 99%

Increased risk of death from pneumonia among cancer survivors: A propensity score‐matched cohort analysis

Tanaka¹,

Inoue²,

Yamaji³

et al. 2022

Cancer Medicine

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Background The repeated global pandemic of the new virus has led to interest in the possibility of severe pneumonia among cancer patients and survivors. Here, we aimed to assess the association between incident cancer and risk of death from pneumonia in Japanese in a large population‐based cohort study. Methods We used the data from The Japan Public Health Center‐based Prospective Study (JPHC Study), which enrolled subjects aged 40 to 69 between 1990 and 1994 and followed their cancer incidence and mortality until 2013. After identifying 103,757 eligible subjects for analysis and imputing missing data on covariates by the chained equations approach, we conducted propensity score‐matched analysis for 1:4 matching, leaving 14,520 cases diagnosed with cancer and 48,947 controls without cancer during the study period for final analysis. A Cox proportional hazards regression model was used to estimate the hazard ratio (HR) and corresponding confidence interval (CI) for the risk of death from pneumonia with comparison of cancer cases and cancer‐free controls. Results Compared to cancer‐free individuals, risk of death from pneumonia was significantly higher among those who had any diagnosed cancer (HR, 1.41; 95%CI, 1.08–1.84); those within 1 year of diagnosis (HR, 23.0; 95% CI, 2.98–177.3); within 1 to <2 years (HR, 3.66; 95% CI, 1.04–12.9); and those with regional spread or distant metastatic cancer at initial diagnosis (HR, 2.01; 95% CI, 1.26–3.21). A history of lung, oesophageal, and head and neck cancer conferred the higher risk among site‐specific cancers. Conclusion We found a positive association between incident cancer and risk of death from pneumonia in this study. These results imply the possibility that the immunocompromised status and respiratory failure due to antitumor treatment may have resulted in a more severe outcome from pneumonia among cancer survivors than the general population.

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Section: Discussionmentioning

confidence: 99%

Increased risk of death from pneumonia among cancer survivors: A propensity score‐matched cohort analysis

Tanaka¹,

Inoue²,

Yamaji³

et al. 2022

Cancer Medicine

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“…The analysis described above was separately performed in each of the imputed datasets. The resulting regression coefficients and test statistics were subsequently pooled across all imputed datasets [ 25 , 26 ].…”

Section: Methodsmentioning

confidence: 99%

Strategies of Advanced Airway Management in Out-of-Hospital Cardiac Arrest during Intra-Arrest Hypothermia: Insights from the PRINCESS Trial

Tjerkaski

Hermansson

Dillenbeck

et al. 2022

JCM

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Background: Trans-nasal evaporative cooling is an effective method to induce intra-arrest therapeutic hypothermia in out-of-hospital cardiac arrest (OHCA). The use of supraglottic airway devices (SGA) instead of endotracheal intubation may enable shorter time intervals to induce cooling. We aimed to study the outcomes in OHCA patients receiving endotracheal intubation (ETI) or a SGA during intra-arrest trans-nasal evaporative cooling. Methods: This is a pre-specified sub-study of the PRINCESS trial (NCT01400373) that included witnessed OHCA patients randomized during resuscitation to trans-nasal intra-arrest cooling vs. standard care followed by temperature control at 33 °C for 24 h. For this study, patients randomized to intra-arrest cooling were stratified according to the use of ETI vs. SGA prior to the induction of cooling. SGA was placed by paramedics in the first-tier ambulance or by physicians or anesthetic nurses in the second tier while ETI was performed only after the arrival of the second tier. Propensity score matching was used to adjust for differences at the baseline between the two groups. The primary outcome was survival with good neurological outcome, defined as cerebral performance category (CPC) 1–2 at 90 days. Secondary outcomes included time to place airway, overall survival at 90 days, survival with complete neurologic recovery (CPC 1) at 90 days and sustained return of spontaneous circulation (ROSC). Results: Of the 343 patients randomized to the intervention arm (median age 64 years, 24% were women), 328 received intra-arrest cooling and had data on the airway method (n = 259 with ETI vs. n = 69 with SGA). Median time from the arrival of the first-tier ambulance to successful airway management was 8 min for ETI performed by second tier and 4 min for SGA performed by the first or second tier (p = 0.001). No significant differences in the probability of good neurological outcome (OR 1.43, 95% CI 0.64–3.01), overall survival (OR 1.26, 95% CI 0.57–2.55), full neurological recovery (OR 1.17, 95% CI 0.52–2.73) or sustained ROSC (OR 0.88, 95% CI 0.50–1.52) were observed between ETI and SGA. Conclusions: Among the OHCA patients treated with trans-nasal evaporative intra-arrest cooling, the use of SGA was associated with a significantly shorter time to airway management and with similar outcomes compared to ETI.

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“…Proportional hazards assumption was tested using cox.zph() function in R. Multiple imputation (MI) was used to impute missing data. Specifically, MI-derPassive (missing PS variables were imputed before PS was derived) INT-within (IPTW was carried out within each imputed datasets) were implemented [ 33 – 35 ].…”

Section: Methodsmentioning

confidence: 99%

“…Only potential effect modifiers were used to estimate the weights for the Cox model. Similarly to the IPTW analysis described in Section 2.2.4, MI-derPassive INT-within was used to impute missing data [ 33 , 34 ]. Specifically, covariates from both original and target cohorts, treatment and outcome variables from the original cohort were included in the imputation model [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Transporting observational study results to a target population of interest using inverse odds of participation weighting

et al. 2022

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Inverse odds of participation weighting (IOPW) has been proposed to transport clinical trial findings to target populations of interest when the distribution of treatment effect modifiers differs between trial and target populations. We set out to apply IOPW to transport results from an observational study to a target population of interest. We demonstrated the feasibility of this idea with a real-world example using a nationwide electronic health record derived de-identified database from Flatiron Health. First, we conducted an observational study that carefully adjusted for confounding to estimate the treatment effect of fulvestrant plus palbociclib relative to letrozole plus palbociclib as a second-line therapy among estrogen receptor (ER)-positive, human epidermal growth factor receptor (HER2)-negative metastatic breast cancer patients. Second, we transported these findings to the broader cohort of patients who were eligible for a first-line therapy. The interpretation of the findings and validity of such studies, however, rely on the extent that causal inference assumptions are met.

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How to Apply Multiple Imputation in Propensity Score Matching with Partially Observed Confounders: A Simulation Study and Practical Recommendations

Cited by 15 publications

References 90 publications

Increased risk of death from pneumonia among cancer survivors: A propensity score‐matched cohort analysis

Increased risk of death from pneumonia among cancer survivors: A propensity score‐matched cohort analysis

Strategies of Advanced Airway Management in Out-of-Hospital Cardiac Arrest during Intra-Arrest Hypothermia: Insights from the PRINCESS Trial

Transporting observational study results to a target population of interest using inverse odds of participation weighting

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