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Lifetime Data Anal 2021 Oct 30;27(4):737-760. Epub 2021 Sep 30.

Oslo Centre for Biostatistics and Epidemiology, Oslo University Hospital, Oslo, Norway.

Multi-state models are increasingly being used to model complex epidemiological and clinical outcomes over time. It is common to assume that the models are Markov, but the assumption can often be unrealistic. The Markov assumption is seldomly checked and violations can lead to biased estimation of many parameters of interest. This is a well known problem for the standard Aalen-Johansen estimator of transition probabilities and several alternative estimators, not relying on the Markov assumption, have been suggested. A particularly simple approach known as landmarking have resulted in the Landmark-Aalen-Johansen estimator. Since landmarking is a stratification method a disadvantage of landmarking is data reduction, leading to a loss of power. This is problematic for "less traveled" transitions, and undesirable when such transitions indeed exhibit Markov behaviour. Introducing the concept of partially non-Markov multi-state models, we suggest a hybrid landmark Aalen-Johansen estimator for transition probabilities. We also show how non-Markov transitions can be identified using a testing procedure. The proposed estimator is a compromise between regular Aalen-Johansen and landmark estimation, using transition specific landmarking, and can drastically improve statistical power. We show that the proposed estimator is consistent, but that the traditional variance estimator can underestimate the variance of both the hybrid and landmark estimator. Bootstrapping is therefore recommended. The methods are compared in a simulation study and in a real data application using registry data to model individual transitions for a birth cohort of 184 951 Norwegian men between states of sick leave, disability, education, work and unemployment.

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http://dx.doi.org/10.1007/s10985-021-09534-4 | DOI Listing |

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8536588 | PMC |

October 2021

Eur Heart J 2020 10;41(39):3813-3823

Institute for Surgical Research, Oslo University Hospital and University of Oslo, Oslo, Norway.

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http://dx.doi.org/10.1093/eurheartj/ehaa603 | DOI Listing |

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7599033 | PMC |

October 2020

Tidsskr Nor Laegeforen 2020 04 20;140(6). Epub 2020 Apr 20.

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http://dx.doi.org/10.4045/tidsskr.20.0001 | DOI Listing |

April 2020

Tidsskr Nor Laegeforen 2019 08 2;139(11). Epub 2019 Aug 2.

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http://dx.doi.org/10.4045/tidsskr.19.0311 | DOI Listing |

August 2019

Biom J 2020 05 19;62(3):532-549. Epub 2019 Feb 19.

Institute of Clinical Biometrics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.

We discuss causal mediation analyses for survival data and propose a new approach based on the additive hazards model. The emphasis is on a dynamic point of view, that is, understanding how the direct and indirect effects develop over time. Hence, importantly, we allow for a time varying mediator. To define direct and indirect effects in such a longitudinal survival setting we take an interventional approach (Didelez, 2018) where treatment is separated into one aspect affecting the mediator and a different aspect affecting survival. In general, this leads to a version of the nonparametric g-formula (Robins, 1986). In the present paper, we demonstrate that combining the g-formula with the additive hazards model and a sequential linear model for the mediator process results in simple and interpretable expressions for direct and indirect effects in terms of relative survival as well as cumulative hazards. Our results generalize and formalize the method of dynamic path analysis (Fosen, Ferkingstad, Borgan, & Aalen, 2006; Strohmaier et al., 2015). An application to data from a clinical trial on blood pressure medication is given.

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http://dx.doi.org/10.1002/bimj.201800263 | DOI Listing |

May 2020

Eur Heart J 2019 05;40(17):1378-1383

Department of Biostatistics, Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Domus Medica Gaustad, Sognsvannsveien 9, Oslo, Norway.

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http://dx.doi.org/10.1093/eurheartj/ehy770 | DOI Listing |

May 2019

Stat Methods Med Res 2019 01 5;28(1):321-322. Epub 2018 Nov 5.

1 Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Norway.

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http://dx.doi.org/10.1177/0962280218811177 | DOI Listing |

January 2019

BMC Public Health 2018 01 15;18(1):135. Epub 2018 Jan 15.

Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, POB. 1122, Blindern, Oslo, N-0317, Norway.

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http://dx.doi.org/10.1186/s12889-018-5033-5 | DOI Listing |

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5769446 | PMC |

January 2018

Epidemiology 2017 07;28(4):e39-e40

Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway, Diakonhjemmet Hospital, Oslo, Norway, of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.

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http://dx.doi.org/10.1097/EDE.0000000000000653 | DOI Listing |

July 2017

Epidemiology 2017 05;28(3):379-386

From the aDepartment of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway; and bDiakonhjemmet Hospital, Oslo, Norway.

Counter-intuitive associations appear frequently in epidemiology, and these results are often debated. In particular, several scenarios are characterized by a general risk factor that appears protective in particular subpopulations, for example, individuals suffering from a specific disease. However, the associations are not necessarily representing causal effects. Selection bias due to conditioning on a collider may often be involved, and causal graphs are widely used to highlight such biases. These graphs, however, are qualitative, and they do not provide information on the real life relevance of a spurious association. Quantitative estimates of such associations can be obtained from simple statistical models. In this study, we present several paradoxical associations that occur in epidemiology, and we explore these associations in a causal, frailty framework. By using frailty models, we are able to put numbers on spurious effects that often are neglected in epidemiology. We discuss several counter-intuitive findings that have been reported in real life analyses, and we present calculations that may expand the understanding of these associations. In particular, we derive novel expressions to explain the magnitude of bias in index-event studies.

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http://dx.doi.org/10.1097/EDE.0000000000000621 | DOI Listing |

May 2017

Eur J Epidemiol 2017 06 18;32(6):511-520. Epub 2016 Jul 18.

Department of Biostatistics, Oslo Centre for Biostatistics and Epidemiology, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.

After the introduction of the prostate specific antigen (PSA) test in the 1980s, a sharp increase in the incidence rate of prostate cancer was seen in the United States. The age-specific incidence patterns exhibited remarkable shifts to younger ages, and declining rates were observed at old ages. Similar trends were seen in Norway. We investigate whether these features could, in combination with PSA testing, be explained by a varying degree of susceptibility to prostate cancer in the populations. We analyzed incidence data from the United States' Surveillance, Epidemiology, and End Results program for 1973-2010, comprising 511,027 prostate cancers in men ≥40 years old, and Norwegian national incidence data for 1953-2011, comprising 113,837 prostate cancers in men ≥50 years old. We developed a frailty model where only a proportion of the population could develop prostate cancer, and where the increased risk of diagnosis due to the massive use of PSA testing was modelled by encompassing this heterogeneity in risk. The frailty model fits the observed data well, and captures the changing age-specific incidence patterns across birth cohorts. The susceptible proportion of men is [Formula: see text] in the United States and [Formula: see text] in Norway. Cumulative incidence rates at old age are unchanged across birth cohort exposed to PSA testing at younger and younger ages. The peaking cohort-specific age-incidence curves of prostate cancer may be explained by the underlying heterogeneity in prostate cancer risk. The introduction of the PSA test has led to a larger number of diagnosed men. However, no more cases are being diagnosed in total in birth cohorts exposed to the PSA era at younger and younger ages, even though they are diagnosed at younger ages. Together with the earlier peak in the age-incidence curves for younger cohorts, and the strong familial association of the cancer, this constitutes convincing evidence that the PSA test has led to a higher proportion, and an earlier timing, of diagnoses in a limited pool of susceptible individuals.

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http://dx.doi.org/10.1007/s10654-016-0185-z | DOI Listing |

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468491 | PMC |

June 2017

Clin Psychol Sci 2015 Sep 26;3(5):686-701. Epub 2014 Sep 26.

Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, and Research Support Services, Oslo University Hospital.

Researchers have hypothesized that men gain greater reward from alcohol than women. However, alcohol-administration studies testing participants drinking alone have offered weak support for this hypothesis. Research suggests that social processes may be implicated in gender differences in drinking patterns. We examined the impact of gender and alcohol on "emotional contagion"-a social mechanism central to bonding and cohesion. Social drinkers (360 male, 360 female) consumed alcohol, placebo, or control beverages in groups of three. Social interactions were video recorded, and both Duchenne and non-Duchenne smiling were continuously coded using the . Results revealed that Duchenne smiling (but not non-Duchenne smiling) contagion correlated with self-reported reward and typical drinking patterns. Importantly, Duchenne smiles were significantly less "infectious" among sober male versus female groups, and alcohol eliminated these gender differences in smiling contagion. Findings identify new directions for research exploring social-reward processes in the etiology of alcohol problems.

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http://dx.doi.org/10.1177/2167702614548892 | DOI Listing |

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4615679 | PMC |

September 2015

BMC Public Health 2015 Oct 23;15:1082. Epub 2015 Oct 23.

Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, University of Oslo, Oslo, Norway.

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http://dx.doi.org/10.1186/s12889-015-2408-8 | DOI Listing |

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4619267 | PMC |

October 2015

Tidsskr Nor Laegeforen 2015 Sep 8;135(16):1465-7. Epub 2015 Sep 8.

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http://dx.doi.org/10.4045/tidsskr.15.0347 | DOI Listing |

September 2015

Stat Med 2015 Dec 16;34(29):3866-87. Epub 2015 Aug 16.

Oslo Centre for Biostatistics and Epidemiology, Department for Biostatistics, University of Oslo, Oslo, Norway.

When it comes to clinical survival trials, regulatory restrictions usually require the application of methods that solely utilize baseline covariates and the intention-to-treat principle. Thereby, much potentially useful information is lost, as collection of time-to-event data often goes hand in hand with collection of information on biomarkers and other internal time-dependent covariates. However, there are tools to incorporate information from repeated measurements in a useful manner that can help to shed more light on the underlying treatment mechanisms. We consider dynamic path analysis, a model for mediation analysis in the presence of a time-to-event outcome and time-dependent covariates to investigate direct and indirect effects in a study of different lipid-lowering treatments in patients with previous myocardial infarctions. Further, we address the question whether survival in itself may produce associations between the treatment and the mediator in dynamic path analysis and give an argument that because of linearity of the assumed additive hazard model, this is not the case. We further elaborate on our view that, when studying mediation, we are actually dealing with underlying processes rather than single variables measured only once during the study period. This becomes apparent in results from various models applied to the study of lipid-lowering treatments as well as our additionally conducted simulation study, where we clearly observe that discarding information on repeated measurements can lead to potentially erroneous conclusions.

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http://dx.doi.org/10.1002/sim.6598 | DOI Listing |

December 2015

Lifetime Data Anal 2015 Oct 24;21(4):579-93. Epub 2015 Jun 24.

Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.

Statistical methods for survival analysis play a central role in the assessment of treatment effects in randomized clinical trials in cardiovascular disease, cancer, and many other fields. The most common approach to analysis involves fitting a Cox regression model including a treatment indicator, and basing inference on the large sample properties of the regression coefficient estimator. Despite the fact that treatment assignment is randomized, the hazard ratio is not a quantity which admits a causal interpretation in the case of unmodelled heterogeneity. This problem arises because the risk sets beyond the first event time are comprised of the subset of individuals who have not previously failed. The balance in the distribution of potential confounders between treatment arms is lost by this implicit conditioning, whether or not censoring is present. Thus while the Cox model may be used as a basis for valid tests of the null hypotheses of no treatment effect if robust variance estimates are used, modeling frameworks more compatible with causal reasoning may be preferrable in general for estimation.

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http://dx.doi.org/10.1007/s10985-015-9335-y | DOI Listing |

October 2015

Int J Epidemiol 2015 Aug 4;44(4):1426-8. Epub 2015 Apr 4.

Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway.

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http://dx.doi.org/10.1093/ije/dyv047 | DOI Listing |

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4588867 | PMC |

August 2015

Int J Epidemiol 2015 Aug 12;44(4):1408-21. Epub 2014 Dec 12.

Cancer Registry of Norway, Institute of Population-Based Cancer Research, Oslo, Norway.

The concept of frailty plays a major role in the statistical field of survival analysis. Frailty variation refers to differences in risk between individuals which go beyond known or measured risk factors. In other words, frailty variation is unobserved heterogeneity. Although understanding frailty is of interest in its own right, the literature on survival analysis has demonstrated that existence of frailty variation can lead to surprising artefacts in statistical estimation that are important to examine. We present literature that demonstrates the presence and significance of frailty variation between individuals. We discuss the practical content of frailty variation, and show the link between frailty and biological concepts like (epi)genetics and heterogeneity in disease risk. There are numerous suggestions in the literature that a good deal of this variation may be due to randomness, in addition to genetic and/or environmental factors. Heterogeneity often manifests itself as clustering of cases in families more than would be expected by chance. We emphasize that apparently moderate familial relative risks can only be explained by strong underlying variation in disease risk between families and individuals. Finally, we highlight the potential impact of frailty variation in the interpretation of standard epidemiological measures such as hazard and incidence rates.

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http://dx.doi.org/10.1093/ije/dyu192 | DOI Listing |

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4588855 | PMC |

August 2015

Stat Med 2013 Dec;32(30):5221

Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.

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http://dx.doi.org/10.1002/sim.6035 | DOI Listing |

December 2013

Am J Epidemiol 2014 Feb 12;179(4):499-506. Epub 2013 Nov 12.

Using a 2-level hierarchical frailty model, we analyzed population-wide data on testicular germ-cell tumor (TGCT) status in 1,135,320 two-generational Norwegian families to examine the risk of TGCT in family members of patients. Follow-up extended from 1954 (cases) or 1960 (unaffected persons) to 2008. The first-level frailty variable was compound Poisson-distributed. The underlying Poisson parameter was randomized to model the frailty variation between families and was decomposed additively to characterize the correlation structure within a family. The frailty relative risk (FRR) for a son, given a diseased father, was 4.03 (95% confidence interval (CI): 3.12, 5.19), with a borderline significantly higher FRR for nonseminoma than for seminoma (P = 0.06). Given 1 affected brother, the lifetime FRR was 5.88 (95% CI: 4.70, 7.36), with no difference between subtypes. Given 2 affected brothers, the FRR was 21.71 (95% CI: 8.93, 52.76). These estimates decreased with the number of additional healthy brothers. The estimated FRRs support previous findings. However, the present hierarchical frailty approach allows for a very precise definition of familial risk. These FRRs, estimated according to numbers of affected/nonaffected family members, provide new insight into familial TGCT. Furthermore, new light is shed on the different familial risks of seminoma and nonseminoma.

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http://dx.doi.org/10.1093/aje/kwt267 | DOI Listing |

February 2014

Alcohol Clin Exp Res 2013 Nov 26;37(11):1954-62. Epub 2013 Jul 26.

Division of Mental Health and Addiction, Institute of Clinical Medicine University of Oslo, Oslo, Norway; Child and Adolescent Mental Health Research Unit, Oslo University Hospital, Oslo, Norway.

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http://dx.doi.org/10.1111/acer.12182 | DOI Listing |

November 2013

Scand J Work Environ Health 2013 Mar 14;39(2):121-4. Epub 2013 Jan 14.

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http://dx.doi.org/10.5271/sjweh.3343 | DOI Listing |

March 2013

BMC Med Res Methodol 2013 Jan 14;13. Epub 2013 Jan 14.

Drug Abuse Epidemiology Research Group, IMIM-Institut de Recerca Hospital del Mar, Doctor Aiguader 88, Barcelona, Spain.

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http://dx.doi.org/10.1186/1471-2288-13-4 | DOI Listing |

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3552809 | PMC |

January 2013

J R Stat Soc Ser A Stat Soc 2012 10;175(4):831-861

University of Oslo Norway.

Time dynamics are often ignored in causal modelling. Clearly, causality must operate in time and we show how this corresponds to a mechanistic, or system, understanding of causality. The established counterfactual definitions of direct and indirect effects depend on an ability to manipulate the mediator which may not hold in practice, and we argue that a mechanistic view may be better. Graphical representations based on local independence graphs and dynamic path analysis are used to facilitate communication as well as providing an overview of the dynamic relations 'at a glance'. The relationship between causality as understood in a mechanistic and in an interventionist sense is discussed. An example using data from the Swiss HIV Cohort Study is presented.

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http://dx.doi.org/10.1111/j.1467-985X.2011.01030.x | DOI Listing |

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3500875 | PMC |

October 2012

Stat Med 2012 Dec 29;31(28):3731-47. Epub 2012 Jun 29.

Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.

The Armitage-Doll model with random frailty can fail to describe incidence rates of rare cancers influenced by an accelerated biological mechanism at some, possibly short, period of life. We propose a new model to account for this influence. Osteosarcoma and Ewing sarcoma are primary bone cancers with characteristic age-incidence patterns that peak in adolescence. We analyze Surveillance, Epidemiology and End Result program incidence data for whites younger than 40 years diagnosed during the period 1975-2005, with an Armitage-Doll model with compound Poisson frailty. A new model treating the adolescent growth spurt as the accelerated mechanism affecting cancer development is a significant improvement over that model. We also model the incidence rate conditioning on the event of having developed the cancers before the age of 40 years and compare the results with those predicted by the Armitage-Doll model. Our results support existing evidence of an underlying susceptibility for the two cancers among a very small proportion of the population. In addition, the modeling results suggest that susceptible individuals with a rapid growth spurt acquire the cancers sooner than they otherwise would have if their growth had been slower. The new model is suitable for modeling incidence rates of rare diseases influenced by an accelerated biological mechanism.

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http://dx.doi.org/10.1002/sim.5441 | DOI Listing |

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4052707 | PMC |

December 2012

Stat Med 2012 Aug 22;31(18):1903-17. Epub 2012 Mar 22.

Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.

There is a single-minded focus on events in survival analysis, and we often ignore longitudinal data that are collected together with the event data. This is due to a lack of methodology but also a result of the artificial distinction between survival and longitudinal data analyses. Understanding the dynamics of such processes is important but has been hampered by a lack of appreciation of the difference between confirmatory and exploratory causal inferences. The latter represents an attempt at elucidating mechanisms by applying mediation analysis to statistical data and will usually be of a more tentative character than a confirmatory analysis. The concept of local independence and the associated graphs are useful. This is related to Granger causality, an important method from econometrics that is generally undervalued by statisticians. This causality concept is different from the counterfactual one since it lacks lacks the intervention aspect. The notion that one can intervene at will in naturally occurring processes, which seems to underly much of modern causal inference, is problematic when studying mediation and mechanisms. It is natural to assume a stochastic process point of view when analyzing dynamic relationships. We present some examples to illustrate this. It is not clear how survival analysis must be developed to handle the complex life-history data that are increasingly being collected today. We give some suggestions.

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http://dx.doi.org/10.1002/sim.5324 | DOI Listing |

August 2012

J Int AIDS Soc 2012 Mar 14;15(1):14. Epub 2012 Mar 14.

ABSTRACT: BACKGROUND: Incidence is a better measure than prevalence for monitoring AIDS, but it is not often used because longitudinal HIV data from which incidence can be computed is scarce. Our objective was to estimate the force of infection and incidence of HIV in Malawi using cross-sectional HIV sero-prevalence data from the Malawi Demographic and Health Survey conducted in 2004. METHODS: We formulated a recurrence relation of population prevalence as a function of a piecewise-constant force of HIV infection. The relation adjusts for natural and HIV-induced mortality. The parameters of the recurrence relation were estimated using maximum likelihood, and confidence intervals of parameter estimates were constructed by bootstrapping. We assessed the fit of the model using the Pearson Chi-square goodness of fit test. We estimated population incidence from the force of infection by accounting for the prevalence, as the force of infection applies only to the HIV-negative part of the population. RESULTS: The estimated HIV population incidence per 100,000 person-years among men is 610 for the 15-24 year age range, 2700 for the 25-34 group and 1320 for 35-49 year olds. For females, the estimates are 2030 for 15-24 year olds, 1710 for 25-34 year olds and 1730 for 35-49 year olds. CONCLUSIONS: Our method provides a simple way of simultaneously estimating the incidence rate of HIV and the age-specific population prevalence for single ages using population-based cross-sectional sero-prevalence data. The estimated incidence rates depend on the HIV and natural mortalities used in the estimation process.

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http://dx.doi.org/10.1186/1758-2652-15-14 | DOI Listing |

March 2012

Tidsskr Nor Laegeforen 2012 Feb;132(3):267

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http://dx.doi.org/10.4045/tidsskr.11.1507 | DOI Listing |

February 2012

Tidsskr Nor Laegeforen 2011 Sep;131(18):1760-1

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http://dx.doi.org/10.4045/tidsskr.11.0574 | DOI Listing |

September 2011

Stat Med 2011 Oct 29;30(24):2947-58. Epub 2011 Jul 29.

Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Norway.

When applying survival analysis, such as Cox regression, to data from major clinical trials or other studies, often only baseline covariates are used. This is typically the case even if updated covariates are available throughout the observation period, which leaves large amounts of information unused. The main reason for this is that such time-dependent covariates often are internal to the disease process, as they are influenced by treatment, and therefore lead to confounded estimates of the treatment effect. There are, however, methods to exploit such covariate information in a useful way. We study the method of dynamic path analysis applied to data from the Swiss HIV Cohort Study. To adjust for time-dependent confounding between treatment and the outcome 'AIDS or death', we carried out the analysis on a sequence of mimicked randomized trials constructed from the original cohort data. To analyze these trials together, regular dynamic path analysis is extended to a composite analysis of weighted dynamic path models. Results using a simple path model, with one indirect effect mediated through current HIV-1 RNA level, show that most or all of the total effect go through HIV-1 RNA for the first 4 years. A similar model, but with CD4 level as mediating variable, shows a weaker indirect effect, but the results are in the same direction. There are many reasons to be cautious when drawing conclusions from estimates of direct and indirect effects. Dynamic path analysis is however a useful tool to explore underlying processes, which are ignored in regular analyses.

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http://dx.doi.org/10.1002/sim.4324 | DOI Listing |

October 2011

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