Statistical projection methods for lung cancer incidence and mortality: a systematic review

Abstract

ObjectivesTo identify and summarise all studies using statistical methods to project lung cancer incidence or mortality rates more than 5 years into the future.Study typeSystematic review.MethodsWe performed a systematic literature search in multiple electronic databases to identify studies published from 1 January 1988 to 14 August 2018, which used statistical methods to project lung cancer incidence and/or mortality rates. Reference lists of relevant articles were checked for additional potentially relevant articles. We developed an organisational framework to classify methods into groups according to the type of data and the statistical models used. Included studies were critically appraised using prespecified criteria.ResultsOne hundred and one studies met the inclusion criteria; six studies used more than one statistical method. The number of studies reporting statistical projections for lung cancer increased substantially over time. Eighty-eight studies used projection methods, which did not incorporate data on smoking in the population, and 16 studies used a method which did incorporate data on smoking. Age–period–cohort models (44 studies) were the most commonly used methods, followed by other generalised linear models (35 studies). The majority of models were developed using observed rates for more than 10 years and used data that were considered to be good quality. A quarter of studies provided comparisons of fitted and observed rates. While validation by withholding the most recent observed data from the model and then comparing the projected and observed rates for the most recent period provides important information on the model’s performance, only 12 studies reported doing this.ConclusionThis systematic review provides an up-to-date summary of the statistical methods used in published lung cancer incidence or mortality projections. The assessment of the strengths of existing methods will help researchers to better apply and develop statistical methods for projecting lung cancer rates. Some of the common methods described in this review can be applied to the projection of rates for other cancer types or other non-infectious diseases.

Forecasting of Lung Cancer Incident Cases at the Small-Area Level in Victoria, Australia

“…Selecting an appropriate projection method mainly relies on data availability and the purpose of the forecasts. These methods range from the present state method assuming the current rate remains unchanged into the future to more complex statistical models of historical trends, including APC and generalized linear mixed model (GLMM) [ 28 ].…”

“…The APC model is the most commonly used projection method for lung cancer incidence and remains appropriate for long-term projections [ 28 ]. Period effects can represent screening, diagnostic, and treatment factors that led to lung cancer incidence variation across all age groups.…”

How Well Have Projected Lung Cancer Rates Predicted the Actual Observed Rates?

“…A systematic review identified 101 studies published between 1st January 1988 and 14th August 2018 that used statistical methods to project lung cancer incidence or mortality rates (Yu et al, 2019). The aims of this study were to compare previously published lung cancer incidence and/or mortality rate projections to observed data that became available since their publication, and to provide insights into key factors that should be considered when selecting methods for projecting lung cancer rates.…”

“…The literature search (Online resource 1) and review protocol for potentially relevant studies are described in detail in our previously published systematic review (Yu et al, 2019), and the full inclusion and exclusion criteria are summarised in Online resource 2. The results of the literature search and the process for selecting studies are described in Online resource 3.…”

“…These observed data were agestandardised to the same standard population used in the published studies, including the WHO World standard population (Ahmad et al, 2001), Segi World standard population, European standard population, the 1970 and 2000 USA standard populations (SEER, 2018), and the 1985 Japanese standard population (Kuroishi et al, 1992). In order to summarise the differences and similarities between the methods used for projections, we applied our previously developed organisational framework to group these methods (Yu et al, 2019).…”