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  1. Article ; Online: Selection Bias with Outcome-dependent Sampling.

    Sjölander, Arvid

    Epidemiology (Cambridge, Mass.)

    2022  Volume 34, Issue 2, Page(s) 186–191

    Abstract: In a seminal paper, Hernán et al. 2004 provided a systematic classification of selection biases, for scenarios where the selection is a collider between the exposure and the outcome. Hernán 2017 discussed another scenario, where the selection is ... ...

    Abstract In a seminal paper, Hernán et al. 2004 provided a systematic classification of selection biases, for scenarios where the selection is a collider between the exposure and the outcome. Hernán 2017 discussed another scenario, where the selection is statistically independent of the exposure, but associated with the outcome through common causes. In this note, we extend the discussion to scenarios where the selection is directly influenced by the outcome, but not by the exposure. We discuss whether these types of outcome-dependent selections preserve the sharp causal null hypothesis, and whether or not they allow for estimation of causal effects in the selected sample and/or in the source population.
    MeSH term(s) Humans ; Selection Bias ; Epidemiology ; Causality
    Language English
    Publishing date 2022-12-12
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1053263-8
    ISSN 1531-5487 ; 1044-3983
    ISSN (online) 1531-5487
    ISSN 1044-3983
    DOI 10.1097/EDE.0000000000001567
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Why test for proportional hazards - or any other model assumptions?

    Sjölander, Arvid / Dickman, Paul

    American journal of epidemiology

    2024  

    Language English
    Publishing date 2024-02-06
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2937-3
    ISSN 1476-6256 ; 0002-9262
    ISSN (online) 1476-6256
    ISSN 0002-9262
    DOI 10.1093/aje/kwae002
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A Cautionary Note on Extended Kaplan-Meier Curves for Time-varying Covariates.

    Sjölander, Arvid

    Epidemiology (Cambridge, Mass.)

    2020  Volume 31, Issue 4, Page(s) 517–522

    Abstract: The Kaplan-Meier curve is a standard statistical tool that is used in cohort studies to illustrate how survival during follow-up depends on time-fixed covariates that are measured at baseline. For time-varying covariates, an extended Kaplan-Meier curve ... ...

    Abstract The Kaplan-Meier curve is a standard statistical tool that is used in cohort studies to illustrate how survival during follow-up depends on time-fixed covariates that are measured at baseline. For time-varying covariates, an extended Kaplan-Meier curve has been proposed that is constructed by letting subjects move across risk sets as their covariate levels change during follow-up. It has been claimed, but not proven, that, under a particular independence assumption, this extended Kaplan-Meier curve has a causal interpretation as representing a hypothetical cohort whose covariate values remain constant during follow-up. In this note, we show that, in the absence of confounding, this claim is indeed correct. However, we argue that the causal implications of this independence assumptions are highly unrealistic, and that a causal interpretation of the extended Kaplan-Meier curve is therefore typically unwarranted.
    MeSH term(s) Causality ; Cohort Studies ; Humans ; Kaplan-Meier Estimate ; Research Design
    Language English
    Publishing date 2020-04-13
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1053263-8
    ISSN 1531-5487 ; 1044-3983
    ISSN (online) 1531-5487
    ISSN 1044-3983
    DOI 10.1097/EDE.0000000000001188
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Re. E-values for Mendelian Randomization.

    Sjölander, Arvid / Gabriel, Erin E

    Epidemiology (Cambridge, Mass.)

    2023  Volume 35, Issue 1, Page(s) e2–e3

    MeSH term(s) Humans ; Mendelian Randomization Analysis ; Genome-Wide Association Study
    Language English
    Publishing date 2023-11-27
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1053263-8
    ISSN 1531-5487 ; 1044-3983
    ISSN (online) 1531-5487
    ISSN 1044-3983
    DOI 10.1097/EDE.0000000000001673
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  5. Article ; Online: Sensitivity analysis of G-estimators to invalid instrumental variables.

    Vancak, Valentin / Sjölander, Arvid

    Statistics in medicine

    2023  Volume 42, Issue 23, Page(s) 4257–4281

    Abstract: Instrumental variables regression is a tool that is commonly used in the analysis of observational data. The instrumental variables are used to make causal inference about the effect of a certain exposure in the presence of unmeasured confounders. A ... ...

    Abstract Instrumental variables regression is a tool that is commonly used in the analysis of observational data. The instrumental variables are used to make causal inference about the effect of a certain exposure in the presence of unmeasured confounders. A valid instrumental variable is a variable that is associated with the exposure, affects the outcome only through the exposure (exclusion), and is not confounded with the outcome (exogeneity). Unlike the first assumption, the other two are generally untestable and rely on subject-matter knowledge. Therefore, a sensitivity analysis is desirable to assess the impact of assumptions' violation on the estimated parameters. In this paper, we propose and demonstrate a new method of sensitivity analysis for G-estimators in causal linear and non-linear models. We introduce two novel aspects of sensitivity analysis in instrumental variables studies. The first is a single sensitivity parameter that captures violations of exclusion and exogeneity assumptions. The second is an application of the method to non-linear models. The introduced framework is theoretically justified and is illustrated via a simulation study. Finally, we illustrate the method by application to real-world data and provide guidelines on conducting sensitivity analysis.
    MeSH term(s) Humans ; Bias ; Computer Simulation ; Causality
    Language English
    Publishing date 2023-07-27
    Publishing country England
    Document type Journal Article
    ZDB-ID 843037-8
    ISSN 1097-0258 ; 0277-6715
    ISSN (online) 1097-0258
    ISSN 0277-6715
    DOI 10.1002/sim.9859
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  6. Article ; Online: An Approximate Expression for the Proportion Explained by Mediation in Survival Analysis.

    Sjölander, Arvid

    Epidemiology (Cambridge, Mass.)

    2019  Volume 31, Issue 2, Page(s) e21–e22

    MeSH term(s) Data Interpretation, Statistical ; Humans ; Survival Analysis
    Language English
    Publishing date 2019-10-21
    Publishing country United States
    Document type Letter
    ZDB-ID 1053263-8
    ISSN 1531-5487 ; 1044-3983
    ISSN (online) 1531-5487
    ISSN 1044-3983
    DOI 10.1097/EDE.0000000000001117
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  7. Article ; Online: Estimation of marginal causal effects in the presence of confounding by cluster.

    Sjölander, Arvid

    Biostatistics (Oxford, England)

    2019  Volume 22, Issue 3, Page(s) 598–612

    Abstract: A popular way to control for unmeasured confounders is to utilize clusters (e.g. sets of siblings), in which a potentially large set of confounders are constant. By estimating the exposure-outcome association within clusters, rather than between ... ...

    Abstract A popular way to control for unmeasured confounders is to utilize clusters (e.g. sets of siblings), in which a potentially large set of confounders are constant. By estimating the exposure-outcome association within clusters, rather than between unrelated subjects, all cluster-constant confounders are implicitly controlled for. To analyze such clustered data, it is common to use fixed effects models, which absorb all cluster-constant confounders into a cluster-specific intercept. In this article, we show how linear and log-linear fixed effects models can be used to estimate marginal counterfactual means. These counterfactual means can be estimated and presented for each exposure level separately, or contrasted to form a wide range of marginal causal effects. For binary outcomes, we propose to estimate marginal causal effects with marginal logistic between-within models. These models include a constant intercept common for all clusters, and control for unmeasured cluster-constant confounders by adding the mean exposure level in each cluster to the model. We illustrate the proposed methods by re-analyzing data from a co-twin control study on birth weight and Attention-Deficit/Hyperactivity Disorder.
    MeSH term(s) Causality ; Humans ; Linear Models
    Language English
    Publishing date 2019-12-04
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2031500-4
    ISSN 1468-4357 ; 1465-4644
    ISSN (online) 1468-4357
    ISSN 1465-4644
    DOI 10.1093/biostatistics/kxz054
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  8. Article ; Online: Estimation of causal effect measures with the R-package stdReg.

    Sjölander, Arvid

    European journal of epidemiology

    2018  Volume 33, Issue 9, Page(s) 847–858

    Abstract: Measures of causal effects play a central role in epidemiology. A wide range of measures exist, which are designed to give relevant answers to substantive epidemiological research questions. However, due to mathematical convenience and software ... ...

    Abstract Measures of causal effects play a central role in epidemiology. A wide range of measures exist, which are designed to give relevant answers to substantive epidemiological research questions. However, due to mathematical convenience and software limitations most studies only report odds ratios for binary outcomes and hazard ratios for time-to-event outcomes. In this paper we show how logistic regression models and Cox proportional hazards regression models can be used to estimate a wide range of causal effect measures, with the R-package stdReg. For illustration we focus on the attributable fraction, the number needed to treat and the relative excess risk due to interaction. We use two publicly available data sets, so that the reader can easily replicate and elaborate on the analyses. The first dataset includes information on 487 births among 188 women, and the second dataset includes information on 2982 women diagnosed with primary breast cancer.
    MeSH term(s) Causality ; Epidemiologic Research Design ; Humans ; Logistic Models ; Proportional Hazards Models ; Reference Standards
    Language English
    Publishing date 2018-03-14
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 632614-6
    ISSN 1573-7284 ; 0393-2990
    ISSN (online) 1573-7284
    ISSN 0393-2990
    DOI 10.1007/s10654-018-0375-y
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  9. Article ; Online: Are E-values too optimistic or too pessimistic? Both and neither!

    Sjölander, Arvid / Greenland, Sander

    International journal of epidemiology

    2022  Volume 51, Issue 2, Page(s) 355–363

    MeSH term(s) Bias ; Humans ; Pessimism ; Surveys and Questionnaires
    Language English
    Publishing date 2022-03-01
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 187909-1
    ISSN 1464-3685 ; 0300-5771
    ISSN (online) 1464-3685
    ISSN 0300-5771
    DOI 10.1093/ije/dyac018
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  10. Article ; Online: Nonparametric Bounds for Causal Effects in Imperfect Randomized Experiments

    Gabriel, Erin E. / Sjölander, Arvid / Sachs, Michael C.

    Journal of the American Statistical Association. 2023 Jan. 2, v. 118, no. 541 p.684-692

    2023  

    Abstract: Nonignorable missingness and noncompliance can occur even in well-designed randomized experiments, making the intervention effect that the experiment was designed to estimate nonidentifiable. Nonparametric causal bounds provide a way to narrow the range ... ...

    Abstract Nonignorable missingness and noncompliance can occur even in well-designed randomized experiments, making the intervention effect that the experiment was designed to estimate nonidentifiable. Nonparametric causal bounds provide a way to narrow the range of possible values for a nonidentifiable causal effect with minimal assumptions. We derive novel bounds for the causal risk difference for a binary outcome and intervention in randomized experiments with nonignorable missingness that is caused by a variety of mechanisms, with both perfect and imperfect compliance. We show that the so-called worst-case imputation, whereby all missing subjects on the intervention arm are assumed to have events and all missing subjects on the control or placebo arm are assumed to be event-free, can be too pessimistic in blinded studies with perfect compliance, and is not bounding the correct estimand with imperfect compliance. We illustrate the use of the proposed bounds in our motivating data example of peanut consumption on the development of peanut allergies in infants. We find that, even accounting for potentially nonignorable missingness and noncompliance, our derived bounds confirm that regular exposure to peanuts reduces the risk of development of peanut allergies, making the results of this study much more compelling.
    Keywords compliance ; peanuts ; placebos ; risk ; Causal bounds ; Clinical trials ; No defiers ; Noncompliance ; Nonignorable missingness
    Language English
    Dates of publication 2023-0102
    Size p. 684-692.
    Publishing place Taylor & Francis
    Document type Article ; Online
    ZDB-ID 2064981-2
    ISSN 1537-274X
    ISSN 1537-274X
    DOI 10.1080/01621459.2021.1950734
    Database NAL-Catalogue (AGRICOLA)

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